- Ececourtesy-list - ecn.purdue.edu

ACS Faculty Candidate Yingying Li, UIUC: Efficient and Safe Online Learning for Physical systems ~ Thur., Feb. 24, 10:30A, PGSC
by Satterfield, Mary Ann 20 Feb '22

20 Feb '22

[cid:image004.png@01D8269E.8A96B350] Mary Ann Satterfield Sr. Administrative Assistant Elmore Family School of Electrical and Computer Engineering Electrical Engineering Building 465 Northwestern Ave., BHEE 326B West Lafayette, IN 47907 o: 765-494-6389 m: 765-490-6392 f: 765-494-2706 [BB871DD1]<https://www.purdue.edu/?utm_source=signature&utm_medium=email&utm_campaign=…>

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Neil Armstrong Distinguished Visiting Fellow Guest Speaker: "Photonic Time Crystals Crash Course", Moti Segev, Monday, February 21 at 10:00AM, BRK 1001
by Hodges, Kendra Renee 18 Feb '22

18 Feb '22

Dear All, Please join us on Monday, February 21, 2022, at the Birck Nanotechnology Center in RM 1001 for guest speaker, Moti Segev. 10:00am -11:00am - Prof Moti Segev Crash Course 11:30am - 12:30pm - Prof Segev Seminar See attached flyers for more details These events are hosted by Alexandra Boltasseva Light refreshments will be served. [cid:image003.png@01D824F6.4F728690] Kendra Hodges | Administrative Assistant Elmore Family School of Electrical and Computer Engineering Purdue University Materials and Electrical Engineering Building 501 Northwestern Avenue, Suite 150 West Lafayette, Indiana 47907 Phone: 765-494-3540 Email: khodges(a)purdue.edu [download]

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Faculty Candidate Seminar: "Quantum Transport Simulation for 21st Century Electronics", Tillmann Kubis (Purdue University), Wednesday, February 23 at 10:30AM, WANG 1004
by Hodges, Kendra Renee 18 Feb '22

18 Feb '22

In-person attendance is encouraged. [cid:image001.png@01D824DF.C26B52B0] Kendra Hodges | Administrative Assistant Elmore Family School of Electrical and Computer Engineering Purdue University Materials and Electrical Engineering Building 501 Northwestern Avenue, Suite 150 West Lafayette, Indiana 47907 Phone: 765-494-3540 Email: khodges(a)purdue.edu [download]

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Prof of Engr Practice Faculty Candidate: Noori Kim
by Satterfield, Mary Ann 18 Feb '22

18 Feb '22

***In-person attendance is encouraged.*** https://purdue-edu.zoom.us/j/98141148554 Mike Zoltowski, Host ~ mikedz(a)purdue.edu<mailto:mikedz@purdue.edu> or michael.zoltowski(a)gmail.com<mailto:michael.zoltowski@gmail.com> [cid:image001.png@01D824C7.C18C5140]

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REMINDER: Guest Speaker Seminar: "Bounded Rationality Methods in Reinforcement Learning-driven Autonomy", Kyriakos Vamvoudakis (The Daniel Guggenheim School of Aerospace Engineering at Georgia Tech), Thursday, February 17 at 10:30AM, POTR 234 (Fu Room)
by Hodges, Kendra Renee 17 Feb '22

17 Feb '22

Good morning, here is the announcement with the seminar zoom link however, in person attendance is appreciated if possible. [cid:image001.png@01D823DF.D27C72C0] Kendra Hodges | Administrative Assistant Elmore Family School of Electrical and Computer Engineering Purdue University Materials and Electrical Engineering Building 501 Northwestern Avenue, Suite 150 West Lafayette, Indiana 47907 Phone: 765-494-3540 Email: khodges(a)purdue.edu<mailto:khodges@purdue.edu> [download]

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Prof of Engrg Practice Faculty Candidate Noori Kim, Newcastle Univ/Singapore: "Fundamentals of Hearing Measurement Systems" and "Finite State Machines." Tue, Feb. 22, 10:30a, PGSC
by Satterfield, Mary Ann 16 Feb '22

