Nikhil Mehta
Computer Science · Purdue University West Lafayette
Publications
33
Citations
164
Est. group size
~1
Recurring co-author estimate
Active years
35
Publishing since 1991
Nikhil Mehta's research focuses on machine learning methods that help models learn efficiently from limited or evolving data, including continual learning (models that keep learning new tasks without forgetting old ones), few-shot and zero-shot learning (recognizing new categories with little or no labeled examples), and generative modeling techniques such as topic models and graph representation learning. Work also touches on applied areas like crop disease detection using drone imagery and privacy-conscious federated learning. This research would suit students interested in the statistical and algorithmic foundations of adaptable, data-efficient AI systems.
Publication output has been fairly steady over the past several years, rising from none before 2019 to a consistent 2-6 papers annually, with a peak in 2023.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Improving Hyperparameter Optimization with Checkpointed Model Weights
Lecture notes in computer science · 2025
- Meta-Learned Attribute Self-Interaction Network for Continual and Generalized Zero-Shot Learning
2024
- HyperMix: Out-of-Distribution Detection and Classification in Few-Shot Settings
2024
- Pushing the Efficiency Limit Using Structured Sparse Convolutions
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · 2023
- HyperMix: Out-of-Distribution Detection and Classification in Few-Shot Settings
arXiv (Cornell University) · 2023
- Meta-Learned Attribute Self-Interaction Network for Continual and Generalized Zero-Shot Learning
arXiv (Cornell University) · 2023
- A Two-Step Machine Learning Approach for Crop Disease Detection Using GAN and UAV Technology
Remote Sensing · 2022
- WAFFLe: Weight Anonymized Factorization for Federated Learning
IEEE Access · 2022
- Efficient Feature Transformations for Discriminative and Generative Continual Learning
2021
- A two-step machine learning approach for crop disease detection: an application of GAN and UAV technology
arXiv (Cornell University) · 2021
- Meta-Learned Attribute Self-Gating for Continual Generalized Zero-Shot Learning
arXiv (Cornell University) · 2021
- Continual Learning using a Bayesian Nonparametric Dictionary of Weight Factors
International Conference on Artificial Intelligence and Statistics · 2021
- Graph Representation Learning via Ladder Gamma Variational Autoencoders
Proceedings of the AAAI Conference on Artificial Intelligence · 2020
- Continual Learning using a Bayesian Nonparametric Dictionary of Weight Factors
arXiv (Cornell University) · 2020
- Bayesian Nonparametric Weight Factorization for Continual Learning.
arXiv (Cornell University) · 2020
- arXiv (Cornell University)×12
- International Conference on Artificial Intelligence and Statistics×2
- Remote Sensing×1
- IEEE Access×1
- Proceedings of the AAAI Conference on Artificial Intelligence×1
- Yaqing Wang
Computer Science · Purdue University West Lafayette
- Zihan Zhang
Computer Science · The Ohio State University
- Vardaan Pahuja
Computer Science · The Ohio State University
- Kai Zhang
Computer Science · The Ohio State University
- Huan Sun
Computer Science · The Ohio State University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
Claim or correct this profile