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DTSTART;TZID=America/Los_Angeles:20240126T140000
DTEND;TZID=America/Los_Angeles:20240126T150000
DTSTAMP:20240118T174157Z
CREATED:20240118T163958Z
LAST-MODIFIED:20240118T174157Z
UID:15949-1706277600-1706281200@www.cecs.uci.edu
SUMMARY:Modeling\, Analysis\, Simulation\, and Optimization. The Bedrock of Engineering
DESCRIPTION:Title: Modeling\, Analysis\, Simulation\, and Optimization. The Bedrock of Engineering \nSpeaker: Dr. Sani Nassif \nDate and Time: Friday\, January 26th\, 2:00 pm \nLocation: EH 2430 \nAbstract: \nVLSI\, Circuit Design\, Technology Scaling\, Moore’s Law\, EDA. They are our own highly specialized areas of expertise\, and it often feels like our discipline is quite different from other fields in engineering. Indeed\, the many decades of exponential growth are unique to our field and have presented numerous challenges that were met by researchers in this area with clever solutions. But at the core of our work\, we find four steps that we take over and over again. We observe phenomena\, make physical or empirical models for them\, analyze their behavior as it impacts our goals\, create mathematical models that we can use to simulate and predict the behaviors\, and finally apply optimization or improvement techniques to create the best possible outcome. These four activities are universal across engineering practice\, and it is useful to reflect on how our own tricks and techniques -many innovated in response to the severe scaling challenges the community has had to go through\, are good lessons to apply in other fields of engineering or even in medicine. \nBiography: \nSani received his PhD from Carnegie-Mellon university in the eighties. He worked for ten years at Bell Laboratories on various aspects of design and technology coupling. He then joined IBM Research where he was for eighteen years working on DFM in various technical and management roles. In 2014 he formed Radyalis to apply EDA techniques to Cancer Radiation Therapy. Sani is a fellow of the IEEE\, a member of the IBM Academy of Technology\, and an IBM master inventor with 70+ patents. He was the president of IEEE CEDA in 2014-2016\, and has published and presented widely. \nHosted By: Fadi Kurdahi \nMore Information
URL:https://www.cecs.uci.edu/event/modeling-analysis-simulation-and-optimization-the-bedrock-of-engineering/
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/2024/01/Sani-Nassif-picture.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240206T150000
DTEND;TZID=America/Los_Angeles:20240206T160000
DTSTAMP:20240205T182606Z
CREATED:20240124T051514Z
LAST-MODIFIED:20240205T182606Z
UID:15960-1707231600-1707235200@www.cecs.uci.edu
SUMMARY:Electrification of Mobilities\, the Challenges and Opportunities
DESCRIPTION:Title: Electrification of Mobilities\, the Challenges and Opportunities \nSpeaker: Dr. Naehyuck Chang \nDate and Time: Tuesday\, February 6th\, 3:00 pm \nLocation: EH 2430 \nAbstract: \nThe electrification of mobilities is essential for sustainability\, and governments are pushing hard to expedite electric vehicle penetration. As a result\, it is not surprising to see a large proportion of electric vehicles on the road today. Nevertheless\, the battery industry faces significant challenges starting from late 2023\, which will continue years from now. \nIn this talk\, we will introduce industry perspective challenges and opportunities for mobility electrification in both the technical and business aspects. The technical aspects to be covered are energy density\, charging time\, lifetime\, cost\, and safety. We will also bring up infrastructure issues for electric vehicle charging. As per the business aspects\, we will discuss the electrification roadmap\, government support\, marketing challenges\, supply chain\, circularity\, expected battery costs\, raw material costs\, and profit margin. In conclusion\, we will summarize the expected academic contributions to the electrification of mobilities. \nBiography: \nNaehyuck Chang is an Executive Vice President at Samsung SDI America. He was the previous Head of Development at Samsung SDI Headquarters from 2021 to 2023. Dr. Chang was in charge of all the automotive and energy-storage product developments\, from cells to systems. Samsung SDI is one of the world’s leading high-performance battery companies. He is the Founder of EMVcon\, Inc.\, Irvine\, CA\, for vehicle electrification\, funded by Samsung. \nDr. Chang received the B.S.\, M.S.\, and Ph.D. degrees from the Department of Control and Instrumentation\, Seoul National University\, Seoul\, South Korea\, in 1989\, 1992\, and 1996\, respectively. From 1997 to 2014\, he was with the Department of Computer Science and Engineering at Seoul National University. From 2011 to 2013\, Dr. Chang was the Vice Dean of the College of Engineering at Seoul National University. Since 2014\, he has been a Full Professor at the Department of Electrical Engineering\, Korea Advanced Institute of Science and Technology\, Daejeon\, South Korea. He was the Strategy Planning Committee Chair at the College of Engineering\, Korea Advanced Institute of Science and Technology. Dr. Chang was a visiting professor at the Arizona State University and the University of Southern California in 2005 and 2009\, respectively. Dr. Chang’s research interests include low-power cyber-physical systems and Design Automation of Things\, such as systematic design and optimization of fuel cell systems\, energy storage systems\, electric vehicles\, drones\, and energy harvesting. Dr. Chang is specifically interested in mobility electrification in terms of design\, optimization\, and management of optimizing the power\, energy\, and lifetime of the battery cells. Dr. Chang’s initial research also includes nuclear power plant monitoring and control systems\, high-speed train monitoring systems\, and real-time systems. \nHosted By: Fadi Kurdahi \nMore Information
