Investigating the Successes and Failures of BERT for Passage Re-Ranking. - Harshith Padigela; Hamed Zamani; W. Bruce Croft (CoRR, 2019)

Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology. - Syed Ashar Javed; Dinkar Juyal; Harshith Padigela; Amaro Taylor-Weiner; Limin Yu; Aaditya Prakash (NeurIPS, 2022)

Additive MIL: Intrinsic Interpretability for Pathology. - Syed Ashar Javed; Dinkar Juyal; Harshith Padigela; Amaro Taylor-Weiner; Limin Yu; Aaditya Prakash (CoRR, 2022)

Synthetic DOmain-Targeted Augmentation (S-DOTA) Improves Model Generalization in Digital Pathology. - Sai Chowdary Gullapally; Yibo Zhang; Nitin Kumar Mittal; Deeksha Kartik; Sandhya Srinivasan; Kevin Rose; Daniel Shenker; Dinkar Juyal; Harshith Padigela; Raymond Biju; Victor Minden; Chirag Maheshwari; et al. (CoRR, 2023)

SC-MIL: Supervised Contrastive Multiple Instance Learning for Imbalanced Classification in Pathology. - Dinkar Juyal; Siddhant Shingi; Syed Ashar Javed; Harshith Padigela; Chintan Shah; Anand Sampat; Archit Khosla; John Abel; Amaro Taylor-Weiner (WACV, 2024)

PLUTO: Pathology-Universal Transformer. - Dinkar Juyal; Harshith Padigela; Chintan Shah; Daniel Shenker; Natalia Harguindeguy; Yi Liu; Blake Martin; Yibo Zhang; Michael Nercessian; Miles Markey; Isaac Finberg; Kelsey Luu; et al. (CoRR, 2024)

Interpretability analysis on a pathology foundation model reveals biologically relevant embeddings across modalities. - Nhat Le; Ciyue Shen; Chintan Shah; Blake Martin; Daniel Shenker; Harshith Padigela; Jennifer A. Hipp; Sean Grullon; John Abel; Harsha Pokkalla; Dinkar Juyal (CoRR, 2024)

ML-Dev-Bench: Comparative Analysis of AI Agents on ML development workflows. - Harshith Padigela; Chintan Shah; Dinkar Juyal (CoRR, 2025)