- Ai - Engineering Mailing Lists

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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AI Seminar : May 17
by Mangannavar, Rajesh Devaraddi 15 May '24

15 May '24

Dear all, Our next AI seminar is scheduled to be on May 17th (Friday), 2-3 PM. Seminar Location: BEXL 320 Student meeting: 3-3:30 pm, KEC 2057 (sign up here<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdocs.goog…>) Zoom link: https://oregonstate.zoom.us/j/91611213801?pwd=Wm9JSkN1eW84RUpiS2JEd0E5TEVkd…<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> Towards Automated Data Mining: Reinforcement Intelligence for Self-Optimizing Feature Engineering Kunpeng Liu Assistant Professor Department of Computer Science Portland State University Abstract: In recent years, data mining has achieved great success in enormous scenarios. As the foundation of data mining, feature engineering plays an essential role in comprehending and perceiving data. Successful feature engineering can remove irrelevant features, generate informative features, improve model performance, enhance generalization, and provide better interpretation and explanation. However, not all researchers and practitioners are experts in feature engineering, making the automation of feature engineering an indispensable demand. In this talk, I will first introduce what feature engineering is and why it is difficult to automate the feature engineering process. Then, I will focus on (1) automated feature selection (2) automated feature generation, and discuss how the framework of reinforcement learning can be adapted to solve these problems correspondingly. Finally, I will conclude the talk and present the big picture of developing intelligent, interpretable, and interactive automated data science systems. Speaker Bio: Kunpeng Liu is an assistant professor from the Department of Computer Science, Portland State University. His research interests are data mining and machine learning, especially in automated data science systems, with application to big data problems, including smart city, privacy-preserving machine learning, reasoning on recommender systems, and user behavior analysis. He is also interested in efficient RLHF and LLM inference. 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 10
by Mangannavar, Rajesh Devaraddi 08 May '24

08 May '24

Dear all, Our next AI seminar is scheduled to be on May 10th (Friday), 2-3 PM. Seminar Location: BEXL 320 Student meeting: 3-3:30 pm, KEC 2057 (sign up here<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdocs.goog…>) Zoom link: https://oregonstate.zoom.us/j/91611213801?pwd=Wm9JSkN1eW84RUpiS2JEd0E5TEVkd…<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> When Sparse Data is All You Have: Learning World Models for Generalizable Planning and RL with a View Towards AI Assessment Siddharth Srivastava Associate Professor of Computer Science School of Computing and Augmented Intelligence Arizona State University Abstract: Can we build autonomous agents that learn generalizable knowledge and reliably solve previously unseen problems? In this talk I will present some of my group's recent research on neuro-symbolic learning for sequential decision-making problems that feature long-horizons, sparse rewards and vast differences between training and testing problems. I will discuss methods for learning and using abstractions to learn world models that can be easily transferred to new problem instances, often overshadowing the complexity of training instances. We will see how these methods can be used to invent symbolic vocabularies and learn logic-based world-models for robot task and motion planning without any human annotation or hand-written logic; how learning simple abstractions during Q-learning can vastly improve the performance of RL agents; and finally, how abstractions can help address the emerging problem of AI assessment. Speaker Bio: Siddharth Srivastava is an Associate Professor of Computer Science in the School of Computing and Augmented Intelligence at Arizona State University. He received his Ph.D. in Computer Science at the University of Massachusetts, Amherst, and did his postdoctoral research at UC Berkeley. His research focuses on safe and reliable taskable AI systems and AI assessment. He is a recipient of the NSF CAREER award, a Best Paper award at the International Conference on Automated Planning and Scheduling (ICAPS), an Outstanding Dissertation award at UMass Amherst, and a Best Final Year Thesis Award at IIT Kanpur. He served as conference Co-Chair for ICAPS 2019 and currently serves as an Associate Editor for the Journal of AI Research. 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 3
by Mangannavar, Rajesh Devaraddi 01 May '24

