Jeffrey Mark Siskind
Computer Science · Purdue University West Lafayette
Publications
114
Citations
6,695
Est. group size
—
Recurring co-author estimate
Active years
37
Publishing since 1990
Jeffrey Mark Siskind's research spans several areas of computer science, including combining language and vision (e.g., connecting sentences to video and robot navigation instructions), automatic differentiation (mathematical techniques for computing derivatives in programs, important for machine learning), and human-in-the-loop AI systems. His work also touches on analyzing brain activity (EEG) data related to visual recognition tasks. Overall, this represents a mix of core programming-language techniques and applied AI/robotics research.
Publication output peaked around 2018 and has since been modest and irregular, averaging about 1-2 papers per year over the last five years.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Automatic Differentiation: Inverse Accumulation Mode
Society for Industrial and Applied Mathematics eBooks · 2026
- Towards Effective Human-in-the-Loop Assistive AI Agents
arXiv (Cornell University) · 2025
- Towards Effective Human-in-the-Loop Assistive AI Agents
2025
- The Repeated-Stimulus Confound in Electroencephalography
arXiv (Cornell University) · 2025
- Learning Exemplar Representations in Single-Trial EEG Category Decoding
2024
- Talk the talk and walk the walk: Dialogue-driven navigation in unknown indoor environments
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · 2021
- The Amazing Race TM: Robot Edition
arXiv (Cornell University) · 2020
- Perturbation confusion in forward automatic differentiation of higher-order functions
Journal of Functional Programming · 2019
- Divide-and-conquer checkpointing for arbitrary \nprograms with no user annotation
MURAL - Maynooth University Research Archive Library (National University of Ireland, Maynooth) · 2018
- Automatic Differentiationin Machine Learning: a Survey
2018
- A Critical Investigation of Deep Reinforcement Learning for Navigation
arXiv (Cornell University) · 2018
- Floyd-Warshall Reinforcement Learning: Learning from Past Experiences to Reach New Goals
arXiv (Cornell University) · 2018
- Floyd-Warshall Reinforcement Learning: Learning from Past Experiences to\n Reach New Goals
arXiv (Cornell University) · 2018
- Learning Goal-Conditioned Value Functions with one-step Path rewards rather than Goal-Rewards
2018
- Sentence Directed Video Object Codiscovery
International Journal of Computer Vision · 2017
- arXiv (Cornell University)×12
- IEEE Transactions on Pattern Analysis and Machine Intelligence×3
- Journal of Functional Programming×1
- International Journal of Computer Vision×1
- PLoS ONE×1
- AJ Piergiovanni
Computer Science · Indiana University
- Pranav Maneriker
Computer Science · The Ohio State University
- Jihyung Kil
Computer Science · The Ohio State University
- Hoin Jung
Computer Science · Purdue University West Lafayette
- Xiyao Wang
Computer Science · Purdue University West Lafayette
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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