Saurabh Singh Rajput is a Ph.D. student with the SMART-lab at Dalhousie University, under the supervision of Dr. Tushar Sharma. My research lies at the intersection of artificial intelligence, hardware processors and software systems. My goal? To develop innovative techniques that help profile, analyze and enhance the energy and compute efficiency of deep learning systems. By doing so, I aim to contribute to the creation of a greener AI, ensuring our technological advancements support a sustainable future. Before this, I was busy adding software to your finances at Fidelity Investments.
News
- [JUNE. 2024] “Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurement”, Accepted by ACM Transactions on Software Engineering and Methodology (TOSEM)
- [JUNE. 2024] Presented research poster on “Investigating the Energy Efficiency of Popular Quantization Techniques for AI Models” Accepted at Software Engineering for Machine Learning Applications (SEMLA) international symposium, Montreal, Quebec.
- [May. 2024] Presented research poster on “FlipFlop: Predictive Power Modeling and Optimization for Energy-Efficient GPU Computing” Accepted at Dalhousie AI Symposium, Halifax, Nova Scotia.
- [April. 2024] Presented research poster on “FlipFlop: Predictive Power Modeling and Optimization for Energy-Efficient GPU Computing” Accepted at Smart Energy Conference, Halifax, Nova Scotia.
- [MAR. 2024] “Pursuit of Energy-efficient AI: Benchmarking Emerging Neural Network Quantization Methods”, Accepted at International Workshop on Green and Sustainable Software (GREENS’24) - Co-Located with ICSA24
- [Jan. 2024] “Greenlight: Highlighting TensorFlow APIs Energy Footprint”, Accepted in MSR (Data/Tools track) 2024.
- [Nov. 2023] Junior PC for MSR 2024 Technical Track.
Publications
- ”Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurement”, Accepted by ACM Transactions on Software Engineering and Methodology (TOSEM) Preprint
- ”Pursuit of Energy-efficient AI: Benchmarking Emerging Neural Network Quantization Methods”, Accepted at International Workshop on Green and Sustainable Software (GREENS’24) - Co-Located with ICSA24 Preprint
- ”Greenlight: Highlighting TensorFlow APIs Energy Footprint”, Accepted in MSR (Data/Tools track) 2024. Preprint
- ”COMET: Generating Commit Messages using Delta Graph Context Representation”, arXiv preprint arXiv:2402.01841. 2024 Feb Preprint
Services
I’m actively involved in the academic community, contributing my expertise in various capacities:
Conferences
- IEEE/ACM International Conference on Mining Software Repositories (MSR) 2024 (Junior PC)
Subreviewer
- ICSE 2024, ASE 2024, ICSA 2024, FSE 2023, ICPC 2023, SANER 2024, SCAM 2023
Contact
first_name(at)dal.ca to get in touch for collaborating, or if you have any opportunity that suit’s my profile.