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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20241017T150000
DTEND;TZID=America/Los_Angeles:20241017T160000
DTSTAMP:20241014T182231Z
CREATED:20241011T160628Z
LAST-MODIFIED:20241014T182231Z
UID:16367-1729177200-1729180800@www.cecs.uci.edu
SUMMARY:Storage-Centric Computing for Genomics and Metagenomics
DESCRIPTION:Title: Storage-Centric Computing for Genomics and Metagenomics \nSpeaker: Nika Mansouri Ghiasi \nDate and Time: Thursday\, October 17th\, 3:00pm \nLocation: DBH 4011 \nAbstract: \nGenomics and metagenomics applications have enabled significant advancements in many critical areas. The exponential growth of genomic data poses unprecedented challenges in genomics and metagenomic applications. These applications suffer from significant data movement overheads from the storage system. To fundamentally address these overheads\, we make a case for storage-centric computing. \nFirst\, we propose GenStore\, the first in-storage processing system designed for genome sequence analysis that greatly reduces both data movement and computational overheads of genome sequence analysis by exploiting low-cost and accurate in-storage filters. We address the challenges of in-storage processing\, supporting reads with 1) different read lengths and error rates\, and 2) different degrees of genetic variation. Through rigorous analysis of read mapping processes\, we design low-cost hardware accelerators and data/computation flows inside a NAND flash-based SSD. Our evaluation using a wide range of real genomic datasets shows that GenStore significantly improves the read mapping performance of state-of-the-art software (hardware) baselines by 2.07-6.05× (1.52-3.32×) for read sets with high similarity to the reference genome and 1.45-33.63× (2.70-19.2×) for read sets with low similarity to the reference genome. \nSecond\, we propose MegIS\, the first in-storage processing system designed to significantly reduce the data movement overhead of the end-to-end metagenomic analysis pipeline. MegIS is enabled by our lightweight design that effectively leverages and orchestrates processing inside and outside the storage system. Through our detailed analysis of the end-to-end metagenomic analysis pipeline and careful hardware/software co-design\, we address in-storage processing challenges for metagenomics via specialized and efficient 1) task partitioning\, 2) data/computation flow coordination\, 3) storage technology-aware algorithmic optimizations\, 4) data mapping\, and 5) lightweight in-storage accelerators. MegIS’s design is flexible\, capable of supporting different types of metagenomic input datasets\, and can be integrated into various metagenomic analysis pipelines. Our evaluation shows that MegIS outperforms the state-of-the-art performance- and accuracy-optimized software metagenomic tools by 2.7×–37.2× and 6.9×–100.2×\, respectively\, while matching the accuracy of the accuracy-optimized tool. MegIS achieves 1.5×–5.1× speedup compared to the state-of-the-art metagenomic hardware-accelerated (using processing-in-memory) tool\, while achieving significantly higher accuracy. \nBiography: \nNika Mansouri Ghiasi is a Ph.D. candidate in the SAFARI Research Group at ETH Zürich\, working with Professor Onur Mutlu. Her current research interests are in computer architecture\, emerging technologies\, bioinformatics\, and their interactions. \nHosted By: Prof. Alex Veidenbaum \nMore Information
URL:https://www.cecs.uci.edu/event/storage-centric-computing-for-genomics-and-metagenomics/
LOCATION:DBH 4011\, Donald Bren Hall\, University of California\, Irvine\, Irvine\, CA\, 92617\, United States
CATEGORIES:Seminars at CECS
ATTACH;FMTTYPE=image/jpeg:https://cecs.uci.edu/files/2024/10/OIP-e1728930126635.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20241018T140000
DTEND;TZID=America/Los_Angeles:20241018T150000
DTSTAMP:20250404T180934Z
CREATED:20241014T181841Z
LAST-MODIFIED:20250404T180934Z
UID:16376-1729260000-1729263600@www.cecs.uci.edu
SUMMARY:In Vitro Biological Neural Networks
DESCRIPTION:Title: In Vitro Biological Neural Networks \nSpeaker: Prof. Lawrence Rauchwerger \nDate and Time: Friday\, October 18th\, 2:00pm \nLocation: EH 2430 \nAbstract: \nA computing stack powered by living neurons could unlock self-organizing and dynamically rewiring systems with extreme connectivity and parallel processing power\, all while running on sugar with unprecedented energy efficiency. In this presentation we outline a framework that leverages in-silico simulation to establish an engineering testbed with the ultimate goal of learning to harness neural in-vitro systems for computational purposes. We describe an optimization approach to uncover reproducible neural activity present in a system that can be leveraged to carry out basic information processing tasks. We demonstrate the feasibility of this approach by optimizing a simulated neural system to perform digit classification\, offering a proof-of-concept for a potential pathway to leveraging neural computation in vitro. \nBiography: \nLawrence Rauchwerger is a professor in the Siebel School of Computing and Data Science at the University of Illinois Urbana-Champaign. He holds a doctoral degree in computer science from the University of Illinois Urbana-Champaign and an M.S. in electrical engineering from Stanford University. Rauchwerger is known for his approach to auto-parallelization\, thread-level speculation and parallel library (STAPL). His research interests are in compilers for parallel computing\, parallel and distributed C++ libraries\, adaptive runtime optimizations\, and architectures for parallel computing. Lately he has become active in neurocomputing. He received an IBM Faculty Award\, an Intel Faculty Award\, and the NSF Faculty Early Career Development\n(CAREER) Award. He is an IEEE Fellow and a AAAS Fellow. \nHosted By: Prof. Alex Veidenbaum \nMore Information
URL:https://www.cecs.uci.edu/event/in-vitro-biological-neural-networks/
LOCATION:EH 2430\, Engineering Hall\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:Seminars at CECS
ATTACH;FMTTYPE=image/png:https://cecs.uci.edu/files/2024/10/Lawrence-Rauchwerger-e1728930349485.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20241201
DTEND;VALUE=DATE:20241202
DTSTAMP:20251113T232535Z
CREATED:20251113T232500Z
LAST-MODIFIED:20251113T232535Z
UID:16605-1733011200-1733097599@www.cecs.uci.edu
SUMMARY:Chen\, Al Faruque Awarded for Research in Autonomous Security
DESCRIPTION:With autonomous vehicle on the rise\, Assistant Professor Alfred Chen and Professor Mohammad Al Faruque have shown that these vehicles\, like self-driving cars and autonomous drones\, can be hijacked and tricked into dangerous driving. Prof. Chen and Prof. Al Faruque are leading a team to discover end-to-end security vulnerabilities in any cyber-physical system. Their project is part of the Faithful Integration\, Reverse-engineering\, and Emulation (FIRE) program\, backed by a three-year fund of $15 million from the U.S. Defense Advanced Research Projects Agency.
