Scholar
Rishabh Iyer
Google Scholar ID: l_XxJ1kAAAAJ
University of Texas Dallas, Microsoft, UW
Citations & Impact
Citations
3,619
H-index
30
i10-index
58
Publications
20
Co-authors
43
list available
Publications
Resume (English only)
Academic Achievements
- Published multiple papers including SCORE (ICML 2024), SMILE (ECCV 2024), CROWD (NeurIPS 2025), SHaSaM (WACV 2026), GLISTER (AAAI 2021), GRADMATCH (ICML 2021), RETRIEVE (NeurIPS 2021), SELCON (ICML 2021), ORIENT (NeurIPS 2022), AUTOMATA (NeurIPS 2022), SubSelNet (NeurIPS 2023), PGM (EMNLP 2022), INGENIOUS (EMNLP 2023), GCFL (WACV 2024), FASS (ICML 2015), Learning with Less Data (WACV 2019), SIMILAR (NeurIPS 2021), TALISMAN (ECCV 2022), PRISM (AAAI 2022), CLINICAL (MICCAI 2022), DIAGNOSE (MICCAI 2022), DITTO (ACL 2023), CLARIFIER (WACV 2024), STONE (NeurIPS 2024), WRSSL (ICDM 2022), NestedMAML (AAAI 2022), WISDOM (ACL 2022), SPEAR (ACL 2021), RuleAugmentedCP (ACL 2021), GCR (CVPR 2022), PLATINUM (ICML 2022).
Research Experience
- Was a Senior Research Scientist at Microsoft from 2016 to 2019. Current research focuses on combinatorial representation learning, compute-efficient learning, data-efficient learning, robust deep learning, data programming, and weak supervision.
Education
- Completed Ph.D. in 2015 from the University of Washington, Seattle, under the guidance of Jeff Bilmes.
Background
- Currently an Assistant Professor at the University of Texas, Dallas, leading the CARAML Lab. Also a Visiting Assistant Professor at the Indian Institute of Technology, Bombay. Research interests include combinatorial representation learning, compute-efficient learning, data-efficient learning, robust deep learning, data programming, and weak supervision.
Miscellany
- Excited about making machines assist humans in processing massive amounts of data, particularly in understanding videos and images.
