Senem Velipasalar

Professor

Electrical Engineering and Computer Science

3-183 CST

svelipas@syr.edu

315.443.4418

Personal Website

Degrees:

  • Ph. D., Electrical Engineering, Princeton University, Princeton, NJ, 2007
  • M.A., Electrical Engineering, Princeton University, Princeton, NJ, 2004
  • M.S., Electrical Sciences and Computer Engineering, Brown University, Providence, RI, 2001
  • B.S., Electrical and Electronics Engineering, Bogazici University, Istanbul, Turkey, 1999

Lab/ Center/ Institute affiliations:

Director of the Smart Vision Systems Laboratory (http://www.vision.syr.edu/)

Faculty Affiliate, Aging Studies Institute

Areas of Expertise:

  • Machine Learning
  • Computer Vision
  • Wireless Smart Camera Networks
  • Mobile camera applications
  • Signal Processing

Prof. Velipasalar’s primary areas of research are machine learning and computer vision. More specifically, her research has focused on human activity classification and fall detection from egocentric cameras, and applications of machine learning to (i) thermal anomaly detection from UAV-mounted infrared cameras, (ii) driver behavior analysis from in-vehicle mounted cameras, (iii) 3D object detection, (iv) person detection from video data, (v) analysis of functional near infrared spectroscopy (fNIRS) data, (vi) dynamic multi-channel access, and (vii) defense against adversarial jamming attacks.

Honors/Awards:

  • NSF CAREER Award, 2011.
  • 2021 IEEE Region 1 Technological Innovation (Academic) Award.
  • 2021 IAAI Deployed Application Award for our paper titled “Preclinical Stage Alzheimer’s Disease Detection Using Magnetic Resonance Image Scans”.
  • Top 25 most downloaded IEEE Sensors Journal paper in the months of January-September 2017, and June 2018.
  • Graduate School All-University Doctoral Prize, received by my former Ph.D. student Burak Kakillioglu, 2022.
  • Graduate School All-University Doctoral Prize, received by my former Ph.D. student Natalie Sommer, 2021.
  • Graduate School All-University Doctoral Prize, received by my former Ph.D. student Yantao Lu, 2020.
  • 2017 IEEE Green Communications & Computing Technical Committee Best Journal Paper Award for our paper titled “Analysis of Energy Efficiency in Fading Channels under QoS Constraints”.
  • 2nd place Poster Award at the 17th Annual SyracuseCoE Symposium Student Poster Competition for our work titled “Heat Mapping Drones”, October 2017.
  • 2014 Excellence in Graduate Education Faculty Recognition Award.
  • Graduate School All University Doctoral Prize, received by my former Ph.D. student Akhan Almagambetov, 2014.
  • Nunan Research Day Poster Competition EECS Departmental Winner Award, received by Danushka Bandara (co-advised by Dr.Hirshfield), 2014.
  • Intelligent Transportation Society (ITS) of NY Best ITS Student Essay Award, received by my former Ph.D. student Akhan Almagambetov, based on our research on vehicle taillight tracking and alert signal detection, May 2013.
  • The college-wide award for “Applicability of Research to Business and Industry”, received by my former Ph.D. student Akhan Almagambetov, Nunan Lecture and Research Day, April 2013.
  • Third place paper award at the ACM/IEEE International Conference on Distributed Smart Cameras for the paper titled “Energy-efficient Feedback Tracking on Embedded Smart Cameras by Hardware-level Optimization“, 2011
  • EPSCoR First Award, 2009
  • Layman Award as PI, 2007
  • Layman Award as Co-PI, 2009
  • Best Student Paper Award at the IEEE International Conference on Multimedia & Expo (ICME) for the paper titled “Design and Verification of Communication Protocols for Peer-to-Peer Multimedia Systems,” 2006
  • IBM Patent Application Award, 2005
  • Travel Grant, Office of Graduate Affairs, Princeton University, 2005
  • Graduate Fellowship, Princeton University, 2002
  • Graduate Fellowship, Brown University, 1999

Selected Publications:

(Please visit https://ecs.syr.edu/faculty/velipasalar/ for a complete list)

