Scholar
Kaustubh Dhole
Google Scholar ID: xSGbQ3oAAAAJ
BITS Pilani, Emory University & Amelia R&D
Citations & Impact
Citations
3,800
H-index
14
i10-index
18
Publications
20
Co-authors
20
list available
Publications
Resume (English only)
Academic Achievements
- - Publications can be found on Semantic Scholar and Google Scholar.
- - Co-organizer of the Generation, Evaluation & Metrics Workshops (GEM) 2021, GEM 2022, GEM 2023, 2025.
- - Co-organizer of the NL-Augmenter, the largest data augmentation repository, and key contributor to LLM task benchmark BIG-Bench.
- - Presented work on RAG evaluation at the Workshop on Task Focussed IR in the Era of Generative AI at Redmond, Microsoft Research.
- - Gave a talk on Retrieval Augmented Generation at the University of Edinburgh, 2024.
- - Presented IR work at IARPA Demo Day, Maryland.
- - Mentored 5 graduate students on efficient variants of GNNs at the London Geometry & Machine Learning Summer School, 2022.
- - Invited as Speaker & Guest of Honour at VIT's ICAITR 2021, Mumbai, gave a short talk on 'NLP in the Past Decade'.
Research Experience
- - Applied Scientist (Summers), ~1 year (2022-2025), Amazon AGI, Alexa Search Experience Science, Stores Foundational AI
- - AI R&D Lead (Science), ~3.5 years (2017-2021), IPsoft Amelia.ai (now SoundHound), Bangalore & New York
- - AI R&D Engineer (Science), ~3 years (2015-2017), IPsoft Amelia.ai (now SoundHound), Bangalore & New York
- - Researcher, ~1.5 years, TIFR, Mumbai
Education
- PhD: Department of Computer Science, Emory University, Advisor: Prof. Eugene Agichtein; Master's: Emory University; Bachelor of Engineering (Hons): Electrical Engineering, BITS Pilani.
Background
- Research interests include Natural Language Processing and Information Retrieval. Completed bachelor's at BITS Pilani, India, spent a year and a half at Tata Institute of Fundamental Research (TIFR, Mumbai), and worked for 6 years at IPsoft Amelia.ai (now SoundHound) in Bangalore and New York, collaborating with Prof. Chris Manning.
Miscellany
- Areas of interest include Reasoning, NLG Evaluation, Retrieval, Retrieval Augmented Generation, Dialog Systems, Graph Neural Networks, Data Augmentation, Efficient Transformers, Privacy Preserving ML, and the Bigger Picture of LLMs.
