- Ai - Engineering Mailing Lists

information theory class
by Nguyen, Thinh 05 Jan '24

05 Jan '24

Hello everyone, I just want to bring to your attention that I will be teaching information theory class (ECE 566) next term. If you are interested in learning the theories and some foundational connections among various disciplines (compression, compression, machine learning, statistics, ...) then you might want to consider taking ECE 566. I am attaching the ECE 566 syllabus. If enough number of student register for ECE 566, I will offer the second course (special topics) in information theory series in the Spring term. Best, Dr. Nguyen

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SP virtual Seminar: Generative AI for Coronary Imaging (11 am, Dec 19)
by Fu, Xiao 12 Dec '23

12 Dec '23

Hi all, Please mark your calendar for the upcoming seminar before the holidays. The zoom link will be sent later today. Best, Xiao Time: 11 am Dec 19, 2023 Title: Generative AI for coronary imaging Abstract: Coronary artery disease (CAD) has been influencing a market over $2.8 billion with roughly 1,000,000 treatment procedures performed annually. Existing guidance of CAD treatment suffers from the limited spatial resolution and lacks real-time detailed pathological identification. Even the finest coronary imaging system - intravascular optical coherence tomography (IVOCT), is not able to resolve the structure at sub-cellular level and vessel characterization at sub-cellular level can only be obtained after patient’s death through histopathology. We will discuss some examples in our research team of developing a data-driven approach using generative artificial intelligence (AI) to use off-line data and training process, without any hardware modifications, to generatively produce new information that could not be acquired previously or can only be obtained ex vivo. We will also cover some latest progress in developing robust algorithms to segment medical images, including OCT images. Bio: Dr. Yu Gan is currently an Assistant Professor in the Biomedical Engineering Department at Stevens Institute of Technology. He received his Ph. D. degree in Electrical Engineering from Columbia University in 2017. Prior to Stevens, He was an Assistant Professor in the Electrical and Computer Engineering at the University of Alabama from 2018 to 2021. Dr. Gan’s research lies in machine learning and deep learning techniques that facilitate biomedical imaging and dig out hidden information from medical/biomedical images. His research has been supported by National health institute (NIH), NJ Health Foundations, National science foundation (NSF), US Department of Agriculture (USDA), Burrough Wellcome foundation, and other agency (companies). He is the recipient of 2023 NSF CAREER award. ==== Xiao Fu, Associate Professor School of Electrical Engineering and Computer Science Oregon State University Corvallis, OR 97331 http://eecs.oregonstate.edu/~fuxia<https://nam04.safelinks.protection.outlook.com/?url=http%3A%2F%2Fpeople.ore…>

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AI Seminar : Dec 08
by Mangannavar, Rajesh Devaraddi 07 Dec '23

07 Dec '23

Dear all, Our next AI seminar is scheduled to be on December 8th, 1-2 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-related Research in Education and Counseling Cass Dykeman Professor of Counseling College of Education Oregon State University Abstract: In various professions, Artificial Intelligence has significantly influenced both research methodologies and subjects. The fields of Education and Counseling are no exceptions. This presentation will delve into how researchers at OSU’s College of Education utilize constituent elements of AI, including the analysis of: (a) collocations, (b) n-grams/skip-grams, (c) authorial stance, (d) keyness, (e) sentiment, and (f) topic models. Furthermore, I will highlight AI research tools beneficial for graduate students and faculty, such as Noteable.io, Elicit.com, Claude.ai, and Orange Data Mining. We will also address the ethical considerations surrounding the use of AI tools in crafting IMRaD manuscripts intended for submission to peer-reviewed journals. Speaker Bio: Cass Dykeman is a professor of counseling in OSU’s College of Education who received his Ph.D. in counselor education from the University of Virginia and a master's in educational psychology from the University of Washington. Prior to working in higher education, Dykeman served as an elementary school counselor in Seattle. At OSU since 1998, he holds the record for the number of dissertations completed (60), having passed James D. White of Chemistry in 2022. The last 25 of these dissertations have employed AI-related research methods. A former Army officer, Dykeman’s own research focuses on AI-augmented counterintelligence. 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 : Dec 1
by Mangannavar, Rajesh Devaraddi 29 Nov '23

