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X-ORIGINAL-URL:https://www.cecs.uci.edu
X-WR-CALDESC:Events for CECS
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TZID:America/Los_Angeles
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DTSTART:20221106T090000
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231208T090000
DTEND;TZID=America/Los_Angeles:20231208T133000
DTSTAMP:20240320T140831Z
CREATED:20231205T191918Z
LAST-MODIFIED:20240320T140831Z
UID:15923-1702026000-1702042200@www.cecs.uci.edu
SUMMARY:2023 MECPS Symposium
DESCRIPTION:Event: MECPS Symposium 2023\nDate: Friday\, December 8\, 2022 \nTime: 9:00am – 1:30pm \nLocation: Interdisciplinary Science and Engineering Building (ISEB) \nView Details
URL:https://www.cecs.uci.edu/event/2023-mecps-symposium/
LOCATION:ISEB Auditorium\, Interdisciplinary Science and Engineering Building\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:Other Events
ORGANIZER;CN="MECPS":MAILTO:mecps@uci.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231207T143000
DTEND;TZID=America/Los_Angeles:20231207T153000
DTSTAMP:20231101T164355Z
CREATED:20231031T205713Z
LAST-MODIFIED:20231101T164355Z
UID:15888-1701959400-1701963000@www.cecs.uci.edu
SUMMARY:Systematic Security Analysis of Cellular Network Specifications and Implementations
DESCRIPTION:Title: Systematic Security Analysis of Cellular Network Specifications and Implementations \nSpeaker: Dr. Imtiaz Karim \nDate and Time: Thursday\, December 7th\, 2:30 pm \nLocation: EH 2430 \nAbstract: \nCellular networks are the bedrock of modern communication. The recent deployment of the 5G has generated further enthusiasm and opportunities in both academia and industry\, because of its promise of enabling\ninnovative applications\, such as autonomous vehicles\, industrial IoT\, and augmented reality. Therefore\, the security of cellular network protocols is critical. In this talk\, I will elaborate on the critical challenges of ensuring cellular\nnetwork security and move on to the research we are doing to enhance the resilience of the networks. I will detail our developed techniques for both white-box and black-box analysis of cellular implementations. On the whole\, using\nthese approaches we have found 24 critical issues in both commercial and open-source implementations. Lastly\, I will talk about our recent work SPEC5G\, the first-ever public 5G dataset for NLP research\, and discuss how using\nSPEC5G and state-of-the-art Large Language Models (LLMs) we are detecting inconsistencies in the cellular protocol specifications. \nBiography: \nImtiaz Karim is a postdoctoral research associate in the Department of Computer Science at Purdue University working with Prof. Elisa Bertino. He received his Ph.D. degree from the same department in Spring\n2023. His research interests lie in analyzing the security and privacy of wireless communication protocols (e.g.\, 4G\, 5G\, Bluetooth\, VoWiFi\, vehicular\, WiFi\, and IoT)\, their implementations\, networked systems\, and mobile\ncomputing. His research has led to several changes in the design of 4G and 5G cellular standards\, and commercial implementations. He has been inducted into the GSMA Mobile Security Research Hall of Fame\nthree times and has received numerous bug bounties\, CVDs\, and CVEs. He received the best paper award at ACSAC 2019\, and the best paper award nomination at ICDCS 2021. For outstanding security research\, he has\nreceived the Maurice H. Halstead Memorial Award and the Bilsland Dissertation Fellowship Award from Purdue University in 2020 and 2022 respectively. He worked as an Applied Scientist Intern at Amazon (Summer 2021)\nand a Security Researcher Intern at Intel (Summer 2020\, 2019). \nHosted By: Zhou Li \nMore Information
URL:https://www.cecs.uci.edu/event/systematic-security-analysis-of-cellular-network-specifications-and-implementations/
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/2023/10/ImtiazKarim-smaller.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20231201
DTEND;VALUE=DATE:20231202
DTSTAMP:20251113T230627Z
CREATED:20251113T230627Z
LAST-MODIFIED:20251113T230627Z
UID:16577-1701388800-1701475199@www.cecs.uci.edu
SUMMARY:Imani Received Young Investigator Award
DESCRIPTION:The Office of Naval Research (ONR) awarded Assistant Professor Mohsen Imani with the Young Investigator Award. Prof. Imani\, who also was also honored with Defense Advanced Research Projects Agency’s Young Faculty Award\, is a pioneer in research on hyper-dimensional computing as a neuro-symbolic AI technique. Him and his lab investigate the intersection of neuroscience\, AI\, and embedded systems. With this award\, Prof. Imani will also receive a fund of &750K over three years for his brilliant effort.
