Welcome back golden bears! The annual Fall 2018 picnic is on September 20, 2018. IEOR undergraduate and graduate students are invited to come and enjoy free Top Dog with faculty and staff. The event will take place at the Upper Doe Terrance. See you there!
Upper Doe Terrace
3108 Etcheverry Hall
Abstract: High tech products such as smartphones or wearables are multi-generational in the sense that every year, a new version of the product is introduced, with some features that are shared with the previous year’s model as well as added functionality. This setting poses unique problems for manufacturers in making operational decisions regarding production and warranty servicing of these products. For each generation, at some point, a decision must be made to stop producing that version of the product. However, this decision often comes before the items themselves are out of warranty. How should companies manage future warranty claims? Professor Candace Yano and PhD student Erik Bertelli are focused on optimal production and warranty decisions in settings where warranty fulfillment may involve provision of new replacements of the same model, repair using spare parts inventory, and rebates for upgrading to a newer generation of product. This research will enable the manufacturer not only to minimize the expected costs for satisfying warranty claims, but also to reduce electronic waste from unnecessary replacement items and spare parts.
Bio: Erik Bertelli is a third year PhD student in the Industrial Engineering and Operations Research department at UC Berkeley. His research focuses on inventory control for multi-generational high-tech products withuncertain warranty obligations.
Matt Olfat - Fair Optimization: Deep Notions of Fairness in Machine Learning
Abstract: Classification algorithms such as Support Vector Machines (SVM) output an array of probabilities or score functions, which are then thresholded to generate binary predictions. The prevalence of these machine learning methods in commercial use has raised concerns of their ability to codify and amplify biases existent in data. Previous techniques to address this problem mainly rely on pre- or post-processing steps; they are often blind to score functions underlying predictions, making their fairness guarantees non-robust to changes in thresholding. Furthermore, pre- and post-processing is inherently greedy and thus may be prone to excessively penalizing accuracy. In response, we propose the framework of Fair Optimization to enforce robust fairness at training time. The goal of this approach is to capture independence through constraints tractable in an optimization framework, effectively controlling the discriminatory ability of a learner within the learning stage. We show that our method is able to find fair classifiers that retain accuracy for a number of real datasets, and present generalizations of our method to the setting of unsupervised learning as well.
Bio: Matt Olfat is a PhD candidate in IEOR at UC Berkeley. He received his B.S. in Systems Engineering and Mathematics from the University of Virginia in 2014, and his M.S. in Industrial Engineering and Operations Research from UC Berkeley in 2016. His research interests include fairness in machine learning, applications of machine learning in public policy, and decompositions of high-dimensional datasets.
3108 Etcheverry Hall
Bio: Jesús De Loera was born and raised in Mexico City. He received his Bachelor of Science degree in Mathematics from the National University of Mexico in 1989, a M.A. in Mathematics from Western Michigan in 1990, and a Ph.D in Applied Mathematics from Cornell University in 1995. He arrived at UC Davis in 1999, where he is now a professor of Mathematics as well as a member of the Graduate groups in Computer Science and Applied Mathematics. He has held visiting positions at the University of Minnesota, the Swiss Federal Technology Institute (ETH Zürich), the Mathematical Science Institute at Berkeley (MSRI), Universität Magdeburg (Germany), the Institute for Pure and Applied Mathematics at UCLA (IPAM), the Newton Institute of Cambridge Univ. (UK), and the Technische Universität München. He has taught courses at universities in the USA, Mexico, Germany and Switzerland.
He is an expert in the field of Discrete Mathematics, but his research encompasses a diverse set of topics including his work in (pure) Convex Geometry, Algebraic Combinatorics, and Combinatorial Commutative Algebra, as well as his (applied) work in Combinatorial Optimization and Algorithms. In addition to more than 80 published research papers, he has co-written two graduate level textbooks: `` Triangulations: Structures for Algorithms and Applications'' (Springer, with J. Rambau and F. Santos) and `` Algebraic and Geometric Ideas in the Theory of Discrete Optimization'' (SIAM, with R. Hemmecke and M. Koeppe). The first being a treatise about combinatorics of triangulations of polytopes and the second an introduction to the state of the art in algebraic-geometric algorithms in optimization.
