- Ai - Engineering Mailing Lists

AI Seminar : Feb 23
by Mangannavar, Rajesh Devaraddi 22 Feb '24

22 Feb '24

Dear all, Our next AI seminar is scheduled to be on February 23rd , 2-3 PM. It will be followed by a 30-minute Q&A session with the graduate students. Location: KEC 1001 Zoom link: https://oregonstate.zoom.us/j/96491555190?pwd=azJHSXZ0TFQwTFFJdkZCWFhnTW04U…<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> Gradient-free bilevel programming with applications in machine learning Alireza Aghasi Assistant Professor Electrical and Computer Engineering Oregon State University Abstract: Our talk is mainly about a scalable and easy way of addressing bilevel optimization problems when we only have access to noisy evaluations of the objective functions, and absolutely no access to their gradient or Hessian. The talk starts with some motivating applications of bilevel programming in machine learning. We will then briefly overview zeroth-order methods for general optimization problems, and specifically focus on a more recent technique called Gaussian smoothing, which allows estimating the first and second order derivatives of an objective function solely through random queries to the function. Finally, we propose a computational algorithm to address general bilevel programs, without the need to access the Jacobian or Hessian of the underlying objectives. We present theoretical guarantees on the performance of the algorithm, and ultimately present iteration complexity (sample complexity) results. To the best of our knowledge, the proposed algorithm is the first result in the literature about a fully zeroth-order scheme to address general bilevel programs. Speaker Bio: Alireza Aghasi joined the School of Electrical Engineering and Computer Science at Oregon State University in Fall 2022. Between 2017 and 2022, he was an assistant professor in the Department of Data Science and Analytics at the school of business, Georgia State University. Prior to this position he was a research scientist with the Department of Mathematical Sciences, IBM T.J. Watson research center, Yorktown Heights. From 2015 to 2016 he was a postdoctoral associate with the computational imaging group at the Massachusetts Institute of Technology, and between 2012 and 2015 he served as a postdoctoral research scientist with the compressed sensing group at Georgia Tech. His research fundamentally focuses on optimization theory, probability theory and statistical analysis, with applications to various areas of data science, artificial intelligence, and modern signal processing. 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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EECB Seminar: Sara Beery, Wed Feb 21 4pm
by Hutchinson, Rebecca 20 Feb '24

20 Feb '24

Hello! Sara Beery<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fbeerys.gi…> is speaking at OSU tomorrow (Wednesday, February 21 at 4pm). Sara is an assistant professor at MIT CSAIL who works on computer vision for conservation and sustainability. In-person & Zoom details are here<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Ffwcs.oreg…>. Best, Rebecca ======== ======== ======== ======== Rebecca A. Hutchinson, PhD (she/her) Associate Professor https://hutchinson-lab.github.io/<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fhutchinso…> 2071 Kelley Engineering Center School of Electrical Engineering and Computer Science Department of Fisheries, Wildlife, and Conservation Sciences Oregon State University* Corvallis, OR 97331 *Oregon State University in Corvallis, OR, is located within the traditional homelands of the Mary's River or Ampinefu Band of Kalapuya. Following the Willamette Valley Treaty of 1855 (Kalapuya etc. Treaty), Kalapuya people were forcibly removed to reservations in Western Oregon. Today, living descendants of these people are a part of the Confederated Tribes of Grand Ronde Community of Oregon (https://www.grandronde.org<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.grand…>) and the Confederated Tribes of the Siletz Indians (https://ctsi.nsn.us<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fctsi.nsn.…>). I recognize that my work hours may be different than yours, so if you receive this message outside your workday, please feel free to respond during your normal working hours.

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AI Seminar : Feb 16
by Mangannavar, Rajesh Devaraddi 15 Feb '24

