News
03/2024: Two of our papers: (a) "Sparse Linear Regression with Constraints: A Flexible Entropy-based Framework" and (b) "Closed-Loop Identification of Stabilized Models Using Dual Input-Output Parameterization" have been accepted into 22nd European Control Conference (ECC) 2024. Kudos to the team!
07/2023: Continuing our work on System Identification from Closed-loop data, we have recently put out a pre-print on the use of Input-Output Parameters in identifying stabilized linear models. See "arXiv" for more details.
07/2023: Our work titled "A Dual System-Level Parameterization for Identification from Closed-Loop Data" has been accepted into IEEE Conference on Decision and Control 2023.
07/2023: I started my new position at IIT-Delhi.
01/2023: Our work on "Towards Efficient Modularity in Industrial Drying: A Combinatorial Optimization Viewpoint" has been accepted into IEEE American Control Conference 2023.
10/2022: Our work on "Dynamic Parameters in Sequential Decision Making" has been accepted for publication in Automatica, Journal of IFAC.
09/2022: Our work on "Inequality Constraints in Facility Location and Related Problems" has been accepted at the IEEE Indian Control Conference 2022.
09/2022: Our work on "Design of Experiments with Imputable Feature Data" has been accepted for presentation at IEEE Indian Control Conference 2022.
05/2022: Our paper "A Parameterized Sequential Decision Approach to Job-Shop Scheduling" has been accepted for presentation at IEEE Case 2022 as an Industry paper.
05/2022: Our paper "On the choice of Number of Superstates in the Aggregation of Markov Chains" has been accepted for publication in Pattern Recognition Letters. This paper proposes a notion of Marginal Return to compare different aggregated models of a Markov chain, and appropriately identify the best representative model.
03/2022: Our paper "Time-Varying Parameters in Sequential Decision Making Problems" has been posted on arXiv. This paper develops a control-theoretic framework to design the unknown parameter dynamics and the time-varying decision policy of the underlying sequential decision making task.
08/2021: Our paper "Parameterized MDPs and Reinforcement Learning Problems-A Maximum Entropy Principle-Based Framework" has been published on IEEE Transactions on Cybernetics. This work proposes and builds a framework for a new parameterized Sequential Decision Making ( para-SDM)class of problems with applications in diverse fields such as - network science, operations research, clustering and classification.