Research

Distributed and Scalable Deep Learning; Deep Learning for Personal Medicine; Randomness in Deep Learning; Analytics of Rapid Data Streams; Complex Event Processing (CEP); Internet of Things and Smart Systems; Privacy Preserving; Cyber Security; Cloud Management

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.

My research focuses on AI and machine learning, with an emphasis on robotics applications. My long term goal is to bring robots into human-centered domains such as homes and hospitals. Towards this goal, some fundamental questions need to be solved, such as how can machines learn models of their environments that are useful for performing tasks, and how to learn behavior from interaction in an interpretable and safe manner. Most of my work falls under the framework of reinforcement learning, and its connections to representation learning and planning.

Theoretical and systems aspects of data management, enumeration algorithms, probabilistic graphical models.

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

Program Synthesis, Machine Learning for Programming Languages, Neuro-Symbolic Models, Program Analysis

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

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

Machine Learning and Optimization.

"Mathematical optimization and algorithmic theory in continuous optimization. Mainly non-convex optimization models, methods and theory, for finance, machine learning applications, and large-scale problems."

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

Modeling and predicting human behavior and social dynamics; Learning to support human decision-making; Training for human objectives and with humans in the loop; Implications of deploying predictive models in social contexts.

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

Natural Language Processing (NLP). Out-of-distribution generalization in NLP (e.g. cross-language and cross-domain learning); NLP for social, behavioral and health science; Causality and model interpretation.

Geometry-based Data Analysis & Modeling; Signal Processing; Applied Harmonic Analysis; Diffusion Geometry; Biomedical Signal Processing; Computational Neuroscience.

Caching, network monitoring, stream processing, reliable distributed systems, high-availability and fault-tolerance, blockchains, cloud computing, wireless mobile ad hoc network

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

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

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.

Natural language processing; machine learning for language understanding and generation; neural network representations; interpretability and robustness of machine learning models.

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