15 Feb '24

Dear all, Our next AI seminar is scheduled to be on February 16th , 2-3 PM. It will be followed by a 30-minute Q&A session with the graduate students. Location: KEC 1001 Zoom link: https://oregonstate.zoom.us/j/96491555190?pwd=azJHSXZ0TFQwTFFJdkZCWFhnTW04U…<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> Towards AI-Powered 3D Reconstruction and Generation Zexiang Xu Research Scientist Adobe Research Abstract: 3D reconstruction and generation are essential for a variety of vision and graphics applications, including 3D content creation, augmented reality, virtual reality, gaming, and robotics. Our research aims to address the core challenges in these areas by re-inventing neural 3D representations and architectural designs, leveraging advanced AI technologies. This talk will cover the recent progress in the fields and showcase our latest works. We will begin by discussing the recent advancements in neural field representations, which have enabled compact and realistic 3D modeling, thereby facilitating both reconstruction and generation tasks. We will then investigate the efficient estimation of neural fields using deep networks in a feed-forward manner, enabling fast and generalizable 3D reconstruction. The talk will also demonstrate the strong inherent connection between 3D reconstruction and generation, exploring how reconstruction techniques can serve as a foundation for powerful generation models. Our recent research introduces transformer-based 3D large reconstruction models, achieving fast and realistic 3D reconstruction and generation across various tasks, ranging from multi-view reconstruction to single-view reconstruction and text-to-3D generation. Speaker Bio: Zexiang Xu is a research scientist at Adobe Research. His research interests lie at the intersection of computer vision, computer graphics, and machine learning, with the primary focus on enabling efficient and realistic 3D reconstruction and rendering. His work covers key areas in 3D vision and graphics, including 3D reconstruction, 3D generation, neural representations, view synthesis, relighting, rendering, and appearance acquisition. Previously, he earned his Ph.D. in 2020 from UC San Diego working with Prof. Ravi Ramamoorthi. 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 : Feb 9
by Mangannavar, Rajesh Devaraddi 08 Feb '24

08 Feb '24

Dear all, Our next AI seminar is scheduled to be on February 9th , 2-3 PM. It will be followed by a 30-minute Q&A session with the graduate students. Location: KEC 1001 Zoom link: https://oregonstate.zoom.us/j/96491555190?pwd=azJHSXZ0TFQwTFFJdkZCWFhnTW04U…<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> Learning Universal Sequence Representations and Interpretable ML Models for Microbial Communities Gail Rosen Professor Electrical and Computer Engineering Drexel University Abstract: Microbes are found in every nook and cranny of the world and regulate the planetary carbon and oxygen cycles that are vital to life. Through high-throughput DNA/RNA sequencing, we can collect a vast amount of data about communities of microbes in diverse environments. In this talk, I will discuss development of large language models to tackle the first step in understanding this vast amount of data for taxonomy/gene identification, including antimicrobial resistance. Then, I will demonstrate our efforts to predict overall microbiome phenotype from metagenomic data and to explain important features, such as species composition, community hierarchy, functions, and mutations. Finally, I will address scalability of methods for sustainability. Speaker Bio: Gail Rosen received a B.S., M.S., and Ph.D. from the Georgia Institute of Technology. She is a recipient of an NSF CAREER award, a Drexel Faculty Career Development award, and Drexel Provost’s Fellowship. She serves on the editorial board of the Association for Microbiology’s mSystems and BMC Microbiome journals. She heads the Ecological and Evolutionary Signal-processing and Informatics (EESI) lab, organizes the Center for Biological Discovery from Big Data, and serves on the board and is a founding member of the University Research Computing Facility at Drexel. For the past 3 summers, she organized 2-week Drexel-Rowan-UChicago Biological Data Science summer workshops, which reached 8000+ participants around the world. In 2022-2023, she spent a year as a visiting scholar at the National Institute of Standards and Technology, benchmarking cancer-somatic variant calling methods. Her interests are in machine learning and evolution. 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 : Feb 2
by Mangannavar, Rajesh Devaraddi 02 Feb '24

