Ray Ptucha

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Ray Ptucha
Contact Infomation:
Email: rwpeec@rit.edu
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Address: 221 Scofield Rd
  Honeoye Falls
  NY 14472
   
   
Bio
Raymond Ptucha is a Computational Display Technology Leader in the Visual Experience Group at Apple where he is responsible for machine learning and algorithms in display products. He was an Associate Professor in Computer Engineering and Director of the Machine Intelligence Laboratory at Rochester Institute of Technology. His research includes machine learning, computer vision, and robotics, with a specialization in deep learning. Ray was a research scientist with Eastman Kodak Company for 20 years where he worked on computational imaging algorithms and was awarded 33 U.S. patents. He graduated from SUNY/Buffalo with a B.S. in Computer Science (1988) and a B.S. in Electrical Engineering (1989). He earned a M.S. in Image Science from RIT in 2002. He earned a Ph.D. in Computer Science from RIT in 2013. Ray was awarded an NSF Graduate Research Fellowship in 2010 and his Ph.D. research earned the 2014 Best RIT Doctoral Dissertation Award. Ray is a passionate supporter of STEM education, is an NVIDIA certified Deep Learning Institute instructor, Chair of the Rochester area IEEE Signal Processing Society, and is an active member of his local IEEE chapter and FIRST robotics organizations.
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Publications
Journal Articles and Book Chapters
  • M. Sharma, M, Dhanaraj, S. Karnam, D. Chachlakis, R. Ptucha, P. Markopoulos, E. Saber, "YOLOrs: Object Detection in Multimodal Remote Sensing Imagery," Journal of Selected Topics in Applied Earth Observations and Remote Sensing, JSTARS-2020-01033.R2, 2020
  • C. Homan, N. Schrading, R. Ptucha, C. Cerulli, C. Alm, "Quantitative Methods for Analyzing Intimate Partner Violence in Microblogs: Observational Study," Journal of Medical Internet Research, e15347, Vol. 22, No. 11, 2020. [paper]
  • A. Chintha, B. Thai, S. Sohrawardi, K. Bhatt, A. Hickerson, M. Wright, R. Ptucha, "Recurrent Convolutional Structures for Audio Spoof and Video Deepfake Detection," IEEE Journal of Selected Topics in Signal Processing, Volume 14, Issue 5, DOI 10.1109/JSTSP.2020.2999185, 2020. [paper]
  • S. Sah, S. Gopalakrishnan, R. Ptucha, "Aligned Attention for Common Multi-Modal Embeddings," Journal of Electronic Imaging, 29(2), 023013, 2020. [paper]
  • R. Ptucha, F. Petroski Such, S. Pillai, F. Brockler, V. Singh, P. Hutkowski, "Intelligent Character Recognition using Fully Convolutional Neural Networks," Pattern Recognition, PR6747, 2018. [paper]
  • S. Sah , T. Nguyen, R. Ptucha, "Understanding Temporal Structure for Real Time Video Captioning," Journal of Pattern Analysis and Applications, PAAA-D-17-00571, 2018. [paper]
  • S. Chennupati, S. Prasad Nooka, S. Sah, R. Ptucha, "Adaptive Hierarchical Decomposition of Large Deep Networks," IJPRAI - International Journal of Pattern Recognition and Artificial Intelligence, under review.
  • F. Petroski Such*, S. Sah*, M. Dominguez, S. Pillai, Chao Zhang, Andrew Michael, N. Cahill, R. Ptucha, "Robust Spatial Filtering with Graph Convolutional Neural Networks," special issue IEEE Journal of Selected Topics in Signal Processing, Volume 11, Issue 6, 2017. [paper]
  • S. Sah, A. Shringi, R. Ptucha, A. Burry, R. Loce, "Video Redaction: A Survey and Comparison of Enabling Technologies," Journal of Electronic Imaging Special issue on Video Analytics for Public Safety, 2017. [paper]
  • R.W. Ptucha, A. Savakis, "LGE-KSVD: Robust Sparse Representation Classification," IEEE Transactions on Image Processing, Volume 23, Issue 4, 2014. [paper]
  • R.W. Ptucha, A. Savakis, "Manifold Based Sparse Representation for Facial Understanding in Natural Images," Image and Vision Computing, vol. 31, pp. 365-378, 2013. [paper]
  • R.W. Ptucha, A. Savakis, "Facial Expression Recognition," IGI Global Encyclopedia of Information Science and Technology, 3rd Edition, 2013. [paper]
Conference Articles
  • M. Dominguez, R. Ptucha, "Directional Graph Networks with Hard Weight Assignments," 25th IEEE International Conference on Pattern Recognition (ICPR), Milan, Italy, 2021.
  • A. Chintha, A. Rao, S. Sohrawardi, K. Bhatt, M. Wright, R. Ptucha, "Leveraging Edges and Optical Flow on Faces for Deepfake Detection," IEEE International Joint Conference on Biometrics (IJCB), Houston, TX, 2020.
  • R. Ballamajalu, M. Li, F. Sahin, C. Hochgraf, R. Ptucha, M. Kuhl, "Turn and Orientation Sensitive A* for Autonomous Vehicles in Intelligent Material Handling Systems," IEEE 16th International Conference on Automation Science and Engineering (CASE), Hong Kong, 2020.
  • M. Li, A. Ganguly, A. Kwasinski, R. Ptucha, M. Kuhl, "Title: Risk-Based A*: Simulation Analysis of a Novel Task Assignment and Path Planning Method," Winter Simulation Conference, Orlando, FL, 2020.
