I am a post-doc at MIT working with
Faez Ahmed. Recently,
I earned my Ph.D. from the Ohio State University where I worked on machine learning,
representation learning and reinforcement learning for decision making in complex
real-world tasks such as design, manufacturing and robotics.
I was supervised by David Hoelzle. Previously,
I also worked as a research intern and later a research collaborator at the
Autodesk AI Lab where I worked with
Rodger Luo on AI driven
My core research interest is to enable efficient decision making in complex systems.
I draw from artificial intelligence (AI)/machine learning (ML) and control theory to
develop learning algorithms for intelligent manufacturing systems, autonomous robots and
generative/algorithmic engineering design tasks. Within AI/ML my interest lies in representation
learning and reinforcement learning. I am also interested in the AI/ML systems integration for
these critical application domains. During my Ph.D., I developed data-efficient
reinforcement learning algorithms using knowledge transfer between tasks and
probabilistic reward modeling. I was also part of the team that built a
state-of-the-art autonomous manufacturing research robot
that can take decisions in real-time to design and manufacture a complex geometry 3D
artifact without any human intervention and require 10x less data samples than
traditional approaches. I have designed and implemented the AI/ML software architecture
for this autonomous robot. I have also worked as a research intern, and later a collaborator, at the Autodesk
AI Lab where we developed transformer based representation learning and autoregressive generative models
for voxel-based 3D design tasks.
Due to logistics issue the arxiv links
are not working at this moment, please reach out to me if you are interested in my research.
Decision making in manufacturing systems is extremely difficult with limited data
I have developed the open source autonomous manufacturing software architecture which is specifically built for our research robot. This software supports modular implementation of any learning algorithms for real-time decision making in the robot.
Additionally I am developing another open source library for modular implementation of
state-of-the-art AI algorithms. For example, here is the implementation of the vanilla transformer architecture from scratch
and corresponding blog with implementation details.
Implementation of statistical machine learning, deep learning and reinforcement learning algorithms from scratch.
Examples: Deep learing ⇒ Vanilla Transformer: implementaion in pure PyTorch, Reinforcement learing ⇒ SAC: implementaion in pure PyTorch,
Classical machine learning ⇒ PCA, SVM: implementaion in pure python (numpy),
Gaussian process: implementaion in pure python (numpy)
Reviewer: Transactions on Automatic Control, Mechatronics, Journal of Dynamic Systems, Measurement and Control,
Conference on Decision and Control (CDC), American Control Conference (ACC), Manufacturing Science and Engineering Conference (MSEC),
The Graduate Student Perspective Personal Blog Posts