Research

Swarm Intelligence, Distributed Control of Multi-Agent Systems, and Distributed Task Allocation. I also investigate Machine Learning implementations (Supervised & Reinforcement) to expedite traditional planning algorithms.

Schema Matching; Entity Resolution; Semantic Integration of Data Resources; Business Process Management; Temporal Databases and Temporal Evolution of Databases.

Service-Engineering of large operations (e.g. hospitals / emergency-departments, call / contact-centers, courts, …); Operations research; Statistics; Queueing science & theory; Control theory; Data- and process-mining.

Computer Vision, Computer Graphics, Machine Learning, Visualization.

Enviromatics; machine learning methods and mathematical for natural complex environments; hydro-informatics; atmospheric-informatics; precise agriculture; structural health; smart infrastructure systems and connected transportation.

Deep Neural Network representation learning, Machine Learning, Computer Vision, Geometric Deep learning, Algorithms for efficient training and inference of Deep Neural Networks.

"Environmental Fluid dynamics Turbulent atmospheric flows Water waves, and wind-waves interactions Acoustics"

Urban and regional economics, Ecological and environmental economics, Systems modeling, Land use change, Economic geography

Conceptual Modeling, Systems Eng. and Modeling, Systems Architecture, Enterprise Systems Modeling; Object-Process Methodology; Ontologies; Software Development Methodologies, Semantic Web; Systems Biology, Robotics.

Information and coding theory with applications to non-volatile memories, associative memories, data storage and retrieval, distributed storage, privacy, and DNA storage.

Automated Planning, Robotics, Artificial Intelligence.

Caching, Content Distribution, Optimizations for Flash Based Storage, Erasure Coding, Deduplication, Workload Characterization and Improved Analysis Tools.

Distributed and network-based computing, Distributed Storage, Parallel Computing, Blockchains.

Algorithmic game theory; Theory of computation; Optimization; AI; Internet economics; Market design; Auctions.

InterArray processing, signal processing, deep learning, analysis and modeling of acoustic signals, speech enhancement, noise estimation, source localization, blind source separation, system identification and adaptive filtering.

Accessibility, mobility, travel problems, disparities, social exclusion, equity.

Algorithmic aspects of multi-robot systems; smart mobility optimization; autonomous driving; robot control and decision making; societal aspects of autonomous transportation systems

Control policies that facilitates learning and sim2real transfer with applications to robot assembly and legged locomotion; Invasive and non-invasive Brain Machine Interfaces (BMIs) and error related processing;

Machine Learning and Statistics, Combinatorial Optimization and Approximation Algorithms, Algorithmic Dimension Reduction and Applications, Complexity.

I seek to deeply understand and to rigorously address the computational challenges that arise when planning for robots. My research, lying at the intersection of Computer Science and Robotics, is motivated by the key insight that in order to address these challenges, traditional Computer Science algorithms, tools and paradigms need to be revisited. This requires (i) understanding and analyzing the unique domain-specific computational challenges in robotic planning and, subsequently (ii) developing algorithms to address these challenges to provide the robotics community foundational tools to solve real-world problems. For additional details, see my research statement.

Predicting human decision making; Mining behavioral data; behavioral economics; behavioral decision making; human learning processes; behavioral mechanism design; behavioral public policy.

I am interested in understanding and mitigating the negative effects of strategic behavior. Mainly by people interacting via large systems, e.g. congestion in networks or biased group decisions.

Computer vision, graphics, Geometric machine learning and big data, computational medicine and biometry, applied metric and differential geometries.

Mathematical foundations of deep learning, graph neural networks, geometric deep learning, explainable AI.

Geodata science, methods of interpretation, mining, and integration of crowdsourced content to augment and develop location-based services and smart mapping infrastructures. Spatial-cognition, navigation, and the built-environment.

Multi-agent AI, multi-robot systems, collaborative AI, multi-agent environment design, integrated task and motion planning for robotics, and multi-agent reinforcement learning.

Artificial Intelligence, Machine Learning, Natural Language Semantics, Anytime Learning, Active and Selective Learning, Information retrieval, Multi-agent Systems, Adversary search, Opponent Modeling, Resource-bounded reasoning, Cost-sensitive Learning.

AI and machine learning, reinforcement learning and planning; learning, optimization and control under uncertainty, Multi-agent systems, Optimization of large scale problems, application of machine learning to a variety of problems: power grids, communication networks, etc.

Robust and adaptive optimization; Data-driven optimization; Algorithms for nonconvex and mixed-integer optimization; Optimization applications in: energy, inventory systems, estimation and control, statistics and healthcare.

Network and multi-user information theory, Modern Communication networks (Cloud and Fog Radio Networks), Information and Signal Processing (Information-Estimation), Information bottleneck problems in communications and learning.

Continuous Optimization: Theory and Algorithms, development and analysis of Optimization Methods for large-scale optimization problems, Applications of Optimization Methods in Machine/Deep Learning.

Theoretical & practical aspects of machine learning. Mathematical solutions to real-life problems demonstrating non-traditional statistical behavior.

Computer Vision; Machine Learning; Image Processing; Signal Processing.

Transportation systems modeling and analysis, traffic simulation modeling, driving and travel behavior, intelligent transportation systems, traffic management and control.

The intersection of probabilistic perception and inference, learning, and planning under uncertainty, both for single and distributed multi-agent autonomous systems.

Research centers around the theory and practice of statistical inference and machine learning, focusing on the reliability, robustness, and interpretability of modern data-driven algorithms.

Storage devices, systems; Reliable data distribution in networks; Coding theory, Data compression.