URL:https://www.cecs.uci.edu/event/chen-al-faruque-awarded-for-research-in-autonomous-security/
LOCATION:CA
CATEGORIES:Awards
ATTACH;FMTTYPE=image/png:https://cecs.uci.edu/files/2025/11/AV_security.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20241201
DTEND;VALUE=DATE:20241202
DTSTAMP:20251113T232622Z
CREATED:20251113T232622Z
LAST-MODIFIED:20251113T232622Z
UID:16610-1733011200-1733097599@www.cecs.uci.edu
SUMMARY:Do Awarded $2M for Intelligent Biocomputing
DESCRIPTION:Associate Professor of Neurology An Do leads a collaboration project between UCI and USC to combine systems engineering and steam cell research to treat damaged brain regions from strokes or other neurological problems. Do and team hope to apply advanced biofabrication and steam cell to construct 3D neural networks and encourage brain repairment. For their efforts\, NSF granted them $2 million in funding over 4 years. \n 
URL:https://www.cecs.uci.edu/event/do-awarded-2m-for-intelligent-biocomputing/
LOCATION:CA
CATEGORIES:Awards
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;VALUE=DATE:20250301
DTEND;VALUE=DATE:20250302
DTSTAMP:20251113T232737Z
CREATED:20251113T232737Z
LAST-MODIFIED:20251113T232737Z
UID:16614-1740787200-1740873599@www.cecs.uci.edu
SUMMARY:Huang Receives NSF Faculty Early Career Award
DESCRIPTION:Assistant Professor Sitao Huang was recognized with Faculty Early Career Development (CAREER) award from the National Science Foundation (NFS). The CAREER Program supports early-career faculty with potential to serve as role models and lifelong leaders in research and education. NFS’s Division of Computing and Communication Foundation awarded Prof. Huang $650\,000 for his project “CAREER: High-Level Synthesis and Compilation for Domain-Specific Reconfigurable Heterogeneous Systems”. \nAs the complexity and diversity of modern computing workloads grow rapidly\, existing computer systems with homogeneous processors or accelerators show critical limitations in computing these complex workloads. This creates the incentive to create the next generation of heterogeneous hardware acceleration systems with diverse processors and accelerators\, such as CPUs\, GPUs\, DSPs\, and FPGAs. \nProf. Huang’s project will develop an efficient complication and synthesis flow from high-level program\, such as Python\, to domain-specific reconfigurable heterogeneous acceleration systems.
URL:https://www.cecs.uci.edu/event/huang-receives-nsf-faculty-early-career-award/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250301
DTEND;VALUE=DATE:20250302
DTSTAMP:20251113T232827Z
CREATED:20251113T232827Z
LAST-MODIFIED:20251113T232827Z
UID:16618-1740787200-1740873599@www.cecs.uci.edu
SUMMARY:Office of Naval Research Sponsor Award for Imani
DESCRIPTION:Assistant Professor Mohsen Imani was recently awarded $398\,000 by the Office of Naval Research for project title\, “DURIP Integrating AI and Intelligent Sensing for Advanced Naval Operations.” This sponsor award will support Prof. Imani and his lab with data processing equipment during the 1-year contract period. \n 
URL:https://www.cecs.uci.edu/event/office-of-naval-research-sponsor-award-for-imani/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250301
DTEND;VALUE=DATE:20250302
DTSTAMP:20251113T232909Z
CREATED:20251113T232909Z
LAST-MODIFIED:20251113T232909Z
UID:16622-1740787200-1740873599@www.cecs.uci.edu
SUMMARY:Odema Selected as ML and System Rising Stars
DESCRIPTION:CECS PhD alumnus Mohanad Odema was selected as one of 38 graduate students to join the Machine Learning (ML) and Systems Rising Stars 2025 cohort. The MLCommons ML and Systems Rising Stars is an initiative of recently graduated PhD students to foster research and career growth\, enable collaborations\, and discuss career opportunities among rising generations of researchers at intersections of ML and systems.
