Winter Semester 2026-2027
| Course name + number | Lecturer | Description + Link | Level | Language | Prerequisites |
| 02360861 – Geometric Computer Vision | Ron Kimmel
CS (& ECE ) |
This course focuses on geometric computational methods for image and shape processing and analysis. We will investigate topics such as shape reconstruction, point-wise matching, shape similarity, intrinsic symmetry, distance measurement on shapes and minimal geodesics and invariant signatures of shapes. The focus would be to use tools from differential and metric geometry, numerical analysis, calculus of variations and mathematical morphology to formulate and solve problems in computer vision and shape processing. We will relate geometric computational methods to deep learning. | Grad+Undergrad | The course material is in English. If there are more than 2 registered students who do not speak Hebrew – English lectures would be considered. | Calculus, linear algebra, and probability theory. This is an advanced course so “mathematical maturity” is strongly advised. |
| 1960015 | Nir Gavish | The Technion has approximately 80 faculty members whose research incorporates a significant component of applied mathematics. All of them are affiliated with the Interdisciplinary Program in Applied Mathematics.
Each week, a different lecturer presents their research, providing seminar participants with broad exposure to applications of advanced mathematical tools and methods to cutting-edge research problems in the exact sciences, engineering, and life sciences at the Technion. The course is particularly suitable for students nearing the end of their undergraduate studies or beginning their master’s degree. |
Grad+Undergrad | English/Hebrew | |
| 1960013 | Ron Levie |
אלגברה ליניארית נומרית: פירוק ערכים סינגולריים, קירוב ריבועים מינימליים, פירוק QR, בעיות ערכים-עצמיים ואלגוריתם QR התמרת פורייה דיסקרטית והתמרת פורייה מהירה שיטות נומריות למד”ר: בעיות התחלה, שיטות חד-צעדיות ורב-צעדיות, שיטות רונגה-קוטה, עקביות יציבות ודיוק סכמות. תוצאות למידה: בסיום הקורס, הסטודנט יהיה מסוגל: 1. שימוש באלגוריתמים לפתרון בעיות ריבועים מינימליים ולפתרון בעיות ערכים עצמיים. 2. שימוש בשיטות ספקטרליות לפתרון בעיות שפה. 3. פיתוח או בחירת שיטה נומרית מתאימה לפתרון מד”ר נתון. 4. ניתוח דיוק ויציבות של שיטה נומרית נתונה לפתרון מד”ר. |
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| 02360201 – Introduction to Data Processing and representation | Ron Kimmel
CS (& ECE ) |
The course focuses on the basic methods for processing and analyzing data with deterministic and probabilistic tools. It is a preliminary course for deep learning and convolutional neural networks. The course will explain how to digitize signals and data, how to represent them in different bases, and how to use these representations efficiently for various signal processing tasks. Covered topics include signal quantization and sampling for bit-allocation, system and data representations including but not limited to the Fourier representation, optimality of the Fourier representation, convolutions, compression, dimensionality reduction, principal component analysis, restoration of blurred deterministic or randomly distributed data with or without random noise via filtering. Signals and systems are analyzed in the continuous and discrete settings. |
Undergrad + grad | The course material is in English. If there are more than 2 registered students who do not speak Hebrew – English lectures would be considered. | Calculus, linear algebra, and probability theory. |
| 02360379 – Coding and Algorithms for Memory | Eitan Yaakobi CS (&ECE) | This course presents the mathematical foundations of coding and algorithmic techniques for modern memory and data storage systems. Topics include coding for flash memories, write-once and rewrite-constrained memories, write amplification and garbage collection algorithms, constrained coding, coding for defective and stuck-at memory cells, insertion and deletion correcting codes, coding techniques for DNA-based storage systems, and fundamental bounds on error-correcting codes.
