Director, Strategic Program Execution and Innovation
Ryan Tilley currently serves as Director of Strategic Program Execution and Innovation at IHMC. Formerly, he served as the Chief Operating Officer for VetCV Inc. as well as KontactIntelligence. His prior experience includes significant experience in government contracting, working as program manager and senior consultant for H2 Performance Consulting.
Ryan earned his Master of Business Administration and Bachelor of Science in both finance and global economics from the University of West Florida. He also received his Certificate in Entrepreneurship from the University of West Florida. Ryan is a certified Project Management Professional (PMP) with additional certifications in ITILv3 and Security+ and is a Certified Scrum Master.
Associate Director & Senior Research Scientist
Dr. Bonnie J. Dorr, a leading researcher in the field of natural language processing, is an associate director and senior research scientist at IHMC’s Ocala facility.
Natural language processing is a growing research field at IHMC, and Dr. Dorr’s expertise is at the cutting edge. Her extensive research and project management experience includes deep language understanding and semantics, large-scale multilingual processing, and summarization. She and her colleagues have carried out seminal work in cross-language divergence detection, machine translation, paraphrasing, and automatic evaluation metrics.
Dr. Dorr joins IHMC from the University of Maryland, where she is Professor Emerita in the Institute for Advanced Computer Studies and the Department of Computer Science. She was an associate dean of the College of Computer, Mathematical and Natural Sciences and co-founded the Computational Linguistics and Information Processing Laboratory. She was also principal scientist for two years at the Johns Hopkins University Human Language Technology Center of Excellence.
In 2011, she became a program manager at the Defense Advanced Research Projects Agency (DARPA), overseeing research in human language technology. Her significant DARPA projects include Broad Operational Language Translation (BOLT), Deep Exploration and Filtering of Text (DEFT), Multilingual Automatic Document Classification, Analysis, and Translation (MADCAT), and Robust Automatic Transcription of Speech (RATS).
She holds both a master’s degree and a Ph.D. in computer science from the Massachusetts Institute of Technology, with a bachelor’s degree from Boston University. She is a Sloan Fellow, an NSF Presidential Faculty (PECASE) Fellow, and a former president of the Association for Computational Linguistics. She has served on the Executive Council of the Association for Advancement of Artificial Intelligence (AAAI) and on the Executive Board of the Association for Computational Linguistics (ACL). She was elected AAAI Fellow in 2013, was graduated in the Class of XXXIII of Leadership Florida in 2015, and was elected ACL Fellow in 2016.
Ian Perera is a research scientist at IHMC working on applying linguistic and cognitive models and knowledge towards natural language understanding and the intersection of such understanding with computer vision and other data-grounded applications. He graduated magna cum laude with a B.S.E. in Digital Media Design at the University of Pennsylvania, obtained his Ph.D. in Computer Science at the University of Rochester in 2016 with Dr. James F. Allen as his advisor, and has been at IHMC since 2013. For his Ph.D. thesis, he worked with Dr. Allen to develop the SALL-E system, which uses child language learning strategies and pragmatic reasoning to learn object names and properties in real-time from ambiguous natural language descriptions with only a limited number of examples.
Ian’s focus in natural language processing is deep language understanding for human-computer dialogues, focusing on incorporating semantics and pragmatics with context and grounded knowledge. Such experience includes building dialogue systems with belief modeling and pragmatic reasoning for robots in a human-robot collaborative task and performing semantic extraction from text in a variety of domains, such as human-robot communication, human dialogues, and news articles. Tackling theoretical issues, he also has worked on formalizing aspects of the symbol grounding problem to constrain the complexity of perceptual representations to be learned in perceptually grounded systems. Finally, he demonstrated the importance of formally defined perceptual symbol grounding by building a zero-shot learning system that can recognize previously unseen complex objects from only logical definitions and primitive perceptual knowledge.
Arash Mahyari is a research scientist at IHMC working on machine learning and signal processing. Arash joined IHMC from ABB Robotics R&D Center in San Jose, CA, where he worked on developing the next generation of artificial intelligence for industrial robots. He received his dual degree in electrical engineering (Ph.D.) and Statistics (MSc) from Michigan State University and MSc in electrical engineering from Shiraz University.
Prior to joining IHMC, Arash has worked on several projects in different domains. For his MSc thesis, he developed image fusion algorithms to enhance the spatial quality of multispectral images of the LANDSAT satellite. He later developed a computer vision algorithm to detect the surface defects of the cold rolled steels. The algorithm located the defected areas using texture segmentation methods and extracted several statistical features to be classified using a trained neural network. He has worked on an image processing project and developed an embedded system based on Analog Device DSP processors for telecommunication companies. During his Ph.D., he used tensor analysis, compressive sensing, and dictionary learning to develop algorithms for event detection and tracking of functional connectivity networks (cognitive science).