URL:https://www.cecs.uci.edu/event/odema-selected-as-ml-and-system-rising-stars/
LOCATION:CA
CATEGORIES:Awards
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:20250312T140000
DTEND;TZID=America/Los_Angeles:20250312T150000
DTSTAMP:20250404T180622Z
CREATED:20250306T194035Z
LAST-MODIFIED:20250404T180622Z
UID:16409-1741788000-1741791600@www.cecs.uci.edu
SUMMARY:Optimizing AI Performance: Bridging Algorithmic and Hardware Innovations from Inference to Training
DESCRIPTION:Title: Optimizing AI Performance: Bridging Algorithmic and Hardware Innovations from Inference to Training \nSpeaker: Prof. Yu Bai \nDate and Time: Wednesday\, March 12\, 2:00pm \nLocation: ISEB 1200 \nAbstract: \nArtificial intelligence (AI) continues to reshape industries and research\, but its rapid growth comes with significant computational and energy costs. This talk explores the challenges and innovations in optimizing AI efficiency\, bridging algorithmic advancements and hardware design to enhance performance from inference to training. The presentation covers two main avenues for improving AI efficiency: model optimization techniques and hardware specialization. Algorithmic strategies such as low-rank tensor decomposition\, structured sparsity\, quantization-aware training\, and compression of large-scale models (e.g.\, LLMs and diffusion models) are discussed. These methods aim to reduce memory requirements\, computational overhead\, and energy consumption while maintaining model accuracy. On the hardware side\, the talk highlights cutting-edge AI accelerators\, dataflow optimizations\, and emerging techniques like in-storage computing for AI hardware. Novel architectures such as the Tensor-Train-based Inference and energy-efficient training solutions are presented\, demonstrating substantial improvements in model compression\, multi-modal AI\, and real-time generative AI tasks. The talk also touches on the broader implications of AI efficiency\, including its impact on embedded AI platforms\, cloud-based AI\, and future developments in efficient AI hardware and software co-design. Through interdisciplinary collaboration and Industry and NSF-supported research\, this work aims to advance the next generation of AI technology\, making it both powerful and sustainable. \nBiography: \nDr. Yu Bai is an Associate Professor and Director of the Intelligent Computing Research Lab at California State University. His research focuses on emerging AI techniques\, energy-efficient AI systems\, and interdisciplinary AI applications\, spanning neuromorphic computing\, generative AI\, deep learning for IoT devices\, and high-performance computing. Dr. Bai has authored over 50 peer-reviewed publications in top AI conferences and journals\, contributing significantly to efficient neural network optimization\, AI hardware-software co-design\, and hyperspectral imaging. His work has been recognized with numerous accolades\, including the Best Researcher Award (2023) from Citation Award\, the Faculty Scholarly and Creative Activity Award (2021)\, and inclusion in Who’s Who in America. With a proven track record in securing research funding\, Dr. Bai has received over $4 million in competitive grants from agencies such as the National Science Foundation (NSF)\, the Army Research Office\, and industry-academic collaborations. His funded projects include pioneering efforts in edge AI architectures\, AI applications in biological systems\, and inclusive AI education strategies. A National Academies of Sciences\, Engineering\, and Medicine (NASEM) awardee\, Dr. Bai is dedicated to advancing AI research and fostering a diverse\, inclusive STEM community through his teaching\, mentorship\, and collaborative initiatives. \nHosted By: Prof. Fadi Kurdahi and Prof. Mohammad Al Faruque \nMore Information
URL:https://www.cecs.uci.edu/event/optimizing-ai-performance-bridging-algorithmic-and-hardware-innovations-from-inference-to-training/
LOCATION:ISEB 1200\, Interdisciplinary Science and Engineering Building University of California\, Irve\, Irvine\, CA\, 92697\, United States
CATEGORIES:Seminars at CECS
ATTACH;FMTTYPE=image/jpeg:https://cecs.uci.edu/files/2025/03/Yu-Bai-800x500-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250421T110000
DTEND;TZID=America/Los_Angeles:20250421T120000
DTSTAMP:20250417T173945Z
CREATED:20250415T195856Z
LAST-MODIFIED:20250417T173945Z
UID:16437-1745233200-1745236800@www.cecs.uci.edu
SUMMARY:Security in Heterogeneous Systems
DESCRIPTION:Title: Security in Heterogeneous Systems \nSpeaker: Yicheng Zhang \nDate and Time: Monday\, April 21\, 11:00am \nLocation: EH 2430 \nAbstract: \nTo improve the performance and efficiency of modern computing systems\, architects increasingly adopt heterogeneous designs that tightly integrate specialized accelerators such as GPUs and FPGAs. These accelerators enable substantial speedups for compute-intensive workloads like machine learning and AR/VR. However\, they also diverge fundamentally from traditional CPU-based systems and introduce a new class of security risks. Unlike prior work that focused on CPU vulnerabilities such as Spectre and Meltdown\, heterogeneous systems expose new attack surfaces due to their architectural complexity and shared resource usage. In this talk\, I will present my research efforts to uncover and mitigate security vulnerabilities in these heterogeneous platforms. First\, I will introduce a set of side-channel attacks targeting AR/VR devices\, leveraging rendering performance counters to recover sensitive user inputs such as virtual keystrokes and voice commands. I will then shift to large-scale cloud environments\, where I demonstrate the first end-to-end side-channel attack that exploits NVLink\, NVIDIA’s high-speed GPU interconnect\, through contention-based and performance-counter-based microarchitectural channels. To conclude\, I will present a defense framework that uses GPU side-channel signatures to validate kernel execution integrity. This approach detects tampering from attacks like buffer overflows and Rowhammer\, turning side channels into a practical tool for runtime verification. Together\, these efforts lay the groundwork for securing future heterogeneous computing platforms across both edge and cloud environments. \nBiography: \nYicheng Zhang is a fourth-year Ph.D. candidate at the University of California\, Riverside\, advised by Prof. Nael Abu-Ghazaleh. Prior to that\, he received his M.Sc. from UC Irvine and his B.S. from Sichuan University. His research interests lie in computer architecture and system security\, with a focus on AR/VR security and microarchitectural security. His work has been\nrecognized by top-tier conferences and journals including multiple USENIX Security\, IEEE S&P\, IEEE DSN and IEEE TIFS. More information can be found at his website: https://yichez.site/ \nHosted By: Prof. Zhou Li \nMore Information
