- Ai - Engineering Mailing Lists

AI seminar: September 30, 2022
by Mangannavar, Rajesh Devaraddi 26 Sep '22

26 Sep '22

Dear all, Our next AI seminar on *"Understanding Multiview and Self-Supervised Representation Learning: A Nonlinear Mixture Identification Perspective" *by Xiao Fu is scheduled to be on September 30th (Friday), 1-2 PM PST. It will be followed by a 30-minute Q&A session by the graduate students. This is an *in-person* event and will be held at *KEC 1001* *Understanding Multiview and Self-Supervised Representation Learning: A Nonlinear Mixture Identification Perspective* Xiao Fu Assistant Professor School of Electrical Engineering and Computer Science Oregon State University *Abstract:* Central to representation learning is to succinctly represent high-dimensional data using the “essential information’’ while discarding the “redundant information”. Properly formulating and approaching this objective is critical to fending against overfitting, and can also benefit many important tasks such as domain adaptation and transfer learning. This talk aims to deepen understanding of representation learning and using the gained insights to come up with a new learning method. In particular, attention will be paid to two representation learning paradigms using multiple views of data, as both naturally acquired (e.g., image and audio) and artificially produced (e.g., via adding different noise to data samples) multiview data have empirically proven useful in producing essential information-reflecting vector representations. Natural views are often handled by multiview analysis tools, e.g., (deep) canonical correlation analysis [(D)CCA], while the artificial ones are frequently used in self-supervised learning (SSL) paradigms, e.g., BYOL and Barlow Twins. However, the effectiveness of these methods is mostly validated empirically, and more insights and theoretical underpinnings remain to be discovered. In this talk, an intuitive generative model of multiview data is adopted, where the views are different nonlinear mixtures of shared and private components. Since the shared components are view/distortion-invariant, such components may serve for representing the essential information of data in a non-redundant way. Under this model, a key module used in a suite of DCCA and SSL paradigms, namely, latent correlation maximization, is shown to guarantee the extraction of the shared components across views (up to certain ambiguities). It is further shown that the private information in each view can be provably disentangled from the shared using proper regularization design---which can facilitate tasks such cross-view translation and data generation. A finite sample analysis, which has been rare in nonlinear mixture identifiability study, is also presented. The theoretical results and newly designed regularization are tested on a series of tasks. *Speaker Bio:* Xiao Fu received the Ph.D. degree in Electronic Engineering from The Chinese University of Hong Kong (CUHK), Shatin, N.T., Hong Kong, in 2014. He was a Postdoctoral Associate with the Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA, from 2014 to 2017. Since 2017, he has been an Assistant Professor with the School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA. His research interests include the broad area of signal processing and machine learning. Dr. Fu received a Best Student Paper Award at ICASSP 2014, and was a recipient of the Outstanding Postdoctoral Scholar Award at University of Minnesota in 2016. His coauthored papers received Best Student Paper Awards from IEEE CAMSAP 2015 and IEEE MLSP 2019, respectively. He received the National Science Foundation CAREER Award in 2022. He serves as a member of the Sensor Array and Multichannel Technical Committee (SAM-TC) of the IEEE Signal Processing Society (SPS). He is also a member of the Signal Processing for Multisensor Systems Technical Area Committee (SPMuS-TAC) of EURASIP. He is the Treasurer of the IEEE SPS Oregon Chapter. He serves as an Editor of Signal Processing and an Associate Editor of IEEE Transactions on Signal Processing. He was a tutorial speaker at ICASSP 2017 and SIAM Conference on Applied Linear Algebra 2021. *Please watch this space for future AI Seminars :* * https://eecs.oregonstate.edu/ai-events <https://eecs.oregonstate.edu/ai-events>* 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: September 23, 2022
by Mangannavar, Rajesh Devaraddi 23 Sep '22