URL:https://www.cecs.uci.edu/event/electrification-of-mobilities-the-challenges-and-opportunities/
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/2024/01/naehyuck-pic.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240220T103000
DTEND;TZID=America/Los_Angeles:20240220T113000
DTSTAMP:20240130T180258Z
CREATED:20240130T161022Z
LAST-MODIFIED:20240130T180258Z
UID:15965-1708425000-1708428600@www.cecs.uci.edu
SUMMARY:Searching for Digital Evidence in Industrial Control Systems
DESCRIPTION:Title: Searching for Digital Evidence in Industrial Control Systems \nSpeaker: Prof. Irfan Ahmed \nDate and Time: Tuesday\, February 20th\, 10:30am \nLocation: EH 2430 \nAbstract: \nIndustrial control systems (ICS) control significant portions of the U.S. critical infrastructure (e.g.\, power grid\, pipelines\, and water management). These systems are known to be vulnerable to cyberattacks\, as demonstrated in the past\, such as CrashOverride\, Havex\, and HatMan. More recently\, DHS CISA reported Russian state-sponsored cyber operations against Ukrainian critical infrastructure\, causing denial-of-service and deployment of KillDisk and other destructive malware. In case of a security breach or catastrophic event\, digital forensics investigation is crucial to answering questions about an attack. This talk will explore how malicious actors can exploit the vulnerabilities in common design features in real-world field devices into successful attacks on physical processes. It will further discuss research challenges and opportunities to investigate an ICS environment effectively. \nBiography: \nIrfan Ahmed is a VCU NIRA scholar and an Associate Professor of Computer Science at Virginia Commonwealth University (VCU)\, where he runs the Security and Forensics Engineering (SAFE) Lab in the College of Engineering\, focusing on the cybersecurity of industrial control systems (ICS). He has an extensive track record of ICS offensive and defensive research\, including eleven ICS-CERT CVEs for the programmable logic controllers of five vendors\, i.e.\, Siemens\, Schneider Electric\, Rockwell Automation\, SEL\, and Automation Direct. Ahmed is a steering team member of the DHS Industrial Control Systems Joint Working Group (ICSJWG) to engage asset owners\, vendors\, state and local governments\, industry associations\, and consultants/integrators to improve ICS cybersecurity. His work has received three Best Paper Awards\, three Best Student Paper Awards\, and two Outstanding Poster Awards in digital forensics and cybersecurity conferences. He is also the recipient of an Outstanding Research Award from the American Academy of Forensic Sciences\, the ORAU Ralph E. Powe Junior Faculty Enhancement Award\, and the CCI Innovation Award from the Virginia Commonwealth Cyber Initiative. Ahmed has also developed laboratory-scale\, fully functional ICS testbeds on a gas pipeline and a wastewater treatment plant initially funded by the Army Research Office. His research work is regularly funded by the DHS\, NSA\, DOE\, and NSF. For more information\, https://people.vcu.edu/~iahmed3/ \nHosted By: Yasser Shoukry and Mohammad Al Faruque \nMore Information
URL:https://www.cecs.uci.edu/event/searching-for-digital-evidence-in-industrial-control-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/2024/01/Irfan-Ahmed-pic.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240222
DTEND;VALUE=DATE:20240224
DTSTAMP:20240425T214255Z
CREATED:20240205T192719Z
LAST-MODIFIED:20240425T214255Z
UID:16010-1708560000-1708732799@www.cecs.uci.edu
SUMMARY:Self-Awareness in Cyber-Physical Systems 2024
DESCRIPTION:Event: Self-Awareness in Cyber-Physical Systems (SelPhyS) 2024\nDate: February 22-23\, 2024\nLocation: San Diego State University (SDSU) \nRegister by: Wednesday\, February 7\, 2024\nLink to Register: https://www.eventbrite.com \nView Details
URL:https://www.cecs.uci.edu/event/self-awareness-in-cyber-physical-systems-2024/
LOCATION:San Diego State University\, San Diego\, CA\, United States
CATEGORIES:Other Events,Seminars at CECS
ATTACH;FMTTYPE=image/png:https://cecs.uci.edu/files/2024/02/SDSU.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240301
DTEND;VALUE=DATE:20240302
DTSTAMP:20251113T230929Z
CREATED:20251113T230929Z
LAST-MODIFIED:20251113T230929Z
UID:16581-1709251200-1709337599@www.cecs.uci.edu
SUMMARY:Krichmar’s Research with AI Featured on Medium
DESCRIPTION:A groundbreaking project by Professor Jeff Krichmar and the Neurorobotics Lab explored methods to bring primary consciousness into machines using Gerald Edelman’s Extended Theory of Neuronal Group Selection. \nFollowing the theory\, which suggests that consciousness emerges through interactions among neuronal groups\, similar to the process of natural selection\, Prof. Krichmar and team aimed to mimic the evolutionary development of consciousness in artificial intelligence. The Neurorobotics Lab have initiated environments that simulate neuronal group selections\, creating AI that can recognize its environment\, make predictions\, and learn from interactions without human intervention. \nProf. Krichmar and team’s dictation and achievements were featured on popular article platform Medium.