URL:https://www.cecs.uci.edu/event/security-in-heterogeneous-systems/
LOCATION:EH 2430\, Engineering Hall\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:Seminars at CECS
ATTACH;FMTTYPE=image/jpeg:https://cecs.uci.edu/files/2025/04/yicheng.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250506
DTEND;VALUE=DATE:20250510
DTSTAMP:20250404T180356Z
CREATED:20250403T192124Z
LAST-MODIFIED:20250404T180356Z
UID:16421-1746489600-1746835199@www.cecs.uci.edu
SUMMARY:Cyber-Physical Systems and Internet-of-Things (CPS-IoT) Week 2025
DESCRIPTION:CPS-IoT Week is the premier event on Cyber-Physical Systems and the Internet-of-Things. It brings together four top conferences\, multiple workshops\, tutorials\, and competition. This year\, CECS is thrilled to host CPS-IoT Week 2025 at the UC Irvine Student Center! \n  \nConferences: \n\nHSCC – 28th ACM International Conference on Hybrid Systems: Computation and Control\nICCPS – 16th ACM/IEEE International Conference on Cyber-Physical Systems\nSenSys – 23rd ACM Conference on Embedded Networked Sensor Systems\nRTAS – 31st IEEE Real-Time and Embedded Technology and Applications Symposium\n\n  \nWorkshops: \n\nThe 2nd International Workshop on Foundation Models for Cyber-Physical Systems & Internet of Things (FMSys)\nThe 2nd International Workshop on Security and Privacy of Sensing Systems (Sensors S&P)\nThe 4th International Workshop on Real-time and IntelliGent Edge computing (RAGE)\nThe 5th Workshop on Computation-Aware Algorithmic Design for Cyber-Physical Systems\nThe 1st Workshop on Modeling and Verification for Secure and Performant Cyber-Physical Systems (MoVe4SPS)\nThe 3rd International Workshop on Human-Centered Sensing\, Modeling\, and Intelligent Systems (HumanSys)\nThe 1st Workshop on Formal Arguments for Cps CerTification (FACCT)\nThe 13th International Workshop on Energy Harvesting and Energy-Neutral Sensing Systems (ENSsys)\nThe 7th Workshop on Design Automation for CPS and IoT (DESTION)\n\n  \nTutorials: \n\nSafe Building App Deployments in Three Easy Steps with Brick and Playground\nFormal Verification and Control with Conformal Prediction\nAligning Ethics and CPS/IoT\nScenic: An Open-Source Probabilistic Programming System for Data Generation and Safety in AI-Based Autonomy\nGoing Public with Your CPS Code and Data\n\n  \nCompetition: \n\nThe 4th Student Design Competition on Networked Computing on the Edge\n\n  \nLearn more at the CPS-IoT Week website.
URL:https://www.cecs.uci.edu/event/cyber-physical-systems-and-internet-of-things-cps-iot-week-2025/
LOCATION:Student Center\, 311 W Peltason Dr\, Irvine\, CA\, 92697\, United States
CATEGORIES:Other Events
ATTACH;FMTTYPE=image/jpeg:https://cecs.uci.edu/files/2025/04/220315_Studentcenter-scaled.jpg
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:20250519T110000
DTEND;TZID=America/Los_Angeles:20250519T120000
DTSTAMP:20250513T184038Z
CREATED:20250513T183835Z
LAST-MODIFIED:20250513T184038Z
UID:16459-1747652400-1747656000@www.cecs.uci.edu
SUMMARY:Efficient and Robust Defense against Query-based Black-box Attacks
DESCRIPTION:Title: Efficient and Robust Defense against Query-based Black-box Attacks \nSpeaker: Shaofei Li \nDate and Time: Monday\, May 2119\, 11:00am \nLocation: EH 2430 \nAbstract: \nQuery-based black-box attacks have emerged as a significant threat to machine learning systems\, where adversaries can manipulate the input queries to generate adversarial examples that can cause misclassification of the system. To counter these attacks\, researchers have proposed Stateful Defense Models (SDMs) such as BlackLight and PIHA\, which can reject queries that are “similar” to historical queries. However\, recent studies show that existing approaches are vulnerable to a stronger adaptive attack\, Oracle-guided Adaptive Rejection Sampling (OARS). OARS can be easily integrated with existing attack algorithms to evade the SDMs by generating queries with fine-tuned direction and step size of perturbations utilizing the leaked decision boundary from the SDMs. In this talk\, I will present our novel approach\, Query Provenance Analysis (QPA)\, for defending against query-based black-box attacks robustly (against both non-adaptive and adaptive attacks) and efficiently (in real-time). The key insight is that\, instead of focusing on individual queries\, utilizing features from the query sequence (termed query provenance) can distinguish malicious queries from benign queries more effectively. So it constructs a query provenance graph to capture the relationship between a new query and prior historical queries\, and then design efficient algorithms to detect malicious queries based on the query provenance graphs. As a results\, QPA outperforms the baselines regarding defense robustness and efficiency on both non-adaptive and adaptive attacks. It can also be deployed for various attack scenarios\, such as membership inference attack and jailbreaking. \nBiography: \nShaofei Li is currently a fourth-year Ph.D. student in the Department of Computer Science at Peking University\, advised by Prof. Ding Li\, Prof. Yao Guo and Prof. Xiangqun Chen. He received his B.S. degree from the School of Electronics Engineering and Computer Science (EECS) at Peking University in 2021. His research interests lie in cybersecurity\, system security and AI security. His research focuses on developing detection and defense mechanisms for various security threats in the real world systems. He has published several papers in top-tier security conferences and transactions such as S&P\, NDSS\, CCS and TOSEM. More information can be found at his website: https://shaofei-li.org/ \nHosted By: Prof. Zhou Li \nMore Information
