Artificial Intelligence Research

Artificial Intelligence research at UR includes work on knowledge representation and reasoning, machine learning, dialog systems, statistical natural language processing, automated planning, AI-based assistive technology, and computer vision. We collaborate with the departments of linguistics and brain and cognitive sciences.  We participate in the Center for Language Sciences, the Center for Visual Science, and The Goergen Institute for Data Science.

 


Daniel Gildea

Professor of Computer Science

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Interests: Machine translation; Semantic parsing; Algorithms for natural language processing

Dan Gildea is interested in statistical approaches to natural language processing, in particular language understanding and machine translation. He has also worked on language and pronunciation modeling for speech recognition and computational approaches to phonology.

 


Hangfeng He

Assistant Professor of Computer Science and Data Science

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Interests: Machine learning; Natural language processing; Reasoning

Hangfeng He's research interests include machine learning and natural language processing, with a focus on incidental supervision for natural language understanding, interpretability of deep neural networks, and reasoning in natural language.

 


Christopher Kanan

Associate Professor of Computer Science

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Interests: Artificial Intelligence; Deep Learning; Computer Vision; Cognitive Science; Applied Machine Learning (e.g. Medical Computer Vision)

Christopher Kanan's research lies in deep learning, with an emphasis on lifelong (continual) machine learning, bias-robust artificial intelligence, medical computer vision, and language-guided scene understanding. He has worked on online continual learning, visual question answering, computational pathology, semantic segmentation, object recognition, object detection, active vision, object tracking, and more. Beyond machine learning, he also has a strong background in eye tracking, primate vision, and theoretical neuroscience.

  


Christopher Kanan

Associate Professor of Computer Science

Interests: Artificial Intelligence; Deep Learning; Computer Vision; Cognitive Science; Applied Machine Learning (e.g. Medical Computer Vision)

Christopher Kanan's research lies in deep learning, with an emphasis on...


Jiebo Luo

Professor of Computer Science

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Interests: Computer Vision; Machine learning; Data Mining; Social Media; Biomedical Informatics; Human Computer Interaction; Ubiquitous and Mobile Computing

Jiebo Luo's research spans image processing, computer vision, NLP, machine learning, data mining, computational social science, and digital health. He is the co-author of the book Deep Neural Network for Medical Image Computing: Principles and Applications (Elsevier, 2022). He has been involved in numerous technical conferences, including serving as program co-chair of ACM Multimedia 2010, IEEE CVPR 2012, ACM ICMR 2016, and IEEE ICIP 2017, and general co-chair of ACM Multimedia 2018. He has served as the Editor-in-Chief of the IEEE Transactions on Multimedia (2020-2022). He is a Fellow of ACM, AAAI, IEEE, SPIE, and IAPR.

 


Lenhart Schubert

Professor of Computer Science

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Interests: Language and dialogue; Knowledge representation and inference; Schema learning and schema-based behavior; Self-motivated agents

Len Schubert's research interests center around language, knowledge representation, inference and planning. These interests are tied together by the general goal of developing agents with common sense and the ability to converse and acquire knowledge through language.

 


Chenliang Xu

Associate Professor of Computer Science

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Interests: Computer Vision; Video Analysis; Machine Learning; Deep Learning; Multimodal Modeling

Chenliang Xu's research thrusts include computer vision and its relations to natural language, robotics and data science. He primarily focuses on problems in video understanding such as video segmentation, activity recognition, and multimodal vision-and-x modeling. Recent projects include work on cross-modal audio-visual generation, fine-grained actor-action segmentation, and video storytelling.

 


James Allen

Professor Emeritus of Computer Science

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Interests: Natural language understanding; Discourse; Knowledge representation; Common-sense reasoning; Planning

James Allen's research interests span a range of issues covering natural language understanding, discourse, knowledge representation, common-sense reasoning, and planning. He held joint appointments in the brain and cognitive sciences and linguistics departments, the Dessauer Chair in Computer Science, and is a fellow of the AAAI. He is the author of the definitive graduate-level textbook Natural Language Understanding, 2nd ed. (Benjamin Cummings, 1994).

 


Henry Kautz

Professor Emeritus of Computer Science

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Interests: Artificial intelligence; Pervasive computing; Data science

Henry Kautz's research projects include data mining social media in order to track disease and improve public health; grounded language learning by align text and video; and knowledge representation and reasoning systems that combine logic and probability. He was the Robin & Tim Wentworth Director of the Goergen Institute for Data Science, and is a Fellow of the American Association for the Advance of Science (AAAS).

 

 

Project Pages

Project NameBrief Summary
Action Recognition from Unconstrained Amateur Videos

We developed a systematic framework for recognizing realistic actions from unconstrained amateur videos which have tremendous variations due to camera motion, background clutter, changes in object appearance and scale, and so on.

Boundary Extraction by Lineal Feature Growing

Computer vision method for extracting lineal features, both curved and straight, from an image using extended local information to provide robustness and sensitivity.

Digital Analysis and Restoration of Daguerreotypes

Cluster computing allows standard digital analysis and restoration techniques to be applied to high-resolution microscopic digitizations of Daguerreotypes from the collection of the George Eastman House in Rochester. Knowing the image context of a feature (such as a small light spot) affects its probability of being noise (dust effect) or signal (foliage effect). Machine learning can be used to automate some subtle decisions.

Interactive Co-segmentation of Topically Related Images with Intelligent Scribble Guidance

We developed a user-friendly system to facilitate a user to perform interactive segmentation of objects of interest from a group of related images by providing scribble guidance.

Vision+Language

Grounded language learning by aligning text and video.

Mining the Power of 'Like' in Social Media Networks

'Like' has now become a very popular social function on social media networks by allowing users to express their positive opinions of certain objects. It provides an accurate way of gauging user interests and an effective way of sharing or promoting information in social media. We developed a system called LikeMiner using a heterogeneous network model and related mining algorithms to estimate the representativeness and influence of objects.

Planning as Satisfiability

Solving combinatorially challenging planning problems by encoding as Boolean satisfiability and applying state of the art SAT solvers.

Machine Translation

Methods for translating between natural languages (such as English and Chinese) by training statistical models on large collections of text.

Trains

The TRAINS project and its successors form one of the longest running research efforts on practical spoken dialogue: conversation undertaken with a specific task in mind.

Image Sentiment Analysis using Progressively Trained and Domain Transferred Deep Networks

We developed a robust algorithm for predicting the sentiment carried by an image using a progressive training strategy to derive a convolution neural network from samples with noisy sentiment labels

Leveraging User Generated Online Photos to Estimate and Monitor Air Pollution in Major Cities

We developed a novel system that makes use of everyday user shared photos to measure the air quality of different locations.