02 Feb '24

Dear all, Our next AI seminar is scheduled to be on February 2nd (Today), 2-3 PM. It will be followed by a 30-minute Q&A session with the graduate students. Location: KEC 1001 Zoom link: https://oregonstate.zoom.us/j/96491555190?pwd=azJHSXZ0TFQwTFFJdkZCWFhnTW04U…<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> Cross-validation for Geospatial Problems Rebecca Hutchinson Associate Professor School of Electrical Engineering and Computer Science Department of Fisheries, Wildlife, and Conservation Sciences Oregon State University Abstract: Most machine learning practitioners have used cross-validation to assess the generalization performance of a model. However, typical cross-validation methods assume that the data are independent and identically distributed (iid). In geospatial problems, data points have geolocated coordinates (like latitude and longitude) as well as associated features, which are often spatially autocorrelated. Furthermore, geospatial problems may entail prediction to new regions, where the features are distributed differently. These properties violate the iid assumption. With a motivating ecological example of evaluating species distribution models, this talk will address theoretical and practical challenges that arise when conducting cross-validation for geospatial applications. Speaker Bio: Rebecca Hutchinson is an associate professor at Oregon State University, with a joint appointment across the School of Electrical Engineering and Computer Science and the Department of Fisheries, Wildlife, and Conservation Sciences. She is also affiliated with the Center for Quantitative Life Sciences and the Collaborative Robotics and Intelligent Systems (CoRIS) Institute. She became interested in interdisciplinary work in machine learning and quantitative ecology during her postdoctoral studies with the Institute for Computational Sustainability and as an NSF SEES Fellow, advised by Tom Dietterich and Matt Betts. Prior to that, she completed her Ph.D. at Carnegie Mellon University with Tom Mitchell. She is the recipient of an NSF CAREER Award. 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
by Tadepalli, Prasad 30 Jan '24

30 Jan '24

Hi everyone, As some of you know, we had an unfortunate attack on zoom during the AI seminar on Friday. Some person had transmitted racist and inappropriate video and audio during the talk. Thanks to the presence of mind of the speaker, it did not come through in the seminar room, but people on zoom were affected. It took me a few minutes too long to realize what happened and remove the offender. I am very sorry for the incident and apologize for not being better prepared. Please note that I have deactivated the old seminar link. I am trying to keep it open for outsiders in a secure way by making it a Webinar. Please stay tuned. Thank you for your understanding. Prasad

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AI Seminar : Jan 26
by Mangannavar, Rajesh Devaraddi 24 Jan '24

24 Jan '24

Dear all, Our next AI seminar is scheduled to be on January 26th, 2-3 PM. It will be followed by a 30-minute Q&A session with the graduate students. Location: KEC 1001 Zoom link: https://oregonstate.zoom.us/j/98684050301?pwd=ZzhianQxUFBPUmdYVWJKOFhaVURCQ…<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> Value-Based Abstractions for Planning Amy Zhang Assistant Professor Chandra Family Department of Electrical and Computer Engineering University of Texas at Austin Abstract: As the field of robotics continues to advance, the integration of efficient planning algorithms with powerful representation learning becomes crucial for enabling robots to perform complex manipulation tasks. We address key challenges in planning, reward learning, and representation learning through the objective of learning value-based abstractions. We explore this idea via goal-conditioned reinforcement learning, action-free pre-training, and with language. By leveraging self-supervised reinforcement learning and efficient planning algorithms, these approaches collectively contribute to the advancement of robotic systems capable of learning and adapting to diverse tasks in real-world environments. Speaker Bio: I am an assistant professor at UT Austin in the Chandra Family Department of Electrical and Computer Engineering. My work focuses on improving generalization in reinforcement learning through bridging theory and practice in learning and utilizing structure in real world problems. Previously I was a research scientist at Meta AI - FAIR and a postdoctoral fellow at UC Berkeley. I obtained my PhD from McGill University and the Mila Institute, and also previously obtained an M.Eng. in EECS and dual B.Sci. degrees in Mathematics and EECS from MIT. 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 Semin : Jan 19
by Mangannavar, Rajesh Devaraddi 18 Jan '24

18 Jan '24

Dear all, Our next AI seminar is scheduled to be on January 19th, 2-3 PM. It will be followed by a 30-minute Q&A session with the graduate students. Location: KEC 1001 Zoom link: https://oregonstate.zoom.us/j/98684050301?pwd=ZzhianQxUFBPUmdYVWJKOFhaVURCQ…<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> AI for Software Engineering (AI4SE): Opportunities and Challenges Manish Motwani Assistant Professor School of Electrical Engineering and Computer Science Oregon State University Abstract: Software is ubiquitous but often faulty. Software engineers spend 35–50% of their time debugging software, accounting for 50–75% of the total software development budget. With the transformative power of AI, software engineering researchers have begun integrating AI into various phases of the software development lifecycle to address these issues and enhance software quality. In this talk, I will provide a high-level overview of opportunities and challenges for applying AI in software engineering, specifically focusing on software testing and debugging. I will then present my research on using natural language artifacts to automatically improve software quality, including my technique for automatically generating tests with oracles from software specifications and my method for using developer-written tests in conjunction with bug reports to automatically localize and repair bugs. Speaker Bio: Manish Motwani is an Assistant Professor in the School of Electrical Engineering and Computer Science at Oregon State University. Before joining OSU, he worked as a postdoc at Georgia Tech and has four years of industry work experience as a researcher at the Tata Research Development and Design Center, an industrial research lab in India. His interests lie in improving software engineers’ productivity and aiding software engineers in developing high-quality software by automating software engineering practices. His research involves learning phenomena from large software repositories and using that knowledge to design novel automation techniques, such as requirements elicitation, test synthesis, and program repair. 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 : Jan 12
by Mangannavar, Rajesh Devaraddi 10 Jan '24