01 May '24

Dear all, Our next AI seminar is scheduled to be on May 3rd (Friday), 2-3 PM. Seminar Location: BEXL 320 Student meeting: 3-3:30 pm, KEC 2057 (sign up here<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdocs.goog…>) Zoom link: https://oregonstate.zoom.us/j/91611213801?pwd=Wm9JSkN1eW84RUpiS2JEd0E5TEVkd…<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> Teams of mobile robots orienteering in hazardous environments Cory Simon Associate Professor of Chemical Engineering Oregon State University Abstract: Teams of mobile [aerial, ground, or aquatic] robots have applications in delivering resources, patrolling, information-gathering, pesticide application to crops, forest fire fighting, chemical plume source localization and mapping, and search-and-rescue. Some environments contain hazards e.g.,\ rough terrain or seas, strong winds, or adversaries capable of attacking robots. Then, the robots must coordinate their trails in the hazardous environment to (i) cooperatively achieve the team-level objective with robustness to robot failures and (ii) strike some balance between expected robot failures versus utility gained. Herein, we pose the bi-objective, risky team orienteering problem, where (i) a team of robots are mobile within an environment, abstracted as a graph (nodes: locations, edges: spatial connections between locations); (ii) each node offers a reward to the team depending on the number of robots that visit it; (iii) the traversal of each edge in the graph imposes a risk of robot failure/destruction; and (iv) the two [often, conflicting] team objectives are to maximize the expected (a) team reward and (b) number of robots that survive the mission. We then use ant colony optimization to search for the Pareto-optimal set of robot team trail plans. A human decision-maker can then select the robot trail plans that balance, according to their values, the two [conflicting] objectives. Speaker Bio: Cory Simon learned the ropes of scientific research at the University of Akron, Virginia Tech, Okinawa Institute of Science and Technology, University of British Columbia, Lawrence Berkeley National Laboratory, École Polytechnique Fédérale de Lausanne, and Altius Institute for Biomedical Sciences. Simon interned in industry at Bridgestone Research (chemical engineering) and Stitch Fix (data science). He was a summer faculty research fellow at the Naval Information Warfare Center. 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 : Apr 26
by Mangannavar, Rajesh Devaraddi 24 Apr '24

24 Apr '24

Dear all, Our next AI seminar is scheduled to be on April 26th (Friday), 2-3 PM. Seminar Location: BEXL 320 Student meeting: 3-3:30 pm, KEC 2057 (sign up here<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdocs.goog…>) Zoom link: https://oregonstate.zoom.us/j/91611213801?pwd=Wm9JSkN1eW84RUpiS2JEd0E5TEVkd…<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> Towards Safe and Actionable AI: Strategies for Robust Adaptation and Proactive Failure Detection Jay Thiagarajan Machine Learning Researcher Machine Intelligence Group Lawrence Livermore National Labs Abstract: As AI technologies and large-scale models continue to integrate into critical applications, prioritizing the robustness and safety of model design has become imperative. Current approaches focus on refining pre-trained models to establish personalized decision rules, enabling reliable predictions and supporting decision-making. The inherent challenges in generalization and safety of these protocols have sparked increased research interest in characterizing model behavior (e.g., diagnosing statistical biases, detecting distribution shifts, predicting generalization capabilities, and evaluating confidence levels), as well as effectively leveraging general-purpose knowledge sources (e.g., pre-trained representations, generative models, and multimodal embeddings) to devise safer solutions. This presentation will delve into innovative strategies for securely and efficiently adapting predictive models, followed by an exploration of methods for proactively detecting failure modes in classification and regression models. Speaker Bio: Jay Thiagarajan is a Machine learning researcher in the Machine Intelligence Group at Lawrence Livermore National Labs. His research broadly spans deep learning, AI/ML safety, generative AI, and human-centric evaluation. He received his PhD degree from Arizona State University. He has served as the PI for projects funded by the DOE, DARPA and the Office of Science on representation learning, high-dimensional sampling, uncertainty quantification and knowledge-driven ML. He has received the LLNL early career award, the WCI gold award for his contribution to the COVID-19 efforts organized by CDC, and multiple best paper awards. He serves on the applied math visioning committee of the DOE Applied Scientific Computing Research program and is part of the ML Commons initiative. 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-related seminar - Business Analytics - May 3rd, Austin Hall 270 12:30-1:50pm
by Fern, Xiaoli 16 Apr '24