URL:https://www.cecs.uci.edu/event/krichmars-research-with-ai-featured-on-medium/
LOCATION:CA
CATEGORIES:Awards
ATTACH;FMTTYPE=image/png:https://cecs.uci.edu/files/2025/11/krichmar.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240301
DTEND;VALUE=DATE:20240302
DTSTAMP:20251113T231149Z
CREATED:20251113T231132Z
LAST-MODIFIED:20251113T231149Z
UID:16586-1709251200-1709337599@www.cecs.uci.edu
SUMMARY:NDSS’24 Best Technical Poster Award
DESCRIPTION:At the Network and Distributed System Security Symposium (NDSS ‘24)\, PhD student Fayzah Alshammari received Best Technical Poster Award for paper “On the Cyber-Physical Security of Commercial Indoor Delivery Robot Systems.” Alshammari’s research aimed to improve security through analysis on systems in real-world commercial settings. Alshammari is under the advisory of Professor Alfred Chen. \n 
URL:https://www.cecs.uci.edu/event/ndss24-best-technical-poster-award/
LOCATION:CA
CATEGORIES:Awards
ATTACH;FMTTYPE=image/png:https://cecs.uci.edu/files/2025/11/Alshammari.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240401T110000
DTEND;TZID=America/Los_Angeles:20240401T120000
DTSTAMP:20240425T213942Z
CREATED:20240327T223424Z
LAST-MODIFIED:20240425T213942Z
UID:16167-1711969200-1711972800@www.cecs.uci.edu
SUMMARY:Hardware for ML and ML for Hardware
DESCRIPTION:Title: Hardware for ML and ML for Hardware \nSpeaker: Prof. Aman Arora \nDate and Time: Monday\, April 1st\, 11:00am \nLocation: EH 2430 \nAbstract: \nThis talk delves into the exciting intersection of hardware design and machine learning\, showcasing how these fields are mutually benefiting each other. The presentation will feature cutting-edge research projects that exemplify this dynamic. In the first half\, I will discuss two research projects focused on optimizing hardware for machine learning (ML): the first involves architecting ML-specialized Field Programmable Gate Arrays (FPGAs)\, while the second focuses on designing a processing-in-memory (PIM) accelerator for ML. Then\, in the second half\, I will share two innovative projects in which ML is being used to revolutionize hardware design: one employing machine learning for power\, performance\, and area (PPA) prediction for FPGAs\, and another utilizing large language models (LLMs) for hardware design and verification. I will share both published results and ongoing work in these projects. This talk highlights how the interplay between these two fields can be leveraged to advance both fields\, promising a future of intelligent computing. \nBiography: \nAman Arora is an assistant professor in the School of Computing and Augmented Intelligence at Arizona State University. He directs the Advent lab\, where he and his students work on developing computer systems that can efficiently process modern workloads like Machine Learning\, with a special focus on reconfigurable systems. He graduated with a Ph.D. from the University of Texas at Austin in 2023. He has over 10 years of experience in the semiconductor industry in design\, verification\, testing and architecture roles. \nHosted By: Prof. Sitao Huang \nMore Information
URL:https://www.cecs.uci.edu/event/hardware-for-ml-and-ml-for-hardware/
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/2024/03/Aman-Arora-3.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240402T120000
DTEND;TZID=America/Los_Angeles:20240402T130000
DTSTAMP:20240502T215645Z
CREATED:20240502T215253Z
LAST-MODIFIED:20240502T215645Z
UID:16268-1712059200-1712062800@www.cecs.uci.edu
SUMMARY:Fairness-Aware learning over Graphs
DESCRIPTION:Title: Fairness-Aware learning over Graphs \nSpeaker: Oyku Deniz Kose \nDate and Time: Tuesday\, April 2nd\, 12:00pm \nLocation: EH 2430 \nAbstract: \nGraphs are powerful mathematical tools that can represent complex real-world systems\, such as financial markets and social networks\, and the relations within them. Due to this\, machine learning (ML) over graphs has attracted significant attention recently\, and showed promising success in various applications. Despite the success\, the large-scale deployment of graph-based ML algorithms in real-world systems relies heavily on how socially responsible they are. While graph-based ML models nicely integrate the nodal data with the connectivity\, they also inherit potential unfairness. Using such ML models may therefore result in inevitable unfair results in various decision- and policy-making in the related applications. While fairness and explainability have attracted increasing attention in responsible ML\, they are mostly under-explored in the graph domain. Motivated by this\, in this talk\, we first focus on the analysis of sources of bias in graph-based learning and then present fairness-aware learning algorithms that combat the identified bias sources. In addition\, we will talk about certain real-word challenges that need to be considered in addition to fairness\, e.g.\, efficiency\, convergences. \nBiography: \nOyku Deniz Kose received the B.S. and M.S. degrees in electrical and electronics engineering from Bogazici University\, Istanbul\, Turkey\, in 2017 and 2020\, respectively. She is currently working towards a Ph.D. degree with the Department of Electrical Engineering and Computer Science\, University of California Irvine\, Irvine\, CA\, USA. She received the Samueli Endowed Fellowship in 2021 and was a research intern at Google in 2023. Her research mainly focuses on trustworthy machine learning with a specific interest in graph-based learning.
