February 2022 - Ececourtesy-list

Seminar: Jim Spohrer (IBM), Service Innovation in the AI Era
by Hegewald, Lynn P 07 Feb '22

07 Feb '22

Please join us for the following seminar. Additional details in the attached flyer. Service Innovation in the AI Era Tuesday, February 8, 2022 9:30 am ET Zoom link<https://purdue-edu.zoom.us/j/93305345902> Jim Spohrer, Retired IBM executive, and ISSIP.org, International Society of Service Innovation Professionals Abstract What if you had a hundred workers ready to assist you with your startup, both launch and ongoing pivots? To help you answer this question, this talk shares a roadmap for the future of AI and service science. Over the next forty years, AI systems will become more capable and less expensive, progressing through stages: tool, assistant, collaborator, coach, mediator, for the roughly two thousands types of occupation that drive the US economy (O*NET). Service innovation is about increasing the quantity and quality of mutually beneficial interactions as people, businesses, and nations adapt to accelerating change. In the end, learning to invest time, effort, and other resources is fundamentally important. The life of an entrepreneur is about learning to invest in getting better at competing for collaborators – customers, partners, investors. Not surprisingly, this is exactly what is expected of innovators and leaders in large corporations as well.

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Faculty Candidate Seminar: "Ferro-Electronics: From Memory to Computing", Kai Ni (Rochester Institute of Technology), Monday, February 7 at 1:30PM, WANG 1004
by Hodges, Kendra Renee 07 Feb '22

07 Feb '22

[cid:image002.png@01D811F1.92B97D90] 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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TODAY, 10:30a, PGSC: ACS Faculty Candidate Nak-seung Patrick Hyun, RA/PostDoc, Harvard: Autonomous Control of Extreme Behaviors in Bio-Inspired Robotics
by Satterfield, Mary Ann 07 Feb '22

07 Feb '22

[cid:image005.jpg@01D816BD.43DC9830] Faculty Candidate Seminar - Autonomous and Connected Systems [cid:image001.png@01D818E6.D18A34F0]Dr. Nak-Seung Patrick Hyun Research Associate (Postdoctoral Fellow) Harvard University Monday, February 7, 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/97086184212 Autonomous Control of Extreme Behaviors in Bio-Inspired Robotics Abstract: Highly agile and extreme behaviors of many biological systems offer examples for future research directions to target similar mobility in bio-inspired robots understanding of the complex dynamics and subsequent design of a robust and adaptive control framework. Examples of extreme behaviors in biological systems are the fast oscillation-driven maneuvers of bees flapping their wings around 200 Hz and the rapid impulsive striking of mantis shrimp releasing their stored potential energy within milliseconds. The challenges for control of robots with similar extreme behaviors lie in the highly nonlinear dynamics operating over multiple timescales. Specifically, one has to account for fast dynamics (extreme motions) and slow dynamics (time-averaged motion or slower drift in the system), and the time-varying actuation model in the high-frequency regime (fast-dynamics) vs the low-frequency regime (slow dynamics). This talk will address the control-theoretic aspects of dealing with such challenges in bio-inspired robots based on first principles in mathematical system theory. The first part of this talk will address the recent progress on controlling the Harvard Robobee, an insect scale flapping-wing vehicle that flaps its wings around 150Hz. In addition, the recent findings in the nonlinear modeling of the dynamic principles of mantis shrimp strike will be covered, which allows the striking speed to reach 27 m/s within a few milliseconds. The second part of this talk will address the causality of modeling nonlinear impulsive systems, which utilize a singular impulsive contact force in nonlinear mechanical system modeling. The third part of this talk will introduce the recent work on safe trajectory optimization and multi-agent system control, envisioning the future of swarms of flapping wing vehicles. Lastly, I will conclude this talk with future research on the control autonomy of extreme behaviors in bio-inspired robotics. Bio: Nak-seung Patrick Hyun is a research associate at the Harvard Microrobotics Laboratory, hosted by Robert J. Wood. His research focuses on the control-theoretic aspects of bio-inspired robots, emphasizing systems with extreme behaviors such as flapping vehicles and impulsive systems. He is interested in the broad range of nonlinear control, including optimization-based control, geometric control, and contraction-based control. His research program provides a cyclic learning cycle between biology, mathematical system theory, and robotics. He received a Ph.D. in electrical and computer engineering in 2018, an M.S. degree in mathematics in 2013, and an M.S. degree in electrical engineering in 2013 from the Georgia Institute of Technology. His previous research at Georgia Tech addresses a new framework of causal modeling of impulsive systems and optimal safe path planning for multi-agent systems. He was recognized as an outstanding graduate teaching assistant by the Georgia Tech ECE Department in 2011.

