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DTSTART;TZID=America/Los_Angeles:20200310T093000
DTEND;TZID=America/Los_Angeles:20200310T103000
DTSTAMP:20231011T190957Z
CREATED:20231011T190957Z
LAST-MODIFIED:20231011T190957Z
UID:14578-1583832600-1583836200@www.cecs.uci.edu
SUMMARY:Design and Implementation of Robust Full-Duplex Wireless Network
DESCRIPTION:Name: Sergey Shaboyan\nChair: Prof. Ahmed Eltawil \nDate: March 10th\, 2020 \nTime: 09:30 AM \nLocation:  EH 3206 \nCommittee: Prof. Ahmed Eltawil (Chair)\, Prof. Ender Ayanoglu\, Prof. Zhiying Wang \nTitle: “Design and Implementation of Robust Full-Duplex Wireless Network” \nAbstract: \nRecently Full-Duplex (FD) communication has gained significant interest due to demonstrable increase in throughput and spectral efficiency. Conventional Half-duplex (HD) communication systems use either time-duplexing or frequency-duplexing to avoid self-interference. In contrast\, full-duplex systems transmit and receive simultaneously on the same frequency band\, thus optimally utilizing available resources. The main challenge in FD systems is managing the self-interference (SI) signal at each node\, which is typically orders of magnitude larger than the intended signal of interest (SOI). To achieve sufficient SI suppression\, FD systems rely on cancellation across multiple domains such as spatial\, analog\, and digital. However\, number of practical\, FD specific challenges arise impacting quality of service\, when at least one node in a network operates in full-duplex mode. \nIn this thesis\, we consider practical issues of wireless networks containing a full-duplex node. The ultimate goal of this work is to design and implement real-time\, end-to-end networks consisting of at least one FD node that is capable of improving network performance under limited available bandwidth constraint. First\, we identify synchronization issues in a network consisting of a full-duplex base station communicating with half-duplex nodes. Novel synchronization techniques specific for full-duplex networks are proposed that allow compensation of synchronization errors in time and frequency. The proposed techniques are implemented and tested experimentally on a real-time full-duplex wireless network. Second\, we characterize the dynamic environment impact on the received self-interference in the FD system\, which is equipped with a reconfigurable antenna as a passive SI suppression mechanism. The self-interference channel delay profile is measured using the FD system operating on 5MHz\, 10MHz\, and 20MHz bandwidths. The measured channel profiles collected under suppressing and non-suppressing antenna patterns are compared\, and channel changes in static\, as well as dynamic environments\, are highlighted. We then statistically model the SI channel by performing probability distribution fitting to SI channel data. Third\, the thesis proposes a Wi-Fi compliant self-interference active cancellation technique for amplify and forward\, as well as decode and forward full-duplex relays. Finally\, we design and implement an end-to-end wireless network extended with the aid of a custom-designed amplify and forward full-duplex relay. We then analyze the relay coverage limitation under the stability and transmit power constraints. The network performance is analyzed as a function of relay location for constant gain and constant transmit power modes\, consequently suggesting optimal relay location that will maximize signal to noise plus interference ratio (SINR) at the destination node. We evaluate the overall network performance by simulation\, as well as experimentally in outdoor/indoor environments.
URL:https://www.cecs.uci.edu/event/design-and-implementation-of-robust-full-duplex-wireless-network/
LOCATION:CA
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200529T140000
DTEND;TZID=America/Los_Angeles:20200529T150000
DTSTAMP:20231011T190957Z
CREATED:20231011T190957Z
LAST-MODIFIED:20231011T190957Z
UID:14579-1590760800-1590764400@www.cecs.uci.edu
SUMMARY:Advancing Compiler and Simulator Techniques for Highly Parallel Simulation of Embedded Systems
DESCRIPTION:Name: Zhongqi Cheng\nChair: Prof. Rainer Doemer \nDate: May 29th\, 2020 \nTime: 02:00 PM \nLocation:  Zoom \nCommittee: Rainer Doemer (Chair)\, Prof. Mohammad Al Faruque and Prof. Aparna Chandramowlishwaran \nTitle: “Advancing Compiler and Simulator Techniques for Highly Parallel Simulation of Embedded Systems” \nAbstract: \nAs an Electronic System Level (ESL) design language\, the IEEE SystemC standard is widely used for testing\, validation and verification of embedded system models. Discrete Event Simulation (DES) has been used for decades as the default SystemC simulation semantic. However\, due to the sequential nature of DES\, Parallel DES has recently gained an increasing amount of attention for performing high speed simulations on parallel computing platforms. To further exploit the parallel computation power of modern multi- and many-core platforms\, Out-of-order Parallel Discrete Event Simulation (OoO PDES) has been proposed. In OoO PDES\, threads comply with a partial order such that different simulation threads may run in different time cycles to increase the parallelism of execution. The Recoding Infrastructure for SystemC (RISC) has been introduced as a tool flow to fully support OoO PDES. \nTo preserve the SystemC semantics under OoO PDES\, a compiler based approach statically analyzes the race conditions in the input model. However\, there are severe restrictions: the source code for the input design must be available in one file\, which does not scale. This disables the use of Intellectual Property (IP) and hierarchical file structures. In this dissertation\, we propose a partial-graph based approach to scale the static analysis to support separate files and IP reuse. Specifically\, the Partial Segment Graph (PSG) data structure is proposed and is used to abstract the behaviours and communication of modules within a single translation unit. These partial graphs are combined at top level to reconstruct the complete behaviors and communication of the entire model. \nWe also propose new algorithms to support the static analysis for modern SystemC TLM-2.0 standard. SystemC TLM-2.0 is widely used in industrial ESL designs for better interoperability and higher simulation speed. However\, it is identified as an obstacle for parallel SystemC simulation due to the disappearance of channels. To solve the problem\, we propose a compile time approach to statically analyze potential conflicts among threads in SystemC TLM-2.0 loosely- and approximately-timed models. A new Socket Call Path (SCP) technique is introduced which provides the compiler with socket binding information for precise static analysis. Based on SCP\, an algorithm is proposed to analyze entangled variable pairs for automatic and accurate conflict analysis. \nBesides the works on the compiler side\, we focus as well on increasing the simulation speed of OoO PDES. We observe that the granularity of the Segment Graph (SG) data structure used in static analysis has a high impact on OoO PDES. This motivates us to propose a set of coding guidelines for the RISC users to properly refine their SystemC model for a higher simulation speed. \nFurthermore\, in this dissertation\, an algorithm is proposed to optimize directly the event delivery strategy in OoO PDES. Event delivery in OoO PDES was very conservative\, which often postponed the execution of waiting threads due to unknown future behaviors of the SystemC model\, and in turn became a bottleneck of simulation speed. The algorithm we propose takes advantage of the prediction of future thread behaviors\, and therefore allows waiting threads to resume execution earlier\, resulting in significantly increased simulation speed. \nTo summarize\, the contributions of this dissertation include: 1) a scalable RISC tool flow for statically analyzing and protecting 3rd party IPs in models with multiple files\, 2) an advanced static analysis approach for modern SystemC TLM-2.0 models\, 3) a set of coding guidelines for RISC users to achieve higher simulation speed\, and 4) a more efficient event delivery algorithm in OoO PDES scheduler using prediction information. \nTogether\, these compiler and simulator advances enable OoO PDES for larger and modern model simulation and thus improve the design of embedded systems significantly\, leading to better devices at lower cost in the end. \n 
URL:https://www.cecs.uci.edu/event/advancing-compiler-and-simulator-techniques-for-highly-parallel-simulation-of-embedded-systems/
LOCATION:CA
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220516T090000
DTEND;TZID=America/Los_Angeles:20220516T100000
DTSTAMP:20231011T032339Z
CREATED:20231011T032339Z
LAST-MODIFIED:20231011T032339Z
UID:14132-1652691600-1652695200@www.cecs.uci.edu
SUMMARY:Internet of Things-Based Collaborative Position-Sensing Systems for Cardiopulmonary Resuscitation and Indoor Localization
DESCRIPTION:Name: Chen\, Hsin-Chung “Andrew” \nChair: Pai Chou \nDate:  Wednesday\, March 16\, 2022 \nTime: 9:00 am \nLocation:  https://uci.zoom.us/j/93264053012 \nCommittee: Nader Bagherzadeh\, Rainer Doemer \nTitle: Internet of Things-Based Collaborative Position-Sensing Systems for Cardiopulmonary Resuscitation and Indoor Localization \nAbstract: \nThe ubiquitous IoT devices have promoted the growth of applications that require target positioning. While precise positioning remains a challenge for IoT devices\, existing methods mainly focus on optimizing the positioning algorithms or expanding the sensing modalities. One opportunity area of improvement is to leverage their wireless communication capability to exchange sensing data of different modalities with nearby devices. This work proposes collaborative sensing frameworks to enhance the accuracy of position inference. \nOur approach to collaborative sensing is divided into selection of reliable devices\, the data-exchange mechanism\, and the data-fusion algorithm. We demonstrate our approach on two different position-sensing systems: one for chest compression-depth estimation and one for indoor pedestrian localization. The key components used in the frameworks include quality assessment of sensor data using mutual information\, data-fusion algorithms based on the chest-compression model and the pedestrian-encounter model\, and the data-exchange mechanism using Bluetooth Low Energy (BLE). A real-time position-estimation method based on our chest-compression model is proposed to remove the noise and handle the cumulative error. A collaborative conditional random field algorithm is developed to reduce the convergence distance in the localization estimation. Experimental results show our collaborative-sensing approach outperforms the existing solutions in terms of real-time estimated position. \nThis dissertation proposes an alternative approach to optimizing the position-sensing systems through collaborative sensing. Our work represents the first step towards the universal positioning on IoT devices and provides research opportunities for further improvement and exploration. \n\nShort Bio: Hsinchung Chen is a Ph.D. candidate in computer engineering. His research interests include signal processing\, machine learning\, and embedded system design.