URL:https://www.cecs.uci.edu/event/fairness-aware-learning-over-graphs/
LOCATION:EH 2430\, Engineering Hall\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:Student Seminars
ATTACH;FMTTYPE=image/jpeg:https://cecs.uci.edu/files/2024/05/Kose-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240425T100000
DTEND;TZID=America/Los_Angeles:20240425T110000
DTSTAMP:20240425T213745Z
CREATED:20240405T212937Z
LAST-MODIFIED:20240425T213745Z
UID:16183-1714039200-1714042800@www.cecs.uci.edu
SUMMARY:Towards Self-Sustainable Wearable IoT Devices for Reliable Mobile Health Applications
DESCRIPTION:Title: Towards Self-Sustainable Wearable IoT Devices for Reliable Mobile Health Applications \nSpeaker: Prof. Ganapati Bhat \nDate and Time: Thursday\, April 25th\, 10:00am \nLocation: EH 2430 \nAbstract: \nElectronic systems experience a major shift in their form factor every 10 to 15 years\, as evidenced by the popularity of heterogeneous mobile systems in the last decade. Wearable internet of things (IoT) devices are the next big evolution in computing systems. Wearable sensors and IoT devices\, along with smart home technologies\, have the potential to transform healthcare by enabling cost-effective\, reliable\, continuous\, and data-driven monitoring of users in a free-living environment. Despite the impressive potential of wearable technology\, widespread adoption of wearable devices has been limited due to several technology and adaptation challenges. First\, wearable devices have small batteries that necessitate frequent recharging to prolong their operation. Ambient energy sources are promising to tackle this problem\, but they are highly stochastic and must be managed optimally. Second\, wearable devices operate in dynamic environments with potential changes in data distribution. Sensor data could go missing due to energy constraints as well. This must be handled in an energy-efficient manner to enable reliable applications. I will present our solutions towards these challenges in this talk. First\, I will describe imitation learning and uncertainty aware algorithms to enable recharge-free operation of wearable devices that harvest energy from the environment. After that\, I will go over our research on algorithms to handle sensor data shifts and missing data in mobile health applications. I will conclude with some ongoing research and future directions. \nBiography: \nGanapati Bhat is an Assistant Professor in the School of Electrical Engineering and Computer Science at Washington State University. He received his B.Tech degree in Electronics and Communication from Indian Institute of Technology (ISM)\, Dhanbad\, India in 2012 and the PhD degree in Computer Engineering from Arizona State University in 2020. From 2012-2014\, he worked as a software engineer at Samsung Research and Development Institute\, Bangalore\, India. His research interests include energy optimization in computing systems\, dynamic thermal and power management\, and energy management for wearable systems. Dr. Bhat received the NSF CAREER award (2023)\, 2022 ACM Outstanding Ph.D. Dissertation Award in Electronic Design Automation\, the 2021 ACM Transactions on Design Automation of Electronic Systems (TODAES) best paper award\, and 2019 Best Paper Award at CASES: International Conference on Compilers\, architecture\, and Synthesis for Embedded Systems during the Embedded Systems Week (ESWEEK). He serves as an Associate Editor of IEEE Embedded Systems Letters and IEEE Design & Test. He regularly serves as technical program committee member top-tier conferences including DAC\, ICCAD\, ISLPED\, and ESWEEK. \nHosted By: Prof. Mohammad Al Faruque \nMore Information
URL:https://www.cecs.uci.edu/event/towards-self-sustainable-wearable-iot-devices-for-reliable-mobile-health-applications/
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/2024/04/ganapatibhat-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240429
DTEND;VALUE=DATE:20240430
DTSTAMP:20251113T232149Z
CREATED:20251113T232149Z
LAST-MODIFIED:20251113T232149Z
UID:16600-1714348800-1714435199@www.cecs.uci.edu
SUMMARY:Levorato Wins Emerging Innovator of Year Award
DESCRIPTION:Assistant Professor Marco Levorato won Beall Applied Innovation Center’s 2023 UCI Early Career/Emerging Innovator of Year Award. The award recognizes Prof. Levorato’s efforts to “drive innovative research into socially and commercially important applications.” Prof. Levorato’s research focuses on balancing data transfer security and cost for mobile AI applications. Along with the Beall Applied Innovation Center\, Prof. Levorato is working on distributing his findings for real world applications. \n 
URL:https://www.cecs.uci.edu/event/levorato-wins-emerging-innovator-of-year-award/
LOCATION:CA
CATEGORIES:Awards
ATTACH;FMTTYPE=image/png:https://cecs.uci.edu/files/2025/11/levorato.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240501T110000
DTEND;TZID=America/Los_Angeles:20240501T120000
DTSTAMP:20240425T214117Z
CREATED:20240425T183332Z
LAST-MODIFIED:20240425T214117Z
UID:16212-1714561200-1714564800@www.cecs.uci.edu
SUMMARY:Agile Hardware Specialization
DESCRIPTION:Title: Agile Hardware Specialization \nSpeaker: Prof. Yun Eric Liang \nDate and Time: Wednesday\, May 1st\, 11:00am \nLocation: ISEB 4020 \nAbstract: \nAs Moore’s law is approaching to the end\, designing specialized hardware accelerator along with the software that map the applications onto the specialized hardware is a promising solution. However\, the hardware is very difficult to design and optimize due to the low-level programming and huge design space. In this talk\, I will introduce AHS\, an Agile framework for Hardware specialization. AHS can automatically partition the hardware and software space\, generate the hardware accelerator\, and map the software onto the hardware efficiently. \nBiography: \nProf. Yun Eric Liang is currently an Endowed Boya Distinguished Professor in the School of Integrated Circuit and School of EECS at Peking University. His research interest is at the hardware-software interface with work spanning electronic design automation (EDA)\, reconfigurable computing\, computer architecture. He has authored over 100 scientific publications in the leading international journals and conferences. His research has been recognized with two Best Paper Awards (FCCM 2011 and ICCAD 2017)\, six Best Paper Award Nominations. He currently serves as Associate Editor of the ACM Transactions on Embedded Computing Systems (TECS) and ACM Transactions on Reconfigurable Technology and Systems (TRETS). He is the program chair of International Conference on Field Programmable Technology (FPT) 2022 and International Conference on Application-specific Systems\, Architecture and Processors (ASAP) 2019. He also serves in the program committees in the premier conferences including DAC\, ICCAD\, FPGA\, FCCM\, HPCA\, MICRO\, DATE\, ASPDAC\, SysML\, etc. \nHosted By: Prof. Sitao Huang \nMore Information
