Aerospace Engineering
Multiphase flows, flow instabilities, particle-wave interactions, hydrodynamic-quantum analogues
Computational methods for wave problems. Computational methods for inverse problems. Computational methods for nonlinear solid mechanics & structural dynamics. Non-standard finite element methods. Hybrid methods combining analytic and numerical solutions.
Probabilistic inference and decision making under uncertainty, typically considering a partially observable setting and high dimensional state spaces in the context of robotics
and autonomous systems.
Aerodynamic theory of wings and bodies in unsteady motion. Asymptotic wing theories. Flight mechanics (theoretical and experimental). Helicopter dynamics. Basic fluid mechanics and transport phenomena. Animals aero- and hydro-dynamics.
Theoretical and computational investigation of flow physics, with emphasis on transition to turbulence
Rarefied and small-scale gas flows: breakdown of the continuum description, microscale heat and mass transfer phenomena. Aeroacoustics: aerodynamic noise, vortex sound.
My interests lie in the understanding, prediction and control of physical mechanisms underlying laminar flow linear and nonlinear instability and laminar-turbulent transition from the incompressible to the hypersonic regime.
Duality in optimization theory as a tool to analyze and design cooperative control systems Dynamic systems, passivity and dissipasivity theory for cooperative control Dynamic systems evolving over Graphs: interplay between graph theory and control theory
Biotechnology and Food Engineering
The genome harbors additional codes. The genetic code was just the first one to be discovered. In order to decipher and decode the genome, we need to make a Biological Rosetta stone. Our research is dedicated to achieving this goal.
Molecular neuroscience, Systems biology, Analysis of big data, Brain connectomics.
Biology
We study how information is processed in neuronal networks to drive behavior
Computational Molecular Biology, Systems biology, machine learning, Biological Networks, Stem cells, single cell RNA-sequencing
Theoretical modeling of bio-mechanical systems: Morphology of membrane organelles, Cell motility, mechanical forces in the cytoskeleton
The basis of behavioral individuality; regulation of long-term behavior across development
We apply the tools and approaches of engineering to study how animals use their sensory and motor systems to understand the world and act within it.
Biomedical Engineering
Machine/Deep-learning applications in medical imaging, magnetic resonance imaging, medical image analysis
Bio-heat and bio-mass transport. Cell and tissue mechanics. Nano-acoustics medicine: ultrasound and opto-acoustics in medicine and biology as determined by intracellular, intra-membrane cavitation and bubble dynamics. Acoustic neuromodulation. Biomechanics of trauma and decompression. Acoustics of the inner ear.
Applying engineering to biology: Principles of genetic circuit design and synthetic biology.
Chemical Engineering
Theoretical biophysics: Learning and control in biological cells; variability and interactions in cell population.
Next-Generation Membranes for Water-Energy-Environment Nexus, Physics of Desalination in Membranes and Nanomaterials, Surface Science & Engineering.
Physico-chemical hydrodynamics, interfacial and particulate flows, small scale propulsion, microfluidics
We work on Colloid Physics and Acoustic/Kinetic Flows, many times in Boundary Layers and Porous Media. We employ Numerical and Asymptotic analyses of Continuum Transport Equations, Classical Density Functional Theory, and Near Equilibrium Gradient Dynamics.
membrane reactors, integration of exothermic and endothermic reactors, dynamic operation of reactors, process conceptualization and optimization.
Chemistry
Open quantum systems, Quantum thermodynamics, Strong coupling, Non-reciprocal systems, Casimir forces, Non-equlibrium systems, Active matter
Nonequilibrium statistical mechanics, molecular machines, rare events and fluctuations, thermodynamics of information.
Civil and Environmental Engineering
Enviromatics, aiming at devising machine learning methods and mathematical models for a better understanding of built and natural complex environments.
Mechanics of reinforced concrete, Computational mechanics: Finite element technology, Cosserat theories,
Stability of thin structures, Mechanics of soft biological tissues
Computer Science
Computational Geometry, Combinatorics (Polyominoes)
Spatial computing, 3D geometry processing, Shape analysis and understanding, Geometric modeling and animation, Computational fabrication, Quad meshing, Applied conformal geometry, Computer Graphics
Geometric Modeling, B-spline representations, Multivariate splines
Building a set of theoretical and applied tools that support and encourage collaborations between autonomous AI agents and robots, as well as between AI agents and humans. This, using a variety of tools that include automated design and model-based inference, reinforcement learning in multi-agent systems and hierarchical design of robots.
Shape reconstruction and analysis, applied differential and metric geometry, image processing, analysis, and synthesis, manifold learning, deep learning and understanding, biometrics, robot vision, and modeling of physical and artificial structures and forms.
Error correcting codes, Coding for storage systems, Application of coding theory to complexity, Information theory
machine learning, deep learning, deep generative models, scientific reproducibility, selective inference, false discovery rate, knockoffs, uncertainty estimation, fairness, sparse representations, convolutional sparse coding, dictionary learning, image processing, inverse problems.