The course emphasizes the design and analysis of coding schemes and algorithms using methods from combinatorics, algebra, graph theory, probability, and information theory. Students will learn how mathematical tools are applied to develop efficient and reliable storage systems under practical physical constraints. The course is intended for graduate students with a background in algorithms and linear algebra or abstract algebra, and does not require prior knowledge of storage technologies. |
undergrad+grad | The course material is in English. If there are more than 2 registered students who do not speak Hebrew – English lectures would be considered. | Linear algebra + algorithms |
| 02360309 – Introduction to Coding Theory | Ronny Roth (CS) | Error-correcting codes are methods for protecting transmitted or stored data against errors that are caused by factors such as noise, failing nodes in a network, physical defects in a storage medium, or malfunctioning of a memory device. Reed–Solomon codes and BCH codes are examples of error-correcting codes that are widely used in applications such as computer memories (see Link 1 below), disks, solid-state drives (flash memories), optical storage (DVD, Blu-ray), disk arrays (Link 2), and bar codes (Links 3 and 4). Numerous other applications of error-correcting codes can be found in other branches of computer science.
The course provides the basics of the theory of error-correcting codes. The topics to be covered include the following.
Links: |
Graduate/Undergraduate | Hebrew. The course closely follows a textbook which is in English. | Knowledge of basic terms in linear and modern algebra is assumed (but will nevertheless be recapped in the recitation class during the first few weeks of the semester). Examples of such terms include: vector spaces, groups, and rings. |
| Computational Geometry, 02360719 | Gill Barequet, CS | Syllabus: Fundamental techniques, data structures, and algorithms for solving geometric problems such as computing convex hulls, intersection of line segments, the Voronoi diagram and Delaunay triangulation of a point set, polygon triangulation, range search, linear programming, and point location. Some topics of discrete geometry, e.g., the crossing number of a graph and its applications, are also covered. | UG/Graduate | Teaching material and exams: English;
Lectures and recitations: If a non-Hebrew-speaker is present – English, otherwise Hebrew. |
Data structure; Algorithms recommended |
| Nonlinear Control, 00860312 | Christian Grussler,
Aerospace Engineering |
This course studies the basic of nonlinear systems and how to control them.
Topics that will be covered include:
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Graduate/Undergraduate | English | A course on ODEs; basic linear algebra and analysis; some basics in control theory is recommended. |
| Mathematical Foundations – Analysis and Algebra, 00860170 | Christian Grussler,
Aerospace Engineering |
This course is a crash course that summarizes rigorously the first two years of typical mathematics studies in Linear Algebra & Analysis, starting from sequences, series, continuity over switching of limits and integrals, to Banach and Hilbert Spaces, L_2 Approximation Theory, Duality, SVD, Positive Definitness, etc. The purpose of this course is to (re-)encounter these tools in an engineering setting as well also provide students with the rigor of a mathematical education needed for research. | Graduate/Untergraduate | English | None, since we start from zero. |
| Quantum Communication and Resource Theories, 1080002 | Gilad Gour, Mathematics | This advanced course is tailored for students who have a foundational understanding of quantum mechanics and are eager to explore the specialized areas of quantum communication, quantum Shannon theory, entanglement theory, and quantum resource theories. The course aims to deepen understanding of quantum entanglement and its pivotal role in quantum technology, explore advanced concepts and protocols in quantum communication, and examine the theoretical and practical aspects of quantum resource theories with a focus on their applications in quantum computing and information processing. | Graduate/Undergraduate |
Spring Semester 2027
| Course name + number | Lecturer | Description + Link | Level | Language | Prerequisites |
| Mathematical Foundations of Graph Deep Learning 1960016 | Ron Levie, Mathematics | Graph deep learning is a subfield of deep learning that focuses on using neural networks to analyze data represented as graphs. The course will begin with a general introduction to deep learning (no prior background in deep learning is required) and then proceed to develop deep learning methods for graphs. In addition to developing the methodology of graph neural networks (GNNs), the main focus will be on deriving mathematical theory to analyze the capabilities of GNNs. We will analyze properties such as generalization, universal approximation, and expressivity of GNNs. | Calculus, linear algebra, and probability theory. Knowledge in graph theory, point set topology, and functional analysis or measure theory will help, but is not necessary. | ||
| 00880504
Initial and Boundary Value Problems Using the Finite Element Method |
Dan Givoli | This is an advanced course on the Finite Element method for problems governed by nonlinear and time-dependent partial differential equations (PDEs). However, no prior knowledge of the Finite Element Method is required. Applications include nonlinear and time-dependent heat conduction, problems of nonlinear continuum mechanics, acoustic and elastic wave propagation, and more. | Grad | English | A basic course on PDEs, a basic course on numerical analysis |
| Analytical Methods for Differential Equations 01960012 | Lydia Peres Hari, Mathematics | The aim of the course Analytical Methods is to give the students the “standard toolbox” used by all those who deal with continuous Applied Mathematics , such as Mathematical Physics, Theoretical Physics etc.
