- Ai - Engineering Mailing Lists

AI Seminar: What I Learned Serving as an AI Expert in the U.S. Congress
by Abou Mrad, Christian 21 Oct '24

21 Oct '24

Dear all, Our next AI seminar is scheduled for October 25th (Friday). Talk details: AI Seminar: What I Learned Serving as an AI Expert in the U.S. Congress Speaker: Kiri Wagstaff, AAAI Fellow Time: 2:00 PM, October 25 Seminar Location: KEC 1001 Zoom link: https://oregonstate.zoom.us/s/98357211915<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> Talk Abstract: What is Congress doing about artificial intelligence? I will share what I learned during a one-year position inside a U.S. Senate office as an AI expert through the AAAS Science and Technology Policy Fellowship program. There is a high demand for technical expertise within Congress, especially for AI. More than 300 AI-related bills have been introduced in the current congressional session; we’ll look at the key AI topics getting attention. I’ll describe how I developed a consumer AI literacy bill and then worked for months with a talented team to achieve the bill’s introduction in the Senate in July. I’ll also suggest ways that you can contribute your expertise and make a difference locally and nationwide. Speaker Bio: Kiri Wagstaff is a AAAI Fellow with a focus on deployed machine learning systems that help us explore, understand, and learn about the universe. She recently returned to Corvallis from Washington, D.C., after serving as an artificial intelligence expert in the U.S. Senate for a year. She previously served as a principal researcher in machine learning at the NASA Jet Propulsion Laboratory and an associate research professor and senior instructor at Oregon State University. She earned her Ph.D. in Computer Science from Cornell University, followed by an M.S. in Geological Sciences (University of Southern California) and an MLIS in Library and Information Science (San Jose State University). She received the Lew Allen Award for Excellence in Research and two NASA Exceptional Technology Achievement Medals. She serves on the AAAI U.S. Policy Committee and the OSU AI Program Executive Committee. She will teach “Machine Learning Challenges in the Real World” at OSU in Spring 2025. For future AI seminars, please visit: https://engineering.oregonstate.edu/EECS/research/AI-seminars<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…>. Best, Christian Abou Mrad Graduate Student Oregon State University

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AI Seminar: Illuminating the Future of Lithography: AI-Powered Simulation and Optimization
by Abou Mrad, Christian 17 Oct '24

17 Oct '24

Dear all, Reminder that our next AI seminar is scheduled for tomorrow , October 18th (Friday). Talk details: AI Seminar: Illuminating the Future of Lithography: AI-Powered Simulation and Optimization Speaker: Dr. Haoyu Yang, Senior Research Scientist, NVIDIA Research Time: 2:00 PM, October 18 Seminar Location: KEC 1001 Zoom link: https://oregonstate.zoom.us/s/98357211915<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> Talk Abstract: Lithography, the process of transferring chip designs onto silicon wafers, is a critical stage in semiconductor manufacturing. Computational lithography models the lithography system's behavior, focusing on resolution enhancement in response to the aggressive scaling dictated by Moore’s Law. This field encompasses two core challenges: system modeling and photomask optimization, both of which demand high-quality results and face immense computational overhead in terms of time and memory. In this talk, we will explore the key challenges in modern computational lithography and how AI is reshaping its landscape. Topics include: 1) AI-accelerated lithography simulations, 2) AI-assisted inverse design, 3) LLM-powered agents for lithography recipe development, and 4) future directions in AI semiconductor manufacturing and research. Speaker Bio: Haoyu Yang is a Senior Research Scientist at NVIDIA Research, specializing in AI and large language models (LLM) for Electronic Design Automation (EDA), GPU-accelerated EDA, AI-driven MultiPhysics simulation, and AI applications in semiconductor manufacturing. Before joining NVIDIA, he was a Postdoctoral Fellow in the Department of Computer Science and Engineering at the Chinese University of Hong Kong, where he also earned his Ph.D. His accolades include the Best Paper Award from IEEE Transactions on Semiconductor Manufacturing (TSM) in 2022 and a Best Paper Nomination at IEEE ASPDAC in 2019. He was also the sole recipient of the prestigious 2019 Nick Cobb Memorial Scholarship, awarded by SPIE & Mentor Graphics for his groundbreaking work in AI for lithography. For future AI seminars, please visit: https://engineering.oregonstate.edu/EECS/research/AI-seminars<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…>. Best, Christian Abou Mrad Graduate Student Oregon State University

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AI Seminar: Illuminating the Future of Lithography: AI-Powered Simulation and Optimization
by Abou Mrad, Christian 14 Oct '24

