Wheeler Ruml

PROFESSOR
Phone: (603) 862-2683
Office: Computer Science, Kingsbury Hall Rm 215D, Durham, NH 03824
Wheeler Ruml

My main research is in artificial intelligence, although I also have interests in robotics, operations research, information visualization, and cognitive science. Very broadly, my goal is to understand how to build autonomous systems - for example, how a robot should decide what to do next. I'm also interested in decision support systems and in natural examples of cognition, such as humans. My current focus is on methods for heuristic search and planning, especially those that can be useful in robotics. In particular, I am interested in solving problems quickly (rational time-bounded decision-making) and in how on-line learning can inform optimization algorithms. I also enjoy experimental algorithmics.

Education

  • Ph.D., Computer Science, Harvard University
  • A.S./B.S., Computer Science, Harvard University

Research Interests

  • Artificial Intelligence/Cybernetics
  • Operations Research
  • Optimization
  • Robotics

Courses Taught

  • CS 696W: Independent Study
  • CS 730/830: Intro Artificial Intelligence
  • CS 758/858: Algorithms
  • CS 800: Internship
  • CS 931: Planning for Robots
  • CS 980: Top/Planning for Robots
  • CS 999: Doctoral Research

Selected Publications

Lemons, S., López, C. L., Holte, R. C., & Ruml, W. (2022). Beam Search: Faster and Monotonic. Retrieved from http://arxiv.org/abs/2204.02929v1

Miller, B. A., Shafi, Z., Ruml, W., Vorobeychik, Y., Eliassi-Rad, T., & Alfeld, S. (2021). Optimal Edge Weight Perturbations to Attack Shortest Paths. Retrieved from http://arxiv.org/abs/2107.03347v1

Miller, B. A., Shafi, Z., Ruml, W., Vorobeychik, Y., Eliassi-Rad, T., & Alfeld, S. (2021). PATHATTACK: Attacking Shortest Paths in Complex Networks. MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2021: RESEARCH TRACK, PT II, 12976, 532-547. doi:10.1007/978-3-030-86520-7_33

Li, J., Ruml, W., & Koenig, S. (2020). EECBS: A Bounded-Suboptimal Search for Multi-Agent Path Finding. Retrieved from http://arxiv.org/abs/2010.01367v2

Gall, K. C., Cserna, B., Ruml, W., & Intelligence, A. A. A. (2020). Envelope-Based Approaches to Real-Time Heuristic Search. In THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE Vol. 34 (pp. 2351-2358). Retrieved from https://www.webofscience.com/

Shang, Y., Ruml, W., Zhang, Y., & Fromherz, M. (2004). Localization from connectivity in sensor networks. IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 15(11), 961-974. doi:10.1109/TPDS.2004.67

Shang, Y., Ruml, W., & IEEE. (2004). Improved MDS-based localization. In IEEE INFOCOM 2004: THE CONFERENCE ON COMPUTER COMMUNICATIONS, VOLS 1-4, PROCEEDINGS (pp. 2640-2651). Retrieved from https://www.webofscience.com/

Caramazza, A., Papagno, C., & Ruml, W. (2000). The selective impairment of phonological processing in speech production. BRAIN AND LANGUAGE, 75(3), 428-450. doi:10.1006/brln.2000.2379

Ruml, W., Caramazza, A., Shelton, J. R., & Chialant, D. (2000). Testing assumptions in computational theories of aphasia. JOURNAL OF MEMORY AND LANGUAGE, 43(2), 217-248. doi:10.1006/jmla.2000.2730

Ruml, W., & Caramazza, A. (2000). An evaluation of a computational model of lexical access: Comment on Dell et al. (1997). PSYCHOLOGICAL REVIEW, 107(3), 609-634. doi:10.1037/0033-295X.107.3.609

Most Cited Publications