These are the basic tools for solving differential equations, used by scientists in their everyday work, tools that do not call for the use of Functional Analysis. Our aim is to obtain solutions to differential equations, and not approximations (asymptotic or numeric). This course is aimed for graduate students in Applied Mathematics, Theoretical Mathematics, Physics, Chemistry, as well as various engineering disciplines that make use of differential equations. The course could also be relevant to strong and advanced undergraduate students. |
Calculus, ODEs, PDEs, Complex functions.
(Strong undergraduate students may take the course in parallel to taking PDE and Complex functions). |
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| Mathematical Biology, 196020 | Nir Gavish, Mathematics Yoni Savir, Medicine |
This course offers a comprehensive tour of modern mathematical biology, equipping students with the toolkit for thinking quantitatively about the dynamics, function, and evolution of living systems. We will analyze mathematical models to understand the principles governing biological systems at every scale – from molecules to populations. The curriculum covers a wide array of methods and applications, including the deterministic models used in population and evolutionary dynamics, the stochastic processes that describe cellular noise and govern evolution, and the design principles of biological circuits. We will explore how frameworks from information theory and physics are applied to understand phenomena such as spatial pattern formation, disease spread, and biological signaling.
https://math.technion.ac.il/wp-content/uploads/2025/06/1970010.pdf |
Graduate/Under-Graduate | If there is a foreign student – English | ODE, Probability |
| Colloidal particles and intermolecular forces, 056396 | Ofer Manor, Chemical Engineering | This is an introductory course on complex liquids with primary focus on classical theorems of intermolecular interactions and their application to interparticle forces and colloidal dispersion dynamics and stability. We further introduce a variational description of complex liquids based on classical density functional theory to study non-ideal systems.
The primary focus is on colloids—particles ranging in size from nanometer to microns that are suspended in liquid—including bacteria, viruses, proteins, paint pigments, emulsion droplets in milk and shampoo, and blood particulates. Owing to their small size, colloids interact with one another and with their surrounding medium through intermolecular forces that are typically associated with many-body molecular-scale interactions, including Born repulsion, van der Waals attraction, electrostatic (electrical double-layer) interactions, steric effects, depletion forces, and osmotic interactions. We then examine how these interactions, together with the thermal (Brownian) motion of colloidal particles, govern the transition from stable colloidal dispersions to aggregated states through the process of coagulation. For example, blood is a familiar and biologically relevant example of a complex liquid that coagulates under mechanical and chemical stresses. |
Graduate/Under-Graduate | If there is a foreign student – English | Calculus, ODEs, basic thermodynamics |
| Autonomous Decision-Making Under Uncertainty, 086762 | Vadim Indelman,
Aerospace & DDS |
The course provides mathematical tools and approaches for solving planning under uncertainty problems in partially observable domains in the context of AI and robotics.