URL:https://www.cecs.uci.edu/event/internet-of-things-based-collaborative-position-sensing-systems-for-cardiopulmonary-resuscitation-and-indoor-localization/
LOCATION:CA
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220707T100000
DTEND;TZID=America/Los_Angeles:20220707T230000
DTSTAMP:20231011T032340Z
CREATED:20231011T032340Z
LAST-MODIFIED:20231011T032340Z
UID:14136-1657188000-1657234800@www.cecs.uci.edu
SUMMARY:Transaction-Level Modeling of Deep Neural Networks for Efficient Parallelism and Memory Accuracy
DESCRIPTION:Name: Emad Arasteh\nChair: Prof. Rainer Doemer \nDate: July 7\, 2022 \nTime: 10:00 am \nLocation: EH 3206 \nCommittee: Prof. Fadi Kurdahi\, Prof. Ian Harris \nTitle: Transaction-Level Modeling of Deep Neural Networks for Efficient Parallelism and Memory Accuracy \nAbstract:  \nThe emergence of data-intensive applications\, such as Deep Neural Networks (DNNs)\, exacerbates the well-known memory bottleneck in computer systems and demands early attention in the design flow. Electronic System-Level (ESL) design using SystemC Transaction Level Modeling (TLM) enables effective performance estimation\, design space exploration\, and gradual refinement. In this dissertation\, we present our exploratory modeling framework for hardware-software codesign based on SystemC TLM with particular focus on exposing parallelism and memory contention. We demonstrate the effectiveness of our approach for representative complex DNNs such as GoogLeNet and Single Shot MultiBox Detector. \nFirst\, we study the impact of communication mechanisms on the available parallelism in TLM models. Specifically\, we demonstrate the impact of varying synchronization mechanisms and buffering schemes on the exposed parallelism using different modeling styles of a DNN. We measure the performance of aggressive out-of-order parallel discrete event simulation and analyze the available parallelism in the models. Our study suggests that increased parallel simulation performance indicates better models with higher amounts of parallelism exposed. \nSecond\, we explore the critical aspects of modeling and analysis of timing accuracy and memory contention. A major hurdle in tackling the memory bottleneck is the detection of memory contention late in the design cycle once detailed timed or cycle-accurate models are developed. A memory bottleneck detected at such a late stage can severely limit the available design choices or even require costly redesign. To explore new architectures prior to RTL implementation\, we propose a novel TLM-2.0 loosely-timed contention-aware (LT-CA) modeling style that offers high-speed simulation close to traditional loosely-timed (LT) models\, yet shows the same accuracy for memory contention as low level approximately-timed (AT) models. \nFinally\, we refine further the TLM-2.0 AT model by adding a cycle-accurate model of a memory subsystem. This model provides a higher timing accuracy for contention analysis\, hence it gives a more accurate estimation of the performance. We revise our LT-CA memory delay modeling to provide further accuracy comparable to the cycle-accurate TLM model of the shared memory subsystem. The high amount of contention on the shared memory suggests new processor architectures with local memories. \n\nShort Bio: \nEmad Arasteh is a Ph.D. candidate in Computer Engineering at University of California\, Irvine and a graduate researcher at the Center for Embedded and Cyber-physical Systems (CECS). He received the M.Sc. degree in Electronic Design from Lund University in Sweden. His current research interests include system-level modeling and design of embedded systems\, scalable memory system design\, and massively-parallel processor architecture. Previously\, he worked as a hardware and software engineer at semiconductor companies in Sweden and USA including Ericsson\, Canon (Axis Communications) and Samsung.
URL:https://www.cecs.uci.edu/event/transaction-level-modeling-of-deep-neural-networks-for-efficient-parallelism-and-memory-accuracy/
LOCATION:EH 3206\, Engineering Hall University of California\, Irve\, Irvine\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220817T103000
DTEND;TZID=America/Los_Angeles:20220817T113000
DTSTAMP:20231011T032408Z
CREATED:20231011T032408Z
LAST-MODIFIED:20231011T032408Z
UID:14153-1660732200-1660735800@www.cecs.uci.edu
SUMMARY:An End-to-End Platform for Multi-Modal Machine Learning Affective Computing Services
DESCRIPTION:Name: Emad Kasaeyan Naeini \nChair: Nikil Dutt \nDate: August 17\, 2022 \nTime: 10:30 AM \nLocation: DBH 3011 \nCommittee: Amir Rahmani\, Fadi Kurdahi \nTitle:  An End-to-End Platform for Multi-Modal Machine Learning Affective Computing Services \nAbstract: \nSmart affective computing applications deliberately influence pain\, emotion and other affective phenomena\, and are fundamental to human experience\, health and well-being. Affective states such as pain\, stress\, and emotion are intrinsically subjective in nature\, posing challenges for objective assessment and quantification of such affective phenomena. Prior efforts in affective computing provide a foundation for the automated analysis of affective states\, but still face challenges in real-life\, everyday settings. We use pain as an exemplar for affective computing services. Pain assessment is critical for optimal treatment\, and is particularly important during periods of acute pain since inadequately treated acute pain increases the risk of chronic pain. Historically\, patients have served as the main assessment tool as they are able to self-report their pain presence and severity on standard\, but somewhat subjective pain scales. However\, it remains a challenge to assess pain from patients who cannot self-report. Automatic pain recognition systems could be crucial to facilitating accurate\, objective\, and real-time pain measurement\, that can in turn improve pain management and ultimately lead to improved patient outcomes. Using pain as an exemplar of smart affective computing services\, this thesis proposes the use of multimodal sensing of physiological and behavioral input data\, transmits the data to edge and/or cloud nodes\, and processes data with compute-intensive machine learning (ML) algorithms. The efficiency of ML-driven applications for affective computing (e.g.\, pain assessment) is greatly affected by run-time variations resulting from continuous stream of noisy input data\, unreliable network connections\, and the variations in computational requirements of ML algorithms. Towards that end\, this thesis evaluates and automates objective and real-time multimodal pain assessment algorithms\, and performs design space exploration of accuracy-performance-energy trade-offs and sense-compute co-optimization for multimodal machine learning (MMML) methods. The approach developed in this thesis could be used for the design and development of an end-to-end platform for multimodal machine learning affective computing services\, together with a thorough analysis of their roles in the prediction\, performance and energy in future studies. \nShort bio: \nEmad Kasaeyan Naeini is a Ph.D. candidate in Computer Engineering at the University of California\, Irvine. He received his M.Sc. degree in Electrical and Computer Engineering from the Department of Electrical Engineering and Computer Science at the University of California Irvine in 2020 and his B.Sc. in Electrical Engineering from Sharif University of Technology\, Tehran\, Iran\, in 2017. His research interests primarily include deep learning and machine learning\, signal processing\, healthcare Internet of Things (IoT)\, and healthcare data analysis.
URL:https://www.cecs.uci.edu/event/an-end-to-end-platform-for-multi-modal-machine-learning-affective-computing-services/
LOCATION:DBH 3011\, Donald Bren Hall University of California\, Irve\, Irvine\, CA\, 92617\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230222T103000
DTEND;TZID=America/Los_Angeles:20230222T113000
DTSTAMP:20231011T032408Z
CREATED:20231011T032408Z
LAST-MODIFIED:20231011T032408Z
UID:14152-1677061800-1677065400@www.cecs.uci.edu
SUMMARY:Online Learning for Orchestrating Deep Learning Inference at Edge
DESCRIPTION:Name: Sina Shahhosseini \nChair: Nikil Dutt \nDate: February 22\, 2023 \nTime: 10:30 AM \nLocation: 2011 DBH \nCommittee: Amir Rahmani\, Fadi Kurdahi \nTitle:  Online Learning for Orchestrating Deep Learning Inference at Edge \nAbstract:  \nDeep-learning-based intelligent services have become prevalent in cyber-physical applications including smart cities and health-care. Resource-constrained end-devices must be carefully managed in order to meet the latency and energy requirements of computationally-intensive deep learning services. Collaborative end-edge-cloud computing for deep learning provides a range of performance and efficiency that can address application requirements through computation offloading. The decision to offload computation is a communication-computation co-optimization problem that varies with both system parameters (e.g.\, network condition) and workload characteristics. On the other hand\, deep learning model optimization provides another source of tradeoff between latency and model accuracy. An end-to-end decision-making solution that considers such computation-communication problem is required to synergistically find the optimal offloading policy and model for deep learning services. To this end\, we propose a reinforcement-learning-based computation offloading solution that learns optimal offloading policy considering deep learning model selection techniques to minimize response time while providing sufficient accuracy. In addition\, we present a Hybrid Learning approach that reduces the number of interactions with the system environment by combining model-based and model-free reinforcement learning. We demonstrate the efficacy of our strategies through experimental comparison with state-of-the-art RL-based inference orchestration. \nShort Bio: \nSina Shahhosseini received his B.S. and M.S degrees in Electrical Engineering from the University of Tehran and Sharif University of Technology\, in 2013 and 2015\, respectively. Since Fall 2016\, he has been working towards his Ph.D. degree in Computer Engineering at University of California\, Irvine\, with a focus on the application of reinforcement learning for task orchestration in computer systems. He interned with Cruise in the summer of 2022 as a Machine Learning Engineer Intern\, where he worked on optimizing Machine Learning frameworks for autonomous vehicles.
