- Associate Dean for Research, College of Computing
- William and Gloria Jackson Associate Professor of Computer Systems
- Director, Institute of Computing and Cybersystems
- PhD, Electrical and Computer Engineering, University of Missouri
- MS, Electrical Engineering, Michigan Technological University
- BS, Electrical Engineering, Michigan Technological University
Dr. Havens is the Associate Dean for Research in the College of Computing and William and Gloria Jackson Associate Professor in Computer Science, and directs the Institute of Computing and Cybersystems (ICC), the ICC Center for Data Sciences, and the PRIME Lab. 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 PhD, 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, sensor networks, and data fusion. He has coauthored over 140 technical publications and is Conference Publications Editor of the IEEE Computational Intelligence Society and an Associate Editor of the IEEE Trans. Fuzzy Systems.
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
- S.K. Kakula, A.J. Pinar, M.A. Islam, D.T. Anderson, and T.C. Havens. Novel regularization for learning the fuzzy Choquet integral with limited training data. Accepted, IEEE Trans. Fuzzy Systems.
- S. Yazdanparast, T.C. Havens, and M. Jamalabdollahi. Linear time community detection by a novel modularity gain acceleration in label propagation. Accepted, IEEE Trans. Big Data.
- B. Murray, M.A. Islam, A. Pinar, D.T. Anderson, G. Scott, T.C. Havens, and J.M. Keller. Explainable AI for the Choquet integral. Accepted, IEEE Trans. Emerging Topics Comp. Intell.
- S. Yazdanparast, T.C. Havens, and M. Jamalabdollahi. Soft overlapping community detection in large-scale networks via fast fuzzy modularity maximization. Accepted, IEEE Trans. Fuzzy Systems.
- S. Kabir, C. Wagner, T.C. Havens, and D.T. Anderson. A similarity measure based on bidirectional subsethood for intervals. Accepted, IEEE Trans. Fuzzy Systems.
- M.A. Islam, D.T. Anderson, A. Pinar, T.C. Havens, G. Scott, and J.M. Keller (July, 2020). Enabling explainable fusion in deep learning with fuzzy integral neural networks. IEEE Trans. Fuzzy Systems, 28(7), 1291-1300.
- I.T. Cummings, T.J. Schulz, J.P. Doane, and T.C. Havens (Dec, 2020). Aperture-level simultaneous transmit and receive with digital phased arrays. IEEE Trans. Signal Processing, 68(1), 1243-1258.
- J. Bialas, T. Oommen, and T.C. Havens (Oct, 2019). Optimal segmentation for building class in high spatial resolution images using random forests. Int. J. App. Earth Obs. Geoinf. 82, 101895.
- C.D. Demars, M.C. Roggemann, A.J. Webb, and T.C. Havens. (Oct, 2018) Target localization and tracking by fusing Doppler differentials from cellular emanations with a multi-spectral video tracker. Sensors, 18(11), 3687.
- A.J. Webb, T.C. Havens, and T.J. Schulz (Sept, 2018). Fast image reconstruction in forward looking GPR using dual l1 regularization. IEEE Trans. Computational Imaging, 4(3), 470-478.
- Modeling and Algorithm Development for Adaptive Adversarial AI for Complex Autonomy ($428,707), US Army ERDC, 2020-2022, PI
- DURIP: Acoustic Sensing System and High-Throughput Computing for Environment and Threat Monitoring in Naval Environments Using Machine Learning ($243,169), Office of Naval Research, 2020-2021, PI
- Machine Learning and Artificial Intelligence Using Acoustic Sensors in Connected Vehicles and Roadside Units ($149,518), Ford Motor Company, 2020-2021, PI
- Defending the Nation’s Digital Frontier: Cybersecurity Training for Tomorrow’s Officers ($66,377/$248,517), Office of Naval Research, 2020-2021, Co-PI (PI: Andrew Barnard)
- Duty Cycle Aggregation, Warranty Mitigation, and Fleet Prognostics using Customer Usage Data (Part II) ($199,847), Ford Motor Company, 2020-2022, PI
- Algorithms for Look-Down Infrared Target Exploitation – Phase II ($399,994), NGA, 2020-2022, PI
- Machine Learning for Human-Based Visual Detection Metrics ($120,000), Signature Research Inc., 2020-2021, PI
- Localization, Tracking, and Classification of On-Ice and Underwater Noise Sources Using Machine Learning ($299,533), Naval Undersea Warfare Center, 2019-2022, PI