23 Sep '22

Dear all, Our next AI seminar on *"AI Seminar: Universal Representations for AI" *by Sridhar Mahadevan is scheduled to be on September 23rd (Tomorrow), 1-2 PM PST. It will be followed by a 30-minute Q&A session by the graduate students. This is an *in-person* event and will be held at *KEC 1001* *AI Seminar: Universal Representations for AI* Sridhar Mahadevan Director, Adobe Research Research Professor, University of Massachusetts, Amherst *Abstract:* Humans are uniquely endowed in biology with a singular cognitive ability to pass on millennia of accumulated knowledge to our descendants, not solely through our genetic endowment, but also through the manufacture of abstract representations: art, culture, language and writing, music, science, and technology. This ability to transfer non-genetic information has been greatly amplified with the invention of decentralized information repositories, such as the traditional web or the emerging cryptographically more secure blockchain economy. In this talk, I will describe a long-term research program on analyzing four central capabilities that underlie our ability to manufacture representations: Objectification: our ability to denote arbitrary entities as ``objects”, from elementary particles to our society, the U.S. economy, or an entire galaxy or a (meta)universe. Social Interaction: our ability to confer agenthood on both inanimate and animate objects, treating objects not as passive containers of data, but actors defined by social interactions. Metaphors: our ability to use analogies to map unfamiliar situations – understanding an alien civilization with an unknown language -- to ones we are familiar with from our experience. Diagrammatic representations: our ability to draw figures, record a tune as a formal musical score, or write equations reveal how diagrams are a universal cognitive calculus for us. We use the abstract mathematics of category theory to characterize universal properties that define these four core capabilities. Objects in a category are modeled as actors, whose properties emerge from their social interaction with other objects, modeled as morphisms. Functors and natural transformations play the role of metaphors in human language. Adjunctions across categories plays the role of analogies. Diagrams are special functors that map from indexing categories to domain categories and define universal constructions for reasoning about interactions. We illustrate our framework by manufacturing novel representations for modeling causality and conditional independence, creativity and imagination, learning, language, and decision-making. *Speaker Bio:* Sridhar Mahadevan has been thinking about AI every waking day for the past 40 years, since he wrote his first machine learning program in 1982 at the Indian Institute of Technology, Kanpur. He was elected Fellow of AAAI for significant contributions in several areas of machine learning. He has published widely across many subfields of AI, including a book on robot learning in 1993 (Springer), and a book on representation discovery in 2007 (Morgan-Claypool). He is currently working on his third book on Universal AI. He is a Director at Adobe Research, and a Research Professor at the University of Massachusetts, Amherst. *Please watch this space for future AI Seminars :* * https://eecs.oregonstate.edu/ai-events <https://eecs.oregonstate.edu/ai-events>* 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: September 23, 2022
by Mangannavar, Rajesh Devaraddi 20 Sep '22

20 Sep '22

Dear all, Our next AI seminar on *"AI Seminar: Universal Representations for AI" *by Sridhar Mahadevan is scheduled to be on September 23rd (Friday), 1-2 PM PST. It will be followed by a 30-minute Q&A session by the graduate students. This is an *in-person* event and will be held at *KEC 1001* *AI Seminar: Universal Representations for AI* Sridhar Mahadevan Director, Adobe Research Research Professor, University of Massachusetts, Amherst *Abstract:* Humans are uniquely endowed in biology with a singular cognitive ability to pass on millennia of accumulated knowledge to our descendants, not solely through our genetic endowment, but also through the manufacture of abstract representations: art, culture, language and writing, music, science, and technology. This ability to transfer non-genetic information has been greatly amplified with the invention of decentralized information repositories, such as the traditional web or the emerging cryptographically more secure blockchain economy. In this talk, I will describe a long-term research program on analyzing four central capabilities that underlie our ability to manufacture representations: Objectification: our ability to denote arbitrary entities as ``objects”, from elementary particles to our society, the U.S. economy, or an entire galaxy or a (meta)universe. Social Interaction: our ability to confer agenthood on both inanimate and animate objects, treating objects not as passive containers of data, but actors defined by social interactions. Metaphors: our ability to use analogies to map unfamiliar situations – understanding an alien civilization with an unknown language -- to ones we are familiar with from our experience. Diagrammatic representations: our ability to draw figures, record a tune as a formal musical score, or write equations reveal how diagrams are a universal cognitive calculus for us. We use the abstract mathematics of category theory to characterize universal properties that define these four core capabilities. Objects in a category are modeled as actors, whose properties emerge from their social interaction with other objects, modeled as morphisms. Functors and natural transformations play the role of metaphors in human language. Adjunctions across categories plays the role of analogies. Diagrams are special functors that map from indexing categories to domain categories and define universal constructions for reasoning about interactions. We illustrate our framework by manufacturing novel representations for modeling causality and conditional independence, creativity and imagination, learning, language, and decision-making. *Speaker Bio:* Sridhar Mahadevan has been thinking about AI every waking day for the past 40 years, since he wrote his first machine learning program in 1982 at the Indian Institute of Technology, Kanpur. He was elected Fellow of AAAI for significant contributions in several areas of machine learning. He has published widely across many subfields of AI, including a book on robot learning in 1993 (Springer), and a book on representation discovery in 2007 (Morgan-Claypool). He is currently working on his third book on Universal AI. He is a Director at Adobe Research, and a Research Professor at the University of Massachusetts, Amherst. *Please watch this space for future AI Seminars :* * https://eecs.oregonstate.edu/ai-events <https://eecs.oregonstate.edu/ai-events>* 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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Fwd: [Sp-group] SP Seminar Summer 2022 beginning on 14th June !
by Fu, Xiao 03 Jun '22

