Manohar Kaul

I am an Associate Professor in the Department of Computer Science and Engineering (CSE) at the Indian Institute of Technology, Hyderabad (IIT-H), where I head the क्रम (Krama) Lab. My research interests are applied algebraic topology (topological data analysis), geometric machine learning (graph and point-cloud representation learning), and optimal transport (discrete OT and assignment problems). My work involves developing practical machine learning algorithms with rigorous theoretical foundations.

Prior to my transition into academia, I gained 9 years of industry expertise as an architect (technical / systems / database) for companies like Oracle on mission critical projects involving big data. This experience gave me a unique and deep perspective into the kind of AI work that would tightly interweave the future needs of industry, while also pushing the boundaries of current AI research. With this new purpose, I decided to join academia.

First, I was a Masters student and researcher in the Uppsala database laboratory (UDBL) headed by Prof. Tore Risch at the Department of Information Technology in Uppsala University, Sweden, where I investigated the near-future prediction of continuously evolving moving-object trajectories. Then, I pursued a Ph.D. in the Computer Science Department at Aarhus University, Denmark, where I was working on fast and approximate path-finding algorithms on massive 3D polyhedral meshes, under the guidance of Prof. Christian S. Jensen. Post that, I was a postdoctoral researcher at DIMA, Technische Universität (TU) Berlin, Germany, headed by Prof. Volker Markl, investigating scalable machine learning methods, especially, locality sensitive hashing (LSH).

On securing a tenured faculty position at IIT-H, I have aggressively pursued AI research and collaborated with both academia and industry alike to create sophisticated next-generation AI tools. The क्रम (Krama) Lab is a culmination of several years worth of effort to accelerate such collaborations.