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Seminar: Accelerating Digital Transformation Using AI & Automation - Utpal Mangla, IBM
by Hegewald, Lynn P 07 Feb '22

07 Feb '22

Please join us for the following seminar. Additional details in the attached flyer. Accelerating Digital Transformation Using AI & Automation in a Post-COVID World Utpal Mangla, GM, Telco Industry & EDGE Cloud (IBM) Thursday February 10, 2022 9:30 AM ET * Webex<https://purdue.webex.com/purdue/j.php?MTID=mc5b44dbeb59bf381223effc2c6fd4119> Abstract Businesses across all sectors were on a digital transformation journey. They were working on their multi-year roadmap until COVID-19 forced companies to pivot quickly. It became critical for organizations to swiftly accelerate the digital transformation, change how we work, and more importantly, rapidly create a new set of skillsets for jobs of the future. Organizations are looking for ways to scale digital transformation in a post-COVID environment. Open innovation using AI, automation and analytics is CRITICAL for enabling industries to leapfrog, create the escape velocity and build momentum for exponential (quantum) leaps. Bio Utpal Mangla ( MBA, PEng, CMC, ITCP, PMP, ITIL, CSM, FBCS ) is a General Manager responsible for Telco Industry & EDGE Clouds in IBM. Prior to that, he was the Vice President and Senior Partner in IBM. He is a recognized Global Leader of IBM's Telecommunications, Media and Entertainment (TME) Industry. He is focussed on driving AI, 5G EDGE, Hybrid Cloud and Blockchain Innovations for TME clients worldwide. In his role as senior executive in the business and thought leader in emerging technologies, Utpal's mission is to fuel growth by building, selling and implementing differentiated competitive market service solution offerings to meeting critical business imperatives of our customers.

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Faculty Candidate Seminar: "Electro-Fluidics for Health and Environmental Monitoring", Mehdi Javanmard (Rutgers University), Wednesday, February 9 at 9:30AM, MJIS1001/Virtual
by Hodges, Kendra Renee 06 Feb '22

06 Feb '22

Mehdi Javanmard is a BME faculty candidate with potential interest in ECE research areas. [cid:image005.jpg@01D819F4.8C64A3B0] Karen Jurss|Administrative Manager Elmore Family School of Electrical and Computer Engineering Purdue University Materials and Electrical Engineering Building 501 Northwestern Avenue, Suite 150 West Lafayette, Indiana 47907 Office: (765) 494-3539 https://engineering.purdue.edu/ECE [4BEDF607]

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REVISED W/ ITINERARY LINK: ACS Faculty Candidate Presentation: Luana Ruiz, PhD Candidate, UPenn: Machine Learning on Large-Scale Graphs, Thu, Feb 10, 10:30a, PGSC
by Satterfield, Mary Ann 03 Feb '22

03 Feb '22

My apologies! I neglected to include a link to the itinerary<https://docs.google.com/document/d/1adrlT5mpl0B-YIsUt6ydY5WgU5JSG-j0TRhIbvB…>. If you'd like to meet with this candidate, please place the time, your name, and meeting place on the itinerary at https://docs.google.com/document/d/1adrlT5mpl0B-YIsUt6ydY5WgU5JSG-j0TRhIbvB… ________________________________ 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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ACS Faculty Candidate Presentation: Luana Ruiz, PhD Candidate, UPenn: Machine Learning on Large-Scale Graphs, Thu, Feb 10, 10:30a, PGSC
by Satterfield, Mary Ann 03 Feb '22