29 Nov '23

Dear all, Our next AI seminar is scheduled to be on December 1stth, 1-2 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…> Best Practices for Robot Death Heather Knight Assistant Professor Computer Science Oregon State University Abstract: Have you ever been sad after losing a pet, lost all data on your phone, or spilled coffee on your laptop? Imagine how a robot owner might feel after losing their companion robot, or seeing its memory and capability erased. We often replace phones after mere years of use, robot software goes out of date much faster than a dog or cat's lifespan, yet there are few social considerations for the grief a human might feel in losing software support for a robot system, or over robot hardwares for which no new parts are constructed. Yet, just like living systems, impermanence in technological systems is a given. This talk considers ways in which perspectives from Social Robotics — a field that leverages anthropomorphism, i.e., the ways in which humans automatically imbue humanlike characteristics to non-human things — can inform future social considerations and best practices for Robot Death. For example, social structures and legal mechanisms that take human-robot bonding into account by compassionately delivering news, and/or validating naturally existing sadness post-machine decline. Drawing from my personal experience having a Nao robot stolen out of my car in Corvallis, OR, from analyses of robot funerals after Starship Robots are hit by cars or trains, and international examples of ceremonies conducted for lost machines, this talk acknowledges that digital lives can also experience existential 1's and 0's. What can Social Robotics as a field teach us about compassionately handling robot and machine death? Moreover, how might robots and machines help us to compassionately navigate impermanence and suffering in our own lives? We finish with a discussion of opportunities for robot-facilitated mindfulness practices, including "maranasati," which means mindfulness of death and has surprising benefits to one's mental health. Speaker Bio: Dr. Heather Knight is an Assistant Professor of Computer Science at Oregon State University, and directs the CHARISMA Robotics Lab, advising students in Robotics, EECS, AI, and MIME. Her students investigate multi-robot expressive motion, human-robot interactive communication, interfaces for human-in-the-loop robot control, and frequently use entertainment methods to bootstrap the development of everyday social robots. Past projects include Cyberflora, a robot flower garden installation at the Smithsonian/Cooper-Hewitt Design Museum, Silicon Comedy, an online learning system for interactive robot comedy featured on TED.com, and This Too Shall Pass, a music video featuring a two-floor Rube Goldberg Machine that won a British Video Music Award. Her academic background included a postdoc at Stanford University exploring minimal robots and autonomous car interfaces, a Robotics Ph.D. at Carnegie Mellon University on Expressive Motion for Low Degree-of-Freedom Robots, and M.S. and B.S. in Electrical Engineering & Computer Science at Massachusetts Institute of Technology, where she developed a sensate skin for a robot teddy bear at the MIT Media Lab. Outside of academia, she has developed robotics and instrumentation at NASA’s Jet Propulsion Laboratory, and sensor and field applications as an early employee of Aldebaran Robotics, the developers of the Nao and Pepper robots (since acquired by Softbank). 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 : Nov 17
by Mangannavar, Rajesh Devaraddi 16 Nov '23

16 Nov '23

Dear all, Our next AI seminar is scheduled to be on Nov 17th, 1-2 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…> New Frontiers In Adaptive Experimental Design For Multi-objective Optimization Syrine Belakaria Data Science Postdoctoral Fellow Computer Science Stanford University Abstract: Many design optimization problems in science, engineering, and industrial domains are instantiations of the following general problem: adaptive optimization of complex design spaces guided by expensive experiments where the expense is measured in terms of resources consumed by the experiments. This talk focuses on the problem of multi-objective optimization (MOO) using expensive black-box function evaluations (also referred to as experiments) where the goal is to approximate the optimal Pareto set of solutions by minimizing the total resource cost of experiments. For example, in drug design optimization, we need to find designs that trade off effectiveness, safety, and cost using expensive experiments. The key challenge is to select the sequence of experiments to uncover high-quality solutions by minimizing the total resource cost. In this talk, I will describe a general framework for solving MOO problems based on the output space entropy (OSE) search principle: select the experiment that maximizes the information gained per unit resource cost about the optimal Pareto front. I will also explain how to instantiate the principle of OSE search to derive efficient algorithms for the following four MOO problem settings: 1) The most basic single-fidelity setting where experiments are expensive and accurate; 2) Handling black-box constraints that cannot be evaluated without performing experiments; 3) The multi-fidelity setting where candidate experiments can vary in the amount of resources consumed and their evaluation accuracy. I will present experimental results on real-world engineering and science applications to demonstrate the effectiveness of OSE framework in terms of accuracy of MOO solutions and computational efficiency. Speaker Bio: Syrine Belakaria is a Data Science Postdoctoral fellow in the Computer Science department at Stanford University working with Professor Stefano Ermon and Professor Barbara Engelhardt. She obtained her PhD in Computer Science from Washington State University where she was advised by Professor Jana Doppa; MS in Electrical Engineering from the University of Idaho; and Engineering degree in Information Technology from the Higher School of Communication of Tunis, Tunisia. She won IBM PhD Fellowship (2021-2023) and was a Finalist for Microsoft Research Fellowship (2021), was selected for the MIT Rising Stars in EECS (2021), won the WSU Harriet Rigas Outstanding Woman in Doctoral Studies Award (2023), won Outstanding TA award in CS (2019), and two Outstanding Reviewer Awards from the ICML conference. She spent time as a research intern at Microsoft Research and Meta Research. Her general research interests are in the broad area of AI for science and engineering with a current focus on adaptive experiment design to solve real-world problems including hardware design, materials design, electric transportation systems, and Auto ML. 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 : Nov 10
by Mangannavar, Rajesh Devaraddi 08 Nov '23

