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Jim Boerkoel

John V. Sell Professor of Computer Science @ Harvey Mudd College
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Prof. Jim Boerkoel

Prof. Jim Boerkoel

Contact Information

  • boerkoel@hmc.edu
  • McGregor Computer Science Center, Room 311
  • Office: (909) 607-4522

Biography

Jim Boerkoel is the John V. Sell Professor of Computer Science at Harvey Mudd College where he leads the Human Experience & Agent Teamwork (HEAT) Lab. The goal of the HEATLab is to develop techniques that augment humans' own cognitive and physical abilities to create integrated human-robot teams that are more capable than their individual counterparts. Prior to joining HMC, Jim worked as a Postdoctoral Associate with Julie Shah of the Interactive Robotics Group at MIT. Jim completed his doctoral thesis on developing distributed approaches for constraint-based, multi-agent scheduling under the supervision of Ed Durfee at the University of Michigan.

Jim received his B.S. (Summa Cum Laude) in Mathematics and Computer Science from Hope College (2005), and his M.S. (2008) and Ph.D. (2012) in Computer Science and Engineering from the University of Michigan. In 2017, Boerkoel was recognized with an NSF CAREER award for his project "Robust and Reliable Multiagent Scheduling under Uncertainty." His research interests include automated planning and scheduling, multi-robot coordination, human-robot interaction, and AI education.

Education & Experience

  • Professor, Computer Science, Harvey Mudd College (2013 – Present)
  • Chair, Department of Computer Science, Harvey Mudd College (2021 – 2026)
  • Postdoctoral Associate, Interactive Robotics Group, CSAIL, MIT (2012 – 2013)
  • Ph.D., Computer Science & Engineering, University of Michigan (2012)
  • M.S., Computer Science & Engineering, University of Michigan (2008)
  • B.S., Mathematics & Computer Science, Hope College (2005, Summa Cum Laude)

Research Interests

  • AI Education
  • Human-Robot Teamwork
  • Multiagent Coordination
  • Temporal Planning
  • Scheduling
  • Climate Applications

Curriculum Vitae

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Research

Prof. Jim Boerkoel leads the Human Experience & Agent Teamwork Lab (HEATlab) @ HMC. The mission of the HEATlab is to develop robust techniques for human-robot teamwork that exploit the relative strengths of humans and agents. We focus on using ideas from AI to automate the scheduling and coordination of human-robot and robot-robot teams. Read more about our work in this PCMag feature interview.

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Recent Publications

2023

James Boerkoel and Mehmet Ergezer. 2023. An Undergraduate Consortium for Addressing the Leaky Pipeline to Computing Research. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2023), Association for Computing Machinery, Toronto ON, Canada, 687–693. DOI: https://doi.org/10.1145/3545945.3569841
Rosy Chen, Yiran Ma, Siqi Wu, and James C. Boerkoel Jr. 2023. Sensitivity Analysis for Dynamic Control of PSTNs with Skewed Distributions. In Proceedings of the International Conference on Automated Planning and Scheduling, 95–99. DOI: https://doi.org/10.1609/icaps.v33i1.27183
Emily Weiss, Zeneve Jacotin, Ryan Blake Jackson, Amy Yuan, and James C Boerkoel. 2023. Analyzing the Fluency of Human-Robot Interactions. In Proceedings of the AAAI 2023 Spring Symposium Series on HRI in Academia and Industry: Bridging the Gap. PDF

2022

James C Boerkoel, Mehmet Ergezer, Christine Alvarado, and Valerie Taylor. 2022. Expanding Computing Research Pathways (Panel). In The Conference on Research in Equity and Sustained Participation in Engineering, Computing, and Technology (RESPECT).
Malia Morgan, Julianna Schalkwyk, Huaxiaoyue Wang, Hannah Davalos, Ryan Martinez, Vibha Rohilla, and James Boerkoel. 2022. Simple Temporal Networks for Improvisational Teamwork. In Proceedings of the International Conference on Automated Planning and Scheduling, 261–269. DOI: https://doi.org/10.1609/icaps.v33i1.27183