URL:https://www.cecs.uci.edu/event/agile-hardware-specialization/
LOCATION:ISEB 4020\, Interdisciplinary Science and Engineering Building\, Irvine\, CA\, 92697\, United States
CATEGORIES:Seminars at CECS
ATTACH;FMTTYPE=image/png:https://cecs.uci.edu/files/2024/04/Yun-Eric-Liang.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240523T100000
DTEND;TZID=America/Los_Angeles:20240523T110000
DTSTAMP:20240528T214031Z
CREATED:20240516T161556Z
LAST-MODIFIED:20240528T214031Z
UID:16284-1716458400-1716462000@www.cecs.uci.edu
SUMMARY:Dissecting the Software Supply Chain of Modern Industrial Control Systems
DESCRIPTION:Title: Dissecting the Software Supply Chain of Modern Industrial Control Systems \nSpeaker: Prof. Michail Maniatakos \nDate and Time: Thursday\, May 23rd\, 10:00am \nLocation: Zoom \nAbstract: \nRecent years have been pivotal in the field of Industrial Control Systems (ICS) security\, with a large number of high-profile attacks exposing the lack of a design-for-security initiative in ICS. The evolution of ICS abstracting the control logic to a purely software level hosted on a generic OS\, combined with hyperconnectivity and the integration of popular open source libraries providing advanced features\, have expanded the ICS attack surface by increasing the entry points and by allowing traditional software vulnerabilities to be repurposed to the ICS domain. In this seminar\, we will shed light to the security landscape of modern ICS\, dissecting firmware from the dominant vendors and motivating the need of employing appropriate vulnerability assessment tools. We will present methodologies for blackbox fuzzing of modern ICS\, both directly using the device and by using the development software. We will then proceed with methodologies on hotpatching\, since ICS cannot be easily restarted in order to patch any discovered vulnerabilities. We will demonstrate our proposed methodologies on various critical infrastructure testbeds. \nBiography: \nMichail (Mihalis) Maniatakos is an Associate Professor of Electrical and Computer Engineering at New York University (NYU) Abu Dhabi\, UAE\, and a Research Associate Professor at the NYU Tandon School of Engineering\, New York\, USA. He is the Director of the MoMA Laboratory (nyuad.nyu.edu/momalab)\, NYU Abu Dhabi. He received his Ph.D. in Electrical Engineering\, as well as M.Sc.\, M.Phil. degrees from Yale University. He also received the B.Sc. and M.Sc. degrees in Computer Science and Embedded Systems\, respectively\, from the University of Piraeus\, Greece. His research interests\, funded by industrial partners\, the US government\, and the UAE government include privacy-preserving computation and industrial control systems security. \nHosted By: Prof. Mohammad Al Faruque \nMore Information
URL:https://www.cecs.uci.edu/event/dissecting-the-software-supply-chain-of-modern-industrial-control-systems/
LOCATION:Zoom
CATEGORIES:Seminars at CECS
ATTACH;FMTTYPE=image/jpeg:https://cecs.uci.edu/files/2024/05/MichailManiatakos_5-23-24-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240524T093000
DTEND;TZID=America/Los_Angeles:20240524T103000
DTSTAMP:20240528T213610Z
CREATED:20240528T213210Z
LAST-MODIFIED:20240528T213610Z
UID:16300-1716543000-1716546600@www.cecs.uci.edu
SUMMARY:Deep Latent Variable Modeling of Physiological Signals
DESCRIPTION:Name: Khuong An Vo \nChair: Prof. Nikil Dutt \nDate: May 24\, 2024 \nTime:  9:30 AM \nLocation: Zoom \nCommittee: Prof. Erik Sudderth\, Prof. Hung Cao\, and Prof. Ramesh Srinivasan \nTitle: Deep Latent Variable Modeling of Physiological Signals \nAbstract: A deep latent variable model is a powerful tool for modeling complex distributions. These models assume that underlying structures\, but unobserved\, are present within the data. In this dissertation\, we explore high-dimensional problems related to physiological monitoring using latent variable models. First\, we present a novel deep state-space model to generate electrical waveforms of the heart using optically obtained signals as inputs. This can bring about clinical diagnoses of heart disease via simple assessment through wearable devices. Second\, we present a brain signal modeling scheme that combines the strengths of probabilistic graphical models and deep adversarial learning. The structured representations can provide interpretability and encode inductive biases to reduce the data complexity of neural oscillations. The efficacy of the learned representations is further studied in epilepsy seizure detection formulated as an unsupervised learning problem. Third\, we propose a framework for the joint modeling of physiological measures and behavior. Existing methods to combine multiple sources of brain data provided are limited. Direct analysis of the relationship between different types of physiological measures usually does not involve behavioral data. Our method can identify the unique and shared contributions of brain regions to behavior and can be used to discover new functions of brain regions. The success of these innovative computational methods would allow the translation of biomarker findings across species and provide insight into neurocognitive analysis in numerous biological studies and clinical diagnoses\, as well as emerging consumer applications. \nShort Bio: Khuong An Vo is a Ph.D. candidate in Computer Science at UC Irvine\, advised by Prof. Ramesh Srinivasan\, Prof. Nikil Dutt\, and Prof. Hung Cao. Prior\, he received a B.Eng. (Honors) degree in CS from HCMC Vietnam National University with research revolved around statistical natural language processing and has been applied in large-scale commercial systems. Now\, Vo’s research focuses on deep latent variable models in biomedical signal processing and neurocognitive modeling. He is also interested in the application of efficient deep learning in healthcare IoT. Vos’ work is supported by National Science Foundation (NSF) and National Institutes of Health (NIH) grants.