Coding and Information Theory, Non-volatile Memories, Storage Systems, Private Information Retrieval, DNA Storage
Electrical & Computer Engineering
How the dynamics of neural networks in the brain support the processes of learning and memory, using theoretical tools from the worlds of mathematics and physics and analyzing data from experiments obtained from research laboratories with which he collaborates.
Random topology and its applications in data analysis and networks.
Machine learning of mathematical and physical constants, Electromagnetic theory and modeling, pen problems in electrodynamics, Applications of quantum, Electrodynamics, and renormalization challenges,Concepts from topology applied in physics.
Information theory and coded communication, including related fields such as signal processing and mechanical statistics
Multi-robot systems; robot planning and control; sampling-based algorithms; smart mobility; autonomous driving; societal aspects of autonomous mobility systems; discrete and continuous optimization.
Theoretical investigation of learning capabilities in neural networks.
Computer graphics, with an emphasis on geometric aspects of graphics, and deep learning for computer graphics
Signal processing, with an emphasis on geometric methods and manifold learning.
I focus on core problems in data-science, statistical inference and information theory. Topics include: Algorithms and theory for non-parametric regression; Statistical inference in high dimensional models; Exploration-exploitation problems with information-based rewards; Information-theoretic limits in prediction; Theoretical limits of DNA information processing.
Data and Decision Sciences
Game theory, Economic theory, Bayesian learning, evolutionary game theory
Game Theory, Learning and complexity of equilibria, Information in games: Information aggregation, Strategic communication of information.
Conceptual modeling with Object-process Methodology: Graph algorithms, Reasoning, Cause-and-effect explaining
Algorithmic foundations of decentralized and interactive decision making
Algorithmic foundations of Machine Learning, Data Science, and Optimization
Continuous Optimization: Theory and Algorithms, First and Second Order Methods, Nonconvex Optimization, Sparse Optimization: Theory and Algorithms, Applications in Machine Learning, Engineering, Finance and Science
Algorithmic Game Theory, Electronic Commerce, Social Networks Internet Auctions
Combinatorial optimization problems, worst case analysis of algorithms for combinatorial optimization problems
Continuous Optimization, Theory and Algorithms, Methods for large-scale optimization problems, Applications in Engineering and Science.
Causality and causal inference, Machine learning and its applications in healthcare
Optimization under uncertainty, data-driven optimization, large-scale optimization.
SAT and SAT-based solvers and their industrial applications, including optimization problems (e.g., place & route, scheduling), formal verification, test generation, timing.
Game theory, Mechanism design, Artificial intelligence, Strategic behavior
Mathematics
Theory of Machine Learning, Equivariant Machine Learning, and Optimization.
Epidemiological theory, Super-conductivity, Electrolyte solutions
Mathematical foundations of deep learning, Graph neural networks, Applied harmonic analysis, Time-frequency and wavelet analysis
Instabilities and pattern formation, Anomalous transport
Phase transitions, wetting, grain boundary migration, Higher order parabolic equations, Surface diffusion
- giladgour@technion.ac.il
- Amado, room 902
- Website
Mathematical aspects of quantum information science and the foundations of quantum mechanics
Mechanical Engineering
Modeling physical phenomena at the interface between solid- and fluid-mechanics for applications in metafluidics and soft robots.
Nonnegative Matrices, Montone Systems, Low-Complexity Modelling, Low-Rank/Sparse Optimization, Online Learning, Statistics on Manifolds
mathematical modeling of metamaterials, Homogenization of partial differential equations for composite materials
Nonlinear and chaotic dynamical systems (multiple-scale asymptotics, global bifurcation continuation). Synchronization and chimera states in rigid-body and continuous systems (co-existence, stability, control). Fluid-structure interaction (self-excitation, non-stationarity). Thermo-visco-elastic nanomechanical resonators (internal resonances, energy transfer of wide-spaced modes).
Medicine
In the Benisty lab, we formulate learning as a temporal transformation of a network. We develop interpretable models linking dynamic changes in cortical activity, connectivity, and behavior to provide a better understanding of the mechanisms enabling the acquisition of new skills.
3D genome organization, Modelling molecular processes and genomic data, Machine learning & probabilistic models, Biological data analysis
Electrical phenomena in point and extended biological systems; low-dimensional models of excitability.
We develop AI systems that mimic brain functionality and use AI tools to advance neuroscience research, including developing brain-computer interfaces (BCI).
At the Savir lab, our goal is to study, both experimentally and theoretically, Information processing in biological systems and its failure in aged cells
Development of computational methods for bridging the data-insight gap, particularly with focus immunity and their application for understanding the drivers of immune variation and how their maturation to Immune-based Precision Medicine.
Physics
Biophysics, Active Matter, Soft Matter, Non-equilibrium Statistical Mechanics