URL:https://www.cecs.uci.edu/event/imani-received-young-investigator-award/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20231201
DTEND;VALUE=DATE:20231202
DTSTAMP:20251113T230240Z
CREATED:20251113T230240Z
LAST-MODIFIED:20251113T230240Z
UID:16569-1701388800-1701475199@www.cecs.uci.edu
SUMMARY:Shoukry’s UAM Project Awarded 2M
DESCRIPTION:Prof. Yasser Shoukry and team were awarded $2 million over four years by the Smart and Connected Communities and the National Science Foundation for their project “Community-Driven Design of Fair Urban Air Mobility Transportation Management Systems.” Prof. Shoukry and team will investigate and design durable Urban Air Mobility (UAM) infrastructures to help traffic congestion that will not discriminate or reinforce socioeconomic inequality.
URL:https://www.cecs.uci.edu/event/shoukrys-uam-project-awarded-2m/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20231201
DTEND;VALUE=DATE:20231202
DTSTAMP:20251113T230133Z
CREATED:20251113T230133Z
LAST-MODIFIED:20251113T230133Z
UID:16565-1701388800-1701475199@www.cecs.uci.edu
SUMMARY:Jun’s Paper Appears on 25-year Retrospective Edition of ISCA’23
DESCRIPTION:Prof. Sang-Woo Jun explores the possibility of improving system efficient without sacrificing performance in his co-authored paper “GraFBoost: Using Accelerated Flash Storage for External Graph Analytics.” His paper from five years ago explained that using a solid-state drive instead of dynamic RAM for I/O intensive graph analytics can cut cost and power usage by four fold without significant loss of performance. \nFor his extraordinary discovery\, Prof. Jun’s work was selected as one of the 98 papers to appear on the 25-year retrospective edition of the International Symposium on Computer Architecture (ISCA 2023). \n 
URL:https://www.cecs.uci.edu/event/juns-paper-appears-on-25-year-retrospective-edition-of-isca23/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20231201
DTEND;VALUE=DATE:20231202
DTSTAMP:20251113T212842Z
CREATED:20251113T212819Z
LAST-MODIFIED:20251113T212842Z
UID:16560-1701388800-1701475199@www.cecs.uci.edu
SUMMARY:Al Faruque Awarded 378K for Autonomous Vehicle Project
DESCRIPTION:Prof. Mohammad Al Faruque received $378K in award over three years for his project “Adaptive and Efficient Perception for Autonomous Ground Vehicle Operating in Highly Stochastic Environments under Sensing Uncertainties”. With the funding from the U.S. Army Automotive Research Center\, Prof. Al Faruque and his team will study the computational constraints and sensor fusion methods to better military autonomous vehicle on uncertain roads. \n 
URL:https://www.cecs.uci.edu/event/al-faruque-awarded-378k-for-autonomous-vehicle-project/
LOCATION:CA
CATEGORIES:Awards
ATTACH;FMTTYPE=image/png:https://cecs.uci.edu/files/2025/11/alfaruque.png
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:20231103T103000
DTEND;TZID=America/Los_Angeles:20231103T113000
DTSTAMP:20231101T164120Z
CREATED:20231031T234255Z
LAST-MODIFIED:20231101T164120Z
UID:15894-1699007400-1699011000@www.cecs.uci.edu
SUMMARY:Sparse Computing and Large Language Model: from AI 1.0 to AI 2.0
DESCRIPTION:Title: Sparse Computing and Large Language Model: from AI 1.0 to AI 2.0 \nSpeaker: Prof. Guohao Dai \nDate and Time: Friday\, November 3rd\, 10:30 am \nLocation: ISEB 4020 \nAbstract: \nGeneral Artificial Intelligence is going through a new development stage from Computation AI\, Perception AI\, to Cognition AI\, with the new trends of big data\, multi-modality\, and multi-task. However\, due to the slowdown of Moore's Law\, there are bottlenecks in hardware computility\, leaving a huge gap between the supply of computility and the demand for the computility in intelligent computing. Because the AI function has