10 Jan '24

Dear all, Our next AI seminar is scheduled to be on January 12th, 2-3 PM. It will be followed by a 30-minute Q&A session with the graduate students. Location: KEC 1001 Zoom link: https://oregonstate.zoom.us/j/98684050301?pwd=ZzhianQxUFBPUmdYVWJKOFhaVURCQ…<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> Reinforcement Learning from a Bayesian perspective Brendan O'Donoghue Research Scientist DeepMind Abstract: Reinforcement learning (RL) involves an agent interacting with an environment over time attempting to maximize its total return. Initially the agent does not know about the environment and must learn about it from experience. As the agent navigates the environment it receives noisy observations which it can use to update its (posterior) beliefs about the environment. Therefore, the RL problem is a statistical inference problem wrapped in a control problem, and the two problems must be tackled simultaneously for good data efficiency. This is because the policy of the agent affects the data it will collect, which in turn affects the policy, and so on. This is in contrast to supervised learning, where the performance of a classifier (for instance) does not influence the data it will later observe. Failure to properly consider the statistical aspect of the RL problem will result in agents that require exponential amounts of experience for good performance. On the other hand, correctly considering the statistical inference problem and the control problem together has the potential to dramatically reduce the compute requirements to solve problems and potentially unlock new domains and capabilities far outside of the range of current agents. In this talk I will introduce these concepts and discuss how Bayesian techniques can provide principled solutions to the problem. Speaker Bio: Brendan O'Donoghue earned his PhD in 2013 from Stanford working with Stephen Boyd on optimization and control theory. Since then he has worked at DeepMind as a research scientist working on deep reinforcement learning, optimization, and (more recently) large language models. 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: Brendan O'Donoghue
by Tadepalli, Prasad 10 Jan '24

10 Jan '24

Hi all, The first AI Seminar this term is by Brendan O'Donoghue of Deep Mind on Friday, 1/12 2-3 pm (new time) in KEC 1001, which will be followed by a 30 minutes time for Q&A. Zoom link: https://oregonstate.zoom.us/j/98684050301?pwd=ZzhianQxUFBPUmdYVWJKOFhaVURCQ… Title: Reinforcement Learning from a Bayesian Perspective Abstract Reinforcement learning (RL) involves an agent interacting with an environment over time attempting to maximize its total return. Initially the agent does not know about the environment and must learn about it from experience. As the agent navigates the environment it receives noisy observations which it can use to update its (posterior) beliefs about the environment. Therefore, the RL problem is a statistical inference problem wrapped in a control problem, and the two problems must be tackled simultaneously for good data efficiency. This is because the policy of the agent affects the data it will collect, which in turn affects the policy, and so on. This is in contrast to supervised learning, where the performance of a classifier (for instance) does not influence the data it will later observe. Failure to properly consider the statistical aspect of the RL problem will result in agents that require exponential amounts of experience for good performance. On the other hand, correctly considering the statistical inference problem and the control problem together has the potential to dramatically reduce the compute requirements to solve problems and potentially unlock new domains and capabilities far outside of the range of current agents. In this talk I will introduce these concepts and discuss how Bayesian techniques can provide principled solutions to the problem. Speaker Biography Brendan O'Donoghue earned his PhD in 2013 from Stanford working with Stephen Boyd on optimization and control theory. Since then he has worked at DeepMind as a research scientist working on deep reinforcement learning, optimization, and (more recently) large language models. Please check the seminar page https://engineering.oregonstate.edu/EECS/research/AI-seminars<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…> for future talks. Prasad Tadepalli

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