Earth System Modeling with AI (ESM-AI)

PI: Prof. Adway Mitra

This group explores various issues related to climate and sustainable development of the earth with the help of Machine Learning and Data Sciences. The grand aim of the group is to develop a model of the world (with a special focus on India), through which we can simulate the impact of global climate change on regional weather and climate, human settlements, agricultural productions, environment and health.

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Predictive Stochastic Adaptive Learning Machines (PSALM)

PI: Prof. Prabhat Kumar Mishra

PSALM (Predictive Stochastic Adaptive Learning Machines) Lab at IIT Kharagpur is focused on research at the intersection of control theory and Artificial Intelligence (AI). The real world applications of AI require safety guarantee and explainability of the AI module. In addition, several modules such as sensing and perception, planning and action need to interact to complete the feedback loop. Our main focus is in planning and decision making but we also consider sensing and actuation along with the effects of communication channels whenever required.

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Signal processing and AI (SPAI)

PI: Prof. Mahesh Mohan M R

SPAI group is actively involved in investigating cutting-edge research problems that are of contemporary relevance to the AI community. The current areas of research being pursued in the lab include AI for computer vision, agriculture, health, finance, and interdisciplinary fields. The members of the lab mainly comprise of students pursuing Ph.D, Masters, and Undergrads. The group has a vibrant research culture and students regularly aim to publish their research works in top venues, bag prestigious scholarships, and internships/job offers in top companies, etc. The lab also actively collaborates with industries (including Tech Mahindra) and international universities (including UIUC and Harvard Medical School).

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Graph Data Processing and Learning

PI: Prof. Plaban Kumar Bhowmick

This group focusses on analysis of graph data. The types of analysis include trend analysis (like in academic genealogy network) or predictive analysis (e.g., predicting binding sites in proteins ). At the core, we aim to advance the state of the art in geometric graph machine learning. We also focus on graph unlearning which aims at performing privacy preserving AI on graph data.

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