  • M. Dhanaraj, M. Sharma, T. Sarkar, S. Karnam, D. G. Chachlakis, R. Ptucha, P. P. Markopoulos, E. Saber, "Vehicle detection from multi-modal aerial imagery using YOLOv3 with mid-level fusion." SPIE Defense and Commercial Sensing, CA, 2020, [DOI]
  • J. Abru, C. Cassidy, J. Kubeck, J. Laos, M. McGarvey, A. Loui, R. Ptucha, "The Advancement of Autonomous Vehicle Navigation," Proceedings of American Society for Engineering Education, Rochester, NY, 2020.
  • B. Thai, R. Jimerson, E. Prud’hommeaux, R. Ptucha, "Fully Convolutional ASR for Less-Resourced Endangered Languages," Language Resources and Evaluation for Language Technologies, Marseille, France, 2020.
  • J. Murkute, R. Damania, N. Nair, N. Kotecha, C. Wicks, R. Phipps, R. Ptucha, A. Papier, "Adding Uncertainty to Dermatological Assistance," In Medical Imaging 2020: Image Processing, vol. 11313, Houston, TX, 2020, [DOI]
  • S. Sohrawardi, S. Seng, A. Chintha, B. Thai, A. Hickerson, R. Ptucha, M. Wright "DeFaking Deepfakes: Understanding Journalists Needs for Deepfake Detection," Computation + Journalism Symposium, Boston, MA, 2020.
  • C. Foreman, P. Thiel, R. Ptucha, M. Dominguez, C. Alm, "Capturing Laughter and Smiles under Genuine Amusement vs. Negative Emotion," IEEE International Workshop on Human-Centered Computational Sensing (HCCS), Austin, TX, 2020.
  • D. R. Bhanushali, R. Relyea, K. Manghi, A. Vashist, C. Hochgraf, A. Ganguly, M. Kuhl, A. Kwasinski, R. Ptucha, "LiDAR-camera fusion for 3D object detection," IS&T Electronic Imaging, Burlingame, CA, 2020.
  • R. Relyea, D. R. Bhanushali, K. Manghi, A. Vashist, C. Hochgraf, A. Ganguly, A. Kwasinski, M. Kuhl, R. Ptucha, "Improving multimodal localization through self-supervision," IS&T Electronic Imaging, Burlingame, CA, 2020.
  • T. Ananthanarayana, R. Ptucha, S. Kelly, "Deep Learning based Fruit Freshness Classification and Detection with CMOS Image sensors and Edge processors," IS&T Electronic Imaging, Burlingame, CA, 2020.
  • A. Vashist, D. Bhanushali, R. Relyea, C. Hochgraf, A. Ganguly, R. Ptucha, A. Kwasinski, M. Kuhl, "Indoor Wireless Localization Using Consumer-Grade 60 GHz Equipment with Machine Learning for Intelligent Material Handling," IEEE International Conference on Consumer Electronics, Las Vegas, NV, 2020- Best Paper Award -.
  • Maojia Patrick Li, P. Sankaran, M. E. Kuhl, R. Ptucha, A. Kwasinski and A. Ganguly, "Task Selection by Autonomous Mobile Robots in a Warehouse Using Deep Reinforcement Learning," 2019 Winter Simulation Conference, National Harbor, MD, 2019.
  • S. Seng, A. Chintha, B. Thai, A. Hickerson, R. Ptucha, M. Wright, "Towards Robust Open-World Detection of Deepfakes," 26th ACM Conference on Computer and Communications Security, London, England, 2019.
  • M. Saraf, T. Roberts, R. Ptucha, C. Homan, C. Alm, "Multimodal Anticipated verses Actual Perceptual Reactions," ACM International Conference on Multimodal Interaction, Suzhou, Jiangsu, China, 2019.
  • S. Gopalakrishnan, P. Udaiyar, S. Sah, R. Ptucha, "Reference Vector Space for Multi-Modal Embeddings," IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Conference, Washington DC, 2019.
  • M. Dominguez, R. Dhamdhere, N. D. H. Kanamarlapudi, S. Raghupathi, R. Ptucha, "Evolution of Graph Classifiers," Western NY Image & Signal Processing Workshop, Rochester, NY, 2019. – Best Paper Award –
  • B. Thai, R. Jimerson, D. Arcoraci, E. Prud’hommeaux, R. Ptucha, "Synthetic data augmentation for improving low-resource ASR," Western NY Image & Signal Processing Workshop, Rochester, NY, 2019.
  • D. Peri, S. Sah, R. Ptucha, "Show, Translate, and Tell," International Conference on Image Processing, Taipei, Taiwan, 2019.
  • R. Longman, R. Ptucha, "Embedded CycleGAN for Shape-agnostic Image-to-Image Translation," International Conference on Image Processing, Taipei, Taiwan, 2019.
  • T. Yuan, S. Sah, T. Ananthanarayana, C. Zhang, A. Bhat, S. Gandhi, R. Ptucha, "Large Scale Sign Language Interpretation," IEEE International Conference on Automatic Face and Gesture Recognition, Lille, France, 2019.
  • D. Hannon, E. Rantanen, B. Sawyer, R. Ptucha, "A Human Factors Engineering Education Perspective on Data Science, Machine Learning and Automation," Human Factors & Ergonomics Society, Seattle, WA, 2019.
  • R. Ptucha, A. Bhat, A. Kuppusamy, S. Lyshevski, "Object Recognition, Identification and Classification for Intelligent Surveillance and Reconnaissance Platforms," SPIE Defense & Commercial Sensing, Baltimore, MD, 2019.
  • R. Jimerson, R. Hatcher, R. Ptucha, E. Prud’hommeaux, "Speech technology for supporting community-based endangered language documentation," 6th International Workshop on Language Documentation and Conservation (ICLDC), Honolulu, Hawaii, 2019.