URL:https://www.cecs.uci.edu/event/online-learning-for-orchestrating-deep-learning-inference-at-edge/
LOCATION:DBH 2011\, Donald Bren Hall University of California\, Irve\, Irvine\, CA\, 92617\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230501T120000
DTEND;TZID=America/Los_Angeles:20230501T140000
DTSTAMP:20231011T032408Z
CREATED:20231011T032408Z
LAST-MODIFIED:20231011T032408Z
UID:14155-1682942400-1682949600@www.cecs.uci.edu
SUMMARY:Cross-Layer Attacks and Defenses in the Cyber-Physical System (CPS)
DESCRIPTION:Name: Anomadarshi Barua\nChair: Mohammad Abdullah Al Faruque \nDate: May 1\, 2023 \nTime: 12 PM – 2 PM \nLocation: EH 2430-Harut Barsamian \nCommittee: Pramod P Khargonekar\, Zhou Li \nTitle: Cross-Layer Attacks and Defenses in the Cyber-Physical System (CPS) \nAbstract: \nHave you ever wondered how simple music can leak deadly pathogens from a bio-safety lab? Have you ever considered how an industrial robotic arm can be compromised from a cloud infrastructure? Have you ever imagined an attacker could use a magnetic field to shut down a weak microgrid?  If the above questions excite you\, please attend my PhD oral defense\, where my research answers the above questions with a practical demonstration of CPS attacks on industry-used infrastructure. With this seminar\, the audience will gain a deep insight into how hardware/software interaction in CPS’s cross-layer can open a “Pandora’s Box” of unknown threats that can come from very unconventional ways. \nMy research sits at the intersection of hardware and software cross-layers in CPSs\, exploring how interactions of cyber and physical components can affect the safety and controllability of closed-loop control from sensing to actuation. Most attacks on CPSs can be propagated from the physical domain to the cyber domain or vice-versa and hence\, can be termed cross-layer attacks. To understand these cross-layer attacks\, a very different set of methodologies and tools is needed. My research talks about how these tools can be made. Moreover\, the seminar will show how cross-layer vulnerabilities can be handled using new hardware/software co-design approaches to detect\, contain and isolate threats in CPSs. \nShort Bio: \nAnomadarshi Barua is a Ph.D. candidate studying Computer Engineering at the University of California Irvine. He received his M.S. degree in Embedded Computing Systems jointly at the University of Southampton (UoS) and the Norwegian University of Science and Technology (NTNU) in 2016. Now\, He is working under the supervision of Professor Al Faruque in the Autonomous and Intelligent Cyber-Physical Systems (AICPS) Lab. His current research focuses on the system-level design of embedded and Cyber-Physical-Systems (CPSs) with a special interest in CPS security\, low-power design\, and computer microarchitecture. He publishes in the security conferences like ACM CCS\, USENIX\, ACSAC\, RAID\, HOST\, and CHES and system conferences like ICCPS and ICCAD. He also has worked in several industries in North America and Europe\, such as Intel Corporation (USA)\, Solidigm (USA)\, Nordic Semiconductor (Norway)\, and IDEAS (Norway). In his leisure time\, he likes to travel to new places and go for hiking.
URL:https://www.cecs.uci.edu/event/cross-layer-attacks-and-defenses-in-the-cyber-physical-system-cps/
LOCATION:EH 2430\, Engineering Hall\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230505T153000
DTEND;TZID=America/Los_Angeles:20230505T163000
DTSTAMP:20231011T032408Z
CREATED:20231011T032408Z
LAST-MODIFIED:20231011T032408Z
UID:14156-1683300600-1683304200@www.cecs.uci.edu
SUMMARY:Context-Aware and Energy-Efficient Autonomous Systems
DESCRIPTION:Name: Arnav Malawade\nChair: Mohammad Al Faruque \nDate: May 5\, 2023 \nTime: 3:30 PM \nLocation: ISEB 1200 \nCommittee: Marco Levorato\, Yasser Shoukry \nTitle:  Context-Aware and Energy-Efficient Autonomous Systems \nAbstract: \nAutonomous systems (AS) are beginning to play a more significant role in modern society. AS include robotic systems that perceive and react to changes in their environment\, such as aerial drones\, ground/aquatic robots\, and consumer autonomous vehicles (AVs)\, among others. Breakthroughs in deep learning and increased consumer interest in ubiquitous autonomy have fueled recent advances in perception\, modeling\, and control algorithms. However\, these advances come with rising energy costs. Modern AS require large deep-learning (DL) models to perceive the environment and safely detect and avoid objects. The computational demands of these models significantly increase the hardware requirements of the complete system\, such that modern AV systems can require several kilowatts of power to enable autonomy\, reducing range and utility. These impacts affect most AS since AVs\, ground robots\, and drones are typically edge devices that operate in energy-constrained environments. \nThe need for large\, complex DL models is primarily driven by the fact that\, in the real world\, challenging and unpredictable scenarios can occur and must be modeled appropriately by the AS. However\, existing approaches are exceedingly cautious\, preferring to execute a large\, inefficient model even in typical scenarios just in case a difficult edge case arises. This approach to autonomy hinders both the utility of existing AS and the broad-scale adoption of AS. Instead\, an AS should be able to understand the context of its surrounding environment and adapt its model to fit each situation. \nThis dissertation explores methods for developing context-aware AS to improve perception and prediction performance\, reduce energy consumption\, and enable adaptation to dynamic environments. Three different perspectives are studied: (i) scene-graph embedding approaches are proposed and evaluated for improved semantic understanding and state estimation; (ii) methods for implementing split ML models for dynamic\, resource-efficient computation offloading from edge to cloud within real-time latency constraints are studied; and (iii) context-aware\, dynamic ML perception models that jointly optimize performance and energy efficiency are studied from an algorithmic and system-wide optimization perspective. Overall\, the results show that context-aware models achieve state-of-the-art performance across applications\, and dynamic architectures conditioned on context can help resolve the energy-performance trade-off by enabling the joint optimization of these objectives. \nShort Bio: \nArnav Malawade is a PhD Candidate in Computer Engineering and works in the Autonomous and Intelligent Cyber-Physical Systems (AICPS) Lab under the supervision of Professor Mohammad Al Faruque. He received his B.S. in Computer Science and Engineering and his M.S. in Computer Engineering at UC Irvine in 2018 and 2021\, respectively. He was a Doctorate Intern at HRL Laboratories in 2022. His research interests include autonomous driving\, graph-based perception\, dynamic neural networks\, and smart manufacturing.
URL:https://www.cecs.uci.edu/event/context-aware-and-energy-efficient-autonomous-systems/
LOCATION:ISEB 1200\, Interdisciplinary Science and Engineering Building University of California\, Irve\, Irvine\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230522T090000
DTEND;TZID=America/Los_Angeles:20230522T110000
DTSTAMP:20231011T032434Z
CREATED:20231011T032434Z
LAST-MODIFIED:20231011T032434Z
UID:14157-1684746000-1684753200@www.cecs.uci.edu
SUMMARY:Graph Learning for Robust Embedded and Cyber-Physical Systems
DESCRIPTION:Name: Shih-Yuan Yu\nChair: Mohammad Al Faruque \nDate: May 22\, 2023 \nTime: 9:00 AM – 11:00 AM \nLocation: EH 2430 \nCommittee: Pramod Khargonekar\, Yanning Shen \nTitle: Graph Learning for Robust Embedded and Cyber-Physical Systems \nAbstract: \nSince the appearance of microprocessors\, miscellaneous categories of computer systems\, including Embedded and Cyber-Physical Systems (ECPS)\, have become an integral part of our modern society. In contrast to general-purpose computers\, an ECPS is a computing system designed to perform a dedicated or narrow range of functions with minimal human intervention. Today\, the advanced standards in device networking\, ubiquitous access to the Internet\, miniaturization of processors\, and reduced power consumption have taken this field to the next level. However\, despite these technological growths\, designing a robust ECPS remains an open research challenge where the goal is to achieve better functionalities\, encompass complex\, uncertain\, and changing environments\, and ensure system security. Minor failures on ECPS may cause severe collapses or cyberattacks\, impeding progress toward increasing automation and modernizing our computing environments. \nTo achieve robustness\, this dissertation studies the embedding of Computational Intelligence (CI) into emerging Graph Learning (GL) technologies. The CI paradigms mimic the nature of humans\, aiming at solving complex problems and exhibiting a cognitive ability to learn or adapt to new situations to generalize\, abstract\, discover\, and associate. As a prerequisite of this research direction\, Machine Learning (ML) has become increasingly ubiquitous\, with existing works exploring various fields\, from self-driving vehicles\, facial recognition systems\, and real-time language translation to security surveillance\, innovative home applications\, and health monitoring. However\, conventional ML algorithms typically require appropriate vectorized representations crafted by domain experts to accomplish the desired goals. Graph-structured data have imposed unprecedented challenges on ML due to their inherent complexity. Unlike text\, audio\, and images\, graphs are embedded in an irregular dimension\, making some essential operations of ML inapplicable. GL has attracted much attention to new research ideas in several fields. To date\, many researchers have proven the usefulness of GL in social computing\, information retrieval\, computer vision\, bioinformatics\, economics\, and e-commerce. However\, its applications in subfields of ECPS still need to be explored. \nIn this dissertation\, we will cover GL applications for ECPS in robust Integrated Circuit (IC) design analysis for hardware security\, robust binary analysis for enabling software security\, and ultimately how GL brings the scene-understanding capabilities of autonomous driving systems to the next level. In IC design analysis (Chapter 2)\, we explore how GL can be leveraged to resolve challenging problems in hardware security. We propose HW2VEC and demonstrate how it can be utilized for Hardware Trojan (HT) detection and Intellectual Property (IP) piracy detection. Next\, in the binary analysis (Chapter 3)\, we demonstrate how a more advanced GL methodology\, called CFG2VEC\, can reverse-engineer the semantic source-level information lost during the compilation process\, thus making the binary software patching tasks more efficient. Then\, we describe our novel methodology\, SG2VEC\, to enhance autonomous driving systems’ scene understanding capabilities (Chapter 4). To enable this cognitive capability\, we propose to use scene-graph to encode the surrounding traffic participants and a pipeline of spatiotemporal scene-graph embedding networks to process scene-graphs and learn toward goals. During the courses of these projects\, we have developed many research tools to confront the technical challenges (mentioned in Appendices). \nShort Bio: \nShih-Yuan Yu is a 5-th year Ph.D. candidate in Computer Engineering and works in the Autonomous and Intelligent Cyber-Physical Systems lab under the supervision of Professor Mohammad Al Faruque. He received his B.S. and M.S. degrees in Computer Science and Information Engineering from the National Taiwan University in 2014. Before his Ph.D.\, Shih-Yuan worked as a software engineer at MediaTek for 4+ years. During his Ph.D.\, he interned at TuSimple (an autonomous driving company) and Siemens (industrial-academia collaborated projects). During his Ph.D.\, he has published 20+ papers in conferences and journals. His interests are making embedded cyber-physical systems more robust with data-driven methodologies enabled by graph learning.