the learnable characteristics\, the amount of calculation can be reduced through the sparsification method\, thereby breaking through the hardware computility bottleneck of intelligent computing while ensuring task accuracy. This report will focus on the software and hardware co-design method of sparse computing. This report will introduce the definition and process of sparse computing\, as well as the challenges faced in intelligent computing from algorithm\, compilation\, and architecture perspectives. This report will elaborate on the multi-level design method for sparse computing in AI\, and introduce three typical models in the AI ​​1.0 era (Graph Neural Networks\, Point Cloud Networks\, and Conventional Convolution Neural Networks). Finally\, this report takes the Large Language Models (LLMs) algorithm in the AI ​​2.0 era as an example to illustrate the application of the above design method in large language model inference optimization. Compared with the existing model\, the calculation speed on dense hardware architecture is increased by 2 times. \nBiography: \nGuohao Dai is a tenure-track Associate Professor at Shanghai Jiao Tong University\, he received his B.S. and Ph.D. (with honor) degrees from Tsinghua University\, Beijing\, in 2014 and 2019. Prof. Guohao Dai has published more than 50\nhigh-level international journals and conference papers in the fields of Electronic Design Automation (EDA)\, heterogeneous computing\, and system/architecture design. He has received more than 1000 citations in Google Scholar. The published paper won the ASP-DAC 2019 Best Paper Award\, DATE 2023/DAC 2022/DATE 2018 Best Paper Nomination\, and WAIC 2022 Outstanding Youth Paper Award. He has personally won honors such as the WAIC 2022 Yunfan Award\, the global champion of the NeurIPS21 BIGANN competition\, Beijing’s outstanding doctoral graduates\, Tsinghua University's outstanding doctoral graduates\, and Tsinghua University’s outstanding doctoral thesis. Professor Dai has participated in guiding students to rank third in the world in ACM 2021 SRC and first in the world in MICRO 2020 SRC. Currently\, he serves as PI/Co-PI for several projects with a personal share of over RMB 10 million. \nHosted By: Sitao Huang \nMore Information
URL:https://www.cecs.uci.edu/event/sparse-computing-and-large-language-model-from-ai-1-0-to-ai-2-0/
LOCATION:ISEB 4020\, Interdisciplinary Science and Engineering Building\, Irvine\, CA\, 92697\, United States
CATEGORIES:Seminars at CECS
ATTACH;FMTTYPE=image/jpeg:https://cecs.uci.edu/files/2023/10/Guohao_Dai_smaller_11-3-23.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231031T080000
DTEND;TZID=America/Los_Angeles:20231031T170000
DTSTAMP:20231031T195550Z
CREATED:20231031T195550Z
LAST-MODIFIED:20231031T195550Z
UID:15881-1698739200-1698771600@www.cecs.uci.edu
SUMMARY:Quoc-Viet Dang Received Excellence in Digital Learning
DESCRIPTION:Assistant Professor QV Dang received Excellence in Digital Learning from UCI Division of Teaching Excellence and Innovation to recognize his demonstrated excellence in undergraduate teaching at UCI. Congrats Prof. Dang!
URL:https://www.cecs.uci.edu/event/quoc-viet-dang-received-excellence-in-digital-learning/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231031T080000
DTEND;TZID=America/Los_Angeles:20231031T170000
DTSTAMP:20231031T195458Z
CREATED:20231031T195458Z
LAST-MODIFIED:20231031T195458Z
UID:15877-1698739200-1698771600@www.cecs.uci.edu
SUMMARY:Rozhin Yasaei Won 2nd Best Paper Award at WiCys 2023
DESCRIPTION:Assistant Professor Rozhin Yasaei won 2nd Best Paper Award at Women in Cyber Security Conference 2023 with paper titled:  “Attack and Fault Detection in the Internet of Things using Graph Neural Networks.” Congrats Prof. Yasaei!