Topics to be covered include: Probability space, Probabilistic inference, MDP and POMDP problem formulations, belief space planning, information-theoretic costs, nonparametric inference, offline MDP and POMDP approaches (dynamic programming, value iteration, policy iteration, Alpha vectors), online MDP (forward search, branch & bound, sparse sampling, Monte Carlo tree search), online POMDP approaches (POMCP, POMCPOW, PFT-DPW), robust and risk-averse planning under uncertainty. |
Graduate/Under-Graduate | English | At least one of these: Algorithms, Artificial Intelligence, machine learning, random process/signals, Reinforcement Learning (strong students can reach out to the lecturer for inquiries) |
| Discrete Algorithmic Geometry, 02380739 (tentative but not sure) | Gill Barequet, CS | Syllabus: Random sampling, static and dynamic randomized algorithms, convex hulls, linear programming, arrangements of line segments and Davenport-Schinzel sequences, Voronoi diagrams with Euclidean and non-Euclidean metrics, probabilistic proofs, polyomino counting, and variants of Heilbronn’s triangle problem. | Graduate | If a non-Hebrew-speaker is present – English, otherwise Hebrew. | Computational Geometry |
| Neural Data Science 970405 | Hadas Benisty,
Medicine and DDS |
This course introduces data analysis methods in computational neuroscience, focusing on how to quantitatively study brain function from neural and behavioral data. We will cover how biological questions about neural circuits and brain networks can be translated into concrete computational and mathematical models that are testable with data. Students will learn modern tools from machine learning, graph theory, and manifold learning to analyze network-level brain dynamics. Throughout the course, an emphasis will be placed on hands-on work, as students design and implement analysis pipelines for a real-world neural and behavioral dataset using contemporary computational tools. | Graduate/Under-Graduate | If a non-Hebrew-speaker is present – English, otherwise Hebrew. | 97209 – Machine Learning 2 or equivalent |
| Thermodynamics of small systems 1270436 | Saar Rahav
Chemistry |
The course focuses on the nonequilibrium statistical mechanics of small systems such as molecular machines, Relevant mathematical tools, such as Langevin and Fokker Planck equations are covered. The aim is to gain intuition as to why such methods are useful, and when they may break down. The latter part of the course covers various fundamental results on out-of-equilibrium fluctuations. | Joint Undergraduate- Graduate course. | If a non-Hebrew-speaker is present – English, otherwise Hebrew. | A basic course in statistical mechanics/thermodynamics. |
| Mobile Robots 00460213 | Kiril Solovey Electrical and Computer Engineering |
The course introduces fundamental algorithmic tools for control, planning, and perception essential to the deployment of modern mobile robots in the real world (e.g., self-driving cars and autonomous drones), both from theoretical and practical perspectives. Control: motion equations of mobile robots, open and closed loop methods.Motion planning: Geometric, differential, and optimal methods. Perception: Sensors, localization and estimation and mapping. |
Joint Undergraduate- Graduate course. | Hebrew | At least one of these: Algorithms, Artificial Intelligence, machine learning, random process/signals, Reinforcement Learning (strong students can reach out to the lecturer for inquiries) |
| Advanced Topics in Robotics (0048xxxx) | Kiril Solovey Electrical and Computer Engineering |
The course will be conducted in a seminar format. Throughout the course, students will be exposed to recent research articles covering a broad range of topics in robotic systems, with a particular emphasis on control, planning, perception, and learning in both single autonomous robots and multi-robot systems. The course spans both theoretical foundations and practical applications. An introductory overview of the fundamentals of autonomous robotic systems will be provided. The course topics are diverse and vary from year to year. They include, or may include, subjects such as navigation and mapping in unknown environments, multi-robot systems, intelligent transportation, autonomous driving, search-and-rescue robotics, and related areas. | Graduate | Hebrew | At least one of these: Algorithms, Artificial Intelligence, machine learning, random process/signals, Reinforcement Learning (strong students can reach out to the lecturer for inquiries) |