URL:https://www.cecs.uci.edu/event/graph-learning-for-robust-embedded-and-cyber-physical-systems/
LOCATION:EH 2430\, Engineering Hall\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230523T140000
DTEND;TZID=America/Los_Angeles:20230523T150000
DTSTAMP:20231011T032546Z
CREATED:20231010T160244Z
LAST-MODIFIED:20231011T032546Z
UID:13940-1684850400-1684854000@www.cecs.uci.edu
SUMMARY:Reasoning About Emergent Behaviors at Runtime
DESCRIPTION:Name: Caio Batista de Melo\nChair: Nikil Dutt \nDate: May 23\, 2023 \nTime: 2:00 PM \nLocation: DBH 4011 \nCommittee: Fadi Kurdahi\, Tony Givargis \nTitle: Reasoning About Emergent Behaviors at Runtime \nAbstract: \nAs Autonomous Vehicles (AVs) become more common in the market\, the development of new technologies and solutions for them advances quickly. However\, there still are safety concerns regarding the increasing presence of AVs on public roads. For example\, the Cali- fornia Department of Motor Vehicles reported an approximate average of 3 disengagement events for each AV under test in 2021 and 1 event for every 1\,500 driven miles. Considering that a vehicle drives\, on average\, 15\,000 miles per year\, an AV would have ten such incidents annually. With this in mind\, work needs to be done to enable AVs to handle unexpected sit- uations (i.e.\, emergent behaviors) that can arise while driving\, increasing the safety for both humans and AVs on the road. This dissertation discusses possible approaches to improve the detection and reasoning of emergent behaviors at runtime. \nFirst\, it describes SAFER\, a framework to monitor individual systems using runtime verifica- tion alongside machine learning anomaly detection to detect emergent behaviors at runtime. SAFER achieves an average F1-score of 83% over four different example systems\, showcas- ing its efficacy to be on par with related work while being applicable to different classes of applications. Next\, it discusses DRIVE\, a framework for monitoring collective systems that can work together to find global anomalies that affect collective safety by identifying violations in local properties. Throughout a state-of-the-art inspired truck platooning case study\, DRIVE can detect all safety property violations\, with most queries resolved under 1 ms and a maximum latency of 11 ms per query\, demonstrating its promise. Lastly\, it presents LOCoCAT\, a low-overhead framework for emergent behavior reasoning to classify anomaly types based on vehicular data. LOCoCAT achieves an F1-score of up to 99.16% within the first 50 ms of the anomaly\, allowing the system to react quickly. These three techniques can be combined to allow future AVs to operate more safely on the road. \nShort Bio: \nCaio Batista del Melo is a Ph.D. candidate at the Computer Science Department of the University of California\, Irvine\, where he works with Prof. Nikil Dutt on the Information Processing Factory project. His research focuses on emergent behaviors and fault detection of systems. From a big-picture view\, he explored how we can enable self-driving vehicles to detect unexpected situations and decide how to best react to them. Prior to joining UCI\, he attended the University of Brasília\, in Brazil\, where he got a B.Sc. and an M.Sc. in Computer Science. While at UnB\, he worked with Prof. Genaína Rodrigues researching dependable distributed systems\, more specifically\, implied scenarios.
URL:https://www.cecs.uci.edu/event/reasoning-about-emergent-behaviors-at-runtime/
LOCATION:DBH 4011\, Donald Bren Hall\, University of California\, Irvine\, Irvine\, CA\, 92617\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230524T150000
DTEND;TZID=America/Los_Angeles:20230524T160000
DTSTAMP:20231011T032435Z
CREATED:20231011T032435Z
LAST-MODIFIED:20231011T032435Z
UID:14158-1684940400-1684944000@www.cecs.uci.edu
SUMMARY:Integrating Machine Learning for Enhanced Cross-Layer Security and Reliability in Hardware and Cyber-Physical Systems
DESCRIPTION:Name: Rozhin Yasaei\nChair: Mohammad Al Faruque \nDate: May 24\, 2023 \nTime: 3:00 PM \nLocation: EH 2430-Harut Barsamian \nCommittee: Fadi Kurdahi\, Zhou Li \nTitle: Integrating Machine Learning for Enhanced Cross-Layer Security and Reliability in Hardware and Cyber-Physical Systems \nAbstract: \nThis Ph.D. thesis presents a comprehensive investigation into addressing security and reliability challenges in hardware design and cyber-physical systems. The research leverages advanced techniques such as Graph Neural Networks (GNN) and machine learning to develop novel methodologies for anomaly detection\, hardware Trojan detection\, IP piracy detection\, and cross-layer security analysis.\nThe thesis focuses on hardware security\, specifically Hardware Trojan (HT) detection. A golden reference-free approach\, GNN4TJ\, is proposed for detecting HTs at the Register Transfer Level (RTL). By modeling hardware designs as graphs and employing GNN\, GNN4TJ achieves high recall rates for identifying both known and unknown HTs. \nMoving to the IoT domain\, the thesis addresses security challenges in IoT systems through the development of adaptive anomaly detection methods. IoT-CAD utilizes IoT sensor data and fog computing to ensure data integrity and detect anomalous incidents. The proposed methodology incorporates sensor association algorithms\, LSTM neural networks\, and Gaussian estimation for real-time anomaly detection. The thesis further extends the research to multi-modal data fusion\, where the integration of sensor and communication data using GNN enables improved anomaly detection\, source identification\, and recovery in IoT systems. \nIn addition\, the thesis explores the security aspect of wearable devices. A machine learning-based approach is proposed to detect anomalies in wearable devices that rely on NFC protocols. By leveraging system context and sensor correlations\, the methodology achieves high accuracy in detecting anomalies from multiple wearable sensors.\nFurthermore\, the thesis delves into IP piracy detection\, introducing a novel methodology that utilizes GNN to assess circuit similarities and detect IP piracy. The proposed approach proves effective in detecting IP piracy even in the presence of obfuscation techniques\, providing a robust solution for protecting intellectual properties. \nOverall\, this thesis showcases the application of advanced techniques such as GNN and machine learning in enhancing security and reliability in hardware design\, IoT systems\, and wearable devices. The proposed methodologies for anomaly detection\, hardware Trojan detection\, IP piracy detection\, and cross-layer security analysis contribute to advancing the state-of-the-art in ensuring the integrity and security of critical systems in the digital era. \nShort Bio: \nRozhin Yasaei is currently a Ph.D. candidate at the University of California Irvine\, studying computer engineering under Professor Mohammad Al Faruque. She received her M.Sc. degree in computer engineering from the University of California Irvine in 2021 and her B.Sc. in electrical engineering from Sharif University of technology in 2018. Her research interests lie in applied machine learning for embedded and cyber-physical systems security. She develops new methodologies that bring principles and techniques from machine learning\, such as graph neural networks\, to cross-layer system security. Rozhin conducts research that spans the areas of hardware\, embedded systems\, and cyber-physical systems while maintaining relevance to security and reliability. \nRozhin was selected for UC Irvine’s pedagogical fellowship in 2021 and\, through one year of training\, received a teaching excellence certificate and the qualification to design and implement a 3-day training program for incoming graduate students to prepare them for teaching assistance. Furthermore\, she received the Rising Stars in EECS Award in 2022\, the iREDEFINE Professional Development Award in 2022\, UCI UROP Research Discovery Fellowship in 2022\, UCI Henry Samueli Endowed Scholarship in 2018\, and several scholarships from different conferences. \nRozhin is devoted to promoting diversity and inclusion and supporting her fellow collages\, especially the underrepresented population in STEM\, by actively volunteering for panels\, workshops\, and conferences with similar visions and serving the community. She has been the president of the UCI chapter of the Women in CyberSecurity (WiCyS) organization since 2021 to foster women’s participation in this field. Qualified with mentoring excellence certificate\, she has been selected by several programs at UCI to mentor undergraduate and graduate students.