URL:https://www.cecs.uci.edu/event/rozhin-yasaei-won-2nd-best-paper-award-at-wicys-2023/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231031T080000
DTEND;TZID=America/Los_Angeles:20231031T170000
DTSTAMP:20231031T195319Z
CREATED:20231031T195319Z
LAST-MODIFIED:20231031T195319Z
UID:15873-1698739200-1698771600@www.cecs.uci.edu
SUMMARY:Trier Mortlock Won ARC Best Student Poster Award
DESCRIPTION:Trier Mortlock won Best Student Poster Award from the Automotive Research Center (ARC) Program Review for project title: “Adaptive and Efficient Perception for Autonomous Ground Vehicles Operating in Highly Stochastic Environments under Sensing Uncertainties.” Congrats Mortlock!
URL:https://www.cecs.uci.edu/event/trier-mortlock-won-arc-best-student-poster-award/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231031T080000
DTEND;TZID=America/Los_Angeles:20231031T170000
DTSTAMP:20231031T195234Z
CREATED:20231031T195234Z
LAST-MODIFIED:20231031T195234Z
UID:15869-1698739200-1698771600@www.cecs.uci.edu
SUMMARY:Mohsen Imani Won Young Faculty Award
DESCRIPTION:Assistant Professor Mohsen Imani won the Young Faculty Award from Defense Advanced Research Projects Agency to recognize Prof. Imani as a “rising star” in research positions\, supporting his work in the context of national security needs. Congrats Prof. Mohsen!
URL:https://www.cecs.uci.edu/event/mohsen-imani-won-young-faculty-award/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231031T080000
DTEND;TZID=America/Los_Angeles:20231031T170000
DTSTAMP:20231031T195110Z
CREATED:20231031T195110Z
LAST-MODIFIED:20231031T195110Z
UID:15865-1698739200-1698771600@www.cecs.uci.edu
SUMMARY:Yonatan Achamyeleh Receives ECPS PhD Fellowship
DESCRIPTION:Yonatan Achamyeleh Receives ECPS PhD Fellowship. which recognize and encourage ICS/ENG PhD student through the educational efforts. Congrats Achamyeleh!
URL:https://www.cecs.uci.edu/event/yonatan-achamyeleh-receives-ecps-phd-fellowship/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231031T080000
DTEND;TZID=America/Los_Angeles:20231031T170000
DTSTAMP:20231031T195024Z
CREATED:20231031T195024Z
LAST-MODIFIED:20231031T195024Z
UID:15861-1698739200-1698771600@www.cecs.uci.edu
SUMMARY:Mohammad Odema Received Best Paper Nomination at DATE 2023
DESCRIPTION:Mohammad Odema Received Best Paper Nomination at Design Automation and Test in Europe (DATE) 2023 for project title: “HADAS:  Hardware-Aware Dynamic Neural Architecture Search for Edge Performance Scaling.” Congrats Odema!
URL:https://www.cecs.uci.edu/event/mohammad-odema-received-best-paper-nomination-at-date-2023/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231031T080000
DTEND;TZID=America/Los_Angeles:20231031T170000
DTSTAMP:20231031T194917Z
CREATED:20231031T194917Z
LAST-MODIFIED:20231031T194917Z
UID:15857-1698739200-1698771600@www.cecs.uci.edu
SUMMARY:Trier Mortlock Won ARC Excellence Award
DESCRIPTION:Trier Mortlock won the Excellence Award from The Automotive Research Center (ARC) for demonstrated exemplary service to ARC students in the field of electrical and computer engineering. Congrats Mortlock!
URL:https://www.cecs.uci.edu/event/trier-mortlock-won-arc-excellence-award/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231031T080000
DTEND;TZID=America/Los_Angeles:20231031T170000
DTSTAMP:20231031T194815Z
CREATED:20231031T194815Z
LAST-MODIFIED:20231031T194815Z
UID:15853-1698739200-1698771600@www.cecs.uci.edu
SUMMARY:Fadi Kurdahi Won Service Recognition Award
DESCRIPTION:Professor Fadi Kurdahi won Service Recognition Award for serving as Associate Dean at the Henry Samueli School of Engineering. Congrats Prof. Kurdahi!