URL:https://www.cecs.uci.edu/event/efficient-and-robust-defense-against-query-based-black-box-attacks/
LOCATION:EH 2430\, Engineering Hall\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:Seminars at CECS
ATTACH;FMTTYPE=image/jpeg:https://cecs.uci.edu/files/2025/05/shaofei_li.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250519T110000
DTEND;TZID=America/Los_Angeles:20250519T120000
DTSTAMP:20250513T184743Z
CREATED:20250513T184624Z
LAST-MODIFIED:20250513T184743Z
UID:16468-1747652400-1747656000@www.cecs.uci.edu
SUMMARY:TEE-Shielded LLM Partition – Efficiently Protect Model Security on User Devices
DESCRIPTION:Title: TEE-Shielded LLM Partition – Efficiently Protect Model Security on User Devices \nSpeaker: Ziqi Zhang \nDate and Time: Monday\, May 19\, 11:00am \nLocation: EH 2430 \nAbstract: \nIn this talk\, I will introduce recent work on TEE-based LLM protection (TSLP) on the user devices: TEESlice (S&P24) and ArrowCloak (Security25 revision). Existing TEE products provides high security guarantee\, but usually lack GPU support and introduce additional inference latency. TSLP aims to incorporate untrusted GPU into TEE to reduce computation overhead. TSLP partition a LLM into two parts\, offloading the privacy-insensitive part to the GPU while shielding the critical privacy-sensitive part within the TEE. In TEESlice\, we systematically review and benchmark existing TSDP solutions and reveal that offloading model weights in plain-text leaks model privacy. Based on the observation\, TEESlice uses a partition-before-training strategy for accurate separation between privacy-related weights from public weights. In ArrowCloak\, we review existing lightweight weight obfuscation solutions in TSLP and find that these solutions cannot effectively protect vector direction in the weight matrix. We use a proof-of-concept attack to exploit this vulnerability and propose ArrowCloak that uses vector-matrix multiplication to protect vector direction. \nBiography: \nZiqi Zhang is currently a Postdoc Researcher at University of Illinois Urbane-Champaign\, working with Lingming Zhang. In 2023\, he obtained Ph.D degree in the School of Computer Science from Peking University in China. Before that\, he obtained the bachelor degree in the School of EECS of Peking University in 2018. His research interests lie in AI security\, software engineering\, and software security. His interested topics include trusted AI\, AI with trusted hardware\, and AI with SE. He has published several papers at top-tier conferences in various domains\, including computer security (S&P\, USENIX Security\, CCS)\, software engineering (ESEC/FSE\, ICSE\, ISSTA\, ISSRE)\, AI (ICML\, WWW)\, and ubiquitous computing (Ubicomp). More information can be found at https://ziqi-zhang.github.io/ \nHosted By: Prof. Zhou Li \nMore Information
URL:https://www.cecs.uci.edu/event/tee-shielded-llm-partition-efficiently-protect-model-security-on-user-devices/
LOCATION:EH 2430\, Engineering Hall\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:Seminars at CECS
ATTACH;FMTTYPE=image/png:https://cecs.uci.edu/files/2025/05/ziqi_zhang.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250601
DTEND;VALUE=DATE:20250602
DTSTAMP:20251113T233115Z
CREATED:20251113T233115Z
LAST-MODIFIED:20251113T233115Z
UID:16626-1748736000-1748822399@www.cecs.uci.edu
SUMMARY:Dang to Travel to Vietnam as Fulbright Scholar
DESCRIPTION:Next year\, Associate Professor Quoc-Viet Dang will travel to Vietnam as a Fulbright Scholar to collaborate with Vietnam National University in Hanoi at the University of Engineering and Technology school (VNU-UET). Prof. Dang is one of the four UCI faculties to become a US Fulbright Scholar for the 2025-26 school year. AT VNU-UET\, Prof. Dang will work with administrators\, teach courses in system software or embedded systems\, and conduct joint research projects. \n 
URL:https://www.cecs.uci.edu/event/dang-to-travel-to-vietnam-as-fulbright-scholar/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250601
DTEND;VALUE=DATE:20250602
DTSTAMP:20251113T233328Z
CREATED:20251113T233328Z
LAST-MODIFIED:20251113T233328Z
UID:16635-1748736000-1748822399@www.cecs.uci.edu
SUMMARY:Xu Joins DAC25 Young Fellow Program
DESCRIPTION:PhD student Jinyao Xu\, who’s under the guidance of Professor Ian Harris\, has been awarded to attend the Design Automation Conference (DAC) 2025 in San Francisco this June as its Young Fellow. Along with sponsored conference registration\, the Young Fellow Program provided Jinyao with a travel grant and exclusive sessions for poster presentation\, special program\, and labs. \n 
URL:https://www.cecs.uci.edu/event/xu-joins-dac25-young-fellow-program/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250601
DTEND;VALUE=DATE:20250602
DTSTAMP:20251113T233408Z
CREATED:20251113T233408Z
LAST-MODIFIED:20251113T233408Z
UID:16639-1748736000-1748822399@www.cecs.uci.edu
SUMMARY:Gajski Ranked Top Scholar
DESCRIPTION:CECS Founder Professor Emeritus Daniel Gajski is a leader in the areas of embedded systems\, design\, and specification languages. He became an IEEE fellow in 1994 for his work in VLSD\, CAD tools\, and system level design methodology. In 2010\, Prof. Gajski received the European Design and Automation Association Lifetime Achievement Award\, recognizing his outstanding contributions to the state of the art in electronics design and automation. In 2024\, Prof. Gajski was ranked top 0.5% of all scholars worldwide and top 20 scholar in the area of systems design.
