Chief Data Scientist

Dr. Edgar A. Bernal’s research focuses on achieving high-level image and video abstraction and understanding through statistical learning. His current research interests include image and video analytics, computer vision, machine and deep learning, generative learning, deep recurrent models, and multimodal fusion.

Prior to joining FLX, Edgar served as Senior Data Scientist and Associate Director of the Rochester Data Science Consortium.  Before that, he was a Principal Scientist at the United Technologies Research Center (UTRC) in East Hartford, CT (from 2016 to 2018), and a Senior Research Scientist at the Palo Alto Research Center, A Xerox Company, in Webster, NY (from 2014 and 2016).  He obtained MSc and PhD degrees in Electrical Engineering from Purdue University, in West Lafayette, IN in 2002 and 2006, respectively.

Edgar is the author of nine journal publications, four book chapters, and over 40 conference proceedings, and holds 124 issued US patents in areas related to his current and past research interests.  He is a Senior Member of IEEE, and served as the Vice-Chair of the Rochester, NY chapter of the IEEE Signal Processing Society for several years.  He has also served as an adjunct faculty member at the Goergen Institute for Data Science (University of Rochester), and at the Center for Imaging Science (Rochester Institute of Technology), and is a frequent reviewer for IEEE Transactions on Image Processing, IEEE Transactions on Multimedia, the Journal of Electronic Imaging and the Journal of Imaging Science and Technology.  Following is a list of selected publications:

PEER-REVIEWED JOURNAL PAPERS

• D. Mehta, X. Zhao, E. Bernal, D. Wales. The Loss Surface of XOR Artificial Neural Networks. Phys. Rev. E 97, May 2018.
• E. Bernal, X. Yang, Q. Li, J. Kumar, S. Madhvanath, R. Bala. Deep Temporal Multimodal Fusion for Medical Procedure Monitoring using Wearable Sensors. IEEE Transactions on Multimedia, Vol. 20(1), Jan. 2018.
• Q. Li and E. Bernal. Hybrid Tenso-Vectorial Compressive Sensing for Hyperspectral Imaging. Journal of Electronic Imaging, Vol. 25(3), 2016.
• R. Loce, E. Bernal, W. Wu, R. Bala. Computer Vision in Roadway Transportation Systems: A Survey, Journal of Electronic Imaging, Vol. 22(4), Oct.-Dec. 2013.
• Bulan, E. Bernal, R. Loce. Efficient Processing of Transportation Surveillance Videos in the Compressed Domain, Journal of Electronic Imaging, Vol. 22(4), Oct.-Dec. 2013.

BOOK CHAPTERS

• E. Bernal, R. Loce. Video Analytics in the Compressed Domain. To appear in Encyclopedia of Image Processing, Taylor and Francis Group, 2018.
• Bala, E. Bernal. Driver Monitoring. Computer Vision in Intelligent Transportation Systems, IEEE/Wiley, 2016, ISBN: 978-1-118-97160-4.
• Wu, O. Bulan, E. Bernal, R. Loce. Detection of Moving Violations. Computer Vision in Intelligent Transportation Systems, IEEE/Wiley, 2016, ISBN: 978-1-118-97160-4.
• Friedland, Q. Li, D. Schonfeld, E. Bernal. Two Algorithms for Compressed Sensing of Sparse Tensors, Compressed Sensing and its Applications, Springer, 2015.

