Keynote Speakers
Dr. Ermin Wei, Northwestern University
Ermin Wei is an Associate Professor at the Electrical and Computer Engineering Department and Industrial Engineering and Management Sciences Department of Northwestern University. She completed her PhD studies in Electrical Engineering and Computer Science at MIT in 2014, advised by Professor Asu Ozdaglar, where she also obtained her M.S. She received her undergraduate triple degree in Computer Engineering, Finance and Mathematics with a minor in German, from University of Maryland, College Park. Her team won the 2nd place in the GO-competition Challenge 1, an electricity grid optimization competition organized by Department of Energy. Wei's research interests include distributed optimization methods, convex optimization and analysis, smart grid, communication systems and energy networks and market economic analysis.
Dr. David Hoelzle, The Ohio State University
Prof. David Hoelzle is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the Ohio State University. He received his MS and PhD from the University of Illinois at Urbana-Champaign in 2007 and 2011, respectively, in Mechanical Science and Engineering and his BS from the Ohio State University in 2005 in Mechanical Engineering. Between his PhD and current position, he completed a post-doc in the Department of Integrative Biology and Physiology at the University of California, Los Angeles and held the position of Assistant Professor in the Department of Aerospace and Mechanical Engineering at the University of Notre Dame. His research interests lie in applied control theory and dynamics for applications in additive manufacturing, hybrid autonomous manufacturing, and surgical robotics. Prof. Hoelzle is a recipient of the 2016 CAREER Award, the 2016 Society of Manufacturing Engineers Outstanding Young Manufacturing Engineer Award, 2020 ASME Manufacturing Science and Engineering Conference Best Paper Award, and the 2023 OSU Dept. of Mechanical and Aerospace Engineering Ralph Boyer Young Achiever Alumni Award.
Keynote Lecture Abstracts
Flexible and Incentive Aligned Federated Learning Methods
Dr. Ermin Wei
Federated learning enables machine learning algorithms to be trained over decentralized edge devices without requiring the exchange of local datasets. We consider two scenarios in this talk. In the first scenario, we have cooperative agents running distributed optimization methods. Current literature fail to capture the heterogeneity in agents’ local computation capacities. We propose FedHybrid as a hybrid primal-dual method that allows heterogeneous agents to perform a mixture of first and second order updates. We prove that FedHybrid converges linearly to the exact optimal point for strongly convex functions. In the second scenario, we consider strategic agents with different data distributions. We analyze how the distribution of data affects agents' incentives to voluntarily participate and obediently follow traditional federated learning algorithms. We design a Faithful Federated Learning (FFL) mechanism based on FedAvg method and VCG mechanism which achieves (probably approximate) optimality, faithful implementation, voluntary participation, and balanced budget. Lastly, we analyze an alternative approach to align individual agent’s incentive to participate by allowing them to opt in or out. We propose a game theoretic framework and study the equilibrium properties with both rational and bounded rational agents.
Control, Optimization, and Network Analysis and Application to Two Metal Processing Technologies
Dr. David Hoelzle
The primary metal processing technologies that have enabled our modern era of infrastructure, transportation, healthcare, and consumer products have been largely unchanged for decades. This presentation will highlight two emerging changes to metal processing that are firmly rooted in robotics – metal additive manufacturing and hybrid autonomous manufacturing – and how controls, optimization, and network analysis can transform the current heuristic, open-loop approach to one with feedback-controlled regulation of part quality. The key objectives of the talk are to communicate the primary challenges in metal processing with respect to control, optimization, and network analysis and the implications for control system design. Two preliminary results in estimator and control application to the processes will elucidate potential improvements enabled by feedback control application and some unsolved problems. The talk will conclude with a projection on the future of metal processing and key challenges that will require expertise from the control, optimization, and networks community.