  • R. Relyea, D. Chanushali A. Vashist, A. Ganguly, A. Kwasinski, M. Kuhl, R. Ptucha, "Multimodal localization for autonomous agents," Proceedings of Electronic Imaging: Image Processing Algorithms and Systems, San Francisco, CA, 2019.
  • Z. Carmichael, B. Glasstone, F. Cwitkowitz, K. Alexopoulos, R. Relyea, R. Ptucha, "Autonomous navigation using localization priors, sensor fusion, and terrain classification," Proceedings of Electronic Imaging: Image Processing Algorithms and Systems, San Francisco, CA, 2019.
  • B. Blakeslesee, A. Savakis, R. Ptucha., "Faster Art-CNN: An Extremely Fast Style Transfer Network," Western NY Image & Signal Processing Workshop, Rochester, NY, 2018.
  • F. Petroski Such, D. Kumar Peri, F. Brockler, P. Hutkowski, R. Ptucha, "Fully Convolutional Networks for Handwriting Recognition," International Conference on Frontiers of Handwriting Recognition, Niagara Falls, NY, 2018.
  • R. Jimerson, K. Simha, R. Ptucha, E. Prud'hommeaux, "Improving ASR Output for Endangered Language Documentation," 6th International Workshop on Spoken Languages for Under-resources Languages (SLTU), Gurugram, India, 2018.
  • P. Sankaran, M. Li , M. Kuhl, A. Ganguly, A. Kwasinski, R. Ptucha, "Simulation Analysis of Deep-Learning Approaches to Task Selection by Autonomous Vehicles for Material Handling," Winter Simulation Conference, Gothenburg, Sweden, 2018.
  • S. Sah, D. Peri, A. Shringi, C. Zhang, A. Savakis, R. Ptucha, "Semantically Invariant Text-to-Image Generation," International Conference on Image Processing, Athens, Greece, 2018.
  • S. Sah, A. Shringi, D. Peri, M. Dominguez, J. Hamilton, A. Savakis, R. Ptucha, "Multimodal Reconstruction Using Vector Representation," International Conference on Image Processing, Athens, Greece, 2018.
  • S. Sah, S. Gopalakrishnan, R. Ptucha, "Cross Modal Retrieval using Common Vector Space," Image and Vision Workshop at IEEE Computer Vision and Pattern Recognition, Salt Lake City, Utah, 2018.
  • J. Allison, R. Ptucha, S. Lyshevshki, "Resilient Communication, Object Classification and Data Fusion in Unmanned Aerial Systems," International Conference on Unmanned /aircraft Systems (ICUAS), Dallas, TX, 2018.
  • McKenna Tornblad, Luke Lapresi, Raymond Ptucha, Christopher Homan and Cecilia Ovesdotter Alm, "Sensing and Learning Human Annotators Engaged in Narrative Sensemaking," Student Research Workshop at NAACL, New Orleans, LA, 2018.
  • M. Dominguez, R. Dhamdhere, A. Petkar, S. Jain, S. Sah, R. Ptucha, "General-Purpose Deep Point Cloud Feature Extractor," Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, 2018.
  • T. Nguyen, S. Sah, R. Ptucha., "Multistream Hierarchical Boundary Network for Video Captioning," Western NY Image & Signal Processing Workshop, Rochester, NY, 2017.
  • R. Dhamdhere, T. Nguyen, L. Rausch, R. Ptucha, "Deep Learning for Philately Understanding," Western NY Image & Signal Processing Workshop, Rochester, NY, 2017.
  • M. Dominguez, M. Daigneau, R. Ptucha, "Source-Separated Audio Input for Accelerating Convolutional Neural Networks," Western NY Image & Signal Processing Workshop, Rochester, NY, 2017.
  • S. Sah, C. Zhang, D. Kumar, T. Nguyen, R. Ptucha, "Vector Learning for Cross Domain Representations," IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Conference, 2017.
  • R. Kangutkar, J. Lauzon, A. Synesael, N. Jenis, A. Bhatt, K. Simha, R. Ptucha, "ROS Navigation Stack for Smart Indoor Agents," IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Conference, 2017.
  • T. Thomas, M. Dominguez, R. Ptucha, "Deep Independent Audio-Visual Affect Analysis," Global Conference on Signal and Information Processing, Montreal, CA, 2017.
  • C. Zhang, S. Sah, T. Nguyen, D. Peri, A. Loui, C. Salvaggio, R. Ptucha, "Semantic Sentence Embeddings for Paraphrasing and Text Summarization," Global Conference on Signal and Information Processing, Montreal, CA, 2017.
  • S. Sah, T. Nguyen, M. Dominguez, F. Petroski Such, R. Ptucha, "Temporally Steered Gaussian Attention for Video Understanding," CVPR Workshop Deep-Vision: Deep Learning in Computer Vision, Honolulu, Hawaii, 2017.
  • S. Sah, R. Longman, A. Shringi, R. Loce, M. Rabbani, R. Ptucha, "Detection without Recognition for Redaction," CVPR Workshop The First International Workshop on The Bright and Dark Sides of Computer Vision: Challenges and Opportunities for Privacy and Security, Honolulu, Hawaii, 2017.
  • M. Dominguez, F. Petroski Such, S. Sah, R. Ptucha, "Towards 3D Convolutional Neural Networks with Meshes," International Conference on Image Processing, Beijing, China, 2017.
  • C. Zhang, T. Nguyen, S. Sah, R. Ptucha, A. Loui, C. Salvaggio, "Bath-Normalized Recurrent Highway Networks," International Conference on Image Processing, Beijing, China, 2017.