14 Oct '24

Dear all, Our next AI seminar is scheduled for October 18th (Friday). Talk details: AI Seminar: Illuminating the Future of Lithography: AI-Powered Simulation and Optimization Speaker: Dr. Haoyu Yang, Senior Research Scientist, NVIDIA Research Time: 2:00 PM, October 18 Seminar Location: KEC 1001 Zoom link: https://oregonstate.zoom.us/s/98357211915<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> Talk Abstract: Lithography, the process of transferring chip designs onto silicon wafers, is a critical stage in semiconductor manufacturing. Computational lithography models the lithography system's behavior, focusing on resolution enhancement in response to the aggressive scaling dictated by Moore’s Law. This field encompasses two core challenges: system modeling and photomask optimization, both of which demand high-quality results and face immense computational overhead in terms of time and memory. In this talk, we will explore the key challenges in modern computational lithography and how AI is reshaping its landscape. Topics include: 1) AI-accelerated lithography simulations, 2) AI-assisted inverse design, 3) LLM-powered agents for lithography recipe development, and 4) future directions in AI semiconductor manufacturing and research. Speaker Bio: Haoyu Yang is a Senior Research Scientist at NVIDIA Research, specializing in AI and large language models (LLM) for Electronic Design Automation (EDA), GPU-accelerated EDA, AI-driven MultiPhysics simulation, and AI applications in semiconductor manufacturing. Before joining NVIDIA, he was a Postdoctoral Fellow in the Department of Computer Science and Engineering at the Chinese University of Hong Kong, where he also earned his Ph.D. His accolades include the Best Paper Award from IEEE Transactions on Semiconductor Manufacturing (TSM) in 2022 and a Best Paper Nomination at IEEE ASPDAC in 2019. He was also the sole recipient of the prestigious 2019 Nick Cobb Memorial Scholarship, awarded by SPIE & Mentor Graphics for his groundbreaking work in AI for lithography. For future AI seminars, please visit: https://engineering.oregonstate.edu/EECS/research/AI-seminars<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…>. Best, Christian Abou Mrad Graduate Student Oregon State University

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AI Seminar: Change Point Detection with Localization
by Abou Mrad, Christian 10 Oct '24

10 Oct '24

Dear all, Reminder that our next AI seminar is scheduled for tomorrow, October 11th (Friday). Talk details: AI Seminar: Change Point Detection with Localization Speaker: Prof. Sharmodeep Bhattacharyya, Associate Professor in the Department of Statistics and AI Degree Program, EECS Department, Oregon State University Time: 2:00 PM, October 11 Seminar Location: KEC 1001 Zoom link: https://oregonstate.zoom.us/s/98357211915<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> Talk Abstract: Changepoint detection methods for networks and precision matrices, fall into two extremes. At one extreme, global changepoint detection methods identify when matrices differ in terms of overall properties, such as their L-infinity norms. This global approach is very coarse-grained in terms of dimensions and may not identify the locations of the changes if they take place on a subset of the dimensions. On the other extreme, local changepoint detection methods inspect individual pairwise components of a precision or adjacency matrix. These methods can identify changes in individual dimensions, but they are generally computationally expensive in higher dimensions, require careful handling of a large number of hypothesis tests simultaneously, and do not identify subsets in which a change takes place. Our work aims to fill a gap in the middle of these two extremes by efficiently detecting and localizing changepoints caused by changes to a subset of dimensions. Our approach for networks is based on CUSUM statistic for rooted subgraphs. Our approach for precision matrices, which we refer to as LD-CPD (Linear Decomposition Changepoint Detection), uses a linear decomposition of the precision matrix and tests for changes to the components of this decomposition, where these components correspond to a group of dimensions. We benchmark our algorithm on several simulation studies with changes involving groups of dimensions, as well as on real-world sensor networks and stock market data. We show that our approach is effective at identifying changepoints in both regimes in a computationally efficient manner while providing a convenient interpretation of the changes through this linear framework. Speaker Bio: Sharmodeep Bhattacharyya is an Associate Professor in the Department of Statistics and AI Degree Program, EECS Department at Oregon State University. His research interests lie in several fields of statistics, including statistical inference of networks, high-dimensional statistical inference, clustering, semiparametric inference, and hypothesis testing. He is also interested in the application of statistical methods in neuroscience, epidemiology, public health, and public policy. Before joining Oregon State University, Dr. Bhattacharyya completed his Ph.D. in Statistics at the University of California, Berkeley, under the supervision of Prof. Peter J. Bickel. Before that, he completed a B.Stat and M.Stat at the Indian Statistical Institute in Kolkata. For future AI seminars, please visit: https://engineering.oregonstate.edu/EECS/research/AI-seminars<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…>. Best, Christian Abou Mrad Graduate Student Oregon State University

