People

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.

Contributing the theory of machine learning through connections to information theory, and computational complexity theory.

Computational imaging

Human behavior in the context of organizational service interactions. Big data and automation tools are used to objectively analyze emotion and behavior of participants in online conversations.

Proteomics; Cancer Vaccines; Antigen Processing and Presentation; Big data of proteomics and peptidomics; Bioinformatics of proteomics and mass spectrometry.

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.

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.

Applied biomechanics and investigation of the interaction of mechanics, biology, and structure of musculoskeletal joint pathologies. Implementing smart wearable technologies that record large data sets of bio-signal motion data.

Our lab focuses on the evolutionary processes that shaped the human cranium and pelvis in evolution. We study human morphology from a comparative point of view in order to understand these processes.

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

Nanotechnology, cancer, Parkinson's disease, AI

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.

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

Enumeration of query results, probabilistic, incomplete and inconsistent databases, infrastructure for text analytics, databases for preferences and social choice, and database aspects of machine learning

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"

" Safety guarantees to deep learning by leveraging formal methods, such as analysis and synthesis."

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

Self-learning materials, disordered materials, exotic order

Deep Learning, i.e., neural networks: understanding them theoretically (e.g., their implicit bias) and improving them (e.g., their resource efficiency and speed during training and inference).

The theory and applications of efficient network and system management, in particular, concentrating on cloud resource management, NFV, SDN, TE, and network aware services.

Mechanobiology of cancer and wounds. Early-prognosis of cancer metastasis. Wound healing and prevention. Experiments, Finite element modeling, machine learning.

Optimal designs for clinical trials, Regression, Semi-supervised learning, Statistical theory.

"David Broday's research focuses on application of data science tools and methods on large atmospheric databases, mainly air pollution (both standard monitoring data and data streams of distributed air pollutant sensor arrays), aerosols (satellite remotely sensed products), meteorological observations, and climate change. "

Recording and analysis of nerve cell populations in the brain during tasks of spatial learning and memory, using electrode probes, tetrodes, and calcium-imaging.

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

Computational biology; Clinical informatics

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

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

Automated Planning, Robotics, Artificial Intelligence.

Establishing and analyzing large-scale clinical and biological/genetic bio-banks and databases to study disease etiology and clinical behavior, especially with regards to malignancies.

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

Service Engineering/Service Operations; Behavioral Operations; Queueing Networks and Approximations; Healthcare and Call-center Operational Design.

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.

Image processing and computer vision, with strong focus on mathematical models related to calculus of variations and nonlinear spectral theory.

Nano and micro-fabricated surfaces for cell biology research; Mechanobiology of Cells.

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.

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

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.

Using learning analytics and artificial intelligence to design intelligent environments that help students become better learners. Assessing and supporting competencies such as creativity and scientific reasoning.

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

Using machine learning in datacenter networks and high-performance routers, e.g., for congestion control, flow classification, and buffer management.

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.

Genomics and epigenetics and cardiac imaging.

The Artificial Intelligence in Medicine Laboratory (AIMLab.) researches innovative pattern recognition algorithms to exploit the information encrypted within large datasets of physiological time series.

Data Science Methods: Spectral Total Variation Techniques; Biomedical Engineering: PET/CT and SPECT/CT data fusion, characterization, quantitation, artifact removal, optimization of clinical imaging protocols.

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

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

Computational Cancer Genomics; Our lab addresses emerging challenges in computational cancer genomics by developing and applying computational methods for cancer data analysis.

Machine Learning and Optimization.

Machine Learning in Healthcare, Chemoinformatics, Data Mining, Natural Language Processing, Information Retrieval, Knowledge Discovery, Machine Learning, Artificial Intelligence, Digital Healthcare

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

The psychology of AI, Answers to human related questions with big data, Consumer behavior as reflected in big data.

Computer Vision, e.g., data generation, multi-label classification; Machine Learning with focus on designing and training efficient and effective Neural Networks; Tactile sensing and haptic feedback

Using microscopy technologies, we capture the dynamic function of cell nuclei and neural circuits as well as their 3D nano-scale structure. Via machine learning, we extract the information of interest from these datasets, linking biological structure to function.

Machine learning for material design.

Data models; Sparse Representations – Theory and Practice and their Connection to Deep-Learning; Inverse Problems in Signal and Image Processing; Image Denoising; Graph-Based Signal Processing; Patch-Based Image Processing; Super-Resolution

Image analysis, statistical analysis of visual tasks, and the application of deep learning to vision tasks.