  • A. Calderwood, E. Pruett, R. Ptucha, C. Homan, C. Alm, "Understanding the Semantics of Narratives of Interpersonal Violence through Reader Annotations and Physiological Reactions," Computational Semantics Beyond Events and Roles (SemBEaR), Valencia, Spain, 2017.
  • S. Sah, S. Kulhare, A. Gray, S. Venugopalan, E. Prud"hommeaux, R. Ptucha, "Semantic Text Summarization of Long Videos," WACV, Santa Rosa, CA, 2017.
  • S. Bag, V. Venkatachalapathy, R. Ptucha, "Motion Estimation Using Visual Odometry and Deep Learning Localization," Proceedings of Electronic Imaging: Image Processing Algorithms and Systems, San Francisco, CA, 2017.
  • M. Hssayeni, S. Saxena, A. Savakis, R. Ptucha, "Distracted Driver Detection: Deep Learning vs Handcrafted Features," Proceedings of Electronic Imaging: Image Processing Algorithms and Systems, San Francisco, CA, 2017.
  • S. Echefu, J. Lauzon, S. Bag, R. Kangutkar, A. Bhatt, R. Ptucha, "Milpet " The Self-driving Wheelchair," Proceedings of Electronic Imaging: Image Processing Algorithms and Systems, San Francisco, CA, 2017.
  • S. Nooka, S. Chennupati, N. Karthik, S. Sah, R. Ptucha, "Adaptive Hierarchical Classification Networks," Proceedings of International Conference on Pattern Recognition, Cancun, Mexico, 2016.
  • M. Dushkoff, R. McLaughlin, R. Ptucha, "A Temporally Coherent Neural Algorithm for Artistic Style Transfer," Proceedings of International Conference on Pattern Recognition, Cancun, Mexico, 2016.
  • S. Kulhare, S. Sah, S. Pillai, R. Ptucha, "Key Frame Extraction for Salient Activity Recognition," Proceedings of International Conference on Pattern Recognition, Cancun, Mexico, 2016.
  • R. Oruganti, S. Sah, Suhas Pillai, and R. Ptucha, "Image Description through Fusion Based Recurrent Multi-Modal Learning," Proceedings of International Conference on Image Processing, Phoenix, AZ, 2016.
  • M. Oak, A. Behera, T. Thomas, C. Alm, C. Homan, E. Prud"hommeaux, R. Ptucha, "Generating Clinically Relevant Texts: A Case Study on Life-changing Events," Proceedings of CLPysch Workshop, North American Chapter of the Association for Computational Linguistics- Human Language Technologies (NAACL-HLT), San Diego, CA, 2016.
  • D. Simon, S. Sridharan, S. Sah, R. Ptucha, C. Kanan, R. Bailey, "Automatic Scanpath Generation With Deep Recurrent Neural Networks," SAP"16 Proceedings of the ACM Symposium on Applied Perception, Anaheim, CA, 2016.
  • M. Dushkoff and R. Ptucha, "Adaptive activation functions for deep networks," Proceedings of Electronic Imaging: Computational Imaging, San Francisco, CA, 2016.
  • V. Chennupati, S. Nooka, S. Sah, R. Ptucha, "Hierarchical decomposition of large deep networks," Proceedings of Electronic Imaging: Computational Imaging, San Francisco, CA, 2016.
  • A. Rajanna, R. Ptucha, S. Sinha, N. Rao, "Prostate cancer detection using photoacoustic imaging and deep learning," Proceedings of Electronic Imaging: Computational Imaging, San Francisco, CA, 2016.
  • N. Schrading, C. Alm, R. Ptucha, C. Homan, "An Analysis of Domestic Abuse Discourse on Reddit," Proceedings of Empirical Methods in Natural Language Processing (EMNLP), Lisbon, Portugal, 2015.
  • A. Rajanna, K. Aryafar, A. Shokoufandeh, R. Ptucha, "Deep Neural Networks: A Case Study for Music Genre Classification," Proceedings of 14th IEEE International Conference on Machine Learning and Applications, Miami, FL, 2015.
  • N. Briviano, M. Daigneau, J. Danko, C. Goss, A. Hintz, S. Kuhr, B. Tarloff, J. Kaemmerlen, R. Ptucha, "Autonomous People Mover: Adding Sensors," NY Cyber Security & Eng. Tech. Assoc., Rochester, NY, 2015.
  • H. Kumar, R. Ptucha, "Gesture Recognition Using Active Body Parts and Active Difference Signatures," Proceedings of International Conference on Image Processing, Quebec City, Canada, 2015.
  • N. Schrading, C. Alm, R. Ptucha, and C. Homan, "#WhyIStayed, #WhyILeft: Microblogging to Make Sense of Domestic Abuse," Proceedings of North American Chapter of the Association for Computational Linguistics- Human Language Technologies (NAACL-HLT), Denver, CO, 2015.
  • K. Knowles, N. Bovee, P. Gelose, D. Le, K. Martin, M. Pressman, J. Zimmerman, R. Lux, R. Ptucha, "Autonomous People Mover," Proceedings of American Society for Engineering Education, Syracuse, NY, 2015.
  • A. Wong, M. Yousefhussien, R.W. Ptucha, "Localization Using Omnivision-based Manifold Particle Filters," Proceedings of Electronic Imaging: Computational Imaging, San Francisco, CA, 2015.
  • M. Yousefhussien, et al., "Flatbed Scanner Simulation to Analyze the Effect of Detector"s Size on Color Artifacts," Proceedings of Electronic Imaging: Computational Imaging, San Francisco, CA, 2015.
  • A. Savakis, R. Rudra, R.W. Ptucha, "Gesture Control Using Active Difference Signatures and Sparse Learning," Proceedings of International Conference on Pattern Recognition, Stockholm, Sweden, 2014.