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Nobel Prize in Physics in 2024
by Tumer, Kagan 09 Oct '24

09 Oct '24

Well, this is an interesting development: https://www.nobelprize.org/prizes/physics/2024/press-release/<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.nobel…> Certainly, Hopfield networks and Boltzmann machines are inspired by physics, but at the root, this Nobel prize is for training neural networks. Might be the first truly AI-based Nobel prize. So, keep coding out there, you never know where it may lead! Kagan

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AI Seminar: Sharmodeep Bhattacharyya, October 11
by Tadepalli, Prasad 07 Oct '24

07 Oct '24

Hi Everyone, As many of you know Rajesh Mangannavar has been doing the AI seminar announcements the past few years and has done a great job. I'd like to give him a break and thank him for his service. Christian Abou Mrad (cc'ed) has volunteered to do this from now on. He is a new student working with Prof. Houssam Abbas. Please look for his emaisl from now on. . This week's AI seminar speaker is Prof. Sharmodeep Bhattacharyya, an AI faculty from OSU's Statistics department. The seminar is at 2 pm on Friday in KEC 1001 and on zoom. AI Seminar: Change Point Detection with Localization Speaker: Sharmodeep Bhattacharyya 2 pm, October 11, KEC 1001, zoom<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> Abstract Changepoint detection methods for networks and precision matrices, fall into two extremes. At one extreme, global changepoint detection methods identify when matrices differ in terms of overall properties, such as their L-infinity norms. This global approach is very coarse-grained in terms of dimensions and may not identify the locations of the changes if they take place on a subset of the dimensions. On the other extreme, local changepoint detection methods inspect individual pairwise components of a precision or adjacency matrix. These methods can identify changes in individual dimensions, but they are generally computationally expensive in higher dimensions, require careful handling of a large number of hypothesis tests simultaneously, and do not identify subsets in which a change takes place. Our work aims to fill a gap in the middle of these two extremes by efficiently detecting and localizing changepoints caused by changes to a subset of dimensions. Our approach for networks is based on CUSUM statistic for rooted subgraphs. Our approach for precision matrices, which we refer to as LD-CPD (Linear Decomposition Changepoint Detection), uses a linear decomposition of the precision matrix and tests for changes to the components of this decomposition, where these components correspond to a group of dimensions. We benchmark our algorithm on several simulation studies with changes involving groups of dimensions, as well as on real-world sensor networks and stock market data. We show that our approach is effective at identifying changepoints in both regimes in a computationally efficient manner while providing a convenient interpretation of the changes through this linear framework. Speaker Biography Sharmodeep Bhattacharyya is an Associate Professor in the Department of Statistics and AI Degree Program, EECS Department at Oregon State University. His research interests lie in several fields of statistics, including statistical inference of networks, high-dimensional statistical inference, clustering, semiparametric inference, and hypothesis testing. He is also interested in the application of statistical methods in neuroscience, epidemiology, public health, and public policy. Before joining Oregon State University, Dr. Bhattacharyya completed his Ph.D. in Statistics at the University of California, Berkeley, under the supervision of Prof. Peter J. Bickel. Before that, he completed a B.Stat and M.Stat at the Indian Statistical Institute in Kolkata. Please follow https://engineering.oregonstate.edu/EECS/research/AI-seminars<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…> for future seminar announcements. Best, Prasad Tadepalli

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Fall AI seminar
by Tadepalli, Prasad 03 Oct '24

