Gobinda Saha


Deep learning optimization, Lifelong learning, Meta-learning, Online learning, Transfer and multitask learning

View the Project on GitHub sahagobinda/portfolio

Machine Learning Researcher

I am a PhD candidate at the Department of Electrical and Computer Engineering at Purdue University where I am advised by Professor Kaushik Roy. I am also affiliated as a graduate student researcher at the Center for Brain-Inspired Computing (C-BRIC) at Purdue. During my PhD, I spent time at the Memory Solution Team at GlobalFoundries as a research intern.

During my PhD, I worked on deep learning optimizer and algorithm design, specifically within the application domains of computer vision and reinforcement learning. My expertise extends to various areas, including online learning, continual learning, meta-learning, and decentralized learning algorithms. Furthermore, I gained valuable industry experience through my involvement in hardware-software co-design for ML acceleration during my internship. I am deeply passionate about advancing the field of AI, particularly in the areas of computer vision, natural language processing, autonomous AI, and generative AI applications, with a strong emphasis on scalability and efficiency.

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Work Experience

Graduate Research Assistant @ Purdue University (August 2017 - Present)

Research Intern, Memory Solution Team @ GlobalFoundries, USA (June 2019 - August 2019)



Continual Learning with Scaled Gradient Projection

Gobinda Saha, Kaushik Roy
AAAI Conference on Artificial Intelligence (AAAI 2023)
[Paper] [Code] [Talk Video]

SGP overview

Saliency Guided Experience Packing for Replay in Continual Learning

Gobinda Saha, Kaushik Roy
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2023)
[Paper] [Code] [Talk Video]

EPR overview

Gradient Projection Memory for Continual Learning

Gobinda Saha, Isha Garg, Kaushik Roy
International Conference on Learning Representations (ICLR 2021) (Oral - top 1% paper)
[Paper] [Code] [Talk Video] [Poster]

GPM overview

SPACE: Structured Compression and Sharing of Representational Space for Continual Learning

Gobinda Saha, Isha Garg, Aayush Ankit, Kaushik Roy
IEEE Access 2021
[Paper] [Code]

SPACE overview

CoDeC: Communication-Efficient Decentralized Continual Learning

Sakshi Choudhary, Sai Aparna Aketi, Gobinda Saha, Kaushik Roy
Transactions on Machine Learning Research (TMLR), 2024 [Paper]

codec overview

Homogenizing Non-IID datasets via In-Distribution Knowledge Distillation for Decentralized Learning

Deepak Ravikumar, Gobinda Saha, Sai Aparna Aketi, Kaushik Roy
Preprint (arxiv), 2023 [Paper]

Deep Unlearning: Fast and Efficient Training-free Approach to Controlled Forgetting

Sangamesh Kodge, Gobinda Saha, Kaushik Roy
Preprint (arxiv), 2023 [Paper]

Unlearning overview

An Energy-Efficient and High Throughput in-Memory Computing Bit-Cell With Excellent Robustness Under Process Variations for Binary Neural Network

Gobinda Saha, Zhewei Jiang, Sanjay Parihar, Cao Xi, Jack Higman, Muhammed Ahosan Ul Karim
IEEE Access, 2021

Global overview

Photonic In-Memory Computing Primitive for Spiking Neural Networks Using Phase-Change Materials

Indranil Chakraborty, Gobinda Saha, Kaushik Roy
Physical Review Applied, 2019

Optical overview

Toward Fast Neural Computing using All-Photonic Phase Change Spiking Neurons

Indranil Chakraborty, Gobinda Saha, Abhronil Sengupta, Kaushik Roy
Scientific Reports, 2018