April 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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Special seminars
by Tadepalli, Prasad 25 Apr '23

25 Apr '23

Dear all, We will not have AI seminar this week on Friday at the regular time, but will have two special seminars by Wu Feng, a Computer Science professor at Virginia Tech. The first one is at 10 AM in KEC 1005 and the second is at noon in KEC 1003. Details are linked below. Please try to attend if possible. Special Seminar: Confessions of an Accidental Greenie: From Green Destiny to the Green500<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fevents.or…> Special Seminar: At the Synergistic Intersection of Parallel Computing, Data Analytics, and Machine Learning<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fevents.or…> Thanks, Prasad

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AI Seminar : April 21, 2023
by Mangannavar, Rajesh Devaraddi 20 Apr '23

20 Apr '23

Dear all, Our next AI seminar on *"Planning and Learning for Reliable Autonomy in the Open World" *by Sandhya Saisubramanian is scheduled to be on April 21st (Tomorrow), 1-2 PM PST (Add to Google Calendar <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcalendar.…> ). It will be followed by a 30-minute Q&A session with the graduate students. *Location: CRPS 122* *Planning and Learning for Reliable Autonomy in the Open World*Sandhya Saisubramanian, Assistant Professor Electrical Engineering and Computer Science Oregon State University *Abstract:* Safe and reliable decision-making is critical for long-term deployment of autonomous systems. Despite the recent advances in artificial intelligence and robotics, ensuring safe and reliable operation of human-aligned autonomous systems in open-world environments remains a challenge because these systems often operate based on incomplete information. In this talk, I will present an overview of some of our recent efforts in mitigating the undesirable impacts arising due to model incompleteness. First, I will present techniques to overcome Markovian and non-Markovian negative side effects. Second, I will present an approach for reward alignment using explanations. Finally, I will present a technique to maintain and restore safety of autonomous systems using meta-reasoning. *Speaker Bio:* Sandhya Saisubramanian is an Assistant Professor in EECS at Oregon State University. Her research focus is on reliable decision making in single and multiple agents that operate in fully and partially observable open-world environments. She is a recipient of the Outstanding Program Committee member award at the ICAPS 2022 and a Distinguished Paper award at IJCAI 2020. She received her Phd from the University of Massachusetts Amherst. *Please watch this space for future AI Seminars :* 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 : April 21, 2023
by Mangannavar, Rajesh Devaraddi 19 Apr '23

19 Apr '23

Dear all, Our next AI seminar on *"Planning and Learning for Reliable Autonomy in the Open World" *by Sandhya Saisubramanian is scheduled to be on April 21st (Friday), 1-2 PM PST (Add to Google Calendar <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcalendar.…> ). It will be followed by a 30-minute Q&A session with the graduate students. *Location: CRPS 122* *Planning and Learning for Reliable Autonomy in the Open World*Sandhya Saisubramanian, Assistant Professor Electrical Engineering and Computer Science Oregon State University *Abstract:* Safe and reliable decision-making is critical for long-term deployment of autonomous systems. Despite the recent advances in artificial intelligence and robotics, ensuring safe and reliable operation of human-aligned autonomous systems in open-world environments remains a challenge because these systems often operate based on incomplete information. In this talk, I will present an overview of some of our recent efforts in mitigating the undesirable impacts arising due to model incompleteness. First, I will present techniques to overcome Markovian and non-Markovian negative side effects. Second, I will present an approach for reward alignment using explanations. Finally, I will present a technique to maintain and restore safety of autonomous systems using meta-reasoning. *Speaker Bio:* Sandhya Saisubramanian is an Assistant Professor in EECS at Oregon State University. Her research focus is on reliable decision making in single and multiple agents that operate in fully and partially observable open-world environments. She is a recipient of the Outstanding Program Committee member award at the ICAPS 2022 and a Distinguished Paper award at IJCAI 2020. She received her Phd from the University of Massachusetts Amherst. *Please watch this space for future AI Seminars :* 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 : April 14, 2023
by Mangannavar, Rajesh Devaraddi 13 Apr '23