URL:https://www.cecs.uci.edu/event/deep-latent-variable-modeling-of-physiological-signals/
LOCATION:Zoom
CATEGORIES:PhD Defenses
ATTACH;FMTTYPE=image/jpeg:https://cecs.uci.edu/files/2024/05/Khuong-An-Vo-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240528T110000
DTEND;TZID=America/Los_Angeles:20240528T120000
DTSTAMP:20240827T000948Z
CREATED:20240827T000948Z
LAST-MODIFIED:20240827T000948Z
UID:16359-1716894000-1716897600@www.cecs.uci.edu
SUMMARY:Differential Privacy for Non-standard Settings
DESCRIPTION:Name: Joann Chen \nChair: Prof. Zhou Li \nDate: May 28\, 2024 \nTime:  11:00 AM \nLocation: EH 5204 \nCommittee: Prof. Yanning Shen\, Prof. Gene Tsudik \nTitle: Differential Privacy for Non-standard Settings \nAbstract: In today’s technologically driven world\, personal data stands at a crucial juncture\, representing both immense potential and significant peril. Such potential\, when leveraged properly\, can lead to groundbreaking advancements in sectors such as healthcare and finance. Yet\, the misuse of this same data can result in severe privacy breaches. This thesis is dedicated to navigating the complex terrain of personal data privacy\, highlighting Differential Privacy (DP) as a pivotal solution that provides strong\, provable privacy guarantees. \nWith a focus on DP-aware system design\, this thesis delves into the challenges of integrating DP into various systems\, emphasizing the necessity for infrastructures that prioritize privacy preservation. It outlines the progression of DP from theoretical concepts to practical implementations\, detailing both the challenges encountered and the progress made in applying academic DP concepts to real-world solutions. Moreover\, this thesis extends beyond the scope of DP\, exploring the broader realm of privacy-enhancing technologies (PETs). These technologies are crucial not only for meeting legal compliance and ensuring privacy in systems and machine learning but also for paving the way towards a future where data utility and individual privacy coexist in harmony. \nShort Bio: Joann is a fifth-year Ph.D. candidate in the EECS department advised by Prof. Zhou Li. Her research interests center around Differential Privacy (DP)\, privacy-enhancing technologies\, and privacy in machine learning. She has experience in quantifying privacy risks in machine learning and building DP into DNS resolution\, data stream release\, resource allocator\, ad conversion measurements\, and network data synthesis. Her research aims to bridge the gap between DP theory and its practical applications for real-world deployments.
URL:https://www.cecs.uci.edu/event/differential-privacy-for-non-standard-settings/
LOCATION:EH 5204
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240531T140000
DTEND;TZID=America/Los_Angeles:20240531T150000
DTSTAMP:20240516T201808Z
CREATED:20240516T201808Z
LAST-MODIFIED:20240516T201808Z
UID:16295-1717164000-1717167600@www.cecs.uci.edu
SUMMARY:Multi-Sensor Data Fusion and Machine Learning for Adaptive Autonomous Systems
DESCRIPTION:Name: Trier Mortlock \nChair: Prof. Mohammad Al Faruque \nDate: May 31\, 2024 \nTime:  2 PM \nLocation: ISEB 1200 \nCommittee: Prof. Faryar Jabbari\, Prof. Pramod Khargonekar \nTitle: Multi-Sensor Data Fusion and Machine Learning for Adaptive Autonomous Systems \nAbstract: Autonomous systems rely on sensors to collect data about their surrounding environments to influence their decision-making algorithms.  Sensor fusion involves combining data from different sensors to reduce sensing uncertainties and better understand the environment. Advances in computing platforms and the growing availability of data have ushered a wave of machined learning-based sensor fusion methods that can extend fusion capabilities beyond the current understanding of systems present in mathematical models. However\, autonomous systems are increasingly tasked with operating in dynamic settings that require high levels of adaptability\, and how best to employ adaptive sensor fusion strategies is a challenging question. This dissertation examines how\, where\, why\, and when to perform multi-sensor data fusion for autonomous systems operating in dynamic environments. A novel sensor fusion architecture that improves autonomous systems’ adaptation capabilities is introduced. The proposed fusion architecture dynamically adapts both how and when fusion is performed by using a machine learning model that learns to identify the optimal sensor fusion strategy for the given sensing context. Use cases across different types of autonomous systems are examined\, highlighting the ability of the proposed fusion approach to generalize across various systems and autonomy tasks. Overall\, the methodologies and ideas presented in this dissertation can be applied broadly across engineering disciplines and can enhance the development of future technologies that require adaptive sensing capabilities. \nShort Bio: Trier Mortlock is a Ph.D. candidate in the Mechanical and Aerospace Engineering program at UC Irvine working under the supervision of Professor Mohammad Al Faruque in the Autonomous and Intelligent Cyber-Physical Systems Lab. Trier obtained his B.S. in Mechanical Engineering from the University of California\, Berkeley and his M.S. in Mechanical and Aerospace Engineering from UC Irvine. During his Ph.D.\, Trier interned at the Frontier Development Lab at the SETI Institute\, NASA and was a Doctoral Research Fellow at the Automotive Research Center\, a U.S. Army Center of Excellence for Modeling and Simulation of Ground Vehicle Systems.