03 Jun '22

Hi all, just in case you hope to attend the summer SP seminar. ======================= Xiao Fu, Assistant 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/ Begin forwarded message: From: "Shrestha, Sagar" <shressag(a)oregonstate.edu<mailto:shressag@oregonstate.edu>> Subject: [Sp-group] SP Seminar Summer 2022 beginning on 14th June ! Date: June 3, 2022 at 4:02:12 PM PDT To: sp-group(a)lists.oregonstate.edu<mailto:sp-group@lists.oregonstate.edu> Hello all, We are happy to announce that the Signal Processing seminar 2022 will start from June 14th, 2022. The seminar will be held in-person, weekly in the month of June and biweekly in July and August. We will have our first talk of the seminar by Dr. Xiao Fu. The talk is scheduled at 4pm to 5pm on June 14th, Tuesday. The talk will be held in-person in KEC 1005. Please see below for the details of the talk. Speaker: Dr. Xiao Fu Title: Understanding Multiview and Self-Supervised Representation Learning: A Nonlinear Mixture Identification Perspective Abstract: Central to representation learning is to succinctly represent high-dimensional data using the “essential information’’ while discarding the “redundant information”. Properly formulating and approaching this objective is critical to fending against overfitting, and can also benefit many important tasks such as domain adaptation and transfer learning. This talk aims to deepen understanding of representation learning and using the gained insights to come up with a new learning method. In particular, attention will be paid to two representation learning paradigms using multiple views of data, as both naturally acquired (e.g., image and audio) and artificially produced (e.g., via adding different noise to data samples) multiview data have empirically proven useful in producing essential information-reflecting vector representations. Natural views are often handled by multiview analysis tools, e.g., (deep) canonical correlation analysis [(D)CCA], while the artificial ones are frequently used in self-supervised learning (SSL) paradigms, e.g., BYOL and Barlow Twins. However, the effectiveness of these methods is mostly validated empirically, and more insights and theoretical underpinnings remain to be discovered. In this talk, an intuitive generative model of multiview data is adopted, where the views are different nonlinear mixtures of shared and private components. Since the shared components are view/distortion-invariant, such components may serve for representing the essential information of data in a non-redundant way. Under this model, a key module used in a suite of DCCA and SSL paradigms, namely, latent correlation maximization, is shown to guarantee the extraction of the shared components across views (up to certain ambiguities). It is further shown that the private information in each view can be provably disentangled from the shared using proper regularization design---which can facilitate tasks such cross-view translation and data generation. A finite sample analysis, which has been rare in nonlinear mixture identifiability study, is also presented. The theoretical results and newly designed regularization are tested on a series of tasks. Biography: Xiao Fu received the Ph.D. degree in Electronic Engineering from The Chinese University of Hong Kong (CUHK), Shatin, N.T., Hong Kong, in 2014. He was a Postdoctoral Associate with the Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA, from 2014 to 2017. Since 2017, he has been an Assistant Professor with the School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA. His research interests include the broad area of signal processing and machine learning.Dr. Fu received a Best Student Paper Award at ICASSP 2014, and was a recipient of the Outstanding Postdoctoral Scholar Award at University of Minnesota in 2016. His coauthored papers received Best Student Paper Awards from IEEE CAMSAP 2015 and IEEE MLSP 2019, respectively. He received the National Science Foundation CAREER Award in 2022. He serves as a member of the Sensor Array and Multichannel Technical Committee (SAM-TC) of the IEEE Signal Processing Society (SPS). He is also a member of the Signal Processing for Multisensor Systems Technical Area Committee (SPMuS-TAC) of EURASIP. He is the Treasurer of the IEEE SPS Oregon Chapter. He serves as an Editor of Signal Processing and an Associate Editor of IEEE Transactions on Signal Processing. He was a tutorial speaker at ICASSP 2017 and SIAM Conference on Applied Linear Algebra 2021. Time: 4:00 PM to 5:00 PM Date: June 14, Tuesday, 2022 Location: KEC 1005 Have a nice end of the term ! Best, Sagar Shrestha _______________________________________________ sp-group mailing list sp-group(a)lists.oregonstate.edu<mailto:sp-group@lists.oregonstate.edu> https://lists.oregonstate.edu/mailman/listinfo/sp-group