URL:https://www.cecs.uci.edu/event/integrating-machine-learning-for-enhanced-cross-layer-security-and-reliability-in-hardware-and-cyber-physical-systems/
LOCATION:EH 2430\, Engineering Hall\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230706T090000
DTEND;TZID=America/Los_Angeles:20230706T100000
DTSTAMP:20231011T032435Z
CREATED:20231011T032435Z
LAST-MODIFIED:20231011T032435Z
UID:14161-1688634000-1688637600@www.cecs.uci.edu
SUMMARY:Self-aware Memory Management for Emerging Energy-Efficient Architectures
DESCRIPTION:Name: Biswadip (Deep) Maity \nChair: Nikil Dutt \nDate: July 6\, 2023 \nTime: 9:00 AM \nLocation: DBH 3011 \nCommittee: Fadi Kurdahi\, Nalini Venkatasubramanian \nTitle: Self-aware Memory Management for Emerging Energy-Efficient Architectures \nAbstract: \nThe ever-increasing demands of data-intensive applications have presented significant challenges in memory performance and energy efficiency. Traditional approaches to memory management\, relying on workload-specific optimizations and static memory configurations\, have become inadequate in addressing the dynamic nature of modern computing systems. Inspired by principles from psychology and neuroscience\, computational self-awareness aims to develop intelligent systems capable of learning from past experiences\, reasoning about their current state\, and making informed decisions at runtime. My research investigates the application of computational self-awareness in memory management\, with a specific focus on understanding the impact of different degrees of self-awareness within the memory subsystem. By applying self-awareness principles to memory management\, we show that it is possible to create an energy-efficient memory subsystem that adapts to changing workloads and optimizes memory usage across various architectures and layers of the system abstraction. \nShort Bio: \nBiswadip Maity is a Ph.D. candidate at the Computer Science department of University of California\, Irvine. Previously\, he was a Software Engineer at Microsoft. His research is in the area of runtime resource allocation and power management for data-centric architectures with a special focus on self-aware embedded systems\, memory systems\, system software and architecture for heterogeneous systems. He is a member of the Dutt Research Group working with Prof. Nikil Dutt. His work spans across resource-constrained microcontrollers\, IoT systems and Cloud computing performance (in collaboration with Facebook). Biswadip Maity is a Charpak Scholar\, a DAAD Scholar\, and a recipient of a Facebook Research Grant.
URL:https://www.cecs.uci.edu/event/self-aware-memory-management-for-emerging-energy-efficient-architectures/
LOCATION:DBH 3011\, Donald Bren Hall University of California\, Irve\, Irvine\, CA\, 92617\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231130T093000
DTEND;TZID=America/Los_Angeles:20231130T103000
DTSTAMP:20240104T190401Z
CREATED:20240104T185923Z
LAST-MODIFIED:20240104T190401Z
UID:15937-1701336600-1701340200@www.cecs.uci.edu
SUMMARY:Towards Finding the Optimization Potential of Fine-Grained Locks
DESCRIPTION:Name:  Ahmad Nahian \nChair: Professor Brian Demsky \nDate: November 30\, 2023 \nTime:  9:30 AM \nLocation: ISEB 1010 \nCommittee: Professor Rainer Doemer\, Professor Quoc-Viet Dang \nTitle: Towards Finding the Optimization Potential of Fine-Grained Locks \nAbstract: Fine-grained locking is an effective way of eliminating lock contention in multi-threaded applications. However\, it is comparatively difficult and error prone to implement. To help programmers know the optimization potential of implementing fine-grained locking we have developed an approach that overcomes several shortcomings of prior approaches like record/replay. Our approach executes the existing implementation of the multi-threaded program with a custom thread scheduler that schedules threads according to their logical time\, which is the time it would have taken to execute the thread without waiting for unnecessary lock contention. The custom thread scheduler leverages a prediction mechanism to predict conflict relations among critical sections so that it schedules threads to generate the execution of an implementation with optimal fine-grained locking. We have implemented our approach in FGLPerf and tested it on several multi-threaded benchmarks. \nShort Bio: Ahmad Nahian is a PhD candidate in the EECS Computer Engineering program at UC Irvine\, working under the supervision of Professor Brian Demsky. Ahmad Nahian attained his BS in Computer Science and Engineering from Bangladesh University of Engineering and Technology and MS in Computer Engineering from UC Irvine. A notable work of his research is information flow profiler that finds optimization opportunities in multi-threaded programs. The information flow profiler is a powerful tool in that it can find new optimization opportunities along with optimization insights to eliminate synchronization bottlenecks that cannot be discovered by other state of the art profilers.
URL:https://www.cecs.uci.edu/event/towards-finding-the-optimization-potential-of-fine-grained-locks/
LOCATION:ISEB 1010\, Interdisciplinary Science and Engineering Building\, University of California\, Irvie\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240524T093000
DTEND;TZID=America/Los_Angeles:20240524T103000
DTSTAMP:20240528T213610Z
CREATED:20240528T213210Z
LAST-MODIFIED:20240528T213610Z
UID:16300-1716543000-1716546600@www.cecs.uci.edu
SUMMARY:Deep Latent Variable Modeling of Physiological Signals
DESCRIPTION:Name: Khuong An Vo \nChair: Prof. Nikil Dutt \nDate: May 24\, 2024 \nTime:  9:30 AM \nLocation: Zoom \nCommittee: Prof. Erik Sudderth\, Prof. Hung Cao\, and Prof. Ramesh Srinivasan \nTitle: Deep Latent Variable Modeling of Physiological Signals \nAbstract: A deep latent variable model is a powerful tool for modeling complex distributions. These models assume that underlying structures\, but unobserved\, are present within the data. In this dissertation\, we explore high-dimensional problems related to physiological monitoring using latent variable models. First\, we present a novel deep state-space model to generate electrical waveforms of the heart using optically obtained signals as inputs. This can bring about clinical diagnoses of heart disease via simple assessment through wearable devices. Second\, we present a brain signal modeling scheme that combines the strengths of probabilistic graphical models and deep adversarial learning. The structured representations can provide interpretability and encode inductive biases to reduce the data complexity of neural oscillations. The efficacy of the learned representations is further studied in epilepsy seizure detection formulated as an unsupervised learning problem. Third\, we propose a framework for the joint modeling of physiological measures and behavior. Existing methods to combine multiple sources of brain data provided are limited. Direct analysis of the relationship between different types of physiological measures usually does not involve behavioral data. Our method can identify the unique and shared contributions of brain regions to behavior and can be used to discover new functions of brain regions. The success of these innovative computational methods would allow the translation of biomarker findings across species and provide insight into neurocognitive analysis in numerous biological studies and clinical diagnoses\, as well as emerging consumer applications. \nShort Bio: Khuong An Vo is a Ph.D. candidate in Computer Science at UC Irvine\, advised by Prof. Ramesh Srinivasan\, Prof. Nikil Dutt\, and Prof. Hung Cao. Prior\, he received a B.Eng. (Honors) degree in CS from HCMC Vietnam National University with research revolved around statistical natural language processing and has been applied in large-scale commercial systems. Now\, Vo’s research focuses on deep latent variable models in biomedical signal processing and neurocognitive modeling. He is also interested in the application of efficient deep learning in healthcare IoT. Vos’ work is supported by National Science Foundation (NSF) and National Institutes of Health (NIH) grants.
URL:https://www.cecs.uci.edu/event/deep-latent-variable-modeling-of-physiological-signals/
LOCATION:Zoom
CATEGORIES:PhD Defenses
ATTACH;FMTTYPE=image/jpeg:https://cecs.uci.edu/files/2024/05/Khuong-An-Vo-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240528T110000
DTEND;TZID=America/Los_Angeles:20240528T120000
DTSTAMP:20240827T000948Z
CREATED:20240827T000948Z
LAST-MODIFIED:20240827T000948Z
UID:16359-1716894000-1716897600@www.cecs.uci.edu
SUMMARY:Differential Privacy for Non-standard Settings
DESCRIPTION:Name: Joann Chen \nChair: Prof. Zhou Li \nDate: May 28\, 2024 \nTime:  11:00 AM \nLocation: EH 5204 \nCommittee: Prof. Yanning Shen\, Prof. Gene Tsudik \nTitle: Differential Privacy for Non-standard Settings \nAbstract: In today’s technologically driven world\, personal data stands at a crucial juncture\, representing both immense potential and significant peril. Such potential\, when leveraged properly\, can lead to groundbreaking advancements in sectors such as healthcare and finance. Yet\, the misuse of this same data can result in severe privacy breaches. This thesis is dedicated to navigating the complex terrain of personal data privacy\, highlighting Differential Privacy (DP) as a pivotal solution that provides strong\, provable privacy guarantees. \nWith a focus on DP-aware system design\, this thesis delves into the challenges of integrating DP into various systems\, emphasizing the necessity for infrastructures that prioritize privacy preservation. It outlines the progression of DP from theoretical concepts to practical implementations\, detailing both the challenges encountered and the progress made in applying academic DP concepts to real-world solutions. Moreover\, this thesis extends beyond the scope of DP\, exploring the broader realm of privacy-enhancing technologies (PETs). These technologies are crucial not only for meeting legal compliance and ensuring privacy in systems and machine learning but also for paving the way towards a future where data utility and individual privacy coexist in harmony. \nShort Bio: Joann is a fifth-year Ph.D. candidate in the EECS department advised by Prof. Zhou Li. Her research interests center around Differential Privacy (DP)\, privacy-enhancing technologies\, and privacy in machine learning. She has experience in quantifying privacy risks in machine learning and building DP into DNS resolution\, data stream release\, resource allocator\, ad conversion measurements\, and network data synthesis. Her research aims to bridge the gap between DP theory and its practical applications for real-world deployments.
