Research

My research and design work focus on the synergy between information technologies, computational languages, and digital fabrication systems, examining the way in which technology informs and generates innovative approaches to design processes.

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

My lab’s work focuses on robotics and autonomous systems, mostly for field applications in unstructured environments with applications such as search and rescue and agriculture. We investigate the use of dynamic legged locomotion, wheeled locomotion, collaboration of ground and aerial robots in field robotics.

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.

We study fundamental epigenetics processes affecting gene expression and regulation by using bioinformatics, generating pipelines, and experimental biology.

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"

Self-learning materials, disordered materials, exotic order

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.

The CDML(Computational Design & Machine Learning) lab was founded by Dr. Guy Austern in 2022. The lab’s goal is to solve problems of architectural design and fabrication using state of the art computational methods. Our current research focuses on applying Machine Learning models to architectural questions of different scales.

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.

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

Modeling and understanding the geometry of shapes, differential geometry, architectural geometry, remeshing for fabrication, numerical optimization and harmonic analysis, animation, fluid simulation.

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.

Formal verification, computational logic, discrete optimization.

Biostatistics and clinical trial design and analysis, experimental design, inference for stochastic processes, classification and regression modelling.

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

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

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.

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.

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

Design computation, computer aided design and fabrication.

Computational imaging, fluorescence microscopy, cellular imaging, 3D imaging, super-resolution microscopy, wavefront shaping