16 Feb '22

[cid:image004.png@01D82356.5ED226A0] Zoom: https://purdue-edu.zoom.us/j/98141148554?pwd=dE5oaXVFSjk0ZEV3R3VmZGhXYWZEZz… Mary Ann Satterfield Sr. Administrative Assistant Elmore Family School of Electrical and Computer Engineering Electrical Engineering Building 465 Northwestern Ave., BHEE 326B West Lafayette, IN 47907 o: 765-494-6389 m: 765-490-6392 f: 765-494-2706 [BB871DD1]<https://www.purdue.edu/?utm_source=signature&utm_medium=email&utm_campaign=…>

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ACS Faculty Candidate Lars Lindemann, PostDoc/UPenn: Safe AI-Enabled Autonomy, Mon., Feb. 21, 10:30A, PGSC
by Satterfield, Mary Ann 11 Feb '22

11 Feb '22

[cid:image001.png@01D81F6C.726832B0] Mary Ann Satterfield Sr. Administrative Assistant Elmore Family School of Electrical and Computer Engineering Electrical Engineering Building 465 Northwestern Ave., BHEE 326B West Lafayette, IN 47907 o: 765-494-6389 m: 765-490-6392 f: 765-494-2706 [BB871DD1]<https://www.purdue.edu/?utm_source=signature&utm_medium=email&utm_campaign=…>

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ACS Faculty Candidate Lars Lindemann
by Satterfield, Mary Ann 11 Feb '22

11 Feb '22

If you'd like to meet with this candidate, please contact Philip E. Paré philpare(a)purdue.edu<mailto:philpare@purdue.edu> or (765) 496-4818 ________________________________ Faculty Candidate Seminar - Autonomous and Connected Systems [cid:image002.jpg@01D81F6B.C2CC99E0]Lars Lindemann Postdoctoral Researcher University of Pennsylvania Monday, February 21, 2022 Presentation: 10:30 A.M. - 11:30 A.M. Q & A: 11:30 A.M. - 12:00 P.M. Purdue Graduate Student Center 504 Northwestern Ave. ~ Room 105A & B https://purdue-edu.zoom.us/j/98587059936 Safe AI-Enabled Autonomy Abstract: AI-enabled autonomous systems show great promise to enable many future technologies such as autonomous driving, intelligent transportation, and robotics. Over the past years, there has been tremendous success in the development of autonomous systems, which was especially accelerated by the computational advances in machine learning and AI. At the same time, however, new fundamental questions were raised regarding the safety and reliability of these increasingly complex systems that often operate in uncertain and dynamic environments. In this seminar, I will provide new insights and exciting opportunities to address these challenges. In the first part of the seminar, I will present a data-driven optimization framework to learn safe control laws for dynamical systems. For most safety-critical systems such as self-driving cars, safe expert demonstrations in the form of system trajectories that show safe system behavior are readily available or can easily be collected. At the same time, accurate models of these systems can be identified from data or obtained from first order modeling principles. To learn safe control laws, I present a constrained optimization problem with constraints on the expert demonstrations and the system model. Safety guarantees are provided in terms of the density of the data and the smoothness of the system model. Two case studies on a self-driving car and a bipedal walking robot illustrate the presented method. In the past years, it was shown that neural networks are fragile and that their use in AI-enabled systems has resulted in systems taking excessive risk. The second part of the seminar is motivated by this fact and presents a data-driven verification framework to quantify and assess the risk of AI-enabled systems. I particularly show how risk measures, classically used in finance, can be used to quantify the risk of not being robust to failure, and how we can estimate this risk from data. We will compare and verify four different neural network controllers in terms of their risk for a self-driving car. I will conclude by sharing exciting research directions in this area. Bio: Lars Lindemann is currently a Postdoctoral Researcher in the Department of Electrical and Systems Engineering at the University of Pennsylvania. He received his B.Sc. degrees in Electrical and Information Engineering and his B.Sc. degree in Engineering Management in 2014 from the Christian-Albrechts-University (CAU), Kiel, Germany. He received his M.Sc. degree in Systems, Control and Robotics in 2016 and his Ph.D. degree in Electrical Engineering in 2020, both from KTH Royal Institute of Technology, Stockholm, Sweden. His current research interests include systems and control theory, formal methods, data-driven control, and autonomous systems. Lars received the Outstanding Student Paper Award at the 58th IEEE Conference on Decision and Control and was a Best Student Paper Award Finalist at the 2018 American Control Conference. He also received the Student Best Paper Award as a co-author at the 60th IEEE Conference on Decision and Control.