  • C. Merkel, D. Kudithipudi, R.W. Ptucha, "Heterogeneous CMOS/Memristor Neural Networks for Real-time Target Classification," SPIE Machine Intelligence and Bio-inspired Computation, STA14-ST141-28, 2014.
  • R.W. Ptucha, D. Kloosterman, B. Mittelstaedt, A. Loui, "Automatic Image Assessment from Facial Attributes," Proceedings of Electronic Imaging: Computational Imaging, San Francisco, CA, 2014.
  • P. Hays, R.W. Ptucha, R. Melton, "Mobile Device to Cloud Co-processing of ASL Finger Spelling to Text Conversion," Proceedings of IEEE Western NY Image Processing Workshop, Rochester, NY, 2013.
  • R.W. Ptucha, S. Azary, A. Savakis, "Keypoint Matching and Image Registration Using Sparse Representations," Proceedings of International Conference on Image Processing, Melbourne, Australia, 2013.
  • C. Bellmore, R.W. Ptucha, A. Savakis, "Fusion of Depth and Color for an Improved Active Shape Model," Proceedings of International Conference on Image Processing, Melbourne, Australia, 2013.
  • R.W. Ptucha, A. Savakis, "LGE-KSVD: Flexible Dictionary Learning for Optimized Sparse Representation Classification," Proceedings of AMFG Workshop, Computer Vision and Pattern Recognition, Portland, OR, 2013.
  • R.W. Ptucha, A. Savakis, "Joint Optimization of Manifold Learning and Sparse Representations," Proceedings of Automatic Face and Gesture Recognition, Shanghai, China, 2013.
  • R.W. Ptucha, D. Rhoda, B. Mittelstaedt, "Auto Zoom Crop from Face Detection and Facial Features," Proceedings of Electronic Imaging: Computational Imaging, San Francisco, CA, 2013.
  • R.W. Ptucha, A. Savakis, "Fusion of Static and Temporal Predictors for Unconstrained Facial Expression Recognition," Proceedings of International Conference on Image Processing, Orlando, FL, 2012.
  • R.W. Ptucha, A. Savakis, "Towards the Usage of Optical Flow Temporal Features for Facial Expression Classification," Proceedings of International Symposium on Visual Computing, Crete, Greece, 2012.
  • R.W. Ptucha, A. Savakis, "How Connections Matter: Factors Affecting Student Performance in STEM Disciplines," Proceedings of IEEE Integrated STEM Education Conference, Princeton, NJ, 2012.
  • C. Bellmore, R.W. Ptucha, A. Savakis, "Interactive Display Using Depth and RGB Sensors for Face and Gesture Control," IEEE Western NY Image Processing Workshop, Rochester, NY, 2011.
  • R.W. Ptucha, G. Tsagkatakis, A. Savakis, "Manifold Based Sparse Representation for Robust Expression Recognition without Neutral Subtraction," Proceedings of BeFIT Workshop, International Conference on Computer Vision, Barcelona, Spain, 2011.
  • R.W. Ptucha, G. Tsagkatakis, A. Savakis, "Manifold Learning for Simultaneous Pose and Facial Expression Recognition," Proceedings of International Conference on Image Processing , Brussels, Belgium, 2011.
  • R.W. Ptucha, A. Savakis, "Facial Expression Recognition Using Facial Features and Manifold Learning," Proceedings of International Symposium on Visual Computing, Las Vegas, NV, 2010.
  • R.W. Ptucha, A. Savakis, "Pose Estimation Using Facial Feature Points and Manifold Learning," Proceedings of International Conference on Image Processing, Hong Kong, 2010.
  • R.W. Ptucha, A. Savakis, "Facial Pose Tracking for Interactive Display," IEEE Western NY Image Processing Workshop, Rochester, NY, 2009.
  • R.W. Ptucha, A. Savakis, "Facial Pose Estimation Using a Symmetrical Feature Model," Proceedings of ICME- Workshop on Media Information Analysis for Personal and Social Applications, New York, NY, 2009.
  • R.W. Ptucha, "Correction of High Frequency Smear in Thermal Printers," Proceedings of IS&T 22nd Annual Non-Impact Printing (NIP22) Conference, Denver, CO, 2006.
  • R.W. Ptucha, "Image Quality Assessment of Digital Scanners and Electronic Still Cameras," Proceedings of IS&T Image Processing, Image Quality, Image Capture Systems (PICS) Conference, Savannah GA, 1999.
  • C.M. Daniels, R.W. Ptucha, L. Schaefer, "The Necessary Resolution to Zoom and Crop Hardcopy Images," Proceedings of IS&T Image Processing, Image Quality, Image Capture Systems (PICS) Conference, Savannah GA, 1999.
Competitions and Presentations
  • R. Ptucha, "Deep Learning and the Road Towards Autonomous Driving," AutoSense, Detroit, 2020.
  • R. Ptucha, "Graph Convolutional Neural Networks and the Machine Intelligence Laboratory," Department Computer Science and Engineering Seminar, Buffalo, NY, 2019.
  • R. Ptucha, "What is and How Will Machine Learning Change Our Lives," Keynote at 2019 Engineering Symposium, Rochester, NY, 2019. [PDF]
  • R. Ptucha, "Expanding the Impact of Deep Learning," PMII Invited at Electronic Imaging, San Francisco, CA, 2019. [PDF]
  • R. Ptucha, "Graph Convolutional Neural Networks," NASA Goddard Workshop on Artificial Intelligence, Baltimore, MD, 2018. [PDF]
  • R. Ptucha, "Extending the Reach of Deep Learning," University of Rochester Big Data Series, Rochester, NY, 2018. [PDF]
  • R. Ptucha, "Deep Learning Fundamentals," Tutorial at International Conference on Frontiers of Handwriting Recognition, Niagara Falls, NY, 2018. [PDF]
  • R. Ptucha, "Introduction to Deep Learning and the Machine Intelligence Laboratory," University of Rochester Laser Laboratory, Rochester, NY, 2018.
