Analytical and computational methods for data-driven optimization under uncertainty with applications in smart transportation
Prof. Siqian Shen, Industrial & Operations Engineering
Albert S. Berahas is an Assistant Professor of Industrial & Operations Engineering. Dr. Berahas received a BSC in Operations Research and Industrial Engineering from Cornell University, and his MSC and Ph.D.degrees in Engineering Sciences and Applied Mathematics from Northwestern University. Before coming to the University of Michigan, Dr. Berahas held positions as a Postdoctoral Research Fellow at Lehigh University and at Northwestern University.
Dr. Berahas’ research broadly focuses on designing, developing and analyzing algorithms for solving large scale nonlinear optimization problems. Such problems are ubiquitous, and arise in a plethora of areas such as engineering design, economics, transportation, robotics, machine learning and statistics. Specifically, he is interested in and has explored several sub-fields of nonlinear optimization such as: (i) general nonlinear optimization algorithms, (ii) optimization algorithms for machine learning, (iii) constrained optimization, (iv) stochastic optimization, (v) derivative-free optimization, and (vi) distributed optimization.
Today’s real-world problems are complex and large, often with an overwhelmingly large number of unknown variables which render them doomed to the so-called “curse of dimensionality”. For instance, in machine learning, it is important to obtain simple, interpretable, and parsimonious models of high-dimensional and noisy data. As another example, in large-scale dynamic systems, a long standing question is how to efficiently design distributed control policies for unknown and interconnected systems.
Our research is centered around two main objectives: (1) to model these problems as tractable optimization problems; and (2) to develop structure-aware and scalable computational methods for solving these optimization problems that come equipped with certifiable optimality guarantees. At the core of our research is to show that exploiting hidden structures in these problems—such as graph-induced or spectral sparsity—is a key game-changer in the pursuit of massively scalable and guaranteed computational methods.
Yafeng Yin is a Professor of Civil and Environmental Engineering. He investigates critical issues associated with the design, operations, regulation, and management of innovative mobility services and systems. The goal is to support them in becoming integral components of transportation systems, improving system connections and integration, yielding efficient and multimodal mobility of people and goods, and enhancing rural underserved communities’ access to employment, education and other lifeline opportunities. He is focus on understanding the interaction between travelers, transportation modes and infrastructure, and then modeling the consequence of the interaction. With the model established, he then investigates how to optimize the design and operations of transportation systems. In his work, he often needs to solve large-scale optimization models.
Rael Al Kontar is an Assistant Professor in the department of Industrial & Operations Engineering. His research broadly focuses on developing data analytics and decision-making methodologies specifically tailored for Internet of Things (IoT) enabled smart and connected products/systems. He envisions that most (if not all) engineering systems will eventually become connected systems in the future. Therefore, his key focus is on developing next-generation data analytics, machine learning, individualized informatics and graphical and network modeling tools to truly realize the competitive advantages that are promised by smart and connected products/systems.