2021

James Boerkoel, James Mason, Daniel Wang, Steve Chien, and Adrien Maillard. 2021. An Efficient Approach for Scheduling Imaging Tasks Across a Fleet of Satellites. In Proceedings of 2021 International Workshop on Planning & Scheduling for Space (IWPSS 21). PDF

2020

Maya Abo Dominguez, La William, and Jim Boerkoel. 2020. Modeling Human Temporal Uncertainty in Human-Agent Teams. In Proc. of Artificial Intelligence in Human Robot Interaction AAAI Fall Symposium Series (AI-HRI 2020). DOI: https://doi.org/10.48550/arXiv.2010.04849
Shyan Akmal, Savana Ammons, Hemeng Li, Michael Gao, Lindsay Popowski, and James C. Boerkoel. 2020. Quantifying controllability in temporal networks with uncertainty. Artificial Intelligence 289, (2020), 103384. DOI: https://doi.org/10.1016/j.artint.2020.103384
Steve Ankuo Chien, James Boerkoel, James Mason, D. Wang, Ashley Gerard Davies, Joel Mueting, Vivek Vittaldev, Vishwa Shah, and Ignacio Zuleta. 2020. Space Ground Sensorwebs for Volcano Monitoring. In The International Symposium on Artificial Intelligence, Robotics and Automation in Space. PDF
Steve Chien, James Boerkoel, James Mason, Daniel Wang, Ashley Davies, Joel Mueting, Vivek Vittaldev, Vishwa Shah, and Ignacio Zuleta. 2020. Leveraging Space and Ground Assets in A Sensorweb for Scientific Monitoring: Early Results and Opportunities for the Future. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2020), IEEE, 3833–3836. DOI: https://doi.org/10.1109/IGARSS39084.2020.9324049
Michael Gao, Lindsay Popowski, and James Boerkoel. 2020. Dynamic control of probabilistic simple temporal networks. In Proceedings of the AAAI Conference on Artificial Intelligence, 9851–9858. DOI: https://doi.org/10.1609/aaai.v34i06.6538

2019

J. R. Abrahams, D. A. Chu, G. Diehl, M. Knittel, J. Lin, W. Lloyd, J. C. Boerkoel, and J. Frank. 2019. Dream: An algorithm for mitigating the overhead of robust rescheduling. In Proc. of the 29th International Conference on Automated Planning and Scheduling (ICAPS-19), 3–12. DOI: https://doi.org/10.1609/icaps.v29i1.3454
Shyan Akmal, Savana Ammons, Hemeng Li, and James C. Boerkoel. 2019. Quantifying degrees of controllability in temporal networks with uncertainty. In Proc. of the 29th International Conference on Automated Planning and Scheduling (ICAPS-19), 22–30. DOI: https://doi.org/10.1609/icaps.v29i1.3456
J. Y. Lee, V. Ojha, and J. C. Boerkoel. 2019. Measuring and optimizing durability against scheduling disturbances. In Proc. of the 29th International Conference on Automated Planning and Scheduling (ICAPS-19), 264–268. DOI: https://doi.org/10.1609/icaps.v29i1.3486

2018

A. Huang, L. Lloyd, M. Omar, and James Boerkoel. 2018. New perspectives on flexibility in simple temporal planning. In Proc. of the 28th International Conference on Automated Planning and Scheduling (ICAPS-18), 123–131. DOI: https://doi.org/10.1609/icaps.v28i1.13907
V. V. Unhelkar, S. Dörr, A. Bubeck, P. A. Lasota, J. Perez, H. C. Siu, J. C. Boerkoel, Q. Tyroller, J. Bix, S. Bartscher, and J. A. Shah. 2018. Mobile robots for moving-floor assembly lines: Design, evaluation, and deployment. IEEE Robotics & Automation Magazine 25, 2 (2018), 72–81. DOI: https://doi.org/10.1109/MRA.2018.2815639

