LabCompass

Tejas Kulkarni

Computer Science · Purdue University West Lafayette

Mid career · publishing since 2011Rising activity

Publications

44

Citations

1,162

Est. group size

~1

Recurring co-author estimate

Active years

16

Publishing since 2011

Research summary
AI-generated

This researcher's work centers on differential privacy, a set of mathematical techniques for analyzing data or training machine learning models while limiting what can be learned about any individual's information. Recent projects apply these privacy methods to federated learning (training models across many devices without centralizing data), Bayesian inference, hyperparameter tuning, and large language model in-context learning, with some earlier work on robotics perception and information-hiding techniques like steganography.

Differential privacyPrivacy-preserving machine learningFederated and decentralized learningBayesian inference under privacy constraintsRobotics perception and representation learning

Publication output peaked around 2018-2019, dropped off in the following years including a gap in 2022, and has shown a modest, irregular resurgence from 2023 onward.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026

Publication cadence
Publications per year over the last 10 years — averaging 2.2/year recently
2017: 2 publications172018: 8 publications8182019: 8 publications8192020: 3 publications202021: 4 publications21222023: 4 publications232024: 2 publications242025: 3 publications252026: 2 publications26
Recent publications
Publishes in
  • arXiv (Cornell University)×15
  • Research Explorer (The University of Manchester)×2
  • Proceedings of the VLDB Endowment×1
  • Internet Mathematics×1
  • Proceedings on Privacy Enhancing Technologies×1
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By research-topic overlap
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  • Chenghong Wang

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  • Fang-Yu Rao

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  • Joshua C. Zhao

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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.

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