Speakers

Data Scientist by profession and CEO by the needs. His personal vision is to advance the field of AI that can ultimately contribute towards solutions for social issues.

IIT Roorkee alumnus and Director of Purdue's Institute for Physical AI; works on parallel & distributed computing and large-scale data analytics.

Directs the IDEAS Lab; research in robotics, embodied AI, human-robot collaboration and intent-aware planning.

His research focuses on computer vision and machine learning, particularly visual analytics, graph-based data association, data fusion, biomedical image analysis, and video surveillance.

Principal Investigator of the IITB-HRTI Centre for Computer Vision, MultiModal AI and Geo-Intelligence, and serves as an AI Engineering Advisor for AWL Inc., Japan.

Research in human-robot interaction, embodied AI, NLP for robotics and multi-robot systems.

Expert in Reinforcement Learning, Markov Decision Models, Queueing Models, and Game Theory. A co-recipient of the 2024 Google India Research Award.

Works on fundamental artificial intelligence and machine learning problems, in particular generalisability (through transfer learning, meta-learning, zero-shot learning, and few-shot learning), generative models, and explainability

Leads the Vision & AI Lab; research in computer vision, deep learning and machine learning. Fellow of INAE.

Dean of the School of Computing, Information & Data Sciences; research in embedded and cyber-physical systems and data science. IEEE/ACM/AAAS Fellow.

Also working as Head, Manekshaw Centre of Excellence for National Security Studies and Research.

Heads the Machine Intelligence, Signals & Networks (MISN) Lab; works on machine learning, graphical models and large-scale optimization.

Works on textual generative models and large-scale information retrieval; earlier developed theory for GANs and diffusion models using classical signal processing.

Leads the DeCLaRe Lab; research in large language models, multimodal AI, NLP and affective computing.

Leads the StARLinG Lab; research in statistical relational learning, probabilistic & neuro-symbolic AI, and AI for healthcare.

An expert in Artificial Intelligence, computational neuroscience, brain imaging, and biomedical engineering. He is FNAE, FNASc., FAIIA, SMIEEE.

A pioneer of data mining and high-performance computing; applies machine learning to global challenges such as climate and the environment.

Directs the iPAL Lab; research in signal & image processing, computational imaging and interpretable deep learning. IEEE Fellow.

His research focuses on cybersecurity, AI-driven security, cyber warfare, and cyber risk management.

Over 15 years of experience building enterprise-scale AI systems across generative AI, computer vision and biometrics, with earlier leadership roles at Intel and Samsung.

Works at the intersection of neuroscience and AI: brain-inspired AI, explainable AI and neural signal processing. Former postdoctoral researcher at MIT.

Research in wavelet and fractional transform theory with applications to multimedia security, watermarking and encryption. Listed among the world's top 2% scientists by Stanford University.

Works on biometric authentication, security and privacy, and deep-learning methods for detecting deepfakes.

Studies graph searching, parameterized complexity, approximation algorithms and the theory of AI and ML; published at ICALP, AAAI, MFCS and ISAAC.

Leads the Machine Learning and Optimization Lab; works on deep and transfer learning, evolutionary algorithms, federated learning and adversarial machine learning.

Research in image processing, computer vision and applied deep learning, including computational imaging, image forensics and assistive technologies.

Research in computer vision, affective computing and deep learning, including all-weather surveillance and human emotion recognition.

Research in visual perception and computer vision, including multimodal LLMs and deep learning; earlier worked at ASTRI, Hong Kong and NTU Singapore.