URL:https://www.cecs.uci.edu/event/differential-privacy-for-non-standard-settings/
LOCATION:EH 5204
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240531T140000
DTEND;TZID=America/Los_Angeles:20240531T150000
DTSTAMP:20240516T201808Z
CREATED:20240516T201808Z
LAST-MODIFIED:20240516T201808Z
UID:16295-1717164000-1717167600@www.cecs.uci.edu
SUMMARY:Multi-Sensor Data Fusion and Machine Learning for Adaptive Autonomous Systems
DESCRIPTION:Name: Trier Mortlock \nChair: Prof. Mohammad Al Faruque \nDate: May 31\, 2024 \nTime:  2 PM \nLocation: ISEB 1200 \nCommittee: Prof. Faryar Jabbari\, Prof. Pramod Khargonekar \nTitle: Multi-Sensor Data Fusion and Machine Learning for Adaptive Autonomous Systems \nAbstract: Autonomous systems rely on sensors to collect data about their surrounding environments to influence their decision-making algorithms.  Sensor fusion involves combining data from different sensors to reduce sensing uncertainties and better understand the environment. Advances in computing platforms and the growing availability of data have ushered a wave of machined learning-based sensor fusion methods that can extend fusion capabilities beyond the current understanding of systems present in mathematical models. However\, autonomous systems are increasingly tasked with operating in dynamic settings that require high levels of adaptability\, and how best to employ adaptive sensor fusion strategies is a challenging question. This dissertation examines how\, where\, why\, and when to perform multi-sensor data fusion for autonomous systems operating in dynamic environments. A novel sensor fusion architecture that improves autonomous systems’ adaptation capabilities is introduced. The proposed fusion architecture dynamically adapts both how and when fusion is performed by using a machine learning model that learns to identify the optimal sensor fusion strategy for the given sensing context. Use cases across different types of autonomous systems are examined\, highlighting the ability of the proposed fusion approach to generalize across various systems and autonomy tasks. Overall\, the methodologies and ideas presented in this dissertation can be applied broadly across engineering disciplines and can enhance the development of future technologies that require adaptive sensing capabilities. \nShort Bio: Trier Mortlock is a Ph.D. candidate in the Mechanical and Aerospace Engineering program at UC Irvine working under the supervision of Professor Mohammad Al Faruque in the Autonomous and Intelligent Cyber-Physical Systems Lab. Trier obtained his B.S. in Mechanical Engineering from the University of California\, Berkeley and his M.S. in Mechanical and Aerospace Engineering from UC Irvine. During his Ph.D.\, Trier interned at the Frontier Development Lab at the SETI Institute\, NASA and was a Doctoral Research Fellow at the Automotive Research Center\, a U.S. Army Center of Excellence for Modeling and Simulation of Ground Vehicle Systems.
URL:https://www.cecs.uci.edu/event/multi-sensor-data-fusion-and-machine-learning-for-adaptive-autonomous-systems/
LOCATION:ISEB 1200\, Interdisciplinary Science and Engineering Building University of California\, Irve\, Irvine\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240605T110000
DTEND;TZID=America/Los_Angeles:20240605T120000
DTSTAMP:20240606T203433Z
CREATED:20240606T203408Z
LAST-MODIFIED:20240606T203433Z
UID:16310-1717585200-1717588800@www.cecs.uci.edu
SUMMARY:Advanced Machine Learning and AI Techniques for Enhancing Wearable Health Monitoring Systems
DESCRIPTION:Name: Amir Hosein Afandizadeh Zargari \nChair: Prof. Fadi Kurdahi \nDate: June 5\, 2024 \nTime:  11 AM \nLocation: EH 3206 \nCommittee: Prof. Peter Tseng\, Prof. Amir Rahmani \nTitle: Advanced Machine Learning and AI Techniques for Enhancing Wearable Health Monitoring Systems \nAbstract: This research advances wearable technology for real-time health monitoring using advanced machine learning (ML) and innovative hardware solutions. Key contributions include a novel ML-based model for accurate tracking of in-mouth nutrient sensors\, employing a wide range of frequencies and advanced algorithms to enhance measurement reliability. An innovative use of Cycle Generative Adversarial Networks (CycleGAN) addresses motion artifacts in photoplethysmography (PPG) signals\, transforming noisy data into clean\, artifact-free signals without relying on accelerometers. The development of smart clothing systems with passive sensors and copper coils\, using Near Field Communication (NFC) and energy harvesting technologies\, ensures wireless connectivity and power in various environments\, including underwater. AI-Coach\, a personalized wearable system\, integrates multiple sensors and a BERT model for real-time workout monitoring\, adapting to individual needs for accurate activity tracking. The research also addresses security vulnerabilities threatening health monitoring systems with robust anomaly detection systems and adversarial training methods. These advancements aim to improve the accuracy\, power efficiency\, and security of wearable health devices\, with significant implications for individual and public health monitoring. \nShort Bio: Amir Hosein Afandizadeh Zargari is a Ph.D. candidate in Computer Engineering at the University of California\, Irvine. He earned his B.Sc. in Computer Engineering from Sharif University of Technology in Iran in 2018 and his M.S. in Computer Engineering from UC Irvine in 2020. Throughout his master’s and Ph.D. studies\, Amir has published multiple papers in prestigious journals\, including Natures\, ACMs\, IEEEs\, and conferences like ESWEEK. His research focuses on advancing wearable technology for real-time health monitoring using machine learning and artificial intelligence.
URL:https://www.cecs.uci.edu/event/advanced-machine-learning-and-ai-techniques-for-enhancing-wearable-health-monitoring-systems/
LOCATION:EH 3206\, Engineering Hall University of California\, Irve\, Irvine\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240606T083000
DTEND;TZID=America/Los_Angeles:20240606T093000
DTSTAMP:20240611T214758Z
CREATED:20240611T214758Z
LAST-MODIFIED:20240611T214758Z
UID:16321-1717662600-1717666200@www.cecs.uci.edu
SUMMARY:Object-Oriented Visual Localization in Urban Mobile-Edge Environments
DESCRIPTION:Name: Bo Tsai \nChair: Prof. Kwei-Jay Lin \nDate: June 6\, 2024 \nTime:  8:30 AM \nLocation: Virtual/Zoom \nCommittee: Prof. Rainer Doemer\, Prof. Yasser Shoukry \nTitle: Object-Oriented Visual Localization in Urban Mobile-Edge Environments \nAbstract: The advent of the Internet of Things (IoT) and the progress in robotic technology have brought a new era where intelligent services significantly benefit society. As robots evolve from executing simple tasks to providing complex\, interactive services\, the need of precise and reliable localization becomes paramount. In this context\, the ubiquity of low-cost cameras and the maturity of object detection technologies have paved the way for advanced semantic visual-based localization solutions. “OOPose\,” introduced in this thesis\, exemplifies this advancement by providing an object-oriented pose estimation framework tailored for robots with small embedded devices. It leverages dense features from conventional object detection neural networks\, striking a balance between pixel-matching accuracy and processing speed\, thus enhancing the effectiveness and efficiency of robot localization. Amidst escalating demand for robots capable of facilitating intelligent interactive activities\, integrating high-quality cameras on mobile IoT devices\, combined with wireless connectivity\, edge computing\, and advanced computer vision algorithms\, enables a novel approach to processing offload with mobile video. This thesis details the deployment of OOPose in a mobile-edge computing paradigm\, emphasizing real-time performance and the feasibility of interactive applications. The research encapsulates the development and application of the OOPose framework\, demonstrating its significant role in the ecosystem of mobile-edge computing. This approach not only satisfies the requirements for real-time performance but also opens avenues for innovative robot localization applications\, ensuring interactive\, intelligent services across various environments. \nShort Bio: Bo Tsai is a PhD candidate in EECS advised by Professor Kwei-Jay Lin. His research focuses on intelligent distributed systems\, specifically addressing applications in visual-based localization\, autonomous robots\, and real-time internet of things.
URL:https://www.cecs.uci.edu/event/object-oriented-visual-localization-in-urban-mobile-edge-environments/
LOCATION:Zoom
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240809T143000
DTEND;TZID=America/Los_Angeles:20240809T153000
DTSTAMP:20240814T140023Z
CREATED:20240814T140023Z
LAST-MODIFIED:20240814T140023Z
UID:16354-1723213800-1723217400@www.cecs.uci.edu
SUMMARY:Hardware/Software Co-design Methodologies for Efficient AI Systems and Applications
DESCRIPTION:Name: Mohanad Odema \nChair: Prof. Mohammad Abdullah Al Faruque \nDate: August 9\, 2024 \nTime:  2:30 PM \nLocation: EH 2430 \nCommittee: Prof. Marco Levorato\, Prof. Hyouk Jun Kwon \nTitle: Hardware/Software Co-design Methodologies for Efficient AI Systems and Applications \nAbstract: The landscape of AI research is dominated by the search for powerful deep learning models and architectures that enable fascinating applications from the edge to the cloud. Indeed\, we have witnessed the emergence of efficient\, on-device deep learning models that facilitate smart edge applications (autonomous vehicles\, AR/VR systems)\, and the emergence of billion parameter foundation/LLM models that excel at tasks thought achievable only through human-level understanding. On the other hand\, the calls for more advanced hardware and systems continue to grow considering the scale at which deep learning model workloads evolve\, and to facilitate sustainable\, efficient model operation across the various application contexts. \nThis suggests a natural way to design deep learning models and their systems: viz\, through hardware/software co-design methodologies\, capturing the interplay and mutual dependencies across various HW/SW layers of the computing stack to guide different design choices. From the algorithmic side\, an awareness of the target platform’s compute capabilities and resources guides the deep learning model architectural and optimization choices (e.g.\, compression) towards maximizing performance efficiency on the target hardware at deployment time. From the hardware side\, understanding the deep learning workloads and computing kernels can shape future architectures of AI hardware that improves on efficiency from the lower levels (as seen through customized accelerators trend). \nAs hardware and software continue to undergo continuous innovation\, this dissertation aims to investigate relevant emergent technologies and challenges at this unified research frontier to guide the design of future AI systems and models. The dissertation focuses on characterizing nascent design spaces\, exploring various optimization opportunities\, and developing new methodologies to maximize the impact of such innovations. In brief\, this dissertation goes over the following topics: \n– Understanding the benefits of dynamic neural networks for efficient inference\, and how to optimize their design for target platform deployment \n– Understanding how multi-model workloads can be scheduled and co-located on multi-chip AI Accelerator modules based on 2.5D chiplets technology while accounting for workloads’ diversity\, affinities\, and memory access patterns \n– Exploring new methodologies to maximize the impact of split computing inference in edge-cloud architectures\, and elevate resource efficiency of edge devices \nShort Bio: Mohanad received a B.Sc degree in Electronics and Communications and a M.Sc degree in Computer Engineering from Ain Shams University\, Egypt. He is a PhD Candidate in Computer Engineering at UCI. He was an intern at MediaTek with the AI Architecture and Algorithm team. His research interests are focused on HW/SW co-design solutions for efficient AI\, bridging model- and system-level optimizations to enhance performance for core industry AI workloads and applications from the edge to the cloud.