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

Science and engineering education, Ethics in AI research and development, Technology-enhanced education, 21st century skills.

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;

Improving patient care and outcome through better characterization of the underlying physiological and structural factors in human diseases by developing novel deep-learning-based methods for MRI acquisition and analysis.

The research in our lab focuses on development and theoretical analysis of machine learning algorithms, using mathematical tools from fields such as approximation theory, optimization, or invariant theory

"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."

Markov Decision Processes and Stochastic Control; Reinforcement Learning; Online Learning and Bandit Processes; Game Theoretic Analysis of Queueing Systems; Trajectory Optimization; Cooperative Mission Planning.

The Lab focuses on T cell immunology in the interface between cancer and autoimmunity, with a particular interest in chemokine-chemokine receptor interactions. The lab develops unique strategies for cancer immunotherapies and for restraining autoimmunity.

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.

information theory, probability and statistics in high dimension, nonparametric regression, large deviations bounds, analysis of Boolean functions.

3D genomics, nuclear organization, computational biology, epigenetics, classical and deep machine learning, probabilistic models.

Formal verification, computational logic, discrete optimization.

Artificial Intelligence; Human-Computer Interaction; Intelligent User Interfaces; Explainable AI.

My research interests lie at the intersection of machine learning and computational game theory. More specifically, I am interested in strategic, societal, and economic aspects of ML, developing both theory and practical tools.

Theoretical neuroscience. How complex systems adapt to their environment? Such systems include brains, cancer cells, and artificial neural networks.

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

Information retrieval, with specific interest in ranking models for search engines.

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.

Cognitive and social processes of computer science, software engineering and data science education, on the individual, the team and the organization levels, in all kinds of organizations.

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

When facing challenging tasks in computerized and daily tasks, people must engage in mental effort management. Professor Ackerman studies factors that drive cognitive biases and waste of thinking time.

Game theory, Economic theory, Privacy.

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.

Ethical issues involved in using AI and Social-AI in the context of education; Human-Robot Interaction; Computer Science Education; Instructional Design

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.

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.

Information processing, learning and control in natural and artificial systems, reinforcement learning, lifelong learning, multi agent learning, the perception action cycle.

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

Developing and applying advanced machine-learning and experimental tools at the frontiers of biomedicine with a specific focus on antimicrobial multi-drug therapy.

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.

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.

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.

Research interests (Max. 200 Chr): Theoretical Machine Learning, focusing on: learning in adversarial and stochastic environments; generalization theory; differentially private machine learning; new theoretical definitions for learnability (that are inspired by applied ML); data compression and information-theoretic methods; combinatorial and geometric problems that arise in ML.

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.

Surgical Data Science, Computer Vision, Automatic Surgical Workflow Analysis, , Automatic Assessment of Competency-Based Medical Education in Surgery and Anesthesiology

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.

Algebraic and Geometric Methods for Discrete Optimization.

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

Neuro-AI: the intersection between neuroscience, machine learning and theoretical methods.

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.

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.

Relating brain functional / structural patterns to children’s abilities (reading, language, memory, attention). Modeling physiological datasets (speech, heart rate, eye-movement, brain markers etc) for intervention and diagnostic tools.

Machine learning; causal inference; machine learning for healthcare; deep learning.

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.

HousingLab develops research methods that address the large scale and multifaceted nature of housing. HousingLab engages in developing archival methods for visual digital data as primary sources. Specifically: developing computer vision capacities for the built environment.

Automatic diseases classification, Cell Biophysics, Heart rate variability analysis, Mobile health devices, Prediction and detection of atrial and ventricular fibrillation, Sinoatrial node cell activity.

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

Survival analysis, Empirical processes, Machine learning, Semiparametric models.

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.

Design computation, computer aided design and fabrication.

Natural Language Processing, Computational Psycholinguistics, AI, Eye-tracking, Neuroimaging.

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

Higher-level cognition; Cognitive complexity; Creativity; Network science in cognitive science; Network neuroscience; Clinical cognitive Networks; Cognitive search

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

Keshet's research concerns both machine learning and the computational study of human speech and language. His work on speech and language concentrates on speech processing, automatic speech recognition, speaker recognition, automating laboratory phonology, and pathological speech. His research on machine learning focuses on core machine learning and deep learning algorithms, specifically, that capture the structure of complex tasks, such as automatic speech recognition. But also - how to make them reliable and trustworthy.

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