LabCompass

Avimanyu Sahoo

Computer Science · Purdue University West Lafayette

Mid career · publishing since 2013Rising activity

Publications

89

Citations

1,528

Est. group size

Recurring co-author estimate

Active years

14

Publishing since 2013

Research summary
AI-generated

Avimanyu Sahoo works on control systems and machine learning, focusing on adaptive and data-driven control for robots and nonlinear systems, as well as neural network methods for tasks like battery health prediction, graph learning, and signal processing. The work often combines theoretical system control techniques (like adaptive dynamic programming) with modern deep learning tools (like physics-informed neural networks and graph neural networks) applied to engineering problems such as robotic manipulators and lithium-ion batteries.

Adaptive and data-driven control of robots and nonlinear systemsPhysics-informed neural networks for battery modelingGraph neural networks and representation learningSystem identification and estimation methodsMachine learning for engineering and security applications

Publication output was relatively steady but modest through the mid-2010s to early 2020s, then increased sharply starting in 2024, suggesting a recent growth in research activity.

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

Publication cadence
Publications per year over the last 10 years — averaging 9.0/year recently
2017: 2 publications172018: 7 publications182019: 5 publications192020: 7 publications202021: 5 publications212022: 3 publications222023: 3 publications232024: 19 publications19242025: 12 publications252026: 8 publications26
Recent publications
Publishes in
  • arXiv (Cornell University)×7
  • IEEE Transactions on Neural Networks and Learning Systems×2
  • Neural Networks×2
  • IEEE Transactions on Industrial Informatics×2
  • 2020 ASEE Virtual Annual Conference Content Access Proceedings×2
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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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