  • J. Chen, B. Kakillioglu and S. Velipasalar, “Background-Aware 3D Point Cloud Segmentation with Dynamic Point Feature Aggregation,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, April 2022.
  • F. Altay and S. Velipasalar, “The Use of Thermal Cameras for Pedestrian Detection,” IEEE Sensors Journal, vol. 22, issue:12, 11489 – 11498, May 2022.
  • Y. Chu, D. Mitra, K. Cetin, N. Lajnef, F. Altay, S. Velipasalar, “Development and Testing of a Performance Evaluation Methodology to Assess the Reliability of Occupancy Sensor Systems in Residential Buildings,” Energy and Buildings, vol. 268, pp. 112148, 2022.
  • J. Wang, T. Grant, S. Velipasalar, B. Geng and L. Hirshfield, “Taking a Deeper Look at the Brain: Predicting Visual Perceptual and Working Memory Load from High-Density fNIRS Data,” IEEE Journal of Biomedical and Health Informatics, vol. 26, issue:5, pp. 2308-2319, December 2021.
  • J. Wang, W. Chai, A. Venkatachalapathy, K. L. Tan, A. Haghighat, S. Velipasalar, Y. Adu-Gyamfi, A. Sharma, “A Survey on Driver Behavior Analysis from In-Vehicle Cameras,” accepted to appear in the IEEE Transactions on Intelligent Transportation Systems, early access version available, November 2021.
  • F. Wang; C. Zhong, M. Cenk Gursoy, S. Velipasalar, “Resilient Dynamic Channel Access via Robust Deep Reinforcement Learning,” IEEE Access Journal, vol. 9 , pp. 163188 – 163203, December 2021.
  • N. M. Sommer, B. Kakillioglu, T. Grant, S. Velipasalar and L. Hirshfield, “Classification of fNIRS Finger Tapping Data with Multi-Labeling and Deep Learning,” IEEE Sensors Journal, vol. 21, issue: 21, pp. 24558-24559, doi: 10.1109/JSEN.2021.3115405, Nov. 2021.
  • Y. Zheng, Y. Lu, and S. Velipasalar, “An Effective Adversarial Attack on Person Re-identification in Video Surveillance via Dispersion Reduction,” IEEE Access Journal, vol. 8, pp. 183891 – 183902, Sept. 2020.
  • N. Sommer, S. Velipasalar, L. Hirshfield, Y. Lu and B. Kakillioglu, “Simultaneous and Spatiotemporal Detection of Different Levels of Activity in Multidimensional Data,” IEEE Access Journal, vol. 8, pp. 118205 – 118218, June 2020.
  • D. Bandara, T. Grant, L. Hirshfield and S. Velipasalar, “Identification of Potential Task Shedding Events Using Brain Activity Data,” Augmented Human Research, 5. 10.1007/s41133-020-00034-y, 2020.
  • M. Cornacchia and S. Velipasalar, “Autonomous Selective Parts-Based Tracking,” IEEE Transactions on Image Processing, vol. 29, pp. 4349-4361, January 2020.
  • B. Kakillioglu, A. Ren, Y. Wang and S. Velipasalar, “3D Capsule Networks for Object Classification with Weight Pruning,” IEEE Access Journal, pp. 27393-27405, Febr. 2020.
  • C. Zhong, M. Cenk Gursoy and S. Velipasalar, “Deep Reinforcement Learning-Based Edge Cashing in Wireless Networks,” IEEE Transactions on Cognitive Communications and Networking, vol. 6 , issue 1, pp. 48-61, March 2020.
  • Y. Hu, Y. Li, M. C. Gursoy, S. Velipasalar, and A. Schmeink, “Throughput Analysis of Low-Latency IoT Systems with QoS Constraints and Finite Blocklength Codes,” IEEE Transactions on Vehicular Technology, vol. 69, issue 3, pp. 3093-3104, March 2020.
  • C. Zhong, Z. Lu, M. Cenk Gursoy and S. Velipasalar, “A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access,” IEEE Transactions on Cognitive Communications and Networking, vol. 5, issue 4, pp. 1125-1139, Dec. 2019.
  • Y. Lu and S. Velipasalar, “Autonomous Human Activity Classification from Wearable Multi-Modal Sensors,” IEEE Sensors Journal, vol. 19, issue: 23, pp. 11403-11412, Dec. 2019.
  • D. Qiao, M. Cenk Gursoy and S. Velipasalar, “Throughput-Delay Tradeoffs with Finite Blocklength Coding over Multiple Coherence Blocks,” IEEE Transactions on Communications, pp. 5892 – 5904, volume: 67 , Issue: 8 , Aug. 2019.
  • D. Bandara, L. Hirshfield and S. Velipasalar, “Classification of Affect Using Deep Learning on Brain Blood Flow Data,” Journal of Near Infrared Spectroscopy, 27(3), pp. 206-219, doi: 10.1177/0967033519837986, April 2019.
  • C. Ye, M. Cenk Gursoy and S. Velipasalar, “Power Control for Wireless VBR Video Streaming: From Optimization to Reinforcement Learning,” IEEE Transactions on Communications, pp. 5629 – 5644, volume: 67 , Issue: 8 , Aug. 2019.
  • M. Cornacchia, B. Kakillioglu, Y. Zheng and S. Velipasalar, “Deep Learning Based Obstacle Detection and Classification with Portable Uncalibrated Patterned Light,” IEEE Sensors Journal, vol. 18, issue: 20, pp. 8416-8425, Oct 2018.
  • Y. Lu and S. Velipasalar, “Autonomous Footstep Counting and Traveled Distance Calculation by Mobile Devices Incorporating Camera and Accelerometer Data,” IEEE Sensors Journal, vol. 17, issue: 21, pp. 7157-7166, Nov. 2017.
  • K. Ozcan, S. Velipasalar and P. Varshney, “Autonomous Fall Detection with Wearable Cameras by using Relative Entropy Distance Measure,” IEEE Transactions on Human-Machine Systems, vol. 47, issue: 1, pp. 31-39, Febr. 2017.
  • M. Cornacchia, K. Ozcan, Y. Zheng and S. Velipasalar, “A Survey on Activity Detection and Classification Using Wearable Sensors,” IEEE Sensors Journal, vol. 17, issue: 2, pp. 386-403, Jan. 2017. Top 25 most downloaded IEEE Sensors Journal paper for nine consecutive months in 2017, and in June 2018 .
  • F. Erden, S. Velipasalar, A. Z. Alkar, A. Enis Cetin, “Sensors in Assisted Living: A Survey of Signal and Image Processing Methods ,” IEEE Signal Processing Magazine, volume:33, issue:2, pp. 36-44, March 2016.
  • K. Ozcan and S. Velipasalar, “Wearable Camera- and Accelerometer-based Fall Detection on Portable Devices ,” IEEE Embedded Systems Letters, volume: 8, issue: 1, pp. 6-9, March 2016.
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