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AI Alignment Reading Group on hold over the summer
by Quintin Pope 03 Jun '22

03 Jun '22

Hello everyone, I’ve decided to put the alignment reading group on hold from now until summer break is over. I look forward to meeting you all fall term! All the best, Quintin Pope Sent from my iPhone

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[CoRIS-List] OSU Robotics Seminar 06/03: Edward Wang, "Healthcare in the Age of Information Technology"
by Ross L. Hatton 31 May '22

31 May '22

Hi All, Our final OSU Robotics seminar of the term will be presented by Edward Wang from the University of California, San Diego, on "Healthcare in the Age of Information Technology: Opportunities and Real-world Challenges". The seminar will be from 10-11am, followed by an 11-11:30 student-only Q&A session. The seminar will be an *in person* event, simulcast over Zoom, and will be immediately followed by a half-hour students-only Q&A session with the speaker. June 3, 10am LINC 268 https://oregonstate.zoom.us/j/93908195212?pwd=cEdoQVdac0JxMUpOSS9xVzZjVG5xU… <https://oregonstate.zoom.us/j/93908195212?pwd=cEdoQVdac0JxMUpOSS9xVzZjVG5xU…> ----------------- Seminar Title: Healthcare in the Age of Information Technology: Opportunities and Real-world Challenges Abstract: With the ubiquity of digital technologies that is seeping into every fabric of our lives, we are presented with an opportunity to harness the economy of scale that is driving down the cost of distributing a wide array of sensors that can be repurposed and reprogrammed. This effect is particularly impactful for the way healthcare is and can be designed, not only as an opportunity to completely change the way we deliver care, but also the potential issues it may cause. Smartphones stand to become the Tricorder that will deliver an ever-expanding library of health metrics through software applications. The induction of wearable devices like the Fitbit and the Apple Watch is introducing passive monitoring of physiological signals that once was exclusive to ICUs (not even primary care) at unprecedented scales. Even something as “mundane” today as a video call, is creating an entire new paradigm of telemedicine. In this talk, I will share with you late breaking work that my group, the DigiHealth Lab is doing around discovering new digital biomarkers for Alzheimer’s screening, improving breastfeeding efficacy for mothers, making blood pressure monitoring as continuous, and passively enabling mass monitoring of individuals for upper-respiratory infections with wearable sensors. It is in this context of developing and designing these technologies beyond the theory and simulation, however, where we are faced with the real-world challenges of bringing such a distributed and federated healthcare system together. I hope to use this opportunity to not only share with you the impact that digital health technologies will make, but also the difficulties and challenges to make such technologies work safely, inclusively, and ultimately better. Bio: Dr. Edward Jay Wang is an Assistant Professor of Electrical and Computer Engineering & The Design Lab at UC San Diego, where he directs the DigiHealth Lab. He earned his Ph.D. from the University of Washington under Dr. Shwetak Patel and his B.S. from Harvey Mudd College His research work explores practical solutions to address real-world medical needs drawn from collaborations with clinicians and world health organizations, but solved in new, creative ways that leverage state-of-the-art applied machine learning, embedded systems, and mobile sensors. His work is currently being funded by the NIH, National Institute of Aging, and UC San Diego Galvanizing Engineering in Medicine (GEM). He has been the recipient of the Google Faculty Research Scholar Award, NSF Graduate Fellowship Award, the ARCS Foundation Fellowship, and 5 publication paper awards. He has published in top-tier venues in Computer Science and Biology, including ACM UbiComp, CHI, UIST, and Nature Scientific Reports. He is currently a member of the UCSD Center for Wireless & Populational Health Systems and a Board of Director of the Center for Mental Health Technologies. =========== Ross L. Hatton Associate Professor, Robotics and Mechanical Engineering Collaborative Robotics and Intelligent Systems Institute Oregon State University coris.oregonstate.edu <http://coris.oregonstate.edu/> research.engr.oregonstate.edu/lram/ <http://research.engr.oregonstate.edu/lram/> rosslhatton.com <http://rosslhatton.com/> ross.hatton(a)oregonstate.edu

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AI seminar: Jun 1, 2022
by Mangannavar, Rajesh Devaraddi 31 May '22

