Timothy Havens

Timothy Havens
"The realization that you can't predict the future--and mold it--could only come as a shock to an academic."
—David Harsanyi


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  • William and Gloria Jackson Associate Professor of Computer Systems
  • Director, Center for Data Sciences
  • Director, Data Science Graduate Program
  • PhD, Electrical and Computer Engineering, University of Missouri
  • MS, Electrical Engineering, Michigan Technological University
  • BS, Electrical Engineering, Michigan Technological University


Dr. Havens is the Director of the ICC Center for Data Sciences and the PRIME Lab, and Director of the MTU Data Science Graduate Program. Prior to joining Michigan Tech in 2012, Tim was an NSF/CRA Computing Innovation Fellow at Michigan State University (MSU) and a Research Associate at the University of Missouri (MU). At MSU, Dr. Havens developed machine-learning methods for clustering in large heterogeneous data sets. His work at MU focused on multi-modal data fusion and detection algorithms and fuzzy clustering. Before working on his Ph.D., he was an Associate Technical Staff at MIT Lincoln Laboratory, where he analyzed airborne directed energy systems, laser-illuminated target ID systems, and GPS signals in support of the U.S. Air Force. His interests include mobile robotics, explosive hazard detection, heterogeneous and big data, fuzzy sets, and sensor networks, and data fusion. He has coauthored over 110 technical publications and is an Associate Editor of the IEEE Trans. Fuzzy Systems. He has been funded by MIT Lincoln Laboratory, Akela Inc., Michigan DOT, USDOT, Army Research Office, US Army, NSF, the RAND/John A. Hartford Foundation, and the Leonard Wood Institute.

Links of Interest

Areas of Interest

  • Pattern Recognition and Machine Learning
  • Signal and Image Processing
  • Sensor and Data Fusion
  • Heterogeneous Data Mining
  • Explosive Hazard Detection

Recent Publications

  • T.C. Havens, J.C. Bezdek, C. Leckie, L.O. Hall, and M. Palaniswami (2012). Fuzzy c-means algorithms for very large data. IEEE Trans. Fuzzy Systems, 20(6), 1130-1146. Read More
  • T.C. Havens and J.C. Bezdek (2012). An efficient formulation of the improved visual assessment of tendency (iVAT) algorithm. IEEE Trans. Knowledge and Data Engineering 24(5), 813-822. Read More
  • D.T. Anderson, T.C. Havens, C. Wagner, J.M. Keller, M.F. Anderson, and D.J. Wescott (2014). Extension of the fuzzy integral for general fuzzy set-valued information. IEEE Trans. Fuzzy Systems., 22(6), 1625-1639
  • C. Wagner, S. Miller, J.M. Garibaldi, D.T. Anderson, and T.C. Havens (2015). From interval-valued data to general type-2 fuzzy sets. IEEE Trans. Fuzzy Systems, 23(2), 248-269.
  • J. Su and T.C. Havens (Oct, 2015). Quadratic program-based modularity maximization for fuzzy community detection in social networks. IEEE Trans. Fuzzy Systems, 23(5), 1356-1371.
  • T.C. Havens, D.T. Anderson, and C. Wagner (Oct, 2015). Data-informed fuzzy measures for fuzzy integration of intervals and fuzzy numbers. IEEE Trans. Fuzzy Systems, 23(5), 1861-1875.
  • D. Kumar, J.C. Bezdek, M. Palaniswami, S. Rajasegarar, C. Leckie, and T.C. Havens (Oct, 2016). A hybrid approach to clustering in big data. IEEE Trans. Systems, Man, and Cybernetics, 46(10), 2372-2385.
  • D.T. Anderson, P. Elmore, F. Petry, and T.C. Havens (Oct, 2016). Fuzzy Choquet integration of homogeneous possibility and probability distributions. Info Sciences, 363, 24-39.
  • S. Yazdanparast and T.C. Havens (April, 2017). Modularity maximization using completely positive programming. Physica A: Statistical Mechanics and its Applications, 471(1), 20-32.
  • H. Deilamsalehy, T.C. Havens, J. Manela (April, 2017). Heterogeneous multi-sensor fusion for mobile platform 3D pose estimation. J. Dynamic Systems, Measurement, and Control, 139(7), 071002.

Recent Funding

  • NURail Center - Tier I ($299,966), US DOT, 2013-2018, Co-PI (PI: Pasi Lautala)
  • Heterogeneous Multisensor Buried Target Detection Using Spatiotemporal Feature Learning ($381,200), Army Research Office, 2015-2018, PI
  • Implementation of Unmanned Aerial Vehicles (UAVs) for Assessment of Transportation Infrastructure ($598,526), MDOT, 2016-2019, Co-PI (PI: Colin Brooks)
  • Multisensor Analysis and Algorithm Development for Detection and Classification of Buried and Obscured Targets ($99,779), Army Research Office, 2016-2019, PI
  • Self-Interference Modeling in Active Phased Arrays ($15,000), MIT Lincoln Laboratory, 2017, PI