Papers
31
Total Citations
1,758
H-Index
14
About
Jeff Schneider is a pioneering researcher whose work sits at the intersection of robotics, machine learning, and autonomous systems. His contributions span reinforcement learning, multi-robot coordination, and Bayesian optimization, establishing him as a significant voice in applied artificial intelligence. Schneider's most influential work tackles some of the field's hardest problems head-on. His 2019 paper on multimodal trajectory predictions for autonomous driving, accumulated over 670 citations, demonstrates his ability to bridge deep learning and real-world safety-critical applications. Earlier foundational work on autonomous helicopter control using reinforcement learning policy search methods (278 citations) showcased his talent for translating theoretically complex frameworks — such as partially observable Markov decision processes — into working physical systems. His research consistently grapples with coordination under uncertainty, particularly in multi-agent and multi-robot settings. Papers on game-theoretic control for robot teams and partially observable stochastic games reflect a sophisticated understanding of decentralized decision-making. Meanwhile, his contributions to Bayesian and multi-fidelity optimization — including Gaussian process bandit optimization and expensive multiobjective optimization for robotics — address the practical challenge of learning efficiently when experiments are costly. Across a career spanning more than two decades, Schneider has shaped how autonomous systems learn, plan, and collaborate, leaving a lasting imprint on both academic research and real-world robotics applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2Autonomous helicopter control using reinforcement learning policy search methods278 citations · 2002
- 3
- 4Policy Search by Dynamic Programming133 citations · 2018
- 5
- 6Game Theoretic Control for Robot Teams71 citations · 2006
- 7Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations68 citations · 2016
- 8Learning Opportunity Costs in Multi-Robot Market Based Planners47 citations · 2006
- 9Expensive multiobjective optimization for robotics36 citations · 2013
- 10Q2: memory-based active learning for optimizing noisy continuous functions27 citations · 2002