URL:https://www.cecs.uci.edu/event/fadi-kurdahi-won-service-recognition-award/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231012T110000
DTEND;TZID=America/Los_Angeles:20231012T120000
DTSTAMP:20231011T203102Z
CREATED:20231010T174533Z
LAST-MODIFIED:20231011T203102Z
UID:13955-1697108400-1697112000@www.cecs.uci.edu
SUMMARY:Human-Centered Visual Computing: Harnessing Symbiosis Between Computer Architecture\, Imaging\, and Biological Perception
DESCRIPTION:Title: Human-Centered Visual Computing: Harnessing Symbiosis Between Computer Architecture\, Imaging\, and Biological Perception \nSpeaker: Dr. Yuhao Zhu \nDate and Time: Thursday\, October 12th\, 11:00 am \nLocation: EH 2430 \nAbstract:  \nEmerging platforms such as Augmented Reality (AR)\, Virtual Reality (VR)\, and autonomous machines\, while are of a computing nature\, intimately interact with both the environment and humans. They must be built\, from the ground up\, with principled considerations of three main components: imaging\, computer systems\, and human perception. This talk will make a case for this tenet and discuss some of our recent work on this front. I will first talk about in-sensor visual computing\, the idea that co-designing the image sensor with the computer systems will significantly improve the overall system efficiency and\, perhaps more importantly\, unlock new machine capabilities. We will show a number of case studies in AR/VR and autonomous machines. I will then discuss our work on human-systems co-optimizations\, where we 1) exploit human color vision to build energy-efficient AR/VR devices and 2) computationally “cure” color blindness without gene therapy. \nBiography:  \nYuhao Zhu is an Assistant Professor in the Department of Computer Science and a Bridging Fellow faculty in the Department of Brain and Cognitive Sciences at University of Rochester. He graduated from The University of Texas at Austin\, and previously held visiting researcher positions at Arm Research and Harvard University. Yuhao Zhu does software and hardware research for visual computing\, an exciting area where a challenging\nproblem in computing might become significantly easier when one considers how computing interacts with conventionally non-computing domains such as imaging and human perception at the system level. \nHosted By: Prof. Hyoukjun Kwon \nMore Information
URL:https://www.cecs.uci.edu/event/human-centered-visual-computing-harnessing-symbiosis-between-computer-architecture-imaging-and-biological-perception/
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/2023/10/Yuhao-Zhu.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230926T140000
DTEND;TZID=America/Los_Angeles:20230926T150000
DTSTAMP:20241014T182326Z
CREATED:20231010T222422Z
LAST-MODIFIED:20241014T182326Z
UID:13934-1695736800-1695740400@www.cecs.uci.edu
SUMMARY:Pareto Exploration Methodology for Future Logic Technology Options in Domain-Specific Processors
DESCRIPTION:Speaker: Dr. Francky Catthoor \nDate and Time: Tuesday\, September 26th\, 2:00 pm \nLocation: EH 2430 \nAbstract:   \nScaling of Si-CMOS based logic processor circuits is hitting limits on application-level performance (throughput and latency)\, dynamic and leakage power\, and even cost characteristics. For that reason\, several researchers have started to study alternatives based on so-called beyond CMOS devices. Many emerging devices have been studied in the last decade\, especially in academia. And some of these looks promising for specific figures of-merit\, like only dynamic power (but not energy)\, only leakage (but not dynamic power) and so on. Hence\, they are almost never simultaneously improving several axes (the desirable figures-of-merit) in the Pareto trade-off space. And as a result\, they rarely improve the true Pareto front in that space. A need exists to better compare potential options in a systematic way\, with emphasis on their system-level impact\, and as much as possible in a future-proof way\, including the link to the processor architecture design and hence to the overall processing platform. In this talk\, a systematic approach will be advocated to characterize these options in a multi-dimensional Pareto exploration space\, and a few interesting directions will be positioned in this space to highlight the potential of this approach. The main architecture context will be domain-specific processor instances. \nBiography: \nFrancky Catthoor received a Ph.D. in EE from the Katholieke Univ. Leuven\, Belgium in 1987. Between 1987 and 2000\, he has headed several research domains in the area of synthesis techniques and architectural methodologies. Since 2000 he is strongly involved in other activities at IMEC including deep submicron technology aspects\, IoT and biomedical platforms\, and smart photovoltaic modules\, all at IMEC Leuven\, Belgium. Currently\, he is an IMEC fellow. He is also a part-time full professor at the EE department of the KULeuven. He has been associate editor for several IEEE and ACM journals and was elected IEEE fellow in 2005. \nHosted By: Prof. Nikil Dutt \nMore Information
