Vineeth N Balasubramanian


Our group’s research interests lie at the intersection of the theory and application of machine learning - with a focus on applications in computer vision. With a strong interest in the mathematical fundamentals and a passion for real-world application, our group aims on being at the forefront of the field, by publishing in the best of venues in the areas of machine learning and computer vision, while at the same time, being guided by application contexts derived from real-world use.

In recent years, our work on the algorithmic side has focused on non-convex optimization, explainable machine learning and deep generative models; while our applied work is on domains including agriculture, e-learning, security/surveillance and human behavior understanding.

As machine learning and Artificial Intelligence continue to make inroads into the daily life of every human, our future research plans focus on the development of machine learning methods for real-world application contexts and challenges, with an emphasis on both theoretical and empirical analysis.

Ongoing Projects (Selected)

  • Non-convex Optimization and Deep Learning (Intel PhD Fellowship)

  • Explainable Machine Learning (Funded by MHRD, DST and Honeywell through the UAY program)

  • Deep Generative Models: Going Beyond Supervised Learning (Intel PhD Fellowship)

  • Object Detection in Unconstrained Settings, including Drone Images and Low-resolution Thermal Images (Funded by DRDO and IBM)

  • Learning with Weak Supervision for Autonomous Vehicles (Funded by Intel)

  • Deep Learning for Agriculture (A DST-JST SICORP Collaborative Project with Univ of Tokyo, IIT-B, IIIT-H, and PJTSAU)

My past research focused extensively on the use of machine learning and computer vision in assistive technology applications. Please see this link for past/completed projects.


We are grateful to the following organizations whose support sustains our research.

Funding Organizations