Qualification → PhD
Area of Interest →
Field of Interest → Development Economics, Political Economy, Social Networks
Qualification → Ph.D. (Fellow, IIM Calcutta)
Area of Interest →
Field of Interest →
Qualification → B.A. (Hons.), M.A., M.Phil., Ph.D. in Economics (Jadavpur University)
Area of Interest →
Field of Interest →
Qualification → Ph.D. in Mathematics (IIT Kharagpur)
Area of Interest →
Field of Interest → Optimization
Qualification → PhD
Area of Interest →
General Management and Human Resource
Field of Interest → Human Resource
Qualification → Ph.D. (Computer Science)
Area of Interest →
Field of Interest → Healthcare Stream Analytics, Vehicular Analytics, Climate Extremes, Hyperspectral Image Processing, Precision Agriculture
Qualification → PhD
Area of Interest →
Bayesian Analysis, Bayesian Inferences
Field of Interest → Reliability, Copula Models
Qualification → Ph.D.
Area of Interest →
Statistical Inference, Data Science
Field of Interest → Statistical Inference
Qualification → Ph.D.
Area of Interest →
Law and Economics, Economics of Conflict, Geopolitics
Field of Interest → Law and Economics, Economics of Conflict, Microeconomics, Contract Economics
Qualification → PhD
Area of Interest →
Field of Interest → Bayesian Data Analysis, Statistical Inference , Decision Theory, Mathematical StatisticsÂÂ
Qualification → B.tech, M.Tech, Ph.D
Area of Interest →
Machine Learning
Deep Learning
Image Processing
Field of Interest → Machine Learning, Image Processing
Qualification → PhD
Area of Interest →
Dynamical systems & control: Modelling forced or unforced evolution in a state space & establishing its stability and control. Theoretical & data driven system dynamics in statistical learning framework, computing system dynamics, stability & control to estimate perception, prediction & planning in an autonomous control system.
Statistical learning: Designing a predictive function in statistical learning framework with discriminative modelling (e.g. regression) or generative modelling (e.g. multi-modality with CVAE) by minimizing a suitable loss function. Transforming a learning problem as an optimization problem.
Optimization: first order & second order adaptive descent algorithms, interior point method for convex problems. Convexification or relaxation or breaking symmetry or introducing uncertainty in non-convex problems & transforming geometrical or physical intuition of the problem into a scalable optimization algorithm.
Field of Interest → Learning dynamical systems and control, autonomous control systems