URL:https://www.cecs.uci.edu/event/gajski-ranked-top-scholar/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250606
DTEND;VALUE=DATE:20250607
DTSTAMP:20251113T233254Z
CREATED:20251113T233254Z
LAST-MODIFIED:20251113T233254Z
UID:16630-1749168000-1749254399@www.cecs.uci.edu
SUMMARY:Kurdahi Receives Engineering Faculty Service Award
DESCRIPTION:On June 6\, CECS Director and Professor Fadi Kurdahi received the Faculty Service Award at the 2025 Spring Faculty Awards. As director of CECS and founder of our Master of Embedded & Cyber-Physical Systems (MECPS) program\, Prof. Kurdahi made outstanding service contributions to the department of school and to advance our mission of education\, research\, and outreach. Prof. Kurdahi also partake in many activities aimed at promoting participation and opportunity from under-represented minorities in IoT. \n 
URL:https://www.cecs.uci.edu/event/kurdahi-receives-engineering-faculty-service-award/
LOCATION:CA
CATEGORIES:Awards
ATTACH;FMTTYPE=image/png:https://cecs.uci.edu/files/2025/11/kurdahi.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250609T110000
DTEND;TZID=America/Los_Angeles:20250609T120000
DTSTAMP:20261027T171544Z
CREATED:20250522T200123Z
LAST-MODIFIED:20261027T171544Z
UID:16475-1749466800-1749470400@www.cecs.uci.edu
SUMMARY:Confidential Computing and Trusted Execution Environment: Challenges\, Opportunities\, and the Future
DESCRIPTION:Title: Confidential Computing and Trusted Execution Environment: Challenges\, Opportunities\, and the Future \nSpeaker:  Mengyuan Li \nDate and Time: Monday\, June 9\, 11:00am \nLocation: EH 2430 \nAbstract: \nConfidential Computing\, or Trusted Execution Environment (TEE)\, represents a cutting-edge design in server-grade CPUs. This technology acts as a protective shield for cloud tasks\, safeguarding the confidentiality and integrity of cloud workloads against a range of threats\, including attacks from privileged software\, physical attackers\, and untrustworthy hypervisors. As the demand for secure private data handling continues to rise\, the adoption of Confidential Computing has become widespread across various industries. Evidence of this includes the adoption of TEE in server-grade CPUs from major vendors like Intel\, AMD\, and ARM. Furthermore\, leading cloud service providers\, such as AWS\, Google Cloud\, Microsoft Azure\, and IBM Cloud\, now offer commercial Confidential Computing services. In this talk\, I will outline my contributions to the study of complex\, heterogeneous Confidential Computing systems. I will share my insights into two real-world vulnerabilities we uncovered within commercial Confidential Computing systems\, along with our joint efforts with CPU manufacturers to address these issues in the latest server-grade CPUs. At the hardware design level\, I will discuss a novel ciphertext side-channel attack targeting hardware-accelerated memory encryption\, which is a crucial hardware feature to protect the memory of cloud workloads. Moving to the software system design level\, I will illustrate how inadequately designed TEE operating systems can pose a threat to the security of Confidential VMs. Finally\, I will outline my ongoing efforts and future directions in enhancing the security and effectiveness of Confidential Computing and my research vision towards building secure and performant hardware systems. \nBiography: \nDr. Mengyuan Li is an Assistant Professor of Computer Science at the University of Southern California (with a courtesy appointment in the ECE department) . Before joining USC\, he was a postdoctoral researcher at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). He holds a Ph.D. from Ohio State University and a bachelor’s degree from Shanghai Jiao Tong University. His research vision is to contribute to the creation of a secure and trustworthy computing environment where private data and programs are well protected. In pursuit of this vision\, his research interests include system security and privacy\, cloud security\, Trusted Execution Environments\, confidential computing\, CPU security\, micro-architectural security\, GPU security\, and AI security. He has published over 20 papers at leading security and privacy conferences\, including IEEE S&P\, Usenix Security\, and CCS\, and holds multiple technology patents. \nHosted By: Prof. Zhou Li \nMore Information
URL:https://www.cecs.uci.edu/event/confidential-computing-and-trusted-execution-environment-challenges-opportunities-and-the-future/
LOCATION:EH 2430\, Engineering Hall\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:Seminars at CECS
ATTACH;FMTTYPE=image/jpeg:https://cecs.uci.edu/files/2025/05/Mengyuan_pic-e1793121341316.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;VALUE=DATE:20250721
DTEND;VALUE=DATE:20250722
DTSTAMP:20251113T234224Z
CREATED:20251113T234224Z
LAST-MODIFIED:20251113T234224Z
UID:16648-1753056000-1753142399@www.cecs.uci.edu
SUMMARY:Alshammari Won Best Demo at USENIX Security Symposium
DESCRIPTION:At the 3rd USENIX Symposium on Vehicle Security and Privacy (VehicleSec ‘25)\, PhD student Fayzah Alshammari presented “CPExploiter: from Cyber to Physical: Understanding the End-to-End Physical Attack Capability of Cyber-Attacks on Robotic Vehicles.” Alshammari’s paper expresses that previous research focuses on vulnerability discovery without understanding the end-to-end impact of robotic vehicle cyber-attacks. Her demo uses VEI scenarios and automates escalation discovery via UAV-specific parameters to expose physical failure. CPExploiter won the Best Demo Award at VehicleSec ‘25. \n 
URL:https://www.cecs.uci.edu/event/alshammari-won-best-demo-at-usenix-security-symposium/
LOCATION:CA
CATEGORIES:Awards
ATTACH;FMTTYPE=image/png:https://cecs.uci.edu/files/2025/11/alshammari-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250901
DTEND;VALUE=DATE:20250902
DTSTAMP:20251113T234035Z
CREATED:20251113T234035Z
LAST-MODIFIED:20251113T234035Z
UID:16643-1756684800-1756771199@www.cecs.uci.edu
SUMMARY:Kurdahi Promoted to Distinguished Professor
DESCRIPTION:Prof. Fadi Kurdahi was promoted to Distinguished Professor with joint appointment under the Department of Electrical Engineering and Computer Science and Donald Bren School of Information and Computer Science. A Distinguished Professor is the university’s highest academic title and a reflection of outstanding leadership\, deep commitment to education\, and lasting impact in the field. Congratulations\, Prof. Kurdahi! \n 
URL:https://www.cecs.uci.edu/event/kurdahi-promoted-to-distinguished-professor/
LOCATION:CA
CATEGORIES:Awards
ATTACH;FMTTYPE=image/jpeg:https://cecs.uci.edu/files/2025/11/kurdahi_distingushed_prof3.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250927
DTEND;VALUE=DATE:20250928
DTSTAMP:20251113T234331Z
CREATED:20251009T222739Z
LAST-MODIFIED:20251113T234331Z
UID:16520-1758931200-1759017599@www.cecs.uci.edu
SUMMARY:Fadi Kurdahi Honored at WAAAUB Orange County Chapter Annual Dinner
DESCRIPTION:On September 27\, 2025\, the Worldwide Alumni Association of American University of Beirut (WAAAUB) Orange County Chapter hosted a memorable dinner to honor CECS director Professor Fadi Kurdahi with the WAAAUB Orange County Chapter Lifetime Achievement Award. The event recognized Prof. Kurdahi’s remarkable contributions to education and mentorship since he received his Bachelor of Engineering at AUB in 1981. As he accepted the award\, Prof. Kurdahi urged alumni to invest in one another’s futures: “Support one another\, host interns\, be mentors. Together\, we can create a network of AUB alumni who uplift one another and future generations.” \nCongratulations Prof. Kurdahi!