  • R. Ptucha, "What is and How Will Machine Learning Change Our Lives," 2018 Engineering Symposium, Rochester, NY, 2018. [PDF]
  • R. Ptucha, "Explorations of Deep Learning," Tutorial at UP-STAT Statistics Conference, Rochester, NY, 2018.
  • R. Ptucha, "Deep Learning Demystified," NVIDIA Deep Learning Institute Course, Rochester Institute of Technology, Rochester, NY, 2018.
  • R. Ptucha, "Deep Learning Fundamentals for Computer Vision," NVIDIA Deep Learning Institute Course, Rochester Institute of Technology, Rochester, NY, 2018.
  • R. Ptucha, "Image Classification with DIGITS," NVIDIA Deep Learning Institute Course, ACM Special Interest Group on Computer Science in Education Conference, Baltimore, MD, 2018.
  • R. Ptucha, "Deep Learning Demystified," NVIDIA Deep Learning Institute Course, University of Rochester, Rochester, NY 2018.
  • J. Luazon, R. Kangutkar, N. Soures, A. Synesael, A. Bhatt, A. Bhat, S. Jain, K. Simha, R.W. Ptucha, 3rd place. "Milpet- Voice Activated Wheelchair," Effective Access Technology Conference, Rochester, NY, 2017.
  • R. Ptucha, Allison Gray, "Fundamentals of Deep Learning," Tutorial at Electronic Imaging, San Francisco, CA, 2016-2018.
  • A. Bhatt, M. Dushkoff, S. Echefu, M. Shoaib, K. Wieszchowski, R.W. Ptucha, "Voice Activated Wheelchair," Effective Access Technology Conference, Rochester, NY, 2015.
  • R.W. Ptucha, "Enabling Ubiquitous Computing," keynote speaker at Rochester Institute of Technology's Graduate Research and Creativity Symposium, Rochester, NY, 2015.
  • R.W. Ptucha, "Deep Belief Networks," IEEE Society for Imaging Science & Technology, Rochester, NY, 2015.
  • R.W. Ptucha, "Machine Learning with Deep Belief Networks," Invited tutorial: Western New York Image and Signal Processing Workshop, Rochester, NY, 2014.
  • R. Rudra, H. Kumar, R.W. Ptucha, "Sparse Based Gesture Recognition," 2014 ChaLearn Gesture Challenge, 2014.
  • R.W. Ptucha, "Machine Learning for Intelligent Behavior," IEEE Rochester Section Joint Chapters Meeting, Rochester, NY, 2014.
  • C. Bellmore, R.W. Ptucha, A. Savakis, "Interactive Storefront Display with Face and Gesture Control," ChaLearn Gesture Challenge entry at International Conference on Pattern Recognition, Tsukuba, Japan, 2012.
  • R.W. Ptucha, "Joint Optimization of Manifold Learning and Sparse Representations for Face and Gesture Analysis," Artificial Intelligence Seminar, Cornell University, 2013.
  • S. Azary, R.W. Ptucha, A. Savakis, "Pervasive Intelligence Aided by Depth Sensors," International Conference on Computational Photography, Cambridge, MA, 2013.
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Projects
MIL
  • Deep Learning: Inspired by the human brain, deep learning methods have revolutionized the computer vision, natural language processing, and pattern recognition communities. The Machine Intelligence Laboratory, directed by Dr. Ptucha, is researching advanced deep learning algorithms: hierarchical architectures, adaptive activation functions, statistical convolutional networks, and temporal recurrence. This algorithm research is yielding state-of-the-art results in still and video understanding, natural language processing, and graph theory. Applications of this research include medical imaging, image annotation, activity understanding, object recognition, voice recognition, advanced human-computer interaction, and autonomous driving.
GraphCNN
  • Graph CNN: CNN's have transformed the pattern recognition commuity, but only work on fixed lattice grids such as images, video, and audio spectrograms. Graph CNN affords the wonderful CNN benefits to non-gridded problems such as trade, security, protein structures, weather, brain scans, etc. Graph CNN develops research into novel graph convolution, pooling, and fully connected layers. Vector representations of graphs enable multi-stream CNNs, feeding into RNNs, and fixed length representations.
CVS
  • Common Vector Space: Discovery of low dimensional latent representations where similar concepts (such as different types of cars) lie close, while dissimilar concepts (such as cars vs. elephants) lie far aprt. CVS does this irrespective of modality such that pictures, videos, audio, text, and 3D images of cars all lie close in this latent representation. This researach uses adveserial contrastive loss functions and novel attention models as well as sequence models for text generation and generative adveserial networks for image generation.
ASR
  • Resource Constrained Automatic Speech Recognition: Automatic Speech Recognition has reached new levels, but requires thousands of hours of transcribed audio. Endangered languages such as Seneca and Mohawk only have a few hours of recorded audio. Use adversarial networks and deep learning architectures for language preservation. Research includes novel methods for both acoustic and language models, including multistage data augmentation, GANs for data generation, and self-supervised learning for improved weight initialization.
iMHS
  • Intelligent Material Handling: Large fullfillment warehouses have hundreds of robots scurrying about. These robots rely on guide wires and RFID tags for localization and route following. This research removes these limitiations and achieves advanced map building and localization using LiDAR, omnidirectional cameras, UWB, 60 GHz WiFi, and adaptive particle filters. Multi-modal models are used for localization, scene understanding, and pixel-level segmentation.