16 Apr '24

See below for a Business analytics seminar that might be of interest to AI folks. -Xiaoli An Alternative Approach to Transparent Decision-Making with Deep Machine Learning Algorithms in Mental Health Autism spectrum disorder (ASD) is a developmental disorder that affects 1 in 36 children in the US (1). The economic cost is estimated to be $66 billion per year in the US, from medical care and lost parental productivity (2). Early diagnosis is crucial since it allows for early treatment and the best long-term outcome. However, identifying children at an early age is challenging due to the limited ale mental health expertise, long wait lists, and misdiagnosis. Automated diagnosing and early referrals through machine learning (ML) can be of significant help. However, there are significant challenges to be overcome. Even with this growing patient population, this is a low-resource domain. The datasets are small, expensive, and difficult to create. The data itself is extremely diverse and training ML models for high performance is difficult. Transparency of models and support tools is highly valued. Finally, transfer learning is essential when applying models to data from different clinicians, clinics, hospitals, and populations. We are creating deep ML algorithms that rely on free text data in electronic health records (EHR) to support non-expert clinicians in identifying children at high risk for ASD. I will show early and recent results of our algorithms (using LSTM, GRU, and BERT models), highlight obstacles and how we are attempting to overcome them: synthetic and parental data to increase data size, transfer learning between datasets, relying on redundancy to increase performance, and avoiding binary black box labeling to provide transparency. [A person in a black suit Description automatically generated] Gondy Leroy, Ph.D., is Professor of MIS, Associate Dean for Research at the University of Arizona's Eller College of Management, and Research Director for the Center for Management Innovations in Healthcare. Her research focuses on natural language processing (NLP) and machine learning (ML) as they are part of artificial intelligence (AI). She has won grants from NIH (NLM, NIMH), AHRQ, NSF, Microsoft Research, and several foundations. She earned a combined BS and MS (1996) in cognitive, and experimental psychology from the Catholic University of Leuven (1996) in Belgium and a MS (1999) and PhD (2003) in management information systems from the University of Arizona. She serves on the editorial board of the Journal of Healthcare Informatics Research, Journal of Database Management, International Journal of Social and Organizational Dynamics in IT, Health Systems, Journal of Business Analytics, and co-chairs several special issues, sessions, tracks, workshops, and conferences focusing on design science and healthcare IT. She serves frequently on NIH grant review panels. She is the author of the book "Designing User Studies in Informatics (Springer, 2011). Finally, she actively contributes to increasing diversity and inclusion in computing through mentoring students early in their academic careers starting in high school and through graduate school.

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AI Seminar : Apr 19
by Mangannavar, Rajesh Devaraddi 16 Apr '24

16 Apr '24

Dear all, Our next AI seminar is scheduled to be on April 19th (Friday), 2-3 PM. The post seminar Q&A session will be held in KEC 2057. Please sign up here<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdocs.goog…> by Wednesday evening to attend the Q&A session. Note that seminar location is BEXL 320. Location: BEXL 320 Zoom link: https://oregonstate.zoom.us/j/91611213801?pwd=Wm9JSkN1eW84RUpiS2JEd0E5TEVkd…<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> Equivariance in Learning for Perception Kostas Daniilidis Ruth Yalom Stone Professor of Computer and Information Science University of Pennsylvania Abstract: Equivariant representations are crucial in various scientific and engineering domains because they encode the inherent symmetries present in physical and biological systems, thereby providing a more natural and efficient way to model them. In the context of machine learning and perception, equivariant representations ensure that the output of a model changes in a predictable way in response to transformations of its input, such as 2D or 3D rotation or scaling. In this talk, we will show a systematic way of how to achieve equivariance by design and how such an approach can yield efficiency in training data and model capacity. We will present examples on spherical networks, equivariant representation for point clouds, and a novel definition of convolution and attention on lightfields. Speaker Bio: Kostas Daniilidis is the Ruth Yalom Stone Professor of Computer and Information Science at the University of Pennsylvania where he has been faculty since 1998. He is an IEEE Fellow. He was the director of the GRASP laboratory from 2008 to 2013, Associate Dean for Graduate Education from 2012-2016, and Faculty Director of Online Learning from 2013- 2017. He obtained his undergraduate degree in Electrical Engineering from the National Technical University of Athens, 1986, and his PhD in Computer Science from the University of Karlsruhe, 1992, under the supervision of Hans-Hellmut Nagel. He received the Best Conference Paper Award at ICRA 2017. He co-chaired ECCV 2010 and 3DPVT 2006. His most cited works have been on event-based vision, equivariant learning, 3D human pose, and hand-eye calibration. 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: Kostas Daniilidis
by Tadepalli, Prasad 12 Apr '24

