September 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

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AI Seminar : Sept 29, 2023
by Mangannavar, Rajesh Devaraddi 28 Sep '23

28 Sep '23

Dear all, Our first AI seminar for the fall term on the topic "Enhancing healthcare with AI-in-the-loop" by Sriraam Natarajan is scheduled to be on Sept 29th (Tomorrow), 1-2 PM PST. It will be followed by a 30-minute Q&A session with the graduate students. Location: KEC 1001 Enhancing healthcare with AI-in-the-loop Sriraam Natarajan Professor and the Director for Center for ML Department of Computer Science University of Texas Dallas Abstract: Historically, Artificial Intelligence has taken a symbolic route for representing and reasoning about objects at a higher-level or a statistical route for learning complex models from large data. To achieve true AI in complex domains such as healthcare, it is necessary to make these different paths meet and enable seamless human interaction. First, I will introduce learning from rich, structured, complex and noisy data. One of the key attractive properties of the learned models is that they use a rich representation for modeling the domain that potentially allows for seam-less human interaction. I will present the recent progress that allows for more reasonable human interaction where the human input is taken as “advice” and the learning algorithm combines this advice with data. I will present these algorithms in the context of several healthcare problems -- learning from electronic health records, clinical studies, and surveys -- and demonstrate the value of involving experts during learning. Speaker Bio: Sriraam Natarajan is a Professor and the Director for Center for ML at the Department of Computer Science at University of Texas Dallas, a hessian.AI fellow at TU Darmstadt and a RBDSCAII Distinguished Faculty Fellow at IIT Madras. His research interests lie in the field of Artificial Intelligence, with emphasis on Machine Learning, Statistical Relational Learning and AI, Reinforcement Learning, Graphical Models and Biomedical Applications. He is a AAAI senior member and has received the Young Investigator award from US Army Research Office, Amazon Faculty Research Award, Intel Faculty Award, XEROX Faculty Award, Verisk Faculty Award, ECSS Graduate teaching award from UTD and the IU trustees Teaching Award from Indiana University. He is the program chair of AAAI 2024, the general chair of CoDS-COMAD 2024, AI and society track chair of AAAI 2023 and 2022, senior member track chair of AAAI 2023, demo chair of IJCAI 2022, program co-chair of SDM 2020 and ACM CoDS-COMAD 2020 conferences. He was the specialty chief editor of Frontiers in ML and AI journal, and is an associate editor of JAIR, DAMI and Big Data journals. 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 : Sept 29, 2023
by Mangannavar, Rajesh Devaraddi 25 Sep '23

25 Sep '23

Dear all, Our first AI seminar for the fall term on the topic "Enhancing healthcare with AI-in-the-loop" by Sriraam Natarajan is scheduled to be on Sept 29th (Friday), 1-2 PM PST. It will be followed by a 30-minute Q&A session with the graduate students. Location: KEC 1001 Enhancing healthcare with AI-in-the-loop Sriraam Natarajan Professor and the Director for Center for ML Department of Computer Science University of Texas Dallas Abstract: Historically, Artificial Intelligence has taken a symbolic route for representing and reasoning about objects at a higher-level or a statistical route for learning complex models from large data. To achieve true AI in complex domains such as healthcare, it is necessary to make these different paths meet and enable seamless human interaction. First, I will introduce learning from rich, structured, complex and noisy data. One of the key attractive properties of the learned models is that they use a rich representation for modeling the domain that potentially allows for seam-less human interaction. I will present the recent progress that allows for more reasonable human interaction where the human input is taken as “advice” and the learning algorithm combines this advice with data. I will present these algorithms in the context of several healthcare problems -- learning from electronic health records, clinical studies, and surveys -- and demonstrate the value of involving experts during learning. Speaker Bio: Sriraam Natarajan is a Professor and the Director for Center for ML at the Department of Computer Science at University of Texas Dallas, a hessian.AI fellow at TU Darmstadt and a RBDSCAII Distinguished Faculty Fellow at IIT Madras. His research interests lie in the field of Artificial Intelligence, with emphasis on Machine Learning, Statistical Relational Learning and AI, Reinforcement Learning, Graphical Models and Biomedical Applications. He is a AAAI senior member and has received the Young Investigator award from US Army Research Office, Amazon Faculty Research Award, Intel Faculty Award, XEROX Faculty Award, Verisk Faculty Award, ECSS Graduate teaching award from UTD and the IU trustees Teaching Award from Indiana University. He is the program chair of AAAI 2024, the general chair of CoDS-COMAD 2024, AI and society track chair of AAAI 2023 and 2022, senior member track chair of AAAI 2023, demo chair of IJCAI 2022, program co-chair of SDM 2020 and ACM CoDS-COMAD 2020 conferences. He was the specialty chief editor of Frontiers in ML and AI journal, and is an associate editor of JAIR, DAMI and Big Data journals. 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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Special AI Seminar Thursday at 4 pm
by Tadepalli, Prasad 14 Sep '23