08 Nov '23

Dear all, Our next AI seminar on "New Frontiers In Adaptive Experimental Design For Multi-objective Optimization" by Syrine Belakaria is scheduled to be on Nov 10th (Friday), 1-2 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…> Learning Interpretable Models on Complex Medical Data Syrine Belakaria Data Science Postdoctoral Fellow Computer Science Stanford University Abstract: Many design optimization problems in science, engineering, and industrial domains are instantiations of the following general problem: adaptive optimization of complex design spaces guided by expensive experiments where the expense is measured in terms of resources consumed by the experiments. This talk focuses on the problem of multi-objective optimization (MOO) using expensive black-box function evaluations (also referred to as experiments) where the goal is to approximate the optimal Pareto set of solutions by minimizing the total resource cost of experiments. For example, in drug design optimization, we need to find designs that trade off effectiveness, safety, and cost using expensive experiments. The key challenge is to select the sequence of experiments to uncover high-quality solutions by minimizing the total resource cost. In this talk, I will describe a general framework for solving MOO problems based on the output space entropy (OSE) search principle: select the experiment that maximizes the information gained per unit resource cost about the optimal Pareto front. I will also explain how to instantiate the principle of OSE search to derive efficient algorithms for the following four MOO problem settings: 1) The most basic single-fidelity setting where experiments are expensive and accurate; 2) Handling black-box constraints that cannot be evaluated without performing experiments; 3) The multi-fidelity setting where candidate experiments can vary in the amount of resources consumed and their evaluation accuracy. I will present experimental results on real-world engineering and science applications to demonstrate the effectiveness of OSE framework in terms of accuracy of MOO solutions and computational efficiency. Speaker Bio: Syrine Belakaria is a Data Science Postdoctoral fellow in the Computer Science department at Stanford University working with Professor Stefano Ermon and Professor Barbara Engelhardt. She obtained her PhD in Computer Science from Washington State University where she was advised by Professor Jana Doppa; MS in Electrical Engineering from the University of Idaho; and Engineering degree in Information Technology from the Higher School of Communication of Tunis, Tunisia. She won IBM PhD Fellowship (2021-2023) and was a Finalist for Microsoft Research Fellowship (2021), was selected for the MIT Rising Stars in EECS (2021), won the WSU Harriet Rigas Outstanding Woman in Doctoral Studies Award (2023), won Outstanding TA award in CS (2019), and two Outstanding Reviewer Awards from the ICML conference. She spent time as a research intern at Microsoft Research and Meta Research. Her general research interests are in the broad area of AI for science and engineering with a current focus on adaptive experiment design to solve real-world problems including hardware design, materials design, electric transportation systems, and Auto ML. 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 : November 3
by Mangannavar, Rajesh Devaraddi 03 Nov '23