URL:https://www.cecs.uci.edu/event/prof-fadi-kurdahi-honored-at-waaaub-orange-county-chapter-annual-dinner/
LOCATION:CA
CATEGORIES:Awards
ATTACH;FMTTYPE=image/jpeg:https://cecs.uci.edu/files/2025/10/WAAAUB_OC_Kurdahi2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251015T140000
DTEND;TZID=America/Los_Angeles:20251015T150000
DTSTAMP:20261027T171519Z
CREATED:20251007T230120Z
LAST-MODIFIED:20261027T171519Z
UID:16512-1760536800-1760540400@www.cecs.uci.edu
SUMMARY:Unleashing Multi-GPU Computing to the Next-Level
DESCRIPTION:Title: Unleashing Multi-GPU Computing to the Next-Level \nSpeaker:  Dr. Xulong Tang \nDate and Time: Wednesday\, October 15\, 2:00pm \nLocation: EH 2430 \nAbstract: \nModern computing platforms increasingly rely on multi-GPU systems to deliver the performance\, bandwidth\, and memory capacity required by data-intensive applications spanning cloud and edge services to large language models (LLMs) and quantum workloads. However\, scaling GPUs beyond single-device encounters fundamental system-level challenges\, including address translation overheads\, complex memory management\, and inefficient multi-tenancy in shared environments. In this talk\, I will present our recent efforts on hardware-software co-design to overcome these bottlenecks and unleash the full potential of multi-GPU computing. I will discuss novel techniques such as lightweight invalidations for page translation\, short-circuiting page table walks\, and multi-instance GPU (MIG) reconfiguration strategies that collectively deliver substantial performance and efficiency gains. Looking forward\, I will highlight emerging research directions in scalable heterogeneous GPU architectures and memory management in GPU virtualization. An especially exciting avenue is applying these insights to LLMs\, where efficient GPU memory use and multi-GPU coordination are critical for KV-cache reuse\, reduced serving latency\, and scalable throughput. Together\, these innovations pave the way for the next generation of GPU systems that can power both large-scale scientific computing and next-generation intelligent applications. \nBiography: \nDr. Xulong Tang is an Associate Professor in the Department of Computer Science at the University of Pittsburgh. His research spans computer architecture and systems\, with a focus on multi-GPU architectures\, scalable memory management\, and heterogeneous computing across high-performance\, edge\, and quantum systems. His group has pioneered advances in GPU memory management\, temporal graph neural network training\, and quantum compilation. Dr. Tang has published extensively in premier venues such as MICRO\, HPCA\, ISCA\, ASPLOS\, PLDI\, ICLR\, and NeurIPS\, and his work has received recognition including an Intel Research Award\, an ICLR Spotlight Award\, and a MICRO Best Paper Nomination. He is also active in the community\, serving as General Co-Chair of ASPLOS 2026 and Vice Chair of the IEEE Micro Top Picks 2025 Selection Committee. \nHosted By: Prof. Sitao Huang \nMore Information
URL:https://www.cecs.uci.edu/event/unleashing-multi-gpu-computing-to-the-next-level/
LOCATION:EH 2430\, Engineering Hall\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:Seminars at CECS
ATTACH;FMTTYPE=image/png:https://cecs.uci.edu/files/2025/10/Xulong_10_15_25-e1793121315619.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251104T140000
DTEND;TZID=America/Los_Angeles:20251104T150000
DTSTAMP:20261027T171446Z
CREATED:20251014T210226Z
LAST-MODIFIED:20261027T171446Z
UID:16540-1762264800-1762268400@www.cecs.uci.edu
SUMMARY:Autonomy on a Budget: Enabling Intelligent Monitoring Through Low-Cost Embedded Systems
DESCRIPTION:Title: Autonomy on a Budget: Enabling Intelligent Monitoring Through Low-Cost Embedded Systems \nSpeaker:  Dr. Prabha Sundaravadivel \nDate and Time: Tuesday\, November 4\, 2:00pm \nLocation: EH 2430 \nAbstract: \nThe next generation of environmental monitoring frameworks demands intelligent\, autonomous systems that are affordable\, scalable\, and resilient. However\, high-performance autonomy has traditionally relied on expensive sensors and computational hardware\, limiting deployment in resource-constrained or remote environments. This talk presents recent advances in low-cost embedded autonomy\, where edge AI\, sensor fusion\, and adaptive control enable real-time decision-making within compact\, power-efficient platforms. Drawing on case studies from underwater robots\, UAV-based agricultural monitoring\, and IoT-enabled field systems\, the discussion will highlight how autonomy can be achieved through cost-aware design\, leveraging open-source tools\, lightweight deep learning models\, and modular architectures. The presentation will also explore challenges in integrating computer vision\, localization\, and control on embedded hardware\, and propose pathways toward scalable\, intelligent monitoring frameworks for environmental and industrial applications. \nBiography: \nDr. Prabha Sundaravadivel received her Ph.D. in Computer Science and Engineering from the University of North Texas (UNT)\, Denton\, in 2018. She earned her B.Tech in Electronics and Communication Engineering from SRM University\, Chennai\, in 2011\, and her M.Tech in VLSI Design from VIT University\, India\, in 2015. She is currently working as an Associate Professor in the Department of Electrical and Computer Engineering at the University of Texas at Tyler\, Tyler\, Texas\, USA. She leads a multidisciplinary research group as the Director of the Center for Robotics and Intelligent