Raymond Ptucha Headshot

Raymond Ptucha

Assistant Professor
Department of Computer Engineering
Kate Gleason College of Engineering

585-475-2623
Office Location

Raymond Ptucha

Assistant Professor
Department of Computer Engineering
Kate Gleason College of Engineering

Education

BS, State University of New York at Buffalo; MS, Ph.D., Rochester Institute of Technology

Bio

Ray is an assistant professor in the Computer Engineering Department, Rochester Institute of Technology. Ray received a B.S. degree in Computer Science and a B.S. degree Electrical Engineering, both from the State University of New York at Buffalo. He received a M.S. degree in Imaging Science and a Ph.D. degree in Computer Science, both from Rochester Institute of Technology.

Prior to joining RIT, Ray was a research scientist with Eastman Kodak Company for 20 years where he worked on computational imaging algorithms. He has over two dozen publications and holds 16 U.S. patents with an additional 32 patent applications on file. Ray is Secretary of the IEEE Rochester Section, serves as reviewer for Journal of Electronic Imaging, Journal of Image Science and Technology, and Transactions on Affective Computing, and is on the Technical Committee for the 2014 IEEE International Conference on Multimedia & Expo. His research interests include machine learning, computer vision, and robotics.

Selected Publications

  • R.W. Ptucha, A. Savakis, “LGE-KSVD: Robust Sparse Representation Classification”, IEEE Transactions on Image Processing, Volume 23, Issue 4, 2014.
  • 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.
  • R.W. Ptucha, A. Savakis, “Facial Expression Recognition”, IGI Global Encyclopedia of Information Science and Technology, 3rd Edition, 2013.
  • 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.
  • 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.

Currently Teaching

IMGS-890
1 - 6 Credits
Doctoral-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.
CMPE-677
3 Credits
Machine intelligence teaches devices how to learn a task without explicitly programming them how to do it. Example applications include voice recognition, automatic route planning, recommender systems, medical diagnosis, robot control, and even Web searches. This course covers an overview of machine learning topics with a computer engineering influence. Includes Matlab programming. Course topics include unsupervised and supervised methods, regression vs. classification, principal component analysis vs. manifold learning, feature selection and normalization, and multiple classification methods (logistic regression, regression trees, Bayes nets, support vector machines, artificial neutral networks, sparse representations, and deep learning).
CMPE-679
3 Credits
Deep learning has been revolutionizing the fields of object detection, classification, speech recognition, natural language processing, action recognition, scene understanding, and general pattern recognition. In some cases, results are on par with and even surpass the abilities of humans. Activity in this space is pervasive, ranging from academic institutions to small startups to large corporations. This course emphasizes convolutional neural networks (CNNs) and recurrent neural networks (RNNs), but additionally covers reinforcement learning and generative adversarial networks. In addition to achieving a comprehensive theoretical understanding, students will understand current state-of-the-art methods, and get hands-on experience at training custom models using popular deep learning frameworks.
CMPE-460
4 Credits
This course covers various sensors, motors, signal conditioning circuits including amplification, filtering, level shifting, ADC, and DAC. Modern tools, such as Keil ARM MDK and PSpice will be used to simulate and debug modern microcontrollers, such as NXP Kinetis, analog active filters, and operational amplifier application circuits. Each team of two students is required to design a complete data acquisition system from sensors, amplification, filtering, ADC, and DAC to analog outputs through either wired transmission or wireless transmission circuits
IMGS-699
0 Credits
This course is a cooperative education experience for graduate imaging science students.

Latest News

  • October 15, 2018

    A group of three researchers stand together in a line and smile at the camera.

    Researchers use AI to preserve Seneca language

    Using deep learning, a form of artificial intelligence, RIT researchers are building an automatic speech recognition application to document and transcribe the traditional language of the Seneca people.