URL:https://www.cecs.uci.edu/event/multi-sensor-data-fusion-and-machine-learning-for-adaptive-autonomous-systems/
LOCATION:ISEB 1200\, Interdisciplinary Science and Engineering Building University of California\, Irve\, Irvine\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240601
DTEND;VALUE=DATE:20240602
DTSTAMP:20251113T231952Z
CREATED:20251113T231952Z
LAST-MODIFIED:20251113T231952Z
UID:16595-1717200000-1717286399@www.cecs.uci.edu
SUMMARY:Khargonekar Inducted into UF Hall of Fame
DESCRIPTION:University of Florida’s (UF) Department of Electrical and Computer Engineering has inducted Professor and Vice Chancellor for Research Pramod Khargonekar into their Hall of Fame. As an alumnus of UF\, Prof. Khargonekar demonstrated institutional leadership\, a long-term commitment to UF’s engineering program\, and impactful research and inventions in the field of electrical or computer engineering. His theories and research are recognized to be one of the greatest theoretical achievements in the control systems field.
URL:https://www.cecs.uci.edu/event/khargonekar-inducted-into-uf-hall-of-fame/
LOCATION:CA
CATEGORIES:Awards
ATTACH;FMTTYPE=image/png:https://cecs.uci.edu/files/2025/11/khargonekar.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240605T110000
DTEND;TZID=America/Los_Angeles:20240605T120000
DTSTAMP:20240606T203433Z
CREATED:20240606T203408Z
LAST-MODIFIED:20240606T203433Z
UID:16310-1717585200-1717588800@www.cecs.uci.edu
SUMMARY:Advanced Machine Learning and AI Techniques for Enhancing Wearable Health Monitoring Systems
DESCRIPTION:Name: Amir Hosein Afandizadeh Zargari \nChair: Prof. Fadi Kurdahi \nDate: June 5\, 2024 \nTime:  11 AM \nLocation: EH 3206 \nCommittee: Prof. Peter Tseng\, Prof. Amir Rahmani \nTitle: Advanced Machine Learning and AI Techniques for Enhancing Wearable Health Monitoring Systems \nAbstract: This research advances wearable technology for real-time health monitoring using advanced machine learning (ML) and innovative hardware solutions. Key contributions include a novel ML-based model for accurate tracking of in-mouth nutrient sensors\, employing a wide range of frequencies and advanced algorithms to enhance measurement reliability. An innovative use of Cycle Generative Adversarial Networks (CycleGAN) addresses motion artifacts in photoplethysmography (PPG) signals\, transforming noisy data into clean\, artifact-free signals without relying on accelerometers. The development of smart clothing systems with passive sensors and copper coils\, using Near Field Communication (NFC) and energy harvesting technologies\, ensures wireless connectivity and power in various environments\, including underwater. AI-Coach\, a personalized wearable system\, integrates multiple sensors and a BERT model for real-time workout monitoring\, adapting to individual needs for accurate activity tracking. The research also addresses security vulnerabilities threatening health monitoring systems with robust anomaly detection systems and adversarial training methods. These advancements aim to improve the accuracy\, power efficiency\, and security of wearable health devices\, with significant implications for individual and public health monitoring. \nShort Bio: Amir Hosein Afandizadeh Zargari is a Ph.D. candidate in Computer Engineering at the University of California\, Irvine. He earned his B.Sc. in Computer Engineering from Sharif University of Technology in Iran in 2018 and his M.S. in Computer Engineering from UC Irvine in 2020. Throughout his master’s and Ph.D. studies\, Amir has published multiple papers in prestigious journals\, including Natures\, ACMs\, IEEEs\, and conferences like ESWEEK. His research focuses on advancing wearable technology for real-time health monitoring using machine learning and artificial intelligence.
