Shuchin Aeron
Midway along a multi-pitch climb … “There is no choice …The only way out is up!” - Adam Young
Affiliations
- Professor, Department of ECE, Tufts University 171 College Ave, Medford, MA 02464,
- Office: Halligan Hall, Room 144
- Senior Investigator, NSF IAIFI
- Affiliate Faculty
- Tufts Department of Computer Science
- Tufts Department of Mathematics
Academic and Research Interests
- Statistical Signal Processing
- Information Theory
- High-Dimensional Statistics and Learning
- Optimal Transport, Generative Models
- Sparse Models for Signal Processing
- Contrastive Learning
Applications Areas
- Inverse Problems for Remote Sensing and Imaging (Geophysics, MRI, HEP)
- Bioinformatics
- LLM for Learning Sciences (Education, Student Outcomes)
news
| Oct 30, 2025 | Paper on adversarial In-Context Learning (ICL) accepted to NeuRIPS 2025 main conference |
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| Feb 20, 2025 | Talk at UPRM, Puerto Rico. Optimal Transport: Metric and Geometric Properties with Applications |
| Jan 27, 2025 | Two papers on Optimal Transport accepted to AISTATS 2025 main conference |
| Oct 01, 2024 | Best Paper Award IEEE MLSP on Contrastive Learning with Imbalanced Classes |
selected recent publications
- generative modelingTowards Universal Unfolding of Detector Effects in High-Energy Physics using Denoising Diffusion Probabilistic ModelsMethods, 2024
- optimal transportLinearized Wasserstein Barycenters: Synthesis, Analysis, Representational Capacity, and Applications2024
- optimal transportSynthesis and Analysis of Data as Probability Measures with Entropy-Regularized Optimal Transport2025
- optimal transportMultivariate Soft Rank via Entropy-Regularized Optimal Transport: Sample Efficiency and Generative ModelingJournal of Machine Learning Research, 2023
- generative modelingScore-based Diffusion Models for Generating Liquid Argon Time Projection Chamber ImagesPhysical Review Letters D, 2024
- optimal transportOn Rank Energy Statistics via Optimal Transport: Continuity, Convergence, and Change Point DetectionIEEE Transactions on Information Theory, 2024