Select Scholarship

Invited Keynote/Presentation
Ptucha, Ray. "Graph Convolutional Neural Networks." NASA Goddard Workshop on Artificial Intelligence. NASA. Baltimore, MD. 28 Nov. 2018. Guest Lecture. ∆
Ptucha, Ray. "Deep Learning Fundamentals." Tutorial at International Conference on Frontiers of Handwriting Recognition. IEEE. Niagara Falls, NY. 1 Aug. 2018. Keynote Speech. ∆
Ptucha, Ray. "Introduction to Deep Learning for Facial Analysis." Tutorial at Automatic Face and Gesture Recognition Conference. IEEE. Xi'an, China. 21 May 2018. Keynote Speech. ∆
Ptucha, Ray. "Fundamentals of Deep Learning." Tutorial at Electronic Imaging. SPIE. San Francisco, CA. 21 Jan. 2018. Guest Lecture. ∆
Ptucha, R. and A. Gray. "Fundamentals of Deep Learning." Tutorial at Electronic Imaging. IS&T. San Francisco, CA. 1 Feb. 2017. Lecture.
Ptucha, R. "Machine Learning with Deep Belief Networks." Western New York Image and Signal Processing Workshop. IEEE. Rochester, NY. 7 Nov. 2014. Lecture.
Ptucha, R. "Machine Learning for Intelligent Behavior." IEEE Rochester Section Joint Chapters Meeting. IEEE. Rochester, NY. 7 Apr. 2014. Keynote Speech.
Ptucha, Raymond. "Joint Optimization of Manifold Learning and Sparse Representations for Face and Gesture Analysis." Artificial Intelligence Seminar. Cornell University. Ithaca, NY. 12 Mar. 2013. Lecture. ∆
Journal Paper
Ptucha, Ray, et al. "Intelligent Character Recognition using Fully Convolutional Neural Networks." Pattern Recognition PR6747. (2018): 24. Print. «
Sah, Shagan, Thang Nguyen, and Ray Ptucha. "Understanding Temporal Structure for Video Captioning." Journal of Pattern Analysis and Applications PAAA-D-17-00571. (2018): 14. Print. «
Such, F. Petroski, et al. "Robust Spatial Filtering with Graph Convolutional Neural Networks." IEEE Journal of Selected Topics in Signal Processing 11. 6 (2017): 100-113. Print. «
Sah, S., et al. "Video Redaction: A Survey and Comparison of Enabling Technologies." Journal of Electronic Imaging Special issue on Video Analytics for Public Safet. (2017): 200-214. Print. «
Ptucha, R. and A. Savakis. "LGE-KSVD: Robust Sparse Representation Classification." IEEE Transactions on Image Processing 23. 4 (2014): 14. Print. «
Ptucha, Raymond and Andreas Savakis. "Manifold Based Sparse Representation for Facial Understanding in Natural Images." Image and Vision Computing 31. (2013): 365-378. Print. *
Published Conference Proceedings
Blakeslesse, Bryan, Andreas Savakis, and Ray Ptucha. "Faster Art-CNN: An Extremely Fast Style Transfer Network." Proceedings of the Western NY Image & Signal Processing Workshop. Ed. IEEE. New York, NY: n.p., Web. «
Such, Felipe Petroski, et al. "Fully Convolutional Networks for Handwriting Recognition." Proceedings of the International Conference on Frontiers of Handwriting Recognition. Ed. IEEE. New York, NY: n.p., Web. «
Jimerson, Robbie, et al. "Improving ASR Output for Endangered Language Documentation." Proceedings of the International Workshop on Spoken Languages for Under-resources Languages. Ed. IEEE. New York, NY: n.p., Web. «
Sankaran, Prashant, et al. "Simulation Analysis of Deep-Learning Approaches to Task Selection by Autonomous Vehicles for Material Handling." Proceedings of the Winter Simulation Conference. Ed. IEEE. New York, NY: n.p., Web. «
Sah, Shagan, et al. "Semantically Invariant Text-to-Image Generation." Proceedings of the International Conference on Image Processing. Ed. IEEE. New York, NY: n.p., Web. «
Sah, Shagan, et al. "Multimodal Reconstruction Using Vector Representation." Proceedings of the International Conference on Image Processing. Ed. IEEE. New York, NY: n.p., Web. «