03 Oct '24

Hi all, I am happy to announce that the first AI seminar of this term will be by Prof. Xiao Fu at 2 pm on Friday 9/27 in KEC 1001. The seminars are public, but are also available for credit to in-person attendees by registering for AI 507. Please follow https://engineering.oregonstate.edu/EECS/research/AI-seminars<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…> for future seminar announcements. See you on Friday. Best, Prasad Tadepalli Towards Provable Unaligned Multimodal Learning: A Model Identification Perspective Xiao Fu, Oregon State University 2 PM, Friday 9/27, KEC 1001 and zoom<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> Abstract: 2023 was "the year of AI", highlighted by the release of numerous AI models with remarkable capabilities. Multimodal learning is at the forefront of AI advancements, with state-of-the-art models like GPT-4 and Gemini emphasizing multimodal functionalities as their defining features. Despite its importance, many aspects of multimodal learning, and AI developments in general, still lack a concrete and comprehensive understanding---which is essential for building resilient and trustworthy systems. Our research focuses on the understanding of AI/ML systems to drive theory-backed advancements. From this perspective, this presentation revisits a core component of multimodal learning-Unsupervised Domain Translation (UDT). Many UDT systems, such as CycleGAN, use Distribution Matching (DM) modules, which often fail in content-aligned translations due to measure-preserving automorphism (MPA). Existing remedies fall short of guaranteed performance. In my talk, I will introduce a model identification perspective for UDT, overcoming the MPA issues and ensuring identifiability of the desired translation functions. This is the first proven identification result in UDT under CycleGAN's settings, to our knowledge. We have also broadened these concepts, providing solutions for various translation challenges, enabling provable content-style disentanglement, and offering more versatile cross-domain data generation. These advancements promise significant theoretically supported enhancements for UDT applications, particularly in annotation-limited fields such as medicine and biology. Speaker Biography Xiao Fu<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…> has been with the School of Electrical Engineering and Computer Science, Oregon State University since 2017, where he is currently an Associate Professor. He received the Ph.D. degree in Electronic Engineering from The Chinese University of Hong Kong, in 2014. He was a Postdoctoral Associate with the Department of Electrical and Computer Engineering, University of Minnesota - Twin Cities, from 2014 to 2017. His research interests include the broad area of machine learning and signal processing, especially theory and algorithms. Dr. Fu received the Best Student Paper Award at ICASSP 2014, the 2022 IEEE Signal Processing Society (SPS) Best Paper Award, and the 2022 IEEE SPS Donald G. Fink Overview Paper Award. He also received the Outstanding Postdoctoral Scholar Award at University of Minnesota in 2016, the Engelbrecht Early Career Faculty Award from the College of Engineering at Oregon State University in 2023, and the National Science Foundation (NSF) CAREER Award in 2022.

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AI Seminar: Jun 7
by Mangannavar, Rajesh Devaraddi 05 Jun '24

05 Jun '24

Dear all, Our next AI seminar is scheduled to be on Jun 07th (Friday), 2-3 PM. Seminar Location: BEXL 320 Zoom link: https://oregonstate.zoom.us/j/91611213801?pwd=Wm9JSkN1eW84RUpiS2JEd0E5TEVkd…<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> Adaptive Workload Modeling for Human-Robot Teams Joshua Bhagat Smith Ph.D. Student Robotics and AI Oregon State University Abstract: Real world human-robot teams will be deployed in dynamic, uncertain environments where effective collaboration hinges on the robot's ability to comprehend its human teammate. Robots must have a dynamic awareness of the human’s internal state in order to adapt their behavior and provide appropriate assistance. A critical element of this adaptation is the ability to estimate the human's workload in unknown situations, as many real-world teams may be assigned tasks they have not be trained to perform. In this talk I will present 1) A multi-dimensional workload estimation algorithm capable of estimating all workload components, 2) a meta-learning based approach that adapts a machine learning model's parameters, such that workload can be accurately estimated for both known and unknown tasks, and 3) discuss the system considerations for a executing these algorithms in real-time on-board a robot. Speaker Bio: Joshua Bhagat Smith is a fourth year Robotics & AI dual major Ph.D. student supervised by Julie A. Adams. Prior to grad school he worked in industry for three years developing large-scale sensor processing systems to build maps for autonomous vehicles. He received his Master's in Computer Science and Bachelor's in Computer Engineering from the University of Arkansas, in 2015 and 2017 respectively. His current research interests include human-robot interaction and multi-robot systems, focusing on developing machine learning-based systems for real world human-robot teams. Please watch this space for future AI Seminars : https://engineering.oregonstate.edu/EECS/research/AI<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…> Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> For graduate students in the AI program, attendance is strongly encouraged

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AI Seminar : May 31
by Mangannavar, Rajesh Devaraddi 30 May '24

