Mind, Machines & Computation

Motion and task planning for autonomous robots; Integration of perception and planning for manipulating and interacting with the physical world; Coordination of multiple physical agents, including human-robot interaction.

Machine learning, robotics, planning and learning in partially observable domains, reinforcement learning.

Theoretical and comparative syntax of natural languages focusing on models of parameterization for deriving grammars from universal principles.

Computer Vision and Machine Learning;  statistical models for learning visual manifolds of objects; computational models for recognition of articulated objects; computational art history.

Computer models of syntactic parsing and language comprehension.

The influence of prior knowledge on memory and decision making in naturalistic environments.

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.

Philosophy of Mind, Cognitive Science, Philosophy of Mathematics and Logic, Philosophy of Physics.

Image interpretation using planning and learning techniques; methods of theory formation for classification, configuration, planning and design problems with biomedical applications.

Conceptual development and the representation of the physical world, of Agency, and of "theory of mind" in infants and preschoolers; also their impairment in autism

Philosophical problems concerning intentionality and consciousness; issues of non-monotonic reasoning.

The nature of cognitive architecture, including the connectionism/classicism debate; psychosemantics and the theory of meaning for mental representations.

Study of visual search and saccadic eye movements, short-term visual memory, perceptual learning and cue integration. Formal computational and ideal observer modeling of visual tasks and of population coding in visual cortex

Vladimir's research interests include Bayesian system modeling, time-series analysis, and statistical computer vision. More recently, his research has focused on modeling of human emotions and affect, as well as design of fast, robust, face tracking and identification systems. He is also interested in modeling and analysis of human crowd behavior from the perspective of distributed sensing and decision making systems.

Perceptual content and mental content more generally, attention, perceptual evidence, the relationship between the phenomenological and epistemological role of perceptual experience, the situation-dependency of perceptual experience, imagination, mental capacities. 

Nature and viability of commonsense (or "folk") psychology, moral cognition & moral reasoning and rationality.

Computational models of language learning, phonology, Optimality Theory, the role of linguistics within cognitive science.

Affective neuroscience, the interaction of emotion with attention and decision-making, and the neural substrates of these functions in both health and neuropsychiatric illness.

RBHS/Brain Health Institute