URL:https://www.cecs.uci.edu/event/pareto-exploration-methodology-for-future-logic-technology-options-in-domain-specific-processors/
LOCATION:EH 2430\, Engineering Hall\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:Seminars at CECS
ATTACH;FMTTYPE=image/webp:https://cecs.uci.edu/files/2023/09/Francky_Catthoor_1-e1728930196231.webp
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230807T110000
DTEND;TZID=America/Los_Angeles:20230807T120000
DTSTAMP:20231011T033045Z
CREATED:20231011T032546Z
LAST-MODIFIED:20231011T033045Z
UID:14162-1691406000-1691409600@www.cecs.uci.edu
SUMMARY:"Capstone: A Capability-based Foundation for Trustless Secure Memory Access"
DESCRIPTION:Speaker: Jason Zhijingcheng Yu\nDate and Time: Monday\, August 7th\, 11:00 am \nLocation: EH 2430 \nAbstract: \nCapability-based memory isolation is a promising new architectural primitive. Software can access lowlevel memory only via capability handles rather than raw pointers\, which provides a natural interface to enforce security restrictions. Existing architectural capability designs such as CHERI provide spatial safety but fail to extend to other memory models that security-sensitive software designs may desire. In this paper\, we propose Capstone\, a more expressive architectural capability design that supports multiple existing memory isolation models in a trustless setup\, i.e.\, without relying on trusted software components. We show how Capstone is well-suited for environments where privilege boundaries are fluid (dynamically extensible)\, memory sharing/delegation are desired both temporally and spatially\, and where such needs are to be balanced with availability concerns. Capstone can also be implemented efficiently. We present an implementation sketch and through evaluation show that its overhead is below 50% in common use cases. We also prototype a functional emulator for Capstone and use it to demonstrate the runnable implementations of six real-world memory models without trusted software components: three types of enclave-based TEEs\, a thread scheduler\, a memory allocator\, and Rust-style memory safety—all within the interface of Capstone.\nBiography: \nJason Zhijingcheng Yu is a 4th-year PhD student working with Prof Prateek Saxena at the School of Computing\, National University of Singapore (NUS). He works on operating system and architectural security\, with a focus on trusted computing and capability-based security in architectures. His first-authored work has been published at venues including ACM CCS 2021\, USENIX Security 2022\, and USENIX Security 2023. Homepage: https://www.comp.nus.edu.sg/~yuz1996/\nHosted By: Zhou Li
URL:https://www.cecs.uci.edu/event/capstone-a-capability-based-foundation-for-trustless-secure-memory-access/
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/2023/07/Jason-Yu.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230714T110000
DTEND;TZID=America/Los_Angeles:20230714T120000
DTSTAMP:20231011T043621Z
CREATED:20231011T032435Z
LAST-MODIFIED:20231011T043621Z
UID:14160-1689332400-1689336000@www.cecs.uci.edu
SUMMARY:"An Integrated Environment for Modeling and Deploying Digital Twins"
DESCRIPTION:Speaker: Charles Steinmetz\nDate and Time: Friday\, July 14th\, 11:00am \nLocation: EH 2430 \nAbstract: \nThe Digital Twin (DT) has been the focus of researchers from academia and industry in the last few years. It is one of the key enablers of the current and next industrial revolutions\, such as Industry 4.0\, Industry 5.0\, and Metaverse. The idea of digitalizing real-world “things” can bring many benefits since it can allow monitoring\, simulation\, predictions\, optimizations and so on. However\, representing real-world systems can be complex\, since assets might have several ways of being represented and several stakeholders with different experiences might be involved. Also\, keeping models updated with the current state of the system can also be challenging. In this context\, this thesis proposes an environment that integrates all these perspectives in a common language that different stakeholders can use\, covering different system levels from the device level up to process and workflow levels. To support the creating of semantic DT models\, a methodology has been proposed\, starting from the identification of the real-world assets\, creating the semantic models and linking external ones\, and ending with the deployment of the system. Furthermore\, a 4-layers architecture is presented to help designers to identify the responsibilities of each part of the system. Use cases related with a simulated industrial plant and in the automotive area have been developed to demonstrate the proposed approach.