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TODAY, 10:30A, PGSC: ACS Faculty Candidate Presentation: Luana Ruiz, PhD Candidate, UPenn: Machine Learning on Large-Scale Graphs
by Satterfield, Mary Ann 10 Feb '22

10 Feb '22

*** In person attendance is encouraged! *** Host: Maggie Zhu ~ zhu0(a)purdue.edu<mailto:zhu0@purdue.edu> Faculty Candidate Seminar - Autonomous and Connected Systems [cid:image001.png@01D818E5.3FF8FF90]Luana Ruiz Ph.D. Candidate Dept. of Electrical and Systems Engineering University of Pennsylvania Thursday, Feb. 10, 2022 Presentation: 10:30 A.M. - 11:30 A.M. Q & A: 11:30 A.M. - 12:00 P.M. Purdue Graduate Student Center 504 Northwestern Ave. ~ Room 105A & B https://purdue-edu.zoom.us/meeting/93351017078 Machine Learning on Large-Scale Graphs Abstract: Graph neural networks (GNNs) are successful at learning representations from most types of network data but suffer from limitations in large graphs, which do not have the Euclidean structure that time and image signals have in the limit. Yet, large graphs can often be identified as being similar to each other in the sense that they share structural properties. Indeed, graphs can be grouped in families converging to a common graph limit -- the graphon. A graphon is a bounded symmetric kernel which can be interpreted as both a random graph model and a limit object of a convergent sequence of graphs. Graphs sampled from a graphon almost surely share structural properties in the limit, which implies that graphons describe families of similar graphs. We can thus expect that processing data supported on graphs associated with the same graphon should yield similar results. In my research, I formalize this intuition by showing that the error made when transferring a GNN across two graphs in a graphon family is small when the graphs are sufficiently large. This enables large-scale graph machine learning by transference: training GNNs on moderate-scale graphs and executing them on large-scale graphs. Bio: Luana Ruiz received the B.Sc. degree in electrical engineering from the University of São Paulo, Brazil, and the M.Eng. degree in electrical engineering from the École Supérieure d'Electricité (now CentraleSupélec), France, in 2017. She is currently a Ph.D. candidate with the Department of Electrical and Systems Engineering at the University of Pennsylvania. Her primary research interests are in large-scale graph machine learning and the mathematical foundations of deep learning. Luana was awarded an Eiffel Excellence scholarship from the French Ministry for Europe and Foreign Affairs between 2013 and 2015, and nominated an iREDEFINE fellow in 2019 and a MIT EECS Rising Star in 2021. She has also received two Best Student Paper awards at the European Signal Processing Conference (EUSIPCO), in 2019 and 2021. Mary Ann Satterfield Sr. Administrative Assistant Elmore Family School of Electrical and Computer Engineering Electrical Engineering Building 465 Northwestern Ave., BHEE 326B West Lafayette, IN 47907 o: 765-494-6389 m: 765-490-6392 f: 765-494-2706 [BB871DD1]<https://www.purdue.edu/?utm_source=signature&utm_medium=email&utm_campaign=…>

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REMINDER: Faculty Candidate Seminar: "From Energy-Efficient Digital SoCs to Adaptive Neuromodulation: Opportunities in Co-Design and Run-time Computing", Visvesh Sathe (University of Washington, Seattle), Thursday, February 10 at 1:30PM, Wang 1004
by Hodges, Kendra Renee 10 Feb '22

10 Feb '22

Good morning, here is the announcement with the seminar zoom link however, in person attendance is appreciated if possible. [cid:image001.png@01D81E56.978CD6C0] Kendra Hodges | Administrative Assistant Elmore Family School of Electrical and Computer Engineering Purdue University Materials and Electrical Engineering Building 501 Northwestern Avenue, Suite 150 West Lafayette, Indiana 47907 Phone: 765-494-3540 Email: khodges(a)purdue.edu<mailto:khodges@purdue.edu> [download]

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