In general, he enjoys rethinking Mathematics in terms of algorithmic questions and understanding how computers can be ``taught'' to discover or prove theorems (e.g., automatically produce rational generating functions from geometric counting questions). He approaches difficult computational problems using tools from Algebra, Combinatorics, and Convex Geometry, and Topology. He believes in the exciting future of interdisciplinary work. Some of his favorite mathematical objects are polytopes, which are multidimensional generalizations of polygons and cubes.
3108 Etcheverry Hall
Abstract: This talk addresses the design of monitoring and inspection strategies to improve infrastructure resilience, with focus on urban water and gas networks facing natural or adversarial disruptions. Firstly, we address the problem of finding a monitoring strategy utilizing the minimum number of sensors necessary to ensure a desired detection performance against multiple adversarial disruptions. This problem can be formulated as a mathematical program with constraints involving Nash equilibria of a large bimatrix game. To overcome the computational issues in solving this problem for real-world networks, we develop an approach that computes randomized strategies based on solutions of a minimum set cover problem and a maximum set packing problem. Game-theoretic and combinatorial arguments enable us to derive optimality guarantees of the resulting strategies. Secondly, we present a stochastic orienteering problem for finding an optimal network inspection strategy in the aftermath of an earthquake. Specifically, the objective is to maximize failure localization performance under timing, sensing, and routing constraints. To calibrate the orienteering problem, we develop a predictive failure model using data from SF Bay area’s gas pipeline inspection operations. We propose non-adaptive algorithms based on integer programming, and obtain efficient solutions that capture the key exploration/exploitation trade-off faced by utility crews in choosing their inspection strategy. These results demonstrate the value of utilizing the real- world failure data and network properties for improving response operations.
Bio: Saurabh Amin is Robert N. Noyce Career Development Associate Professor in the Department of Civil and Environmental Engineering at MIT. He is also affiliated with the Institute of Data, Systems and Society and the Operations Research Center at MIT. His research focuses on the design of network inspection and control algorithms for infrastructure systems resilience. He studies the effects of security attacks and natural events on the survivability of cyber-physical systems, and designs incentive mechanisms to reduce network risks. Dr. Amin received his Ph.D. from the University of California, Berkeley in 2011. His research is supported by NSF CPS FORCES Frontiers project, NSF CAREER award, Google Faculty Research award, DoD-Science of Security Program, and Siebel Energy Institute Grant.
3108 Etcheverry Hall
Bio: Jerry Ding is a staff research engineer in the Control Systems group at United Technologies Research Center in East Hartford, CT. He received his PhD in Electrical Engineering and Computer Sciences from University of California, Berkeley in 2012, and his Bachelor of Science in Electrical Engineering from the University of Wisconsin-Madison in 2006. His current responsibilities includes the development of motion planning and control algorithms for autonomous vehicles and the development of resilient estimation architectures for aerospace applications. While at UTRC, he has worked on the Sikorsky MATRIX program, contributing to the development and demonstration of autonomous motion planning algorithms for full-size helicopter platforms in 2015. His research interests include autonomous planning, model-based control and estimation, and formal verification of safety-critical control systems.