03 Feb '22

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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ACS Faculty Candidate Luana Ruiz, PhD Candidate. UPenn: Machine Learning on Large-Scale Graphs
by Satterfield, Mary Ann 03 Feb '22

03 Feb '22

Host: Maggie Zhu ~ zhu0(a)purdue.edu<mailto:zhu0@purdue.edu> Faculty Candidate Seminar - Autonomous and Connected Systems [cid:image001.png@01D818E3.C0A87D20]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.

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Canceled: PoP Faculty Candidate Presentation: Gary Viviani, VivatronX and Montana Tech Univ: Electronic Synthetic Atom; Autonomous Signals and Systems -- Thu, Feb 3, 10:30. A.M., PGSC
by Satterfield, Mary Ann 02 Feb '22

02 Feb '22

*** At the candidate's request, please keep this visit confidential. *** Gary Viviani Owner and Founder, VivatronX Bastrop, TX Thursday, February 3, 2022 10:30 A.M. - 11:30 A.M. Purdue Graduate Student Center 504 Northwestern Ave. ~ Room 105A & B https://purdue-edu.zoom.us/meeting/94166580119 Electronic Synthetic Atom Lecture Abstract: The ultimate autonomous system is an atom. This presentation describes what can be thought of as an electronic synthetic atom. Such a capability gives rise to applications that approximate the efficiency and effectiveness of naturally occurring systems. These revelations have been confirmed with actual prototype devices. The focus of the presentation will be to explain the theory, in the context of previously known results, and to highlight the new developments. These new developments are key to the ability to fabricate devices with desirable characteristics. Actual device performance, applications, and research directions will also be discussed. Autonomous Signals and Systems Analysis (sophomore/junior level subject matter) Teaching Abstract: This lecture will present standard material associated with signals and systems analysis from the perspective of an approach to synthesis. Synthesis is the basis for analysis. By describing how to design an autonomous approach to signal processing, the critical analysis capabilities are solidified and made apparent. Other related considerations will also be addressed. Bio: Dr. Viviani is an Associate Professor at Montana Technological University. He specializes in complex autonomous system control and the means for recognizing the associated information related patterns of interest. He has been developing complex autonomous systems for various industries including chemical, power, semiconductor, aerospace and defense. His work in semiconductor ion implantation autonomous control resulted in a system that sustains unmatched performance for producing most of the chips in the world. He has also developed a variety of smart weapons with desirable cognitive abilities. Some of this work included development of advanced drones. He was Vice President and Chief Scientist from 2006-2016 at Insitu, which is now a wholly owned subsidiary of the Boeing Company. At Insitu he was directly involved with the design and implementation of software and hardware systems for autonomous robotic airplanes. Early in his career he earned tenure as the Gulf States Utilities Research Professor at Lamar University. His research was pursued jointly at the university and utility. Much of it involved solving power system control problems, as well as developing some first-generation smart devices. He has various journal level publications. His most recent work is focused on achieving quantum like performance for dynamic information representation. Gary L. Viviani received his BSEE, MSEE and Ph.D. (Electrical and Computer Engineering) degrees from Purdue University in 1977, 1978 and 1980, respectively. Host: Mike Zoltowski ~ mikedz(a)purdue.edu<mailto:mikedz@purdue.edu>

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Guest Speaker Seminar: "Automated Analysis and Implementation for Modern Networks", Mina Arashloo (Cornell University), Wednesday, February 9 at 10:30AM, WANG 1004
by Hodges, Kendra Renee 01 Feb '22

01 Feb '22

[cid:image001.png@01D81757.DFCD0AA0] 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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