Publications

Intuitive knowledge of object acoustics enables perceptual separation of physical variables from impact sounds

V. Agarwal, J. Traer, J. Schwartz, J. H. McDermott

Proceedings of the National Academy of Sciences (PNAS), 123(47), e2605876123 · bioRxiv preprint

Synthesis and perception of sounds from physical interactions reveals auditory intuitive physics

V. Agarwal

PhD thesis, Massachusetts Institute of Technology, Advisor: Josh H. McDermott

Physics, ecological acoustics and the auditory system

J. H. McDermott, V. Agarwal, J. Traer

Current Biology, 34(20), R1006–R1013

Human perception of impact sounds suggests auditory intuitive physics

V. Agarwal, J. Traer, J. H. McDermott

Journal of the Acoustical Society of America (meeting abstract), 155(3_Supplement), A307

Sample-efficient learning of auditory object representations using differentiable impulse response synthesis

V. Agarwal, J. Traer, J. H. McDermott

ICML 2023 Workshop on Differentiable Almost Everything

Understanding auditory intuitive physics

J. H. McDermott, V. Agarwal, F. De La Torre, J. Traer

Journal of the Acoustical Society of America (meeting abstract), 153(3_supplement), A364

Inferring object interactions from scraping and rolling sounds

V. Agarwal, J. H. McDermott

Proceedings of the 44th Annual Meeting of the Cognitive Science Society (CogSci 2022)

Object-based synthesis of scraping and rolling sounds based on non-linear physical constraints

V. Agarwal, M. Cusimano, J. Traer, J. H. McDermott

24th International Conference on Digital Audio Effects (DAFx20in21), pp. 136–143 · arXiv

Scientific Agent Skills: A Library of Procedural Knowledge for Research Agents

T. Kassis, V. Agarwal, Y. He, D. Patel, A. M. Brueckner

arXiv preprint, arXiv:2609.00065 · GitHub

MetaMuse: A Multi-Agent AI System for Biomedical Metadata Curation and Harmonization

E. Mittal, E. Litman, T. D. Myers, V. Agarwal, A. Gopinath, T. Kassis

bioRxiv preprint

GeneJepa: A Predictive World Model of the Transcriptome

E. Litman, T. D. Myers, V. Agarwal, E. Mittal, O. Li, A. Gopinath, T. Kassis

bioRxiv preprint

Revive-Flow: A Foundation Model for Blood DNAm Aging

E. Litman, T. D. Myers, V. Agarwal, E. Mittal, A. Gopinath, T. Kassis

bioRxiv preprint

Guided multi-agent AI invents highly accurate, uncertainty-aware transcriptomic aging clocks

V. Agarwal, O. Li, C. A. Petty, T. Kassis, P. W. K. Rothemund, D. A. Sinclair, A. Gopinath

bioRxiv preprint

The Limitations of TabPFN for High-Dimensional RNA-seq Analysis

S. Zhou, V. Agarwal, A. Gopinath, T. Kassis

bioRxiv preprint

K-Dense Analyst: Towards Fully Automated Scientific Analysis

O. Li, V. Agarwal, S. Zhou, A. Gopinath, T. Kassis

arXiv preprint, arXiv:2508.07043

Transcriptomic age prediction using mixture-of-experts models reveals tissue-specific aging signatures in large-scale human RNA-sequencing data

Q. Quill, V. Agarwal, O. Li, A. Patil, T. Kassis, A. Gopinath

medRxiv preprint