12 Apr '24

Hi all, There is no AI Seminar today. Please attend the Global Futures Forum<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fleadershi…> on AI by live stream if you can. Next week, April 19, we will have AI seminar<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…> by Kostas Daniilidis. If you would like to meet with him in a small group after the seminar, please sign up here<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdocs.goog…>. Thanks, Prasad

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IEEE SPS Distinguished Lecture (1-2PM April 11, Gesualdo Scutari at Purdue)
by Fu, Xiao 05 Apr '24

05 Apr '24

Hello all, I hope to remind you about this event. Note that the location is updated to Johnson 102. More details are here: https://research.engr.oregonstate.edu/soda/upcoming-events<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fresearch.…> Cheers, Xiao Time: 1pm-2pm, April 11 Venue: Johnson 102 Title: Statistical Inference over Networks: Decentralized Optimization Meets High-dimensional Statistics Abstract: Abstract: There is growing interest in solving large-scale statistical learning problems over decentralized networks, where data are distributed across the nodes of the network and no centralized coordination is present (we termed these systems as “mesh” networks). Inference from massive datasets poses a fundamental challenge at the nexus of the computational and statistical sciences: ensuring the quality of statistical inference when computational resources, like time and communication, are constrained. While statistical-computation tradeoffs have been largely explored in the centralized setting, our understanding over mesh networks is limited: (i) distributed schemes, designed and performing well in the classical low-dimensional regime, can break down in the high-dimensional case; and (ii) existing convergence studies may fail to predict algorithmic behaviors, with some findings directly contradicted by empirical tests. This is mainly due to the fact that the majority of distributed algorithms have been designed and studied only from the optimization perspective, lacking the statistical dimension. This talk will discuss some vignettes from high-dimensional statistical inference suggesting new analyses (and designs) aiming at bringing statistical thinking in distributed optimization. Speaker: Bio: Gesualdo Scutari is a Professor with the School of Industrial Engineering and Electrical and Computer Engineering (by courtesy) at Purdue University, West Lafayette, IN, USA, and he is a Purdue Faculty Scholar. His research interests include continuous (distributed) optimization, equilibrium programming, and their applications to signal processing and statistical learning. Among others, he was a recipient of the 2013 NSF CAREER Award, the 2015 IEEE Signal Processing Society Young Author Best Paper Award, and the 2020 IEEE Signal Processing Society Best Paper Award. He serves as an IEEE Signal Processing Distinguish Lecturer (2023-2024). He served on the editorial broad of several IEEE journals and he is currently an Associate Editor of SIAM Journal on Optimization. He is a fellow of IEEE. ======================= Xiao Fu, Ph.D. Associate Professor School of Electrical Engineering and Computer Science Oregon State University Corvallis OR 97331 3003 Kelley Engineering Center Homepage: https://web.engr.oregonstate.edu/~fuxia/

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AI Seminar: Jean Tarbouriech, April 5.
by Tadepalli, Prasad 03 Apr '24

03 Apr '24

Hi everyone, Please note that the AI Seminar will be in BEXL 320, 2-3 PM from this Friday onwards. The web page has Owen 102, but we are going to change it. This week's speaker is Jean Tarbouriech of Google Mind in London. He will be speaking remotely on "Probabilistic Inference in Reinforcement Learning Done Right." Please refer to the AI Seminar page<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…> for more details including a zoom link if you cannot attend in person. For this term I have decided to move the post-seminar Q&A to a different room (KEC 2057) to guarantee the room for us and also make the discussion more focused. Everybody is welcome. I have the schedule of speakers for this term here<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdocs.goog…>. Please sign up at least two days in advance (by tonight for this week's meeting) to attend this session. Look forward to seeing you on Friday. Prasad

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