November 2023 - 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

1 1

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 We often replace phones after mere years of use, robot software goes out of date much faster than a dog or cat's lifespan, yet there are few social considerations for the grief a human might feel in losing software support for a robot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

1 0

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

1 0

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

1 1

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

1 0

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

1 0