\nBiography: \nCharles Steinmetz received the B.S. degree in Information Systems from the Franciscan University (UFN)\, Santa Maria\, Brazil\, in 2015\, and the M.Sc. degree in Computer Science from the Federal University of Rio Grande do Sul (UFRGS)\, Porto Alegre\, Brazil\, in 2018. He is currently working toward the Ph.D. degree in Computer Science with the University of Oldenburg\, Oldenburg\, Germany. He also works as Research Assistant at the University of Applied Science Hamm-Lippstadt\, Lippstadt\, Germany. His research interests are Internet of Things\, Digital Twins\, semantic models and industry 4.0.\nHosted By: Nikil Dutt
URL:https://www.cecs.uci.edu/event/an-integrated-environment-for-modeling-and-deploying-digital-twins/
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/2023/07/charles-image.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230714T100000
DTEND;TZID=America/Los_Angeles:20230714T230000
DTSTAMP:20231011T032435Z
CREATED:20231011T032435Z
LAST-MODIFIED:20231011T032435Z
UID:14159-1689328800-1689375600@www.cecs.uci.edu
SUMMARY:"Requirements and Applications of  Cloud-based Services within the Automotive Edge"
DESCRIPTION:Speaker: Prof. Dr. Achim Rettberg\nDate and Time: Friday\, July 14th\, 10:00am \nLocation: EH 2430 \nAbstract: \nThe architectures in today’s vehicles are usually still based on decentralized or domain-centric architecture concepts that place a strong focus on individual control units. New applications and services\, which may only be used or required temporarily\, are very difficult to implement with these vehicle architectures. This applies in particular when external data sources and services are also involved\, e.g. a bidirectional exchange of information between vehicles and their infrastructure. The dynamic adaptation to new circumstances\, such as the availability of new data sources or the use of specific vehicle functions by third-party applications\, is hardly or not at all feasible here. These concepts are being investigated in the GAIA-X4AGEDA joint research project\, which aims to develop a software architecture in the edge device vehicle that makes use of the concepts of the cloud initiative “GAIA-X” and “by-design” data-driven applications and the dynamic adaptation of a vehicle with its functions over the entire life cycle of the vehicle. The modeling of this approach with digital twins plays a central role here\, on the one hand to be able to show early statements about characteristics such as real time etc. and on the other hand to make predictions about the state of the system \nBiography: \nProf. Dr. Achim Rettberg works since December 2017 as Professor for Human Machine Interface Technologies at University of Applied Science Hamm/Lippstadt. Previously\, he worked since end of 2014 for HELLA as Methodology expert and for inventing a PhD program. Furthermore\, he is still extraordinary Professor at Carl von Ossietzky University Oldenburg. He received his M.S. (Dipl.-Inform.) in computer science and economics in 1997 and his Ph. D. (Dr. rer. nat.) degree in 2006 from the University of Paderborn\, Germany\, where he studied computer science and economics. He worked from 2007 until 2008 as Post-Doc at C-LAB and managed EU-Projects. In 2008 he became professor of complex integrated systems/embedded systems at the Computer Science department at Carl von Ossietzky University Oldenburg. Prof. Rettberg is founder and General Chair of the International Embedded Systems Symposium (IESS). He is chair of the IFIP working group 10.2 for Embedded Systems still April 2012 and chair of IFIP TC 10  Computer Systems Technology since October 2019. Since September 2022 he is member of the IFIP board. His general research interests are real-time systems and HW/SW architectures. His current focus lies on design methods\, optimization for embedded systems and architectures\, and for human machine interface technologies for embedded devices. \nHosted By: Nikil Dutt
URL:https://www.cecs.uci.edu/event/requirements-and-applications-of-cloud-based-services-within-the-automotive-edge/
LOCATION:EH 2430\, Engineering Hall\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:Seminars at CECS