31 May '22

Dear all, Our next AI seminar on *"Enabling Humans and Robots to Predict the Other’s Behavior from Small Datasets" *by Vaibhav Unhelkar is scheduled to be on June 1st (tomorrow), 1-2 PM PST (Add to google calendar <https://calendar.google.com/event?action=TEMPLATE&tmeid=YXExYTZuY2NiZXRhbHJ…> ). It will be followed by a 30-minute Q&A session by the graduate students. Zoom Link: https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5U… *Enabling Humans and Robots to Predict the Other’s Behavior from Small Datasets* Vaibhav Unhelkar Assistant Professor of Computer Science Rice University *Abstract:* We are steadily moving towards a future where humans work with robotic assistants, teammates, and even trainers. Towards realizing this future and mitigating its adverse side effects, I will share two techniques that together enable humans and robots to predict the other’s task-oriented behavior. First, I will present AI Teacher: an interactive machine teaching framework to assist humans in acquiring mental models of robots. By building upon human’s natural ability to model others (Theory of Mind), the AI Teacher framework reduces the number of interactions it takes for humans to arrive at predictive models of robot behavior. Second, I will discuss BTIL, an imitation learning approach for enabling robots to arrive at predictive models of humans’ collaborative behavior. Discussions of both these techniques will highlight the need as well as solutions for sample-efficient learning in settings of human-robot collaboration. *Speaker Bio:* Vaibhav Unhelkar is an Assistant Professor of Computer Science at Rice University, where he leads a research group in the emerging area of Human-Centered AI. Ongoing research in his group includes development of algorithms and systems that enable “robots to work with humans” and “humans to become informed users of robots.” Unhelkar received his doctorate in Autonomous Systems at MIT (2020) and completed his undergraduate education at IIT Bombay (2012). He serves as an Associate Editor for IEEE Robotics and Automation Letters and is the recipient of JPMC AI Early Career Researcher Award and AAMAS 2022 Best PC Member Award. Before joining Rice, Unhelkar worked as a robotics researcher at X, the Moonshot Factory (formerly, Google X). *Please watch this space for future AI Seminars :* * https://eecs.oregonstate.edu/ai-events <https://eecs.oregonstate.edu/ai-events>* 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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[CoRIS-List] Capprin Bass' Masters Defense: June 8th 10:00AM
by Bass, Capprin James 30 May '22

30 May '22

Hello everyone, I will be defending my masters thesis next Wednesday, June 8th at 10:00AM. You are all welcome to attend; details are below. *When: *Wednesday, June 8th at 10:00AM PDT *Where: *Rogers 226, or simulcast over Zoom *Title: *Geometric Optimization Methods for Mobile Systems *Abstract:* When studying robot systems, it is common to ask about optimal approaches to accomplish a given task. In the context of mobile systems, particularly biomimetic systems, optimization tasks are closely related to the relevant dynamics of locomotion. In this thesis, building on prior work from the geometric mechanics community, we introduce several methods for optimization in the context of mobile systems. Each topic is covered in a separate manuscript: in the first manuscript, we investigate how error accrues when using second order displacement approximations for planar mobile systems, and how coordinate optimization reduces error by accounting for higher order error effects. In the second manuscript, we extend planar coordinate optimization to the group of 3D rotations, clarifying methods presented in prior work. Contributions introduced here further reduce error inherent to displacement approximations. In the third and final manuscript, we use recent robot gait optimization methods to perform dimensionality reduction on robot shape spaces, respecting useful dynamics. The resulting 2D shape spaces have useful topological and geometric properties, and could act as a building block for more general robot dimensionality reduction. Finally, the conclusion summarizes the shared themes of the included papers, and discusses in general the potential of geometric mechanics in future robotics work. *Advisor: *Dr. Ross L. Hatton *Committee: *Dr. Ross L. Hatton, Dr. Joseph Davidson, Dr. Ren Guo, Dr. Barbara Simpson *Zoom Meeting:* Capprin Bass is inviting you to a scheduled Zoom meeting. Topic: Capprin Bass' Masters Thesis Defense Time: Jun 8, 2022 10:00 AM Pacific Time (US and Canada) Join Zoom Meeting https://oregonstate.zoom.us/j/94321608275?pwd=NnFOUHkyREhyL1R0YmdBTm9RcjFxZ… Password: 255135 Phone Dial-In Information +1 971 247 1195 US (Portland) +1 253 215 8782 US (Tacoma) +1 301 715 8592 US (Washington DC) Meeting ID: 943 2160 8275 Join by Polycom/Cisco/Other Room System 94321608275(a)zoomcrc.com Thanks all, Capprin Bass

