As a research scientist, Sajal Dash explores scaling approaches for large-scale deep learning applications by focusing on convergence behavior and problems associated with large batch size. He will also continue 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.
Sajal received his B.Sc. in Computer Science and Engineering from BUET, Bangladesh, and MS in Computer Science from UNC Chapell Hill before getting his Ph.D. in Computer Science from Virginia Tech.
Top 100 in Cancer by Scientific Reports (2019) for the paper "Differentiating between cancer and normal tissue samples using multi-hit combinations of genetic mutations"
Best paper finalist at HPCC'2017 for the paper "Portable Parallel Design of Weighted Multi-Dimensional Scaling for Real-Time Data Analysis"