Systems (CeRIS)\, mentoring graduate\, undergraduate\, and high school students. Her team has received multiple accolades\, including Best Paper\, Poster and Pitch awards\, at both regional and international conferences sponsored by IEEE and ACM. \nHer research has led to successful funded projects totaling over $1.7 Million from organizations like NSF\, NASA\, Oracle\, USDA\, and the Venturewell Foundation. Her research is in the emerging areas of the Internet of Things (IoT)\, on-device learning\, bio-inspired robotics\, Agricultural Robotics and Applied ML. Dr. Sundaravadivel has been recognized as a “Faculty Scholar” at the 2019 Grace Hopper Celebration for Women in Computing and 2022 ACM Richard Tapia Conference and was named as “Innovation Fellow” by Venturewell Foundation in 2023. In 2022\, she was recognized as “Outstanding Early Career Researcher” in the IEEE Bio-Inspired Technical Community. \nHosted By: Prof. Mohammad Al Faruque \nMore Information
URL:https://www.cecs.uci.edu/event/autonomy-on-a-budget-enabling-intelligent-monitoring-through-low-cost-embedded-systems/
LOCATION:EH 2430\, Engineering Hall\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:Seminars at CECS
ATTACH;FMTTYPE=image/jpeg:https://cecs.uci.edu/files/2025/10/Prabha_Sundaravadivel_11_4_25-e1793121283651.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260213T103000
DTEND;TZID=America/Los_Angeles:20260213T113000
DTSTAMP:20261027T171407Z
CREATED:20260115T203609Z
LAST-MODIFIED:20261027T171407Z
UID:16669-1770978600-1770982200@www.cecs.uci.edu
SUMMARY:GOAL: An Industrially Validated Global Optimization Framework for Advanced Macro Placement in Semiconductor Design
DESCRIPTION:Title: GOAL: An Industrially Validated Global Optimization Framework for Advanced Macro Placement in Semiconductor Design \nSpeaker: Jooyoung Lee \nDate and Time: Friday\, February 13\, 10:30am \nLocation: EH 2430 \nAbstract: \nMost current AI solutions in industry rely on LLM architectures\, which are fundamentally unsuited for high-dimensional combinatorial optimization. In contrast\, we target problem classes that LLMs and conventional reinforcement learning (RL) cannot solve. System-level macro placement in advanced semiconductor design represents an extremely large-scale combinatorial optimization space\, with a search complexity exceeding astronomical scales. Prior RL-based approaches from major EDA vendors and research groups have not translated into production-level performance\, largely due to sparse and ambiguous reward structures in real-world design constraints. \nOur CSA global-optimization framework is engineered specifically for such non-convex\, high-dimensional search problems. Its performance has been validated through back-to-back victories in The Critical Assessment of Protein Structure Prediction (CASP) and through “Badugi\,” the first Korean AI system to defeat a professional Go player?both demonstrating its capability to navigate vast\, rugged solution landscapes. \nWe have collaborated with a leading global semiconductor manufacturer to evaluate our technology on a top-tier\, cutting-edge semiconductor device. The CSA-based macro-placement engine produced layouts superior to those of veteran human designers and showed strong potential to reduce overall physical-design cycle time by more than half. \nUnlike costly RL-driven\, general-purpose optimization tools\, our solution is built on a dedicated combinatorial-optimization engine that directly models the search space rather than relying on proxy rewards. We are advancing AI that can reliably solve the hardest optimization problems in semiconductor design and beyond. \nBiography: \nProf. Jooyoung Lee\, Good Intelligence CEO\, received his PhD in Physics in 1990 from Brown University under the supervision of Prof. JM Kosterlitz (2016 Nobel laureate in Physics). Since 1994\, his research focused on solving the protein folding problem\, and he led a KIAS (Korea Institute for Advanced Study) team in the CASP (protein structure modeling) competition for CASP5-CASP12 (2002-2016). Motivated by the superhuman performance of AlphaGo\, with an ultimate eye towards improvements in protein folding prediction\, Prof. Lee’s research interest temporarily turned to deep learning. He started with its application to the game of Go\, and implemented a unique deep learning method to compete in world Go AI competitions (2nd place in the 2019 Fuzhou Go AI competition; 3rd place in the 11th Computer Go UEC Cup). \nProf. Lee\, a professor in the school of computational sciences at KIAS\, has published more than 120 peer-reviewed papers in various SCI journals. Many of these papers are well cited [(over 4700 citations from SCI journals and over 3900 citations to his first/corresponding-authored papers (as of Sept. 2018)]. His publication H-Index is 34. \nHosted By: Prof. Ian Harris \nMore Information
URL:https://www.cecs.uci.edu/event/goal-an-industrially-validated-global-optimization-framework-for-advanced-macro-placement-in-semiconductor-design/
LOCATION:EH 2430\, Engineering Hall\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:Seminars at CECS
ATTACH;FMTTYPE=image/jpeg:https://cecs.uci.edu/files/2026/01/JooyoungLee_Photo-e1793121242113.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
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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
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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
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