URL:https://www.cecs.uci.edu/event/hardware-software-co-design-methodologies-for-efficient-ai-systems-and-applications/
LOCATION:EH 2430\, Engineering Hall\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250224T100000
DTEND;TZID=America/Los_Angeles:20250224T110000
DTSTAMP:20250224T172059Z
CREATED:20250224T172059Z
LAST-MODIFIED:20250224T172059Z
UID:16404-1740391200-1740394800@www.cecs.uci.edu
SUMMARY:Navigating Cybersecurity in the AI Era: Strategies and Defense Mechanisms
DESCRIPTION:Name: Jiacen Xu \nChair: Prof. Zhou Li \nDate: February 24\, 2025 \nTime:  10:00 AM \nLocation: Zoom \nCommittee: Prof. Mohammad Al Faruque\, Prof. Yanning Shen \nTitle: Navigating Cybersecurity in the AI Era: Strategies and Defense Mechanisms \nAbstract: The rise of AI has dramatically reshaped the cybersecurity landscape\, introducing both advanced cyber threats and innovative defense mechanisms. My research delves into the evolving nature of cyber attacks and AI-driven defenses\, with a particular focus on how AI both exacerbates and addresses cybersecurity risks. \nI examine the progression of AI-powered cyber-attacks\, highlighting how these technologies enable more precise\, autonomous\, and scalable breaches across a range of digital infrastructures\, including enterprise network systems and IoT systems like autonomous vehicles. The research explores AI-driven attack vectors such as automated malicious command generation and adversarial machine learning techniques targeting autonomous systems. \nIn response\, my research evaluates AI-powered defense strategies\, such as graph neural networks for threat detection and AI-augmented forensic analyses\, using datasets from DARPA DC and Los Alamos National Lab. These defense mechanisms are assessed for their ability to detect and mitigate threats. \nThe goal of my research is to provide a comprehensive understanding of the dual role of AI in cybersecurity and propose strategic measures to strengthen security practices in response to the rapid pace of technological advancement. \nShort Bio: Jiacen Xu is a Ph.D. candidate in Electrical Engineering and Computer Science at the University of California\, Irvine\, under the supervision of Professor Zhou Li. He holds a B.Sc. and M.Sc. in Information Security and Cyberspace Security from Shanghai Jiao Tong University. His research focuses on Artifact Intelligence for cybersecurity applications such as cyber attack automation and detection. During his Ph.D.\, he interned at Microsoft Research and IBM Research.
URL:https://www.cecs.uci.edu/event/navigating-cybersecurity-in-the-ai-era-strategies-and-defense-mechanisms/
LOCATION:Zoom
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250311T100000
DTEND;TZID=America/Los_Angeles:20250311T110000
DTSTAMP:20250310T202338Z
CREATED:20250310T202256Z
LAST-MODIFIED:20250310T202338Z
UID:16415-1741687200-1741690800@www.cecs.uci.edu
SUMMARY:Modeling and Evaluation of a Cache-less Grid of Processing Cells
DESCRIPTION:Name: Vivek Govindasamy \nChair: Prof. Rainer Doemer \nDate: March 11\, 2025 \nTime: 10:00 AM \nLocation: EH 3206 (CECS Conference Room) \nCommittee: Prof. Mohammad Al Faruque\, Prof. Ian Harris \nTitle: Modeling and Evaluation of a Cache-less Grid of Processing Cells \nAbstract: Modern processors experience memory contention when the speed of their computational units exceeds the rate at which new data is available to be processed. This phenomenon is well known as the memory wall and is a great challenge in computer engineering. The reason for this phenomenon is the unequal growth rate in memory access speeds compared to processor clock rates. In order to mitigate the memory bottleneck in classic computer architectures\, a scalable parallel computing platform called the Grid of Processing Cells (GPC) has been proposed. To evaluate its effectiveness\, we model the GPC in this work at the instruction-level and functional-level using SystemC TLM-2.0\, with a focus on memory contention. In an advanced model\, individual GPC cells can be switched as needed between the two abstraction levels. We then show that our mixed-level system model enables fast and accurate simulations. We test multiple streaming applications on the GPC\, analyze software-based optimization methods and their effects on the GPC\, at both abstraction levels. The performance is then compared against the traditional shared memory processor (SMP) architecture. Finally\, experimental results show improved execution times on the GPC primarily due to a large decrease in main memory contention. \nShort Bio: Vivek Govindasamy received his Bachelor of Technology degree in Electrical Engineering from Vellore Institute of Technology\, India in 2020\, and M.S. degree in Computer Engineering from the University of California Irvine\, Irvine\, CA\, USA in 2022. He is currently pursuing his PhD degree in Computer Engineering under the supervision of Professor Rainer Doemer. His research is focused on modeling and simulation of many-core computer architectures.
URL:https://www.cecs.uci.edu/event/modeling-and-evaluation-of-a-cache-less-grid-of-processing-cells/
LOCATION:EH 3206\, Engineering Hall University of California\, Irve\, Irvine\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250514T110000
DTEND;TZID=America/Los_Angeles:20250514T120000
DTSTAMP:20250513T184214Z
CREATED:20250512T171811Z
LAST-MODIFIED:20250513T184214Z
UID:16450-1747220400-1747224000@www.cecs.uci.edu
SUMMARY:In-Memory Computing to Accelerate Machine Learning
DESCRIPTION:Name: Mariam Rakka \nChair: Prof. Fadi Kurdahi \nDate: May 14\, 2025 \nTime: 11:00 AM \nLocation: EH 2430 \nCommittee: Prof. Nikil Dutt\, Prof. Rainer Döemer \nTitle: In-Memory Computing to Accelerate Machine Learning \nAbstract: The increasing computational demands of Machine Learning (ML) models have made conventional computing architectures inadequate due to energy inefficiencies and data movement bottlenecks. In-memory computing (IMC) offers a promising alternative by enabling computation directly within memory arrays. This dissertation presents novel architectural and algorithmic strategies to accelerate machine learning inference using IMC. It proposes precision-fluid data flows and hardware/software co-design techniques to efficiently accelerate different ML workloads. Performance evaluations demonstrate that IMC-based designs can deliver substantial improvements in energy efficiency and throughput\, making them viable candidates for next-generation AI systems. \nBio: Mariam Rakka received her B.E. degree in Computer and Communications Engineering with high distinction from the American University of Beirut\, Lebanon\, in 2020\, along with two minors in Mathematics and Business Administration. She received her M.S. degree in Electrical and Computer Engineering from the University of California\, Irvine\, in 2022. She is currently pursuing her Ph.D. degree in Electrical and Computer Engineering at the University of California\, Irvine under the mentorship of Professor Fadi J. Kurdahi. Her research interests include systems for machine learning and hardware/software co-design. She has completed research internships at Arm in 2022\, 2023\, and 2024\, where she contributed to the design of future generations of ARM processors. She has published in top-tier journals and conferences and served as a reviewer for several peer-reviewed journals in the field.
