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Abdeslam Boularias
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Machine learning, robotics, planning and learning in partially observable domains, reinforcement learning.
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Mubbasir Kapadia
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Aims to develop agent-centric models for simulating crowd dynamics that challenge foundational assumptions in crowd modeling, while providing solutions that are validated using comparisons to real data, and virtual reality experiments. These solutions can be used to optimize the behavioral dynamics of real crowds and model the relationships between crowd flow and environment features, with applications in predictive analytics and crowd management, and environment layout design. His other research interests include real-time multi-agent planning, character animation for autonomous virtual humans, and digital storytelling.
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Casimir Kulikowski
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Image interpretation using planning and learning techniques; methods of theory formation for classification, configuration, planning and design problems with biomedical applications.
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Dimitris Metaxas
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American Sign Language and Gesture recognition from video, human identification and intent recognition from video, human computer interaction, shape and motion representation for recognition.
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Karl Stratos
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I develop computational models to learn generalizable and human-readable representations from unlabeled data, with a focus on natural language processing. To this end, I rely on mathematical frameworks such as Information theory: a representation is good if it transmits most information. Linear algebra: a representation is good if it lies in an optimal subspace. I am also interested in applications of learned representations to practical problems such as entity linking. I am not a theoretician by trade, but I enjoy working with theoreticians to study topics relevant to representation learning such as estimating mutual information.
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