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DTSTART;TZID=America/Los_Angeles:20240401T110000
DTEND;TZID=America/Los_Angeles:20240401T120000
DTSTAMP:20240425T213942Z
CREATED:20240327T223424Z
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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
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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
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DTSTART;TZID=America/Los_Angeles:20240425T100000
DTEND;TZID=America/Los_Angeles:20240425T110000
DTSTAMP:20240425T213745Z
CREATED:20240405T212937Z
LAST-MODIFIED:20240425T213745Z
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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
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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
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