I am a Senior Researcher and Engineer at Qualcomm developing efficient computer vision/machine learning algorithms. My current work is mainly in the area of image and video processing/understanding and generative modeling.
Before joining Qualcomm, I was a postdoctoral researcher at Northeastern University working with Prof. Jennifer Dy, Prof. Octavia Camps and Prof. Dana H Brooks. I completed my PhD in Computing and Information Sciences from Rochester Institute of Technology under advisement of Prof. Linwei Wang.
What I find exciting and truly satisfying working in AI and machine learning is the richness and diversity of ideas. Ideas from different fields like mathematics, Physics, economics, computer science, statistics, electrical engineering, control and neural network architecture engineering have crossed fields and found interesting adaptations in machine learning and vice versa. My current favorite is how Diffusion model can be simultaneously interpreted in different ways from different perspectives like Bayesian inference, control system, Schrodinger Bridge problem, Stochastic differential equation, Maximum-likelihood generation, Neural-ODE, Optimal transport and Alpha-blending. To learn about yet another novel perspective, please take a look at my recent work where I introduce the geometric interpretation of these Diffusion generative models (see here).
At Qualcomm, I am excited to work on machine learning, computer vision and AI research works that will be realized as product, and will have real-world impact.Resume PhD Thesis
PhD in Computing and Information Science, 2020
Rochester Institute of Technology
BE in Electronics and Communication Engineering, 2012
Institute of Engineering
Can we learn meaningful representation from real world videos or biomedical signals ?
How to understand and improve generalization and robustness in deep networks?
Apply probabilistic modeling, deep generative modeling and inference to solve inverse problem.