Sah, Shagan, Sabarish Gopalakrishnan, and Ray Ptucha. "Cross Modal Retrieval using Common Vector Space." Proceedings of the Image and Vision Workshop at IEEE Computer Vision and Pattern Recognition. Ed. IEEE. New York, NY: n.p., Web. «
Allison, Jacob, Ray Ptucha, and Sergey Lyshevshki. "Resilient Communication, Object Classification and Data Fusion in Unmanned Aerial Systems." Proceedings of the International Conference on Unmanned /aircraft Systems (ICUAS). Ed. IEEE. New York, NY: n.p., Web. «
Tornblad, McKenna, et al. "Sensing and Learning Human Annotators Engaged in Narrative Sensemaking." Proceedings of the Student Research Workshop at NAACL. Ed. IEEE. New York, NY: n.p., Web. «
Dominguez, Miguel, et al. "General-Purpose Deep Point Cloud Feature Extractor." Proceedings of the Winter Conference on Applications of Computer Vision. Ed. IEEE. New York, NY: n.p., Web. «
Nguyen, T., S. Sah, and R. Ptucha. "Multistream Hierarchical Boundary Network for Video Captioning." Proceedings of the Western NY Image & Signal Processing Workshop. Ed. IEEE. Rochester, NY: IEEE, 2017. Web. *
Dhamdhere, R., et al. "Deep Learning for Philately Understanding." Proceedings of the Western NY Image & Signal Processing Workshop. Ed. IEEE. Rochester, NY: IEEE, 2017. Web. *
Dominguez, M., M. Daigneau, and R. Ptucha. "Source-Separated Audio Input for Accelerating Convolutional Neural Networks." Proceedings of the Western NY Image & Signal Processing Workshop. Ed. IEEE. Rochester, NY: IEEE, 2017. Web. *
Sah, S., et al. "Vector Learning for Cross Domain Representations." Proceedings of the IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Conference. Ed. IEEE. Washington, DC: IEEE, 2017. Print. «
Kangutkar, R., et al. "ROS Navigation Stack for Smart Indoor Agents." Proceedings of the IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Conference. Ed. IEEE. Washington, DC: IEEE, 2017. Print. *
Thomas, T., M. Dominguez, and R. Ptucha. "Deep Independent Audio-Visual Affect Analysis." Proceedings of the Global Conference on Signal and Information Processing. Ed. IEEE. Montreal, Canada: IEEE, 2017. Print. «
Zhang, C., et al. "Semantic Sentence Embeddings for Paraphrasing and Text Summarization." Proceedings of the Global Conference on Signal and Information Processing. Ed. IEEE. Montreal, Canada: IEEE, 2017. Print. «
Sah, S., et al. "Temporally Steered Gaussian Attention for Video Understanding." Proceedings of the CVPR Workshop Deep-Vision: Deep Learning in Computer Vision. Ed. IEEE. Honolulu, Hawaii: IEEE, 2017. Web. «
Sah, S., et al. "Detection without Recognition for Redaction." Proceedings of the CVPR Workshop The First International Workshop on The Bright and Dark Sides of Computer Vision: Challenges and Opportunities for Privacy and Security. Ed. IEEE. Honolulu, Hawaii: IEEE, 2017. Web. «
Dominguez, M., et al. "Towards 3D Convolutional Neural Networks with Meshes." Proceedings of the International Conference on Image Processing. Ed. IEEE. Beijing, China: IEEE, 2017. Print. «
Zhang, C., et al. "Batch-Normalized Recurrent Highway Networks." Proceedings of the International Conference on Image Processing. Ed. IEEE. Beijing, China: IEEE, 2017. Print. «
Calderwood, A., et al. "Understanding the Semantics of Narratives of Interpersonal Violence through Reader Annotations and Physiological Reactions." Proceedings of the Computational Semantics Beyond Events and Roles (SemBEaR). Ed. IEEE. Valencia, Spain: IEEE, 2017. Web. «
Sah, S., et al. "Semantic Text Summarization of Long Videos." Proceedings of the WACV. Ed. IEEE. Santa Rosa, CA: IEEE, 2017. Web. «