URL:https://www.cecs.uci.edu/event/in-memory-computing-to-accelerate-machine-learning/
LOCATION:EH 2430\, Engineering Hall\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
ATTACH;FMTTYPE=image/jpeg:https://cecs.uci.edu/files/2025/05/mariam_rakka.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250610T090000
DTEND;TZID=America/Los_Angeles:20250610T100000
DTSTAMP:20260120T201804Z
CREATED:20260120T201633Z
LAST-MODIFIED:20260120T201804Z
UID:16676-1749546000-1749549600@www.cecs.uci.edu
SUMMARY:Full-Stack Resource Management for Predictable and Scalable Autonomous Vehicle Pipelines
DESCRIPTION:Name: Dongjoo Seo \nChair: Prof. Nikil Dutt \nDate: June 10\, 2025 \nTime: 9:00 AM \nLocation: DBH 3011 \nCommittee: Prof. Tony Givargis\, Prof. Sang-Woo Jun \nTitle: Full-Stack Resource Management for Predictable and Scalable Autonomous Vehicle Pipelines \nAbstract: Autonomous vehicles (AVs) rely on tightly coupled perception\, planning\, and control pipelines that demand both high throughput and deterministic performance from the underlying computing platform. In the first part of this dissertation\, we address unpredictable memory-induced latency by introducing two complementary strategies. ProSwap leverages periodic access patterns to proactively swap pages before they incur page‐fault penalties\, reducing end‐to‐end pipeline stalls by up to 26% over Linux’s default swapping. Building on this\, MemScape employs reinforcement‐learning driven demotion and promotion across DRAM and high‐capacity memory tiers to avoid latency spikes—trading minimal peak performance for a 3× improvement in tail‐latency stability under dynamic AV workloads. In the second part\, we tackle I/O and CPU resource management challenges that further impact AV real‐time guarantees. SPEED replaces ROS’s single‐queue architecture with a multi‐queue\, context‐aware I/O scheduler\, achieving an 84% reduction in rosbag latency and a 1.5× gain in throughput by aligning writebacks with application idle periods. To eliminate scheduling jitter\, SCHED distills an offline‐trained reinforcement learning policy into a lightweight decision tree for in‐kernel CPU dispatch\, offering up to 250× faster decision times compared to raw RL inference. Together\, these full‐stack optimizations enable scalable\, predictable\, and safe operation of AV pipelines on commodity Linux platforms. \nBio: Dongjoo Seo received his B.E. in Computer Science and Master’s in Computer Software Engineering from Kookmin University at Seoul\, Korea in 2017 and 2020\, respectively. During this Master’s\, Seo worked alongside Prof. Nikil Dutt at UCI as a visiting researcher where he built the simulator system for Fog computing and made a reinforcement learning-based agent for offloading the task. In 2021\, Seo returned to UCI under Prof. Dutt to pursue his Ph.D. in Computer Science. His research focused on specializing OS\, memory systems\, and CLX. Now\, Seo is a software engineer at Samsung Semiconductor\, building reliable next-generation computing platforms for machine learning.
URL:https://www.cecs.uci.edu/event/full-stack-resource-management-for-predictable-and-scalable-autonomous-vehicle-pipelines/
LOCATION:DBH 3011\, Donald Bren Hall University of California\, Irve\, Irvine\, CA\, 92617\, United States
CATEGORIES:PhD Defenses
ATTACH;FMTTYPE=image/jpeg:https://cecs.uci.edu/files/2026/01/Dongjoo_Seo-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260302T130000
DTEND;TZID=America/Los_Angeles:20260302T140000
DTSTAMP:20260305T195727Z
CREATED:20260305T195727Z
LAST-MODIFIED:20260305T195727Z
UID:16729-1772456400-1772460000@www.cecs.uci.edu
SUMMARY:A Pareto-Optimal Compiler for Mapping Task Graphs to Truly Scalable Grids of Processing Cells
DESCRIPTION:Name: Yutong Wang \nChair: Prof. Rainer Doemer \nDate: March 2\, 2026 \nTime: 1:00 PM \nLocation: EH 3206 \nCommittee: Prof. Brian Demsky\, Prof. Zhou Li \nTitle: A Pareto-Optimal Compiler for Mapping Task Graphs to Truly Scalable Grids of Processing Cells \nAbstract: Programming scalable system-on-chip architectures composed of processor arrays remains challenging due to the complexity of partitioning applications\, assigning tasks and data\, and realizing communication under strict locality constraints. Regular grid-based architectures offer scalability and predictable communication\, yet mapping real applications onto such platforms is often manual\, error-prone\, and difficult to validate. This dissertation presents the Grid of Processing Cells Compiler (GPCC)\, an automated framework that maps application task graphs extracted from C++ specifications onto a truly scalable two-dimensional grid of processing cells with local memories and neighbor-only communication. GPCC automatically analyzes application graphs\, performs task and data partitioning\, places tasks onto grid locations\, and generates consistent on-chip and off-chip communication\, producing executable SystemC TLM-2.0 architecture models for system-level simulation and evaluation. GPCC employs a two-level mapping strategy in which tasks are first placed onto processing cells using a dependency-aware recursive search\, followed by assignment of communication channels and external I/O resources. In addition\, this work introduces an automated design space exploration method that evaluates candidate grid dimensions and identifies Pareto-optimal configurations. Experimental results on embedded applications and synthetic benchmarks demonstrate that GPCC efficiently produces correct executable models\, enables systematic exploration of grid-based architectures\, and determines suitable grid dimensions with minimal manual effort. \nBio: Yutong Wang is a Ph.D. candidate in Computer Engineering at UC Irvine\, working in scalable system-on-chip architectures and SystemC-based modeling. His research centers on automated mapping and design space exploration for grid-based processor arrays through the Grid of Processing Cells Compiler (GPCC).
URL:https://www.cecs.uci.edu/event/a-pareto-optimal-compiler-for-mapping-task-graphs-to-truly-scalable-grids-of-processing-cells/
LOCATION:EH 3206\, Engineering Hall University of California\, Irve\, Irvine\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
ATTACH;FMTTYPE=image/webp:https://cecs.uci.edu/files/2026/03/yutong.webp
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260518T130000
DTEND;TZID=America/Los_Angeles:20260518T140000
DTSTAMP:20260824T233918Z
CREATED:20260824T233717Z
LAST-MODIFIED:20260824T233918Z
UID:16893-1779109200-1779112800@www.cecs.uci.edu
SUMMARY:Securing Cyber-Physical Systems: From Sensor-Layer Attacks to Firmware Binary Analysis
DESCRIPTION:Name: Yonatan Gizachew Achamyeleh \nChair: Mohammad Al Faruque\nMember: Zhou Li\nMember: Yasser Shoukry \nDate: 05/18/2026\nTime: 01:00 PM PT\nLocation: ISEB 1010 \nTitle: Securing Cyber-Physical Systems: From Sensor-Layer Attacks to Firmware Binary Analysis\nAbstract: This dissertation advances security across the full stack of cyber-physical systems\, from the physical sensor layer to the firmware binary layer. Four contributions demonstrate that sensors are a pervasive and underprotected attack surface\, showing that pressure sensors can be exploited to eavesdrop on nearby audio\, acoustic signals can destabilize UAV flight\, physical sensor corruption misaligns Vision-Language Model-driven robots\, and sensor rate of change can trigger hardware Trojans covertly. Three further contributions develop tools for semantic recovery from stripped industrial firmware\, spanning binary embeddings\, function name prediction\, and LLM-guided decompilation\, achieving over 94% recompilability across six architectures. The unifying argument is that the sensor and binary layers are structurally coupled\, and that securing one requires understanding the other. \nBio: Yonatan Gizachew Achamyeleh is a PhD candidate in Computer Engineering at the University of California\, Irvine\, advised by Professor Mohammad Abdullah Al Faruque. His research spans cyber-physical systems security\, embedded binary analysis\, and the intersection of physical-layer attacks with AI-driven autonomy.
URL:https://www.cecs.uci.edu/event/securing-cyber-physical-systems-from-sensor-layer-attacks-to-firmware-binary-analysis/
LOCATION:CA
CATEGORIES:PhD Defenses
ATTACH;FMTTYPE=image/png:https://cecs.uci.edu/files/2026/08/Yonatan-Achamyeleh.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260608T100000
DTEND;TZID=America/Los_Angeles:20260608T110000
DTSTAMP:20260716T025310Z
CREATED:20260716T025012Z
LAST-MODIFIED:20260716T025310Z
UID:16853-1780912800-1780916400@www.cecs.uci.edu
SUMMARY:Adaptive Sparsity for Efficient and Reliable Machine Learning Inference in Autonomous Vehicles
DESCRIPTION:Name: Danny Abraham \nChair: Prof. Nikil Dutt \nDate: June 8\, 2026 \nTime: 10:00 AM \nLocation: DBH 4011 \nCommittee: Prof. Fadi Kurdahi\, Prof. Mohsen Imani \nTitle: Adaptive Sparsity for Efficient and Reliable Machine Learning Inference in Autonomous Vehicles \nAbstract: Autonomous vehicles rely on machine learning models that must make accurate decisions under strict latency\, memory\, and reliability constraints. However\, modern deep learning systems are often designed as fixed\, dense inference pipelines\, even though real driving conditions vary across compute availability\, scene difficulty\, sensor structure\, and modality access.\nThis dissertation presents adaptive sparsity as a framework for making autonomous vehicle inference more efficient and responsive to these changing conditions. It explores three complementary forms of sparsity: parameter sparsity for dynamically adjusting model capacity\, activation sparsity for exploiting input-dependent computation\, and input sparsity for using rich training-time modalities to improve deployment-time models with fewer inputs. Together\, these methods show how sparsity can serve not only as a compression tool\, but as a mechanism for adaptive\, efficient\, and reliable inference in autonomous vehicle systems. \nBio: Danny Abraham is a PhD student in the School of Computer Science at UC Irvine\, working with Dr. Nikil Dutt\, in the field of embedded machine learning. His current line of work is researching adaptive machine learning models for resource-constrained embedded systems\, focusing on dynamic runtime reconfiguration. This enables real-time model adjustment and optimizes the tradeoff between accuracy\, latency\, and energy consumption particularly in the context of autonomous vehicles.
URL:https://www.cecs.uci.edu/event/adaptive-sparsity-for-efficient-and-reliable-machine-learning-inference-in-autonomous-vehicles/
LOCATION:DBH 4011\, Donald Bren Hall\, University of California\, Irvine\, Irvine\, CA\, 92617\, United States
CATEGORIES:PhD Defenses
ATTACH;FMTTYPE=image/jpeg:https://cecs.uci.edu/files/2026/07/danny.jpg
END:VEVENT
END:VCALENDAR