Jessen Havill

Jessen Havill

Professor of Computer Science and Benjamin Barney Chair
Director, Data Analytics Program
Central Campus
Olin Science Hall 208
Position Type 



Jessen Havill joined the Denison faculty in 1998, having spent the six prior years studying at The College of William and Mary in Williamsburg, Virginia. Dr. Havill teaches courses across the computer science curriculum, in both theory and systems, although his specialty is in theory-related courses like Discrete Mathematics, Data Structures, and Algorithm Design and Analysis. He is also very interested in developing courses that explore connections between computer science and other disciplines. In 2009, he developed and started teaching a new introductory computer science course (CS 111: Foundations of Computing for Scientific Discovery) that introduces the principles of computer science in the context of scientific modeling and simulation. In 2012, he and Jeff Thompson, a colleague in the Biology Department, began teaching an interdisciplinary computational biology course (CS/BIOL 309: Computational Biology). In 2013, Dr. Havill was awarded Denison’s Charles A. Brickman Teaching Excellence Award.

B.A., Bucknell University; M.S., Ph.D., College of William and Mary


Algorithms, computational biology — I enjoy designing efficient online algorithms for network and scheduling problems. Like a market speculator, an online algorithm must respond to input as it arrives, without knowing the future. I am also interested in computational biology problems.

My research largely focuses on the design and analysis of algorithms for online network routing and machine scheduling problems. An online algorithm is one that processes its input one element at a time instead of all at once like a traditional algorithm. For example, an online room scheduling algorithm would have to assign a room to each event as it “arrives” without knowing what events might need to be scheduled later. Online algorithms usually cannot come up with optimal solutions due to their lack of knowledge about the future. Instead, we try to design algorithms that find solutions that are provably within some factor of optimal. I have also recently developed an interest in problems in computational biology.


  • Discovering Computer Science: Interdisciplinary Problems, Principles, and Python Programming. Chapman & Hall/CRC Textbooks in Computing, Taylor & Francis Group, 2015.
  • Improved Upper Bounds for Online Malleable Job Scheduling (with Nathaniel Kell '13). Journal of Scheduling, 2014.
  • A New Approach for Detecting Riboswitches in DNA Sequences (with Chinmoy Bhatiya '09, Steven M. Johnson '13, Joseph D. Sheets '12, and Jeffrey S. Thompson). Bioinformatics 30(21), pp. 3012-3019, 2014.
  • Optimal Online Ring Routing [pdf] (with K. R. Hutson)
 Networks 57(2), pp. 187-197, 2011
  • Online Malleable Job Scheduling for m ≤ 3 [pdf] Information Processing Letters111(1), pp. 31-35, 2010
  • Competitive Online Scheduling of Perfectly Malleable Jobs with Setup Times [pdf] (with W. Mao)
 European Journal of Operational Research187(3), pp. 1126-1142, 2008


Student Collaborations 
  • Power Management for Online Malleable Job Scheduling, Andrew Quinn, Senior Research (with Recognition), 2013–2014.
  • A Disequilibrium Multi-Country Macroeconomic Model with Agent-Based Portfolio Investors, Edward Takahashi, Senior Research, Fall 2013.
  • Bringing Extinct Sponges to Life: Modeling Stromatoporoid Growth with OpenGL, Trevor Masters, Summer 2013 (co-advised with David Goodwin, Geosciences)
  • Improved Upper Bounds for Online Malleable Job Scheduling, Nathaniel Kell, 2012–2013
  • A Web Tool for Detecting Riboswitches in Genomic Sequences, Steven Johnson, Summer 2012
  • Towards a More Realistic Metric for Online Ring Routing, Andrew Quinn, Summer 2012
  • Using Computational Algorithms to Further Examine and Visualize Riboswitch Domains, Joseph Sheets, Summer 2011 (co-advised with Jeff Thompson, Biology)