CSL
  • Sign Language to Text: Sign language is the primary way of communication between deaf people, but the majority of hearing people do not know how to sign. The reliance of deaf people on interpreters is both inconvenient and cost inefficient. We introduce the world’s largest sign language dataset to date- a collection of 50,000 video snippets taken from a pool of 10,000 unique utterances signed by 50 signers. We further propose several sequence-to-sequence deep learning approaches to automatically translate from Chinese sign language to both English and Mandarin written text. These methods utilize body joint position, facial expression, as well as finger articulation.
Deepfake
  • Deepfake Detection: Deepfakes are arti cially generated audio or visual renderings of an individual. These recordings, which are typically done without consent, can be used to defame a public gure or influence public opinion. With the recent discovery of generative adversarial networks, an attacker using a normal desktop computer outfi tted with an off -the-self graphics processing unit can make renditions so good, they can easily fool humans and machines alike. This rsearch develops advanced digital forensics to help a human analyst determine if an audio or video recording was generated using deepfake techniques. Rather than concentrate on a single method which attackers can learn to circumvent, we propose a family of methods including contextual and metadata features both for improved detection and better social understanding of deepfakes.
milpet
  • Voice Activated Wheelchair: An effective access wheelchair is being designed to help elderly and disabled patients safely navigate through busy environments while smartly recognizing objects and surroundings. Motorized wheelchairs have improved the lives of countless individuals. For those who have trouble with a joystick, wheelchairs have been retrofitted to move based upon eye blinks, breathing tubes, and head pose. This project is developing a natural wheelchair interface, offering the patient the ability to control their wheelchair using typical and intuitive human-to-human methods such as voice and facial expression. The voice commands give visually impaired or wheelchair patients with restricted hand mobility, such as stroke, arthritis, limb injury, Parkinson"s, cerebral palsy or multiple sclerosis, the freedom to move independently. The advanced object and scene recognition give patients the ability and confidence to move safely and reliably through complex environments.
APM
  • Autonomous People Mover: Self-driving cars will make our roadways safer, our environment cleaner, our roads less congested, and our lifestyles more efficient. Imagine a people mover on the RIT campus that stands ready at all times to give people rides across campus. A person needing a lift sends a text to the people mover service. The people mover (golf cart) drives over to the location of the person, asks where they want to go, and then drives them to the destination of their choosing. The key differentiator with this project is there is no human driver- the people mover is driving fully autonomously. The hardware and software for high speed highway driving and low speed campus driving are very similar. The algorithms, including localization, obstacle avoidance, and navigation, are almost identical. The autonomous people mover project is a multi-year senior design project. Phase I converted the golf cart into a remote control vehicle. Phase II is robustifying the control systems and introducing advanced sensors. Phase III is currently working towards autonomous driving in restricted settings. Future phases will enable the vehicle to drive autonomously in real-world scenarios. Key tasks involve LIDAR and vision integration, localization, path planning, path following, and object detection/recognition/tracking.
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Patents
  • "Imaging Workflow Using Facial and Non-facial Features," U.S. Patent 10,528,795, Jan. 7, 2020.
  • "Group Display System," U.S. Patent 10,075,679, Sept. 11, 2018.
  • "Imaging Workflow Using Facial and Non-Facial Features," U.S. Patent 9,552,374, Jan 24, 2017.
  • "Interactive Digital Advertising System," U.S. Patent 9,349,131, May 24, 2016.
  • "Camera and Display System Interactivity," U.S. Patent 9,319,640, April 19, 2016.
  • "Method for Group Interactivity," U.S. Patent 9,253,447, Feb 2, 2016.
  • "Group Display System," U.S. Patent 9,179,102, Nov. 3, 2015.
  • "Image Recomposition from Face Detection and Facial Features," U.S. Patent 9,025,836, May 5, 2015.
  • "Image Recomposition from Face Detection and Facial Features," U.S. Patent 9,025,835, May 5, 2015.
  • "Image Recomposition from Face Detection and Facial Features," U.S. Patent 9,008,436, April 14, 2015.
  • "Image Recomposition from Face Detection and Facial Features," U.S. Patent 8,811,747, Jan. 20, 2015.
  • "Method for Producing Artistic Image Template Designs," U.S. Patent 8,854,395, Oct. 7, 2014.
  • "Method for Matching Artistic Attributes of a Template and Secondary Images to a Primary Image," U.S. Patent 8,849,853, Sept. 30, 2014.
  • "System for Matching Artistic Attributes of Secondary Image and Template to a Primary Image," U.S. Patent 8,849,043, Sept. 30, 2014.
  • "Image Recomposition from Face Detection and Facial Features," U.S. Patent 8,811,747, Aug. 19, 2014.
  • "Method for Controlling Interactive Display System," U.S. Patent 8,810,513, Aug. 19, 2014.
  • "Multi-user Interactive Display System," U.S. Patent 8,723,796, May. 13, 2014.
  • "System for Coordinating User Images in an Artistic Design," U.S. Patent 8,538,986, Sept. 17, 2013.
  • "System for Matching Artistic Attributes of Secondary Image and Template to a Primary Image," U.S. Patent 8,422,794, April 16, 2013.
  • "Display System for Personalized Consumer Goods," U.S. Patent 8,390,648, March 5, 2013.
  • "Context Coordination for an Artistic Digital Template for Image Display," U.S. Patent 8,345,057, Jan 1, 2013.
  • "Method of Generating Artistic Template Designs," U.S. Patent 8,332,427, Dec 11, 2012.
  • "Method of Making an Artistic Digital Template for Image Display," U.S. Patent 8,289,340, Oct 16, 2012.