14 Sep '23

Hi everyone, I hope you had a good summer. We will resume the regular AI seminar<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineeri…> on Fridays at 1 pm in KEC 1001 starting from September 29'th. It is open to public. Grads, please register for AI 507 to get 1 credit. We will also have a special virtual seminar this week on Thursday, August 14, at 4 pm PST in KEC 1001 by Jonathan Ferrer Mestres, a research scientist at CSIRO, Australia. I hope to see you there. Zoom link: https://oregonstate.zoom.us/j/98684050301?pwd=ZzhianQxUFBPUmdYVWJKOFhaVURCQ…<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonsta…> Title: Towards more interpretable solutions for conservation problems Abstract: Markov Decision Processes (MDPs) provide a convenient model for representing sequential decision-making optimization problems when the decision maker has complete information about the current state of the system and dynamics are non-deterministic. MDPs have been applied to help recover populations of threatened species under limited resources, to control invasive species, to perform adaptive management of natural resources, and to test behavioral ecology theories. These domains are human-operated systems, where MDP policies provide recommendations. Solutions computed for MDPs with thousands of states are difficult to understand. In human-operated systems, it is crucial that solutions provided by artificial intelligence algorithms can be interpreted and explained in order to increase uptake of MDP solutions. Explainable artificial intelligence, also known as the interpretability problem, aims to generate decisions in which one of the criteria is how easily a human can understand these decisions. We propose to increase the interpretability of MDPs by providing explainable artificial intelligence algorithms that can be used to solve conservation decision problems. We define the problem of solving K-MDPs, i.e., given an original MDP and a number of states (K), generate a reduced state space MDP that minimizes the difference between the original and reduced optimal solutions. Abstracting states aims to reduce the size of large state spaces by aggregating states which are equivalent given a metric. We found that K-MDPs can achieve a substantial reduction of the number of states with a small loss of performance on a number of case studies of increasing complexity from the literature. Bio: Jonathan is a Research Scientist within the Conservation Decisions Team, where he focuses on developing trustworthy and explainable artificial intelligence solutions for environmental decision-making. His primary goal is to ensure that artificial intelligence systems are equipped with transparent capabilities, fostering trust among users and experts who rely on these solutions. Jonathan's research centers on making informed decisions in the face of uncertainty to provide effective environmental solutions. Prior to his current role, he earned his PhD from Universitat Pompeu Fabra, in the Artificial Intelligence and Machine Learning Group in Barcelona in 2018, under the supervision of Dr. Hector Geffner. His doctoral thesis explored the integration of task and motion planning. Jonathan's educational journey also includes a Master's degree in Intelligent Interactive Systems and a Bachelor's degree in Computer Science. ------------------------------------------------------- Prasad Tadepalli Director, AI Program School of Electrical Engineering and Computer Science Oregon State University Corvallis, OR 97330

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