3108 Etcheverry Hall
Bio: Yinyu Ye is currently the Kwoh-Ting Li Professor in the School of Engineering at the Department of Management Science and Engineering and Institute of Computational and Mathematical Engineering and the Director of the MS&E Industrial Affiliates Program, Stanford University. He received the B.S. degree in System Engineering from the Huazhong University of Science and Technology, China, and the M.S. and Ph.D. degrees in Engineering-Economic Systems and Operations Research from Stanford University. Ye's research interests lie in the areas of optimization, complexity theory, algorithm design and analysis, and applications of mathematical programming, operations research and system engineering. He is also interested in developing optimization software for various real-world applications. Current research topics include Liner Programming Algorithms, Markov Decision Processes, Computational Game/Market Equilibrium, Metric Distance Geometry, Dynamic Resource Allocation, and Stochastic and Robust Decision Making, etc. He is an INFORMS (The Institute for Operations Research and The Management Science) Fellow, and has received several research awards including the winner of the 2014 SIAG/Optimization Prize awarded every three years to the author(s) of the most outstanding paper, the inaugural 2012 ISMP Tseng Lectureship Prize for outstanding contribution to continuous optimization, the 2009 John von Neumann Theory Prize for fundamental sustained contributions to theory in Operations Research and the Management Sciences, the inaugural 2006 Farkas prize on Optimization, and the 2009 IBM Faculty Award. He has supervised numerous doctoral students at Stanford who received received the 2015 and 2013 Second Prize of INFORMS Nicholson Student Paper Competition, the 2013 INFORMS Computing Society Prize, the 2008 Nicholson Prize, and the 2006 and 2010 INFORMS Optimization Prizes for Young Researchers. Ye teaches courses on Optimization, Network and Integer Programming, Semidefinite Programming, etc. He has written extensively on Interior-Point Methods, Approximation Algorithms, Conic Optimization, and their applications; and served as a consultant or technical board member to a variety of industries, including MOSEK.
3108 Etcheverry Hall
Bio: Professor Sen served as a program director at NSF where he was responsible for the Operations Research, and the Service Enterprise Engineering programs. At NSF, he also headed the Cyber infrastructure planning activities of the Engineering Directorate. Concurrently with his appointment at NSF, he was a professor of Systems and Industrial Engineering at the University of Arizona.
Professor Sen has served on the editorial board of several journals, including Operations Research as Area Editor for Optimization, and as Associate Editor in INFORMS Journal on Computing, Telecommunications Systems, as well as Operations Research. He is the past-Chair of the INFORMS Telecommunications Section and founded the INFORMS Optimization Section. Professor Sen is a Fellow of INFORMS.
3108 Etcheverry Hall
Bio: Roger Wets is a Distinguished Research Professor of Mathematics at the University of California, Davis. He guided nineteen students to their Ph.D. His main research interests have been stochastic optimization and variational analysis. For this work he has received a number of prizes that include Guggenheim and Erskine Fellowships, the SIAM-MPS Dantzig Prize in Mathematical programming and the INFORMS Lanchester prize for the book “Variational Analysis” that he co-authored with R.T. Rockafellar. During the last decade his research has been focused on equilibrium problems, in particular equilibrium problems in a stochastic environment, and on nonparametric estimation, in particular on the fusion of hard and soft information. Over time, he has been associated with the Boeing Scientific Research Labs, the International Institute of Applied Systems Analysis (Laxenburg, Austria), the IBM-T.J. Watson Research Center (Yorktown Heights, N.Y.), the Center for Mathematical Modeling of the University of Chile and the World Bank. This resulted in getting involved in projects related to aerospace, telecommunications, finance, soil management and water resources, manufacturing and energy. He has published about 200 technical articles, mostly in pure and applied mathematical journals, but also in journals dealing with probability, statistics, economics and ecology. He held, or holds, editorial positions on a number of leading journals in mathematics and operations research.
3108 Etcheverry Hall
Research Expertise and Interest
process systems engineering, control theory, energy systems, biological systems
The goal of Professor Mesbah's research is to develop systems analysis techniques and application-relevant control theory for complex chemical and biological systems. The theoretical developments are applied toward societal problems, including energy systems and biomedical applications. His group's research efforts provide a balance between theory, computation, and real-world applications that span from nano- and micro-length scale systems to traditional large-scale (bio)chemical engineering systems.