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AI seminar: May 25, 2022
by Mangannavar, Rajesh Devaraddi 27 May '22

27 May '22

Dear all, Our next AI seminar on *"Toward Addressing Evaluation and Explanation Challenges in Scientific ML Applications" *by Shusen Liu is scheduled to be on May 25th, 1-2 PM PST (Add to google calendar <https://calendar.google.com/event?action=TEMPLATE&tmeid=YXExYTZuY2NiZXRhbHJ…> ). It will be followed by a 30-minute Q&A session by the graduate students. Zoom Link: https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5U… *Toward Addressing Evaluation and Explanation Challenges in Scientific ML Applications* Shusen Liu Research Scientist Machine Intelligence Group Lawrence Livermore National Laboratory *Abstract:* Although the influence of deep learning in scientific domains is unmistakable, there are still fundamental barriers to utilizing these complex models for scientific discovery due in part to our inability to directly translate their predictive capabilities into scientific understanding. The root of the problem is twofold: 1) the challenge to evaluate the model in the context of the application; 2) the difficulties of reasoning about such models in terms that domain scientists can easily understand for knowledge extraction. This talk will provide a closer look at these unique challenges associated with applying deep learning to scientific applications. And cover some of our works for addressing the evaluation and explanation challenges in Scientific ML. Specifically, we will show how topological data analysis plays a crucial role in evaluating deep surrogate models for fusion science, and how deep generative models allow us to explore hypothetical materials and obtain actionable explanations that lead to improved material performance. *Speaker Bio:* Shusen Liu is a research scientist with the Machine Intelligence Group at the Lawrence Livermore National Laboratory (LLNL). His interests include fundamental research in explainable AI and high-dimensional data visualization, as well as their impact on scientific applications for advancing domain understanding. He received a Ph.D.in computing from the University of Utah in 2017. *Please watch this space for future AI Seminars :* * https://eecs.oregonstate.edu/ai-events <https://eecs.oregonstate.edu/ai-events>* 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: Jun 1, 2022
by Mangannavar, Rajesh Devaraddi 26 May '22

26 May '22

Dear all, Our next AI seminar on *"Enabling Humans and Robots to Predict the Other’s Behavior from Small Datasets" *by Vaibhav Unhelkar is scheduled to be on June 1st, 1-2 PM PST (Add to google calendar <https://calendar.google.com/event?action=TEMPLATE&tmeid=YXExYTZuY2NiZXRhbHJ…> ). It will be followed by a 30-minute Q&A session by the graduate students. Zoom Link: https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5U… *Enabling Humans and Robots to Predict the Other’s Behavior from Small Datasets* Vaibhav Unhelkar Assistant Professor of Computer Science Rice University *Abstract:* We are steadily moving towards a future where humans work with robotic assistants, teammates, and even trainers. Towards realizing this future and mitigating its adverse side effects, I will share two techniques that together enable humans and robots to predict the other’s task-oriented behavior. First, I will present AI Teacher: an interactive machine teaching framework to assist humans in acquiring mental models of robots. By building upon human’s natural ability to model others (Theory of Mind), the AI Teacher framework reduces the number of interactions it takes for humans to arrive at predictive models of robot behavior. Second, I will discuss BTIL, an imitation learning approach for enabling robots to arrive at predictive models of humans’ collaborative behavior. Discussions of both these techniques will highlight the need as well as solutions for sample-efficient learning in settings of human-robot collaboration. *Speaker Bio:* Vaibhav Unhelkar is an Assistant Professor of Computer Science at Rice University, where he leads a research group in the emerging area of Human-Centered AI. Ongoing research in his group includes development of algorithms and systems that enable “robots to work with humans” and “humans to become informed users of robots.” Unhelkar received his doctorate in Autonomous Systems at MIT (2020) and completed his undergraduate education at IIT Bombay (2012). He serves as an Associate Editor for IEEE Robotics and Automation Letters and is the recipient of JPMC AI Early Career Researcher Award and AAMAS 2022 Best PC Member Award. Before joining Rice, Unhelkar worked as a robotics researcher at X, the Moonshot Factory (formerly, Google X). *Please watch this space for future AI Seminars :* * https://eecs.oregonstate.edu/ai-events <https://eecs.oregonstate.edu/ai-events>* 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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