2017

D. Fisher, C. Isbell, M. L. Littman, M. Wollowski, T. W. Neller, and James Boerkoel. 2017. Ask me anything about moocs. AI Magazine 38, 2 (2017), 7–12. DOI: https://doi.org/10.1609/aimag.v38i2.2729
K. Lund, S. Dietrich, S. Chow, and James Boerkoel. 2017. Robust execution of probabilistic temporal plans. In Proc. of the 31st National Conference on Artificial Intelligence (AAAI-17), 3597–3604. DOI: https://doi.org/10.1609/aaai.v31i1.11019
M. Wollowski, T. Neller, and James Boerkoel. 2017. Artificial intelligence education: Editorial introduction. AI Magazine 38, 2 (2017), 5–6. DOI: https://doi.org/10.1609/aimag.v38i2.2728

2015

J. Brooks, E. Reed, A. Gruver, and J. C. Boerkoel. 2015. Robustness in probabilistic temporal planning. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15), 3239–3246. https://dl.acm.org/doi/10.5555/2888116.2888167
P. L. Donti, H. Rosenbloom, A. Gruver, and J. C. Boerkoel. 2015. Predicting the quality of user experiences to improve productivity and wellness. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 4154–4155. DOI: https://doi.org/10.1609/aaai.v29i1.9740

2014

E. H. Durfee, J. C. Boerkoel, and J. Sleight. 2014. Using hybrid scheduling for the semi-autonomous formation of expert teams. Future Generation Computer Systems 31, (2014), 200–212. DOI: https://doi.org/10.1016/j.future.2013.04.008
V. V. Unhelkar, J. Perez, J. C. Boerkoel, J. Bix, S. Bartscher, J. Shah, et al. 2014. Towards control and sensing for an autonomous mobile robotic assistant navigating assembly lines. In Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 4161–4167. DOI: https://doi.org/10.1109/ICRA.2014.6907464

2013

J. C. Boerkoel and E. H. Durfee. 2013. Decoupling the multiagent disjunctive temporal problem. In Twenty-Seventh AAAI Conference on Artificial Intelligence, 123–129. https://dl.acm.org/doi/abs/10.5555/2484920.2485113
J. C. Boerkoel and E. H. Durfee. 2013. Distributed reasoning for multiagent simple temporal problems. Journal of Artificial Intelligence Research 47, (2013), 95–156. DOI: https://doi.org/10.1613/jair.3840
J. C. Boerkoel, L. Planken, R. Wilcox, and J. A. Shah. 2013. Distributed algorithms for incrementally maintaining multiagent simple temporal networks. In Proceedings of the 23rd International Conference on Automated Planning and Scheduling (ICAPS), 11–19. https://dl.acm.org/doi/10.5555/3038718.3038721

2012

J. C. Boerkoel and E. H. Durfee. 2012. A distributed approach to summarizing spaces of multiagent schedules. In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 1742–1748. https://dl.acm.org/doi/10.5555/2566972.2566976

2011

J. C. Boerkoel and E. H. Durfee. 2011. Distributed algorithms for solving the multiagent temporal decoupling problem. In The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1 (AAMAS ’11), International Foundation for Autonomous Agents and Multiagent Systems, Taipei, Taiwan, 141–148. https://dl.acm.org/doi/abs/10.5555/2030470.2030491

2010

J. C. Boerkoel and E. H. Durfee. 2010. A comparison of algorithms for solving the multiagent simple temporal problem. In Proceedings of the Twentieth International Conference on International Conference on Automated Planning and Scheduling (ICAPS’10), AAAI Press, Toronto, Ontario, Canada, 26–33. https://dl.acm.org/doi/10.5555/3037334.3037339
J. C. Boerkoel, E. H. Durfee, and K. Purrington. 2010. Generalized solution techniques for preference-based constrained optimization with cp-nets. In Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1-Volume 1, 291–298. https://dl.acm.org/doi/abs/10.5555/1838206.1838247

2009

J. C. Boerkoel and E. H. Durfee. 2009. Evaluating hybrid constraint tightening for scheduling agents. In Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems-Volume 1, 673–680. https://dl.acm.org/doi/pdf/10.5555/1558013.1558106

2008

J. C. Boerkoel and E. H. Durfee. 2008. Hybrid constraint tightening for solving hybrid scheduling problems. In Proceedings of the 23rd National Conference on Artificial Intelligence - Volume 3 (AAAI’08), AAAI Press, Chicago, Illinois, 1446–1449. https://dl.acm.org/doi/abs/10.5555/1620270.1620301