  • "Processing Digital Templates for Image Display," U.S. Patent 8,274,523, Sept 25, 2012.
  • "Image Capture Method with Artistic Template Design," U.S. Patent 8,237,819, Aug 7, 2012.
  • "Artistic Digital Template for Image Display," U.S. Patent 8,212,834, July 3, 2012.
  • "Printer Having Differential Filtering Smear Correction," U.S. Patent 7,847,979, Dec 7, 2010.
  • "Selection of Alternative Image Processing Operations to Maintain High Image Quality," U.S. Patent 7,570,829 B2, Aug 4, 2009.
  • "Applying an Adjusted Image Enhancement Algorithm to a Digital Image," U.S. Patent 7,428,332, Sept 23, 2008.
  • "Sharpening a Digital Image in Accordance With Magnification Values," U.S. Patent 7,269,300, Sept 11, 2007.
  • "Method for Constructing Extended Color Gamut Digital Images From Limited Color Gamut Digital Images," U.S. Patent 7,308,135, Dec 11, 2007.
  • "Method of Color Transformation for Processing Digital Images," U.S. Patent 6,956,967, Oct 18, 2005.
  • "Method For Determining Necessary Resolution For Zoom And Crop Of Images," U.S. Patent 6,643,416, Nov. 4, 2003.
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Teaching: University Courses
  • Deep Learning, CMPE-679, CE Dept, RIT, 2017-2020.
  • Machine Intelligence, CMPE-677, CE Dept, RIT, 2014-2019.
  • Interface & Digital Electronics, CMPE-460, CE Dept, RIT, 2014-2020.
  • Introduction to Computer Engineering, CMPE-110, CE Dept, RIT, 2013.
  • Robotics, 0306-675, 0306-775, CE Dept, RIT, Adjunct Professor: 2007-2013.
  • CE Multi-Disciplinary Senior Design, 0306-656, CE Dept, RIT, Adjunct Professor: 2009.
  • Engineering Fundamentals of Computer Systems, 0306-340, CE Dept, RIT, Adjunct Professor: 2007, 2008.
Teaching: Kodak Courses
  • Facial Processing, Invited Speaker for Kodak Science & Technology Conference, 2010.
  • Halftoning, Tutorial for Kodak Engineering Conference, 2008.
  • Bayesian Nets & Classifications, Tutorial for Kodak Engineering Conference, 2008.
  • Digital Imaging Algorithms- Under the Hood- Parts I & II, Tutorial for Kodak Engineering Conference, 2005.
  • Introduction to Digital Imaging, 12 hour class for Kodak Camera Club, 2005.
  • Digital Technology Module- Input, 8 hour internal class, 1x/year 1998-2006.
  • Digital Imaging Technology, 16 hour internal class, 3-4x/year 1998-2004.
  • Rendered Digital Input Path Algorithms- Under the Hood, Tutorial for Kodak Digital Imaging Conference, 2004.
  • Digital Imaging Algorithms- Under the Hood- Parts I & II, Tutorial for Kodak Digital Imaging Conference, 2003.
  • Digital Image Processing Within the Image State Diagram, Tutorial for Kodak Digital Imaging Conference, 2002.
  • How Can We Use Color To Our Advantage, Tutorial for Kodak Digital Imaging Conference, 2001.
  • Digital Scanner Best Practices: Image Processing Path, Kodak video training series, 1997.
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Awards
  • 2020 IEEE Region 1 Outstanding Teaching in an IEEE Area of Interest (University or College).
  • 2019 Graduate Student Office Volunteer of the Year.
  • 2019 Best Paper Award- Western NY Image & Signal Processing Workshop.
  • 2018 KGCOE Faculty Scholarship Report.
  • 2018 Computer Engineering Most Effective Teacher.
  • 2018 Best Paper Award- Western NY Image & Signal Processing Workshop.
  • 2017 KGCOE Exemplary Performance in Research Proposals Submitted.
  • 2017 KGCOE Exemplary Performance in Teaching.
  • 2017 KGCOE Exemplary Performance in Externally Disseminated Works.
  • 2017 KGCOE Exemplary Performance in Engaging Students in Dissemination.
  • 2017 Best Paper Award- Western NY Image & Signal Processing Workshop.
  • 2016 KGCOE Exemplary Performance in Externally Disseminated Works.
  • 2016 KGCOE Exemplary Performance in Engaging Students in Dissemination.
  • 2016 Computer Engineering Favorite Faculty Mentor Award.
  • 2015 KGCOE Exemplary Performance in Teaching.
  • 2015 KGCOE Exemplary Performance in Engaging Students in Dissemination.
  • 2015 Keynote speaker for RIT Graduate Research Symposium.
  • 2014 Best RIT Doctoral Dissertation.
  • 2013 International Conference on Image Processing- Top 10% Paper Award.
  • 2013 Automatic Face and Gesture Recognition Doctoral Consortium recipient.
  • 2012 Computer Vision and Pattern Recognition Doctoral Consortium recipient.
  • Best presentation/paper, RIT Graduate Research Symposium, 2012.
  • 2010 National Science Foundation Graduate Research Fellowship recipient.
  • Best paper, RIT Graduate Research Symposium, 2009.
  • RIT Outstanding Adult Student Award, 2009.
  • Gold Program for Future Kodak Leaders, 2000-2004.
  • Kodak instructor of the year nominee, 1998-2005.
  • 2003 R&D Team Leadership finalist.
  • SOGP Masters Program, 2001-2002.
  • The DIMA Innovative Digital Product of PIMA "98 for the Advantix FD-300 Film Scanner.
  • Image Science Career Development Program, 1994-1996.
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