CONFERENCE PROCEEDINGS

• E. Bernal. Training Deep Normalizing Flow Models in Highly Incomplete Data Scenarios with Prior Regularization, in Proc. 2021 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Nashville, TN, June 2021. (Oral, 15% acceptance rate)
• T. Richardson, W. Wu, L. Lin, B. Xu, E. Bernal. MC Flow: Monte Carlo Flow Models for Data Imputation, in Proc. 2020 IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, June 2020. (Poster, 22% acceptance rate)
• L. Lin, B. Xu, W. Wu, T. Richardson, E. Bernal, B. Martens, C. Thornton, C. Heatwole. Deep Metric Learning with Triplet Networks: Application to Hand-grip Myotonia, IEEE EMB Special Topic Conference on Healthcare Innovations and Point-of-Care Technologies, Bethesda, MD, Nov. 2019.
• W. Wu, B. Xu, E. Bernal, R. L. Hill, E. B. Brown, and D. Desa. Breast Cancer Tissue Sub-Type Classification from Second Harmonic Generation Images via Machine Learning, IEEE EMB Special Topic Conference on Healthcare Innovations and Point-of-Care Technologies, Bethesda, MD, Nov. 2019.
• T. Cheng, B. Xu, W. Wu, L. Lin, T. Richardson, and E. Bernal. An Unsupervised Machine Learning Framework for Parkinson’s Disease Progression Analysis and Subtyping, IEEE EMB Special Topic Conference on Healthcare Innovations and Point-of-Care Technologies, Bethesda, MD, Nov. 2019.
• O. Oshin, E. Bernal, B. Nair, J. Ding, R. Varma, R. Osborne, E. Tunstel, F. Stramandinoli. Coupling Deep Discriminative and Generative Models for Reactive Robot Planning in Human-Robot Collaboration, in Proc. 2019 IEEE Conference on Systems, Man and Cybernetics.
• T. Wang, Y. Gu, X. Zhao, D. Mehta, E. Bernal. Improving the Sensitivity of Neural Networks to Adversarial Attacks, in Proc. 2019 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
• E. Bernal. Surrogate Contrastive Network for Supervised Band Selection in Multispectral Image Analysis Tasks, in Proc. 2019 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
• L. Lin, B. Xu, W. Wu, T. Richardson, E. Bernal. Interpretable Diagnosis of Myotonic Dystrophy from Handgrip Time Series Data with Attention-based Temporal Convolutional Network, in Proc. 2019 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
• K. G. Lore, K. Reddy, M. Giering, E. Bernal. Generative Adversarial Networks for Spectral Super-resolution and bidirectional RGB-to-multispectral mapping, in Proc. 2019 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
• K. G. Lore, K. Reddy, M. Giering, E. Bernal. Generative Adversarial Networks for Depth Map Estimation from RGB Video, in Proc. 2018 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, June 2018.
• E. Bernal, Q. Li. Tensorial Compressive Sensing of Jointly Sparse Matrices with Applications to Color Imaging, in Proc. 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China, Sept. 2017.
• X. Yang, E. Bernal, et al. Deep Multimodal Representation Learning from Temporal Data, in Proc. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, June 2017.
• M. Shreve, E. Bernal, Q. Li, J. Kumar, R. Bala. A Study on the Discriminability of FACS from Spontaneous Facial Expressions, in Proc. 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, Sept. 2016.
• M. Shreve, E. Bernal, et al. Static Occlusion Detection Handling in Transportation Videos, in Proc. IEEE Intelligent Transportation Systems Conference, Las Palmas, Spain, September 2015.
• J. Kumar, E. Bernal, et al. On-the-fly Training for Hand Detection in Egocentric Video, Oral Presentation, in Proc. IEEE Conference on Computer Vision and Pattern Recognition – Workshops, Boston, MA, June 2015.
• E. Bernal, Q. Li. Hybrid Vectorial and Tensorial Compressive Sensing for Hyperspectral Imaging, Oral Presentation, IEEE Conference on Audio Signal Speech Processing, Brisbane, Australia, April 2015.
• D. Chuang, R. Bala, E. Bernal, A. Burry, P. Paul. Estimating Gaze Direction of Vehicle Drivers using a Smartphone Camera, in Proc. IEEE Conf. on Computer Vision and Pattern Recognition – Workshops, Columbus, OH, June 2014.
• E. Bernal, W. Wu, et al. Monocular Video-Based Vehicular Speed Estimation from Compressed Video Streams, in Proc. IEEE Intelligent Transportation Systems Conference, The Hague, Netherlands, Oct. 2013.
• D. Delibaltov, E. Bernal, et al. Parking Lot Occupancy Determination from Lamp Post Camera Images, in Proc. IEEE Intelligent Transportation Systems Conference, The Hague, Netherlands, Oct. 2013.