Bag, S., V. Venkatachalapathy, and R. Ptucha. "Motion Estimation Using Visual Odometry and Deep Learning Localization." Proceedings of the Proceedings of Electronic Imaging: Image Processing Algorithms and Systems. Ed. IS&T. San Francisco, CA: IS&T, 2017. Web. «
Hssayeni, M., et al. "Distracted Driver Detection: Deep Learning vs Handcrafted Features." Proceedings of the Proceedings of Electronic Imaging: Image Processing Algorithms and Systems. Ed. IS&T. San Francisco, CA: IS&T, 2017. Print. «
Echefu, S., et al. "Milpet — The Self-driving Wheelchair." Proceedings of the Proceedings of Electronic Imaging: Image Processing Algorithms and Systems. Ed. IS&T. San Francisco, CA: IS&T, 2017. Print. «
Ptucha, Ray, et al. "Understanding the Semantics of Narratives of Interpersonal Violence through Reader Annotations and Physiological Reactions." Proceedings of the Computational Semantics Beyond Events and Roles. Ed. IEEE. Valencia, Spain: n.p., Print. «
Sah, Shagan, et al. "Semantic Text Summarization of Long Videos." Proceedings of the WACV. Ed. IEEE. Santa Rosa, CA: IEEE, Print. «
Bag, Suvam, Vishwas Venkatachalapathy, and Raymond Ptucha. "Motion Estimation Using Visual Odometry and Deep Learning Localization." Proceedings of the Electronic Imaging. Ed. IS&T. San Francisco, CA: IS&T, Print. «
Hssayeni, M., et al. "Distracted Driver Detection: Deep Learning vs Handcrafted Features." Proceedings of the Electronic Imaging. Ed. IS&T. San Francisco, CA: n.p., Print. «
Echefu, Sam, et al. "Milpet — The Self-driving Wheelchair." Proceedings of the Electronic Imaging. Ed. IS&T. San Francisco, CA: n.p., Print. «
Nooka, Sai, et al. "Adaptive Hierarchical Classification Networks." Proceedings of the ICPR. Ed. IEEE. Cancun, Mexico: n.p., Print. «
Dushkoff, Michael, Ryan McLaughlin, and Raymond Ptucha. "A Temporally Coherent Neural Algorithm for Artistic Style Transfer." Proceedings of the ICPR. Ed. IEEE. Cancun, Mexico: n.p., Print. «
Kulhare, Sourabh, et al. "Key Frame Extraction for Salient Activity Recognition." Proceedings of the ICPR. Ed. IEEE. Cancun, Mexico: n.p., Print. «
Oruganti, Ram, et al. "Image Description through Fusion Based Recurrent Multi-Modal Learning." Proceedings of the ICIP. Ed. IEEE. Phoenix, AZ: n.p., Print. «
Oak, Mayuresh, et al. "Generating Clinically Relevant Texts: A Case Study on Life-changing Events." Proceedings of the Proceedings of CLPysch Workshop, North American Chapter of the Association for Computational Linguistics- Human Language Technologies (NAACL-HLT). Ed. IEEE. San Diego, CA: n.p., Web. «
Dushkoff, Michael and Raymond Ptucha. "Adaptive Activation Functions for Deep Networks." Proceedings of the Electronic Imaging. Ed. IS&T. San Francisco, CA: n.p., Web. «
Chennupati, Sumanth, et al. "Hierarchical Decomposition of Large Deep Networks." Proceedings of the Electronic Imaging. Ed. IS&T. San Francisco, CA: n.p., Web. «
Rajanna, Arjun Raj, et al. "Prostate Cancer Detection Using Photoacoustic Imaging and Deep Learning." Proceedings of the Electronic Imaging. Ed. IS&T. San Francisco, CA: n.p., Web. «
Avery, Alex, et al. "Autonomous People Mover." Proceedings of the Proceedings of American Society for Engineering Education. Ed. ASEE. Ithaca, NY: n.p., Web. *
Yousefhussien, Mohammed and Raymond Ptucha. "Flatbed Scanner Simulation to Analyze the Effect of Detector’s Size on Color Artifacts." Proceedings of the Electronic Imaging. Ed. IS&T. San Francisco, CA: n.p., 2015. Print. *
Wong, Adelia and Raymond Ptucha. "Localization Using Omnivision-based Manifold Particle Filters." Proceedings of the Electronic Imaging. Ed. IS&T. San Francisco, CA: n.p., 2015. Print. «
