On the energy of bifurcated hydrogen bonds for protein structure prediction. - Sajal Dash; Jack Snoeyink (BIBM Workshops, 2011)

Portable Parallel Design of Weighted Multi-Dimensional Scaling for Real-Time Data Analysis. - Sajal Dash; Anshuman Verma; Chris North 001; Wu-chun Feng (HPCC/SmartCity/DSS, 2017)

Strategies to Deploy and Scale Deep Learning on the Summit Supercomputer. - Junqi Yin; Shubhankar Gahlot; Nouamane Laanait; Ketan Maheshwari; Jack Morrison; Sajal Dash; Mallikarjun Shankar (DLS@SC, 2019)

Towards a Universal Classifier for Crystallographic Space Groups: A Trickle-Down Approach to Handle Data Imbalance. - Sajal Dash; Archi Dasgupta (SMC, 2020)

Exploring the Landscape of Big Data Analytics Through Domain-Aware Algorithm Design. - Sajal Dash (2020)

Scaling Out a Combinatorial Algorithm for Discovering Carcinogenic Gene Combinations to Thousands of GPUs. - Sajal Dash; Qais Al-Hajri; Wu-chun Feng; Harold R. Garner; Ramu Anandakrishnan (IPDPS, 2021)

Mitigating Catastrophic Forgetting in Deep Learning in a Streaming Setting Using Historical Summary. - Sajal Dash; Junqi Yin; Mallikarjun Shankar; Feiyi Wang; Wu-chun Feng (DRBSD@SC, 2021)

Smoky Mountain Data Challenge 2021: An Open Call to Solve Scientific Data Challenges Using Advanced Data Analytics and Edge Computing. - Pravallika Devineni; Panchapakesan Ganesh; Nikhil Sivadas; Abhijeet Dhakane; Ketan Maheshwari; Drahomira Herrmannova; Ramakrishnan Kannan; Seung-Hwan Lim; Thomas E. Potok; Jordan B. Chipka; Priyantha Mudalige; Mark Coletti; et al. (SMC, 2021)

Distilling Knowledge from Ensembles of Cluster-Constrained-Attention Multiple-Instance Learners for Whole Slide Image Classification. - Folami Alamudun; Jacob D. Hinkle; Sajal Dash; Benjamín Hernández; Aristeidis Tsaris; Hong-Jun Yoon (IEEE Big Data, 2022)

Hvac: Removing I/O Bottleneck for Large-Scale Deep Learning Applications. - Awais Khan 002; Arnab K. Paul; Christopher Zimmer 001; Sarp Oral; Sajal Dash; Scott Atchley; Feiyi Wang (CLUSTER, 2022)

A Scalable Pipeline for Gigapixel Whole Slide Imaging Analysis on Leadership Class HPC Systems. - Sajal Dash; Benjamín Hernández; Aristeidis Tsaris; Folami T. Alamudun; Hong-Jun Yoon; Feiyi Wang (IPDPS Workshops, 2022)

Image transformers for classifying acute lymphoblastic leukemia. - Priscilla Cho; Sajal Dash; Aristeidis Tsaris; Hong-Jun Yoon (Computer-Aided Diagnosis, 2022)

Toward an Autonomous Workflow for Single Crystal Neutron Diffraction. - Junqi Yin; Guannan Zhang; Huibo Cao; Sajal Dash; Bryan C. Chakoumakos; Feiyi Wang (SMC, 2022)

Distributing Simplex-Shaped Nested for-Loops to Identify Carcinogenic Gene Combinations. - Sajal Dash; Mohammad Alaul Haque Monil; Junqi Yin; Ramu Anandakrishnan; Feiyi Wang (IPDPS, 2023)

A comparison of histopathology imaging comprehension algorithms based on multiple instance learning. - Adam Saunders; Sajal Dash; Aristeidis Tsaris; Hong-Jun Yoon (Digital and Computational Pathology, 2023)

Scaling Resolution of Gigapixel Whole Slide Images Using Spatial Decomposition on Convolutional Neural Networks. - Aristeidis Tsaris; Joshua Romero; Thorsten Kurth; Jacob D. Hinkle; Hong-Jun Yoon; Feiyi Wang; Sajal Dash; Georgia D. Tourassi (PASC, 2023)

FORGE: Pre-Training Open Foundation Models for Science. - Junqi Yin; Sajal Dash; Feiyi Wang; Mallikarjun Shankar (SC, 2023)

DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies. - Shuaiwen Leon Song; Bonnie Kruft; Minjia Zhang; Conglong Li; Shiyang Chen 004; Chengming Zhang 006; Masahiro Tanaka; Xiaoxia Wu; Jeff Rasley; Ammar Ahmad Awan; Connor Holmes; Martin Cai; et al. (CoRR, 2023)

Ultra-Long Sequence Distributed Transformer. - Xiao Wang 004; Isaac Lyngaas; Aristeidis Tsaris; Peng Chen 035; Sajal Dash; Mayanka Chandra Shekar; Tao Luo 014; Hong-Jun Yoon; Mohamed Wahib; John Gounley (CoRR, 2023)

Optimizing Distributed Training on Frontier for Large Language Models. - Sajal Dash; Isaac Lyngaas; Junqi Yin; Xiao Wang 004; Romain Egele; Guojing Cong; Feiyi Wang; Prasanna Balaprakash (CoRR, 2023)

Evaluation of pre-training large language models on leadership-class supercomputers. - Junqi Yin; Sajal Dash; John Gounley; Feiyi Wang; Georgia D. Tourassi (J. Supercomput., 2023)

Exploring Vision Transformers on the Frontier Supercomputer for Remote Sensing and Geoscientific Applications. - Valentine Anantharaj; Takuya Kurihana; Sajal Dash; Gabriele Padovani; Sandro Fiore (IGARSS, 2024)

Optimizing Distributed Training on Frontier for Large Language Models. - Sajal Dash; Isaac Lyngaas; Junqi Yin; Xiao Wang 004; Romain Egele; J. Austin Ellis; Matthias Maiterth; Guojing Cong; Feiyi Wang; Prasanna Balaprakash (ISC, 2024)

Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars. - Wesley Brewer; Aditya Kashi; Sajal Dash; Aristeidis Tsaris; Junqi Yin; Mallikarjun Shankar; Feiyi Wang (CoRR, 2024)

The Home in Smart Homes: Placemaking Through a Design Framework for Integrating Spatial Design and Smart Capabilities. - Archi Dasgupta; Sajal Dash; Denis Gracanin (HCI (4), 2025)

Compression-Induced Communication-Efficient Large Model Training and Inferencing. - Sudip K. Seal; Maksudul Alam; Jorge Ramirez; Sajal Dash; Hao Lu 001 (CoRR, 2025)

X-MoE: Enabling Scalable Training for Emerging Mixture-of-Experts Architectures on HPC Platforms. - Yueming Yuan; Ahan Gupta; Jianping Li; Sajal Dash; Feiyi Wang; Minjia Zhang (CoRR, 2025)

Bigpicc: a graph-based approach to identifying carcinogenic gene combinations from mutation data. - Vladyslav Oles; Sajal Dash; Ramu Anandakrishnan (BMC Bioinform., 2025)