03 Nov '23

Dear all, Our next AI seminar on "Learning Interpretable Models on Complex Medical Data" by Jennifer G. Dy is scheduled to be on Nov 03rd (Tomorrow), 1-2 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…> Learning Interpretable Models on Complex Medical Data Jennifer G. Dy Professor Department of Electrical and Computer Engineering Northeastern University Abstract: Machine learning as a field has become more and more important due to the ubiquity of data collection in various disciplines. Coupled with this data collection is the hope that new discoveries or knowledge can be learned. My research spans both fundamental research in machine learning and their application to biomedical imaging, health, science and engineering. Multi-disciplinary research is instrumental to the growth of the various areas involved. In many applications, data is often complex, high-dimensional and multi-faceted, where multiple possible interpretations are inherent in the data. Fortunately, domain scientists often have rich knowledge that can guide data driven methods. Thus, it is important to enable incorporation of domain input into the design of algorithms. Furthermore, for clinicians and domain scientists to trust and use the results of learning algorithms, not only are models necessary to be accurate but it is also imperative for learning models to be interpretable. In this talk, I highlight these challenges through our experience in collaborative research working on discovering disease subtypes and then provide examples of how these challenges led to innovations in machine learning and to new discoveries. Speaker Bio: Jennifer G. Dy is a Full Professor at the Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, where she first joined the faculty in 2002. She received her M.S. and Ph.D. in 1997 and 2001 respectively from the School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, and her B.S. degree from the Department of Electrical Engineering, University of the Philippines, in 1993. Her research spans both foundations in machine learning and its application to biomedical imaging, health, science and engineering, with research contributions in unsupervised learning, interpretable models, explainable AI, dimensionality reduction, feature selection/sparse methods, learning from uncertain experts, active learning, Bayesian models, and deep representation learning. She is Director of AI Faculty at the Institute for Experiential AI, an institute with 90+ faculty across all colleges at Northeastern. She is also the Director of the Machine Learning Lab and is a founding faculty member of the SPIRAL (Signal Processing, Imaging, Reasoning, and Learning) Center at Northeastern. She received an NSF Career award in 2004. She has served or is serving as Secretary for the ICML Board (formerly, International Machine Learning Society), associate editor/editorial board member for the Journal of Machine Learning Research, Machine Learning journal, IEEE Transactions on Pattern Analysis and Machine Intelligence, organizing and or technical program committee member for premier conferences in machine learning, AI, and data mining (ICML, NeurIPS, ACM SIGKDD, AAAI, IJCAI, UAI, AISTATS, ICLR, SIAM SDM), Program Chair for SIAM SDM 2013, ICML 2018, AISTATS 2023, and AAAI 2024. 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: Nov 03
by Mangannavar, Rajesh Devaraddi 01 Nov '23

01 Nov '23

Dear all, Our next AI seminar on "Learning Interpretable Models on Complex Medical Data" by Jennifer G. Dy is scheduled to be on Nov 03rd (Friday), 1-2 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…> Learning Interpretable Models on Complex Medical Data Jennifer G. Dy Professor Department of Electrical and Computer Engineering Northeastern University Abstract: Machine learning as a field has become more and more important due to the ubiquity of data collection in various disciplines. Coupled with this data collection is the hope that new discoveries or knowledge can be learned. My research spans both fundamental research in machine learning and their application to biomedical imaging, health, science and engineering. Multi-disciplinary research is instrumental to the growth of the various areas involved. In many applications, data is often complex, high-dimensional and multi-faceted, where multiple possible interpretations are inherent in the data. Fortunately, domain scientists often have rich knowledge that can guide data driven methods. Thus, it is important to enable incorporation of domain input into the design of algorithms. Furthermore, for clinicians and domain scientists to trust and use the results of learning algorithms, not only are models necessary to be accurate but it is also imperative for learning models to be interpretable. In this talk, I highlight these challenges through our experience in collaborative research working on discovering disease subtypes and then provide examples of how these challenges led to innovations in machine learning and to new discoveries. Speaker Bio: Jennifer G. Dy is a Full Professor at the Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, where she first joined the faculty in 2002. She received her M.S. and Ph.D. in 1997 and 2001 respectively from the School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, and her B.S. degree from the Department of Electrical Engineering, University of the Philippines, in 1993. Her research spans both foundations in machine learning and its application to biomedical imaging, health, science and engineering, with research contributions in unsupervised learning, interpretable models, explainable AI, dimensionality reduction, feature selection/sparse methods, learning from uncertain experts, active learning, Bayesian models, and deep representation learning. She is Director of AI Faculty at the Institute for Experiential AI, an institute with 90+ faculty across all colleges at Northeastern. She is also the Director of the Machine Learning Lab and is a founding faculty member of the SPIRAL (Signal Processing, Imaging, Reasoning, and Learning) Center at Northeastern. She received an NSF Career award in 2004. She has served or is serving as Secretary for the ICML Board (formerly, International Machine Learning Society), associate editor/editorial board member for the Journal of Machine Learning Research, Machine Learning journal, IEEE Transactions on Pattern Analysis and Machine Intelligence, organizing and or technical program committee member for premier conferences in machine learning, AI, and data mining (ICML, NeurIPS, ACM SIGKDD, AAAI, IJCAI, UAI, AISTATS, ICLR, SIAM SDM), Program Chair for SIAM SDM 2013, ICML 2018, AISTATS 2023, and AAAI 2024. 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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Halloween special:: Best Practices for Robot Death
by Tadepalli, Prasad 31 Oct '23