Schrading, Nicolas and Raymond Ptucha. "#WhyIStayed, #WhyILeft: Microblogging to Make Sense of Domestic Abuse." Proceedings of the NAACL. Ed. IEEE. Denver, CO: n.p., 2015. Print. «
Kumar, Himanshu and Raymond Ptucha. "GESTURE RECOGNITION USING ACTIVE BODY PARTS AND ACTIVE DIFFERENCE SIGNATURES." Proceedings of the ICIP. Ed. IEEE. Quebec City, Canada: n.p., 2015. Print. «
Rajanna, Arjun Raj and Raymond Ptucha. "Deep Neural Networks: A Case Study for Music Genre Classification." Proceedings of the ICMLA. Ed. IEEE. Miami, FL: n.p., 2015. Print. «
Schrading, Nicolas and Raymond Ptucha. "An Analysis of Domestic Abuse Discourse on Reddit." Proceedings of the EMNLP. Ed. IEEE. Lisbon, Portugal: n.p., 2015. Print. «
Savakis, A., R. Rudra, and R. Ptucha. "Gesture Control Using Active Difference Signatures and Sparse Learning." Proceedings of the International Conference on Pattern Recognition. Ed. . Stockholm, Sweden: n.p., 2014. Print. «
Ptucha, R., et al. "Automatic Image Assessment from Facial Attributes." Proceedings of the Electronic Imaging: Computational Imaging. Ed. SPIE. San Francisco, CA: n.p., 2014. Print. *
Merkel, C., D. Kudithipudi, and R. Ptucha. "Heterogeneous CMOS/Memristor Neural Networks for Real-time Target Classification." Proceedings of the SPIE Machine Intelligence and Bio-inspired Computation. Ed. SPIE. Washington, DC: n.p., 2014. Print. *
Hays, Phil, Raymond Ptucha, and Andreas Savakis. "Mobile Device to Cloud Co-processing of ASL Finger Spelling to Text Conversion." Proceedings of the Western NY Image Processing Workshop. Rochester, NY: IEEE Xplore, 2013. Web. «
Ptucha, Raymond, Sherif Azary, and Andreas Savakis. "Keypoint Matching and Image Registration Using Sparse Representations." Proceedings of the of International Conference on Image Processing. Ed. IEEE Xplore. Melbourne, AU: IEEE Xplore, 2013. Web. «
Bellmore, Colin, Raymond Ptucha, and Andreas Savakis. "Fusion of Depth and Color for an Improved Active Shape Model." Proceedings of the of International Conference on Image Processing. Ed. IEEE Xplore. Melbourne, AU: IEEE Xplore, 2013. Web. «
Ptucha, Raymond and Andreas Savakis. "LGE-KSVD: Flexible Dictionary Learning for Optimized Sparse Representation Classification." Proceedings of the Computer Vision and Pattern Recognition. Ed. IEEE Xplore. Portland, OR: IEEE Xplore, 2013. Web. «
Ptucha, Raymond and Andreas Savakis. "Joint Optimization of Manifold Learning and Sparse Representations." Proceedings of the Face and Gesture Recognition. Ed. IEEE Xplore. Shanghai, China: IEEE Xplore, 2013. Web. «
Peer Reviewed/Juried Poster Presentation
Lauzon, J., et al. "Milpet- Voice Activated Wheelchair." Proceedings of the Effective Access Technology Conference. Ed. IEEE. Rochester, NY: IEEE. *
Provisional Patent
Such, F. Petroski, R. Ptucha, and P. Hutkowski. "System and Method of Character Recognition Using Fully Convolutional Neural Networks." U.S. Provisional Patent Application 20170000000. 1 Oct. 2017.
Such, F. Petroski, R. Ptucha, and P. Hutkowski. "System and Method of Character Recognition Using Fully Convolutional Neural Networks with Attention." U.S. Provisional Patent Application 15812681. 14 Nov. 2017.
Book Chapter
Ptucha, Raymond and Andreas Savakis. "Facial Expression Recognition." IGI Global Encyclopedia of Information Science and Technology. : IGI, 2013. Print. *
Shows/Exhibits/Installations
Ptucha, Raymond. Pervasive Intelligence Aided by Depth Sensors. By Andreas Savakis. 13 Apr. 2013. Harvard University, Boston. Exhibit. *