报告题目：Learning Predictive Models from Observed Network Equilibria: From Transportation to Metabolic Networks

报告人：Yannis Paschalidis, Boston University

报告时间：2018年7月17日 上午9:00—10:00

报告地点：南一楼中311

报告摘要：

Equilibrium modeling is common in a variety of fields such as game theory, transportation science, and cell metabolism. The inputs to these models, however, are often difficult to estimate, while their outputs, i.e., the equilibria they are meant to describe, are often directly observable. By combining ideas from inverse optimization with the theory of variational inequalities, I will present an efficient, data-driven technique for estimating the parameters of these models from observed equilibria. Our framework allows for both parametric and non-parametric estimation and provides probabilistic guarantees on the quality of the estimated quantities.

I will present applications in two seemingly distinct areas. In transportation networks we use these techniques to estimate the congestion function, which determines users' route selection. Using actual traffic data from the Boston area, we have been able to quantify the inefficiency of drivers' selfish behavior compared to a socially optimal solution, also called the price of anarchy.

In biochemical networks, Flux Balance Analysis (FBA) is a widely used predictive model which computes a cell's steady-state chemical reaction fluxes as a solution to an optimization problem. FBA, however, assumes a certain global cellular objective function which is not necessarily known. We will use our new method to estimate such an objective. This enables us to elucidate the cellular metabolic network control mechanisms and infer important information regarding an organism's evolution.

报告人简介：

Yannis Paschalidis is a Professor in the College of Engineering at Boston University with joint appointments in the Department of Electrical and Computer Engineering, the Division of Systems Engineering, and the Department of Biomedical Engineering.

He is the Director of the Center for Information and Systems Engineering (CISE) – a Boston University research center with 39 affiliated faculty and more than $6.6 million of annual research expenditures.

He is also affiliated with the BioMolecular Engineering Research Center (BMERC), the Clinical & Translational Science Institute (CTSI), the Precision Diagnostics Center (PDC), and the Rafik B. Hariri Institute for Computing and Computational Science & Engineering.