13 Apr '23

Dear all, Our next AI seminar on *"**Some Perspectives on Stochastic Gradient Learning and an Application in Neuroprosthesis**" *by John Mathews is scheduled to be on April 14th (Tomorrow), 1-2 PM PST (Add to Google Calendar <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcalendar.…> ). It will be followed by a 30-minute Q&A session with the graduate students. *Location: CRPS 122 (Location changed from the previous seminar)* *Some Perspectives on Stochastic Gradient Learning and an Application in Neuroprosthesis* V John Mathews Professor, Electrical and Computer Engineering Oregon State University *Abstract:* This talk will be divided into two parts. The first part will involve some signal processing/control theory-based perspectives on stochastic gradient learning. In particular, we will discuss a lowpass-regularized framework for accelerated learning that contains many momentum-based learning algorithms as special cases. We will discuss how a control systems perspective may be employed to derive parameter update algorithms and characterize their learning behavior. The second part of this talk will describe efforts to interpret human motor intent from bioelectrical signals. The estimated movement intent may be used to intuitively control (i.e., control by thought) prosthetic limbs. We will present a semi-supervised online learning algorithm for movement intent estimation and share experimental results demonstrating the ability of this approach to tackle time-varying characteristics of the neuro-musculoskeletal system. *Speaker Bio:* V John Mathews is a professor in the School of Electrical Engineering and Computer Science at the Oregon State University. He received his Ph.D. and M.S. degrees in electrical and computer engineering from the University of Iowa, Iowa City, Iowa in 1984 and 1981, respectively, and the B.E. (Hons.) degree in electronics and communication engineering from the Regional Engineering College (now National Institute of Technology), Tiruchirappalli, India in 1980. Prior to 2015, he was with the Department of Electrical & Computer Engineering at the University of Utah. He served as the chairman of the ECE department at Utah from 1999 to 2003, and as the head of the School of Electrical Engineering and Computer Science at Oregon State from 2015 to 2017. His current research interests are in nonlinear and adaptive signal processing and application of signal processing and machine learning techniques in neural engineering, biomedicine, and structural health management. Mathews is a Fellow of IEEE. He has held many leadership positions of the IEEE Signal Processing Society. He is a recipient of the 2008-09 Distinguished Alumni Award from the National Institute of Technology, Tiruchirappalli, India, IEEE Utah Section’s Engineer of the Year Award in 2010, and the Utah Engineers Council’s Engineer of the Year Award in 2011. He was a distinguished lecturer of the IEEE Signal Processing Society for 2013 and 2014, and is the recipient of the 2014 IEEE Signal Processing Society Meritorious Service Award. *Please watch this space for future AI Seminars :* 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 : April 14, 2023
by Mangannavar, Rajesh Devaraddi 11 Apr '23