31 Oct '23

Hi all, We’ll have Jennifer Dy<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…> speaking this week and Syrine Belakaria<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…> on November 17 at the AI Seminar. Here is an early announcement of a “Halloween Special” on December 1 by our own Heather Knight. Happy Halloween (or Dia de los Muertos), Prasad Title: Best Practices for Robot Death Abstract: Have you ever been sad after losing a pet, lost all data on your phone, or spilled coffee on your laptop? Imagine how a robot owner might feel after losing their companion robot, or seeing its memory and capability erased. We often replace phones after mere years of use, robot software goes out of date much faster than a dog or cat's lifespan, yet there are few social considerations for the grief a human might feel in losing software support for a robot system, or over robot hardwares for which no new parts are constructed. Yet, just like living systems, impermanence in technological systems is a given. This talk considers ways in which perspectives from Social Robotics — a field that leverages anthropomorphism, i.e., the ways in which humans automatically imbue humanlike characteristics to non-human things — can inform future social considerations and best practices for Robot Death. For example, social structures and legal mechanisms that take human-robot bonding into account by compassionately delivering news, and/or validating naturally existing sadness post-machine decline. Drawing from my personal experience having a Nao robot stolen out of my car in Corvallis, OR, from analyses of robot funerals after Starship Robots are hit by cars or trains, and international examples of ceremonies conducted for lost machines, this talk acknowledges that digital lives can also experience existential 1's and 0's. What can Social Robotics as a field teach us about compassionately handling robot and machine death? Moreover, how might robots and machines help us to compassionately navigate impermanence and suffering in our own lives? We finish with a discussion of opportunities for robot-facilitated mindfulness practices, including "maranasati," which means mindfulness of death and has surprising benefits to one's mental health. Bio: Dr. Heather Knight is an Assistant Professor of Computer Science at Oregon State University, and directs the CHARISMA Robotics Lab, advising students in Robotics, EECS, AI, and MIME. Her students investigate multi-robot expressive motion, human-robot interactive communication, interfaces for human-in-the-loop robot control, and frequently use entertainment methods to bootstrap the development of everyday social robots. Past projects include Cyberflora, a robot flower garden installation at the Smithsonian/Cooper-Hewitt Design Museum, Silicon Comedy, an online learning system for interactive robot comedy featured on TED.com, and This Too Shall Pass, a music video featuring a two-floor Rube Goldberg Machine that won a British Video Music Award. Her academic background included a postdoc at Stanford University exploring minimal robots and autonomous car interfaces, a Robotics PhD at Carnegie Mellon University on Expressive Motion for Low Degree-of-Freedom Robots, and M.S. and B.S. in Electrical Engineering & Computer Science at Massachusetts Institute of Technology, where she developed a sensate skin for a robot teddy bear at the MIT Media Lab. Outside of academia, she has developed robotics and instrumentation at NASA’s Jet Propulsion Laboratory, and sensor and field applications as an early employee of Aldebaran Robotics, the developers of the Nao and Pepper robots (since acquired by Softbank).

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AI Seminar
by Mangannavar, Rajesh Devaraddi 26 Oct '23

26 Oct '23

Dear all, Our next AI seminar on "Symbolic AI 3.0 (S3): Rise of the LLMs" by Scott Sanner is scheduled to be on Oct 27th (Tommorrow), 1-2 PM. It will be followed by a 30-minute Q&A session with the graduate students. Location: KEC 1001 Symbolic AI 3.0 (S3): Rise of the LLMs Scott Sanner Associate Professor Industrial Engineering and Computer Science University of Toronto Abstract: Large Language Models (LLMs) such as ChatGPT have emerged as a revolutionary technology for natural language reasoning and numerous related AI applications. I'll discuss some of my group's own work on abstract reasoning and interactive conversational systems leveraging LLMs and the game-changing realizations that I have taken away from these investigations. This talk will then discuss some general implications of the LLM era and my conjectures as to how it will shift research foci in the near future and enable levels of user-facing AI deployment that were unthinkable just two years ago. Speaker Bio: Scott Sanner is an Associate Professor in Industrial Engineering and Cross-appointed in Computer Science at the University of Toronto. Scott’s research focuses on a broad range of AI topics spanning sequential decision-making, (conversational) recommender systems, and applications of machine/deep learning. Scott is currently an Associate Editor for ACM Transactions on Recommender Systems (TORS), the Machine Learning Journal (MLJ), and the Journal of Artificial Intelligence Research (JAIR). Scott was a co-recipient of paper awards from the AI Journal (2014), Transport Research Board (2016), and CPAIOR (2018). He was a recipient of a Google Faculty Research Award in 2020 and a Visiting Researcher at Google while on sabbatical during 2022-23. 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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