30 May '24

Dear all, Our next AI seminar is scheduled to be on May 31st (Friday). There are 2 seminars on Friday this week: [Please note that they are in different locations] Talk 1 : Title: Training Machines to Know What They Don't Know Time : 10-11 AM Seminar Location: KEC 1003 Zoom link: https://oregonstate.zoom.us/j/91611213801?pwd=Wm9JSkN1eW84RUpiS2JEd0E5TEVkd…<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> Talk 2 : Title: Inverse constraint learning and risk averse reinforcement learning for safe AI Time : 2-3 PM Seminar Location: BEXL 320 Zoom link: https://oregonstate.zoom.us/j/91611213801?pwd=Wm9JSkN1eW84RUpiS2JEd0E5TEVkd…<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> Speaker Details: Pascal Poupart Professor David R. Cheriton School of Computer Science University of Waterloo Talk 1 Abstract: Training Machines to Know What They Don't Know Modern machine learning predictors often suffer from overconfidence. I will explain why neural predictors with a softmax output layer exhibit arbitrarily high confidence away from the training data. I will describe a simple modification to the softmax layer to prevent this type of overconfidence. I will also describe how product-of-expert and mixture-of-expert approximations in Bayesian inference can lead to over or under confidence. Finally, I will describe a simple interpolation technique to enhance the calibration of predictions in distributed machine learning applications including federated learning. Talk 2 Abstract: Inverse constraint learning and risk averse reinforcement learning for safe AI In many applications of reinforcement learning (RL) and control, policies need to satisfy constraints to ensure feasibility, safety or thresholds about key performance indicators. However, some constraints may be difficult to specify. For instance, in autonomous driving, it is relatively easy to specify a reward function to reach a destination, but implicit constraints followed by expert human drivers to ensure a safe, smooth and comfortable ride are much more difficult to specify. I will present some techniques to learn soft constraints from expert trajectories in autonomous driving and robotics. I will also present an alternative to variance based on Gini deviation for risk-averse reinforcement learning. Speaker Bio: Pascal Poupart is a Professor in the David R. Cheriton School of Computer Science at the University of Waterloo (Canada). He is also a Canada CIFAR AI Chair at the Vector Institute and a member of the Waterloo AI Institute. He serves on the advisory board of the NSF AI Institute for Advances in Optimization (2022-present) at Georgia Tech. He served as Research Director and Principal Research Scientist at the Waterloo Borealis AI Research Lab at the Royal Bank of Canada (2018-2020). He also served as scientific advisor for ProNavigator (2017-2019), ElementAI (2017-2018) and DialPad (2017-2018). His research focuses on the development of algorithms for Machine Learning with application to Natural Language Processing and Material Discovery. He is most well-known for his contributions to the development of Reinforcement Learning algorithms. Notable projects that his research team are currently working on include inverse constraint learning, mean field RL, RL foundation models, Bayesian federated learning, uncertainty quantification, probabilistic deep learning, conversational agents, transcription error correction, sport analytics, adaptive satisfiability and material discovery for CO2 recycling. Please watch this space for future AI Seminars : https://engineering.oregonstate.edu/EECS/research/AI<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…> Note to AI Seminar course students: Credit will be given for both seminars Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> For graduate students in the AI program, attendance is strongly encouraged

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AI Seminar : May 24
by Mangannavar, Rajesh Devaraddi 21 May '24

21 May '24

Dear all, Our next AI seminar is scheduled to be on May 24th (Friday), 3-4 PM. Please note the change in time for this week – It is from 3-4 PM this week. Seminar Location: BEXL 320 Zoom link: https://oregonstate.zoom.us/j/91611213801?pwd=Wm9JSkN1eW84RUpiS2JEd0E5TEVkd…<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> Deep Learning for Radio-Frequency Device Fingerprinting Weng-Keen Wong Professor Computer Science Oregon State University Abstract: Radio frequency (RF) device fingerprinting plays an important role in network security, enabling physical layer-based network access authentication and network device classification through the identification of devices from their transmitted RF signals. The use of deep learning for RF device fingerprinting has become prevalent in recent years as it has enabled the automated extraction of device-specific features and signatures solely from raw RF signals, without the need for expert domain knowledge to engineer informative features. This talk will describe two active areas of research related to RF device fingerprinting: 1) open-set detection for detecting unauthorized devices and 2) domain adaptation to deal with changes in channel conditions and environmental settings. Speaker Bio: Weng-Keen Wong received his Ph.D. and M. S. degrees in computer science from Carnegie Mellon University in 2004 and 2001 respectively. He received his B.Sc. degree from the University of British Columbia in 1997. He is currently a Professor in the School of Electrical Engineering and Computer Science at Oregon State University. From 2016-2018, he served as a program director at the National Science Foundation under the Robust Intelligence Program in the Division of Information and Intelligent Systems. His research areas are in data mining and machine learning, with specific interests in anomaly detection, deep learning, probabilistic graphical models, computational sustainability and human-in-the-loop learning. Please watch this space for future AI Seminars : https://engineering.oregonstate.edu/EECS/research/AI<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…> Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> For graduate students in the AI program, attendance is strongly encouraged

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