URL:https://www.cecs.uci.edu/event/advanced-machine-learning-and-ai-techniques-for-enhancing-wearable-health-monitoring-systems/
LOCATION:EH 3206\, Engineering Hall University of California\, Irve\, Irvine\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240606T083000
DTEND;TZID=America/Los_Angeles:20240606T093000
DTSTAMP:20240611T214758Z
CREATED:20240611T214758Z
LAST-MODIFIED:20240611T214758Z
UID:16321-1717662600-1717666200@www.cecs.uci.edu
SUMMARY:Object-Oriented Visual Localization in Urban Mobile-Edge Environments
DESCRIPTION:Name: Bo Tsai \nChair: Prof. Kwei-Jay Lin \nDate: June 6\, 2024 \nTime:  8:30 AM \nLocation: Virtual/Zoom \nCommittee: Prof. Rainer Doemer\, Prof. Yasser Shoukry \nTitle: Object-Oriented Visual Localization in Urban Mobile-Edge Environments \nAbstract: The advent of the Internet of Things (IoT) and the progress in robotic technology have brought a new era where intelligent services significantly benefit society. As robots evolve from executing simple tasks to providing complex\, interactive services\, the need of precise and reliable localization becomes paramount. In this context\, the ubiquity of low-cost cameras and the maturity of object detection technologies have paved the way for advanced semantic visual-based localization solutions. “OOPose\,” introduced in this thesis\, exemplifies this advancement by providing an object-oriented pose estimation framework tailored for robots with small embedded devices. It leverages dense features from conventional object detection neural networks\, striking a balance between pixel-matching accuracy and processing speed\, thus enhancing the effectiveness and efficiency of robot localization. Amidst escalating demand for robots capable of facilitating intelligent interactive activities\, integrating high-quality cameras on mobile IoT devices\, combined with wireless connectivity\, edge computing\, and advanced computer vision algorithms\, enables a novel approach to processing offload with mobile video. This thesis details the deployment of OOPose in a mobile-edge computing paradigm\, emphasizing real-time performance and the feasibility of interactive applications. The research encapsulates the development and application of the OOPose framework\, demonstrating its significant role in the ecosystem of mobile-edge computing. This approach not only satisfies the requirements for real-time performance but also opens avenues for innovative robot localization applications\, ensuring interactive\, intelligent services across various environments. \nShort Bio: Bo Tsai is a PhD candidate in EECS advised by Professor Kwei-Jay Lin. His research focuses on intelligent distributed systems\, specifically addressing applications in visual-based localization\, autonomous robots\, and real-time internet of things.
URL:https://www.cecs.uci.edu/event/object-oriented-visual-localization-in-urban-mobile-edge-environments/
LOCATION:Zoom
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240809T143000
DTEND;TZID=America/Los_Angeles:20240809T153000
DTSTAMP:20240814T140023Z
CREATED:20240814T140023Z
LAST-MODIFIED:20240814T140023Z
UID:16354-1723213800-1723217400@www.cecs.uci.edu
SUMMARY:Hardware/Software Co-design Methodologies for Efficient AI Systems and Applications
DESCRIPTION:Name: Mohanad Odema \nChair: Prof. Mohammad Abdullah Al Faruque \nDate: August 9\, 2024 \nTime:  2:30 PM \nLocation: EH 2430 \nCommittee: Prof. Marco Levorato\, Prof. Hyouk Jun Kwon \nTitle: Hardware/Software Co-design Methodologies for Efficient AI Systems and Applications \nAbstract: The landscape of AI research is dominated by the search for powerful deep learning models and architectures that enable fascinating applications from the edge to the cloud. Indeed\, we have witnessed the emergence of efficient\, on-device deep learning models that facilitate smart edge applications (autonomous vehicles\, AR/VR systems)\, and the emergence of billion parameter foundation/LLM models that excel at tasks thought achievable only through human-level understanding. On the other hand\, the calls for more advanced hardware and systems continue to grow considering the scale at which deep learning model workloads evolve\, and to facilitate sustainable\, efficient model operation across the various application contexts. \nThis suggests a natural way to design deep learning models and their systems: viz\, through hardware/software co-design methodologies\, capturing the interplay and mutual dependencies across various HW/SW layers of the computing stack to guide different design choices. From the algorithmic side\, an awareness of the target platform’s compute capabilities and resources guides the deep learning model architectural and optimization choices (e.g.\, compression) towards maximizing performance efficiency on the target hardware at deployment time. From the hardware side\, understanding the deep learning workloads and computing kernels can shape future architectures of AI hardware that improves on efficiency from the lower levels (as seen through customized accelerators trend). \nAs hardware and software continue to undergo continuous innovation\, this dissertation aims to investigate relevant emergent technologies and challenges at this unified research frontier to guide the design of future AI systems and models. The dissertation focuses on characterizing nascent design spaces\, exploring various optimization opportunities\, and developing new methodologies to maximize the impact of such innovations. In brief\, this dissertation goes over the following topics: \n– Understanding the benefits of dynamic neural networks for efficient inference\, and how to optimize their design for target platform deployment \n– Understanding how multi-model workloads can be scheduled and co-located on multi-chip AI Accelerator modules based on 2.5D chiplets technology while accounting for workloads’ diversity\, affinities\, and memory access patterns \n– Exploring new methodologies to maximize the impact of split computing inference in edge-cloud architectures\, and elevate resource efficiency of edge devices \nShort Bio: Mohanad received a B.Sc degree in Electronics and Communications and a M.Sc degree in Computer Engineering from Ain Shams University\, Egypt. He is a PhD Candidate in Computer Engineering at UCI. He was an intern at MediaTek with the AI Architecture and Algorithm team. His research interests are focused on HW/SW co-design solutions for efficient AI\, bridging model- and system-level optimizations to enhance performance for core industry AI workloads and applications from the edge to the cloud.
URL:https://www.cecs.uci.edu/event/hardware-software-co-design-methodologies-for-efficient-ai-systems-and-applications/
LOCATION:EH 2430\, Engineering Hall\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles: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
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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
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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
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