As a research scientist, Sajal Dash is exploring scaling approaches for large-scale deep learning models such as Large Language Models (LLM) and Vision Transformers (ViT) and their applications in scientific domains. He is also continuing his research on mitigating catastrophic forgetting during incremental training of deep learning models in a streaming setting.

Before joining Oak Ridge National Laboratory, Sajal completed his Ph.D. in Computer Science at Virginia Tech. His Ph.D. dissertation titled “Exploring the Landscape of Big Data Analytics Through Domain-Aware Algorithm Design” focused on solving large-scale domain problems by leveraging domain-knowledge with properties of big data. His dissertation solved a big data problem in cancer biology by efficiently distributing the combinatorial workload across nodes while regularizing memory access patterns. Dr. Dash’s Ph.D. has been greatly impacted by two summer internships at Oak Ridge National Laboratory in 2018 and 2019 under the mentorship of Dr. Junqi Yin and Dr. Mallikarjun Shankar.

Dr. Dash received his B.Sc. in Computer Science and Engineering from BUET, Bangladesh, and MS in Computer Science from UNC Chapel Hill before getting his Ph.D. in Computer Science from Virginia Tech.


Virginia Tech
Computer Science
Doctor of Philosophy (Ph.D.)
UNC Chapel Hill
Computer Science
Master of Science (M.S.)
Bangladesh University of Engineering and Technology
Computer Science and Engineering
Bachelor of Science (B.S.)


Al Hajri, Qais, Sajal Dash, Wu-chun Feng, Harold R. Garner, and Ramu Anandakrishnan. "Identifying multi-hit carcinogenic gene combinations: Scaling up a weighted set cover algorithm using compressed binary matrix representation on a GPU." Scientific reports 10, no. 1 (2020): 1-18.
Dash, Sajal. "Exploring the Landscape of Big Data Analytics Through Domain-Aware Algorithm Design." PhD diss., Virginia Tech, 2020.
Dash, Sajal, Nicholas A. Kinney, Robin T. Varghese, Harold R. Garner, Wu-chun Feng, and Ramu Anandakrishnan. "Differentiating between cancer and normal tissue samples using multi-hit combinations of genetic mutations." Scientific reports 9, no. 1 (2019): 1-13.
Yin, Junqi, Shubhankar Gahlot, Nouamane Laanait, Ketan Maheshwari, Jack Morrison, Sajal Dash, and Mallikarjun Shankar. "Strategies to Deploy and Scale Deep Learning on the Summit Supercomputer." In 2019 IEEE/ACM Third Workshop on Deep Learning on Supercomputers (DLS), pp. 84-94. IEEE, 2019.
Dash, Sajal, Sarthok Rahman, Heather M. Hines, and Wu-chun Feng. "Incremental BLAST: incremental addition of new sequence databases through e-value correction." bioRxiv (2018): 476218.
Dash, Sajal, Anshuman Verma, Chris North, and Wu-chun Feng. "Portable parallel design of weighted multi-dimensional scaling for real-time data analysis." In 2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pp. 10-17. IEEE, 2017.