Ramakrishna Vedantam

Email: ramakrishna_dot_vedantam_at_nyu_dot_edu

Hi! I am currently a visiting researcher at the NYU Center for Data Science (CDS) working with Prof. Julia Kempe and Prof. Andrew Gordon Wilson. Previously, I was a Research Scientist at Facebook AI Research (FAIR) in New York. I enjoy working on problems in core machine learning that make AI models more general, robust and human-like. In pursuit of these goals, my current focus is on multimodal machine learning, robustness and data efficient learning.

At FAIR, I proposed the Decodable Information Bottleneck for Representation Learning which provides a theoretical and practical foundation for worst case risk mitigation for deep representation learning.

Previously, I graduated with a Ph.D. in Computer Science from the School of Interactive Computing at Georgia Tech. My PhD advisor was Devi Parikh. My thesis was on ā€œInterpretation, Grounding and Imagination for Machine Intelligenceā€. Among other things, during my Ph.D. I proposed the CIDEr metric commonly used for evaluating image captioning models (3000+ citations) and contributed to the Grad-CAM method popularly used for interpreting neural network predictions (13000+ citations).

For more details on my research see my Google Scholar page or checkout my publications. My PhD research was supported by the highly competitive Google Ph.D. fellowship in Machine Perception, Speech Technology and Computer Vision.

I have been fortunate to work with some great mentors and collaborators during grad school, including Larry Zitnick, Dhruv Batra, Kevin Murphy, Gal Chechik, and Samy Bengio.

In a previous life, I was an undergrad in ECE at IIIT-Hyderabad where I worked with K. Madhava Krishna in Robotics. Here is a link to my old website.



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