11 Apr '23

Dear all, Our next AI seminar on *"**Some Perspectives on Stochastic Gradient Learning and an Application in Neuroprosthesis**" *by John Mathews is scheduled to be on April 14th (Friday), 1-2 PM PST (Add to Google Calendar <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcalendar.…> ). It will be followed by a 30-minute Q&A session with the graduate students. *Location: CRPS 122 (Location changed from the previous seminar)* *Some Perspectives on Stochastic Gradient Learning and an Application in Neuroprosthesis* V John Mathews Professor, Electrical and Computer Engineering Oregon State University *Abstract:* This talk will be divided into two parts. The first part will involve some signal processing/control theory-based perspectives on stochastic gradient learning. In particular, we will discuss a lowpass-regularized framework for accelerated learning that contains many momentum-based learning algorithms as special cases. We will discuss how a control systems perspective may be employed to derive parameter update algorithms and characterize their learning behavior. The second part of this talk will describe efforts to interpret human motor intent from bioelectrical signals. The estimated movement intent may be used to intuitively control (i.e., control by thought) prosthetic limbs. We will present a semi-supervised online learning algorithm for movement intent estimation and share experimental results demonstrating the ability of this approach to tackle time-varying characteristics of the neuro-musculoskeletal system. *Speaker Bio:* V John Mathews is a professor in the School of Electrical Engineering and Computer Science at the Oregon State University. He received his Ph.D. and M.S. degrees in electrical and computer engineering from the University of Iowa, Iowa City, Iowa in 1984 and 1981, respectively, and the B.E. (Hons.) degree in electronics and communication engineering from the Regional Engineering College (now National Institute of Technology), Tiruchirappalli, India in 1980. Prior to 2015, he was with the Department of Electrical & Computer Engineering at the University of Utah. He served as the chairman of the ECE department at Utah from 1999 to 2003, and as the head of the School of Electrical Engineering and Computer Science at Oregon State from 2015 to 2017. His current research interests are in nonlinear and adaptive signal processing and application of signal processing and machine learning techniques in neural engineering, biomedicine, and structural health management. Mathews is a Fellow of IEEE. He has held many leadership positions of the IEEE Signal Processing Society. He is a recipient of the 2008-09 Distinguished Alumni Award from the National Institute of Technology, Tiruchirappalli, India, IEEE Utah Section’s Engineer of the Year Award in 2010, and the Utah Engineers Council’s Engineer of the Year Award in 2011. He was a distinguished lecturer of the IEEE Signal Processing Society for 2013 and 2014, and is the recipient of the 2014 IEEE Signal Processing Society Meritorious Service Award. *Please watch this space for future AI Seminars :* 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 : April 7, 2023
by Mangannavar, Rajesh Devaraddi 06 Apr '23

06 Apr '23

Dear all, Our next AI seminar on *"Great Haste Makes Great Waste: Exploiting and Attacking Efficient Deep Learning" *by Sanghyun Hong is scheduled to be on April 7th (Tomorrow), 1-2 PM PST (Add to Google Calendar <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcalendar.…> ). It will be followed by a 30-minute Q&A session with the graduate students. *Location: KEAR 212* *Great Haste Makes Great Waste: Exploiting and Attacking Efficient Deep Learning*Sanghyun Hong Assistant Professor, Computer Science Oregon State University *Abstract:* Recent increases in the computational demands of deep neural networks have sparked interest in efficient deep learning mechanisms, such as neural network quantization or input-adaptive multi-exit inferences. Those mechanisms provide significant computational savings while preserving a model's accuracy, making it practical to run commercial-scale models in resource-constrained settings. However, most methods focus on "hastiness"—i.e., how fast and efficiently they get correct predictions—and it overlooks the security vulnerability that can "waste" their practicality. In this talk, I will revisit efficient deep learning from a security perspective and introduce emerging research on exploiting and attacking them to achieve malicious objectives. First, I will show how an adversary can exploit neural network quantization to induce malicious behaviors. An adversary can manipulate a pre-trained model to behave maliciously upon quantization. Next, I will show how input-adaptive mechanisms, such as multi-exit models, fail to promise computational efficiency in adversarial settings. By adding human-imperceptible input perturbations, an attacker can completely offset the computational savings provided by these input-adaptive models. Finally, I will conclude my talk by encouraging the audience to examine efficient deep learning practices with an adversarial lens and discuss future research directions for building defense mechanisms. I believe that this is the best moment to listen to Benjamin's advice: "Take time for all the things." *Speaker Bio:* Sanghyun Hong is an Assistant Professor of Computer Science at Oregon State University. He works on building trustworthy and socially responsible AI systems for the future. He is the recipient of the Samsung Global Research (GRO) Award 2022 and was selected as a DARPA Riser 2022. He was also an invited speaker at USENIX Enigma 2021, where he talked about practical hardware attacks on deep learning. He earned his Ph.D. at the University of Maryland, College Park, under the guidance of Prof. Tudor Dumitras. He was also a recipient of the Ann G. Wylie Dissertation Fellowship. He received his B.S. at Seoul National University. *Please watch this space for future AI Seminars :* 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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