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: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: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: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: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: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:20230426T150000
DTEND;TZID=America/Los_Angeles:20230426T160000
DTSTAMP:20231011T032408Z
CREATED:20231011T032408Z
LAST-MODIFIED:20231011T032408Z
UID:14154-1682521200-1682524800@www.cecs.uci.edu
SUMMARY:"Intuitive\, Interoperable\, Intelligent – A Look into Current Automation Engineering Research"
DESCRIPTION:Speaker: Dr. Gustavo Quiros\nDate and Time: Wednesday\, April 26th\, 3:00pm \nLocation: EH 2430 \nAbstract:   \nIntuitive\, Interoperable\, Intelligent – The future of automation engineering will embrace these basic characteristics to continuously reduce the effort and expertise required by engineers to implement industrial automation solutions while increasing the flexibility and the capabilities of engineering tools. This talk will give an overview of several research activities in the area of industrial automation engineering\, showing how they aim to realize these three basic characteristics\, and provides a vision for automation engineering in the near future. \nBiography: \nDr. Gustavo Quiros A. is a Research and Technology Manager at Siemens Technology. He specializes in industrial automation engineering\, programming languages and software engineering. He holds a B.Sc. degree in Computer Engineering from Instituto Tecnologico de Costa Rica\, a M.Sc. degree in Software Systems Engineering and a Dr.rer.nat. degree in Process Control Engineering from RWTH Aachen University. He obtained summa cum laude and the Borchers-Badge of RWTH Aachen University for his doctoral dissertation “Model-based Decentralized Automatic Management of Product Flow Paths in Processing Plants”. He also obtained the NAMUR Award “Process Automation” 2010 from the User Association of Automation Technology in Process Industries.
URL:https://www.cecs.uci.edu/event/intuitive-interoperable-intelligent-a-look-into-current-automation-engineering-research/
LOCATION:EH 2430\, Engineering Hall\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:Seminars at CECS
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DTSTART;TZID=America/Los_Angeles:20230306T140000
DTEND;TZID=America/Los_Angeles:20230306T150000
DTSTAMP:20231011T032353Z
CREATED:20231011T032353Z
LAST-MODIFIED:20231011T032353Z
UID:14151-1678111200-1678114800@www.cecs.uci.edu
SUMMARY:“Perception and Computational Efficiency for Autonomous Vehicles”
DESCRIPTION:Speaker: Prof. Marilyn Wolf\nDate and Time: Monday\, March 6th\, 2:00pm \nLocation: EH 2430 \nAbstract:   \nPerception is a critical computational task in autonomous vehicles.  Autonomous vehicles place stringent and somewhat conflicting demands on perception systems: high accuracy\, low latency\, and performance on limited computational resources.  The conflict between these requirements is particularly acute in the case of unmanned aerial vehicles (UAVs) but is also true of ground vehicles.  This talk will describe two related efforts to improve and manage efficiency in perception for autonomous vehicles. Work with Krishna Muuva and Justin Bradley of UNL looks at UAV-UAV tracking.  We show that tracking performance saturates above a given level of perceptual accuracy.  Work with Deep Samal and Saibal Mukhopodhyay of Georgia Tech compares the results of multiple evaluators to improve the accuracy of LIDAR tracking. \nBiography: \nMarilyn Wolf is Elmer E. Koch Professor of Engineering and Director of the School of Computing at the University of Nebraska – Lincoln.  She received her BS\, MS\, and PhD in electrical engineering from Stanford University in 1980\, 1981\, and 1984\, respectively.  She was with AT&T Bell Laboratories from 1984 to 1989.  She was on the faculty of Princeton University from 1989 to 2007 and was Farmer Distinguished Chair at Georgia Tech from 2007 to 2019.   Her research interests include cyber-physical systems\, embedded computing\, embedded video and computer vision\, and VLSI systems. She has received the IEEE Kirchmayer Graduate Education Award\, the IEEE Computer Society Goode Memorial Award\, the  ASEE Terman Award\, and the IEEE Circuits and Systems Society Education Award. She is a Fellow of the IEEE and ACM and an IEEE Computer Society Golden Core member. \nHosted By: Prof. Mohammad Al Faruque \n 
URL:https://www.cecs.uci.edu/event/perception-and-computational-efficiency-for-autonomous-vehicles/
LOCATION:EH 2430\, Engineering Hall\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:Seminars at CECS
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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
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