Research

Closed-loop software and hardware platforms driving chemical discovery through automated hypothesis generation, refinement, validation, and revision, resulting in predictive chemical kinetic models.

Network routing, survivability, QoS provisioning, wireless networks, the application of game theory to computer and power networks, blokchains, the application of machine learning to network protocols.

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

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.

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

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

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

Automated Planning, Robotics, Artificial Intelligence.

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

Nano-array devices for screening, diagnosis and monitoring of disease, nanomaterial-based chemical (flexible) sensors, electronic skin, breath analysis, volatile biomarkers, and cell-to-cell communication.

Implications of quantum mechanics on future technology; Algorithms to automate research in fundamental science and in mathematics; Fundamentals of light-matter interactions; Probing materials with ultrafast electrons and photons.

Environmental quality driven urban design, Generative urban design, Zero energy buildings and districts, Performance-based design using digital tools, Environmental workspace design, Passive low carbon design strategies

Machine Learning and Optimization.

Machine learning for material design.

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

Building physics; Building performance simulation; Bioclimatic design; Urban microclimate; Building technology; History of architecture and architectural technology; Urban history; Digital Humanities.

Inverse molecular design, chemical space exploration, physical organic chemistry.

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.

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

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

Performing logic using memory cells to build the memristive memory processing unit (mMPU), mixed-signal circuits, RF circuits, neuromorphic computing, cytomorphic systems, deep learning accelerators, internet-of-things, and hardware security.

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.

The CFDLAB focuses on the development, implementation and application of high-fidelity numerical methods for solving problems in aerodynamic, combustion, energy, propulsion, and multiphase turbulent flows. We use high-performance computing including multi-GPU platforms to parallelize our simulations and improve performance. We also study algorithms for quantum computers. In all cases, AI/machine learning techniques are considered to implement flow control strategies, better understand and improve predictive accuracy of turbulent flows, and finally to improve performance of quantum algorithms.

Research in Advanced Computer architecture to achieve high Hardware utilization. Leverage the Machine Learning unique characteristics to redesign energy efficient Deep Neural Networks.

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

We are working on AI/ML in battery materials research.

Design computation, computer aided design and fabrication.