Biography
Srinivasa M. Salapaka received the B.Tech. degree in Mechanical Engineering from Indian Institute of
Technology in 1995, the M.S. and the Ph.D. degrees in Mechanical Engineering from the University of
California at Santa Barbara, U.S.A in 1997 and 2002, respectively. During 2002-2004, he was a postdoctoral
associate in the Laboratory for Information and Decision Systems, Massachusetts Institute of Technology,
Cambridge, USA. Since January 2004, he has been a faculty member in Mechanical Science and
Engineering at the University of Illinois, Urbana-Champaign. He got the NSF CAREER award in the year
2005. He is an ASME Fellow since 2015. His areas of current research interest include machine learning,
combinatorial optimization, and control analysis and synthesis for scanning-probe microscopy, X-ray
microscopy, and power-grid systems.
Abstract
This talk introduces a class of optimization problems - Parameterized Sequential Decision Making (para-
SDM) problems, and proposes an algorithmic framework to solve the problems in this class. Para-SDM problems cover a vast range of network logistics and planning application areas such as facility location
with path optimization (FLPO), vehicle routing, sensor network design, manufacturing process parameter
optimization, last mile delivery, industrial robot-resource allocation, and data aggregation, classification,
and clustering algorithms. These problems include large subclasses of problems such as Markov Decision
Processes (MDPs), reinforcement learning (RL), clustering, resource allocation, scheduling, and routing
problems. Conceptually these problems require simultaneously determining the shortest path and
allocating resources in a network, incorporating application-specific capacity and exclusion constraints,
and while respecting the dynamical evolution of the network. In this regard, they generalize Markov
Decision Process (MDP) problems, which have been studied more extensively. This talk will present a
combinatorial-optimization framework that address this class of problems. This talk will present methods
and algorithms that mimic free-energy principle from statistical physics to address a class of combinatorial
optimization problems. In fact, this principle is viewed as maximum entropy principle (MEP) propounded
by E.T. Jaynes. The resulting framework generalized important concepts from clustering/classification
literature as well as tools from machine learning – especially those related to MDPs and RL.
