PhD in

Motion planning is an integral component of robotic applications. It helps the robot to strategically decide on its future moves and when to take them. The course covers the state-of-the-art motion planning techniques along with their applications to different kinds of robots (e.g., ground, aerial, marine, humanoid, manipulator). It provides a theoretical in-depth analysis of such methods and teaches students how to implement them through several programming-based assignments.

The course covers the foundations of multi-robot systems. It introduces students to the state-of-the-art in multi-robot research through a combination of classical teaching and seminar style lectures and labs. Students will learn how to apply a consortium of techniques, such as stochastic processes, graph theoretic methods, geometric concepts, and optimization principles, to model, analyze, and drive multi-robot systems.

Humanoid robots have become more and more prevalent with the increase in the demand of service and human-assistive robots. This specialized course covers various advanced topics in the state-of-the-art of humanoid robots, such as their kinematics, dynamics, modeling, control, motion planning, object grasping and manipulation, perception, learning, and interaction with humans. The course provides a blend of theoretical in-depth analysis of such techniques and hands-on practice through simulation and hardware implementation.

This Ph.D. course focuses on the key advanced computer vision techniques utilized in autonomous robotics, such as image formation, feature detection and description, multiple view geometry, dense reconstruction, tracking, event-based vision, visual-inertial odometry, visual simultaneous localization and mapping (SLAM), locomotion concepts, and deep learning based visual positioning.

This course introduces students to the diverse landscape of biological data, including its types and characteristics and explores the foundational principles of single-cell omics bioinformatics, encompassing key methodologies, tools, and computational workflows, with an emphasis on the development of foundation models for single cell omics data from a research perspective. Single cell omics technologies are a new and fast-growing family of biological assays that enables measuring the molecular contents of individual cells with very high resolution and is key to advancing precision medicine. The course covers essential bioinformatics aspects for working with single cell omics data.

This course provides an introduction to 3D geometry processing, an important field that intersects computer vision, computer graphics, and discrete geometry. With 3D vision systems becoming increasingly sophisticated, object recognition and modeling is no longer limited to abstracted feature representations but are often high-fidelity digitization of real-world objects and environments. While 3D geometry processing has evolved significantly in the areas of visual effects and interactive games, they are impacting other domains, ranging from metaverse technologies to robotics, biomedicine, and additive manufacturing. Augmented/virtual reality systems are using 3D scanned virtual avatars to enable immersive communication, autonomous cars are capturing their 3D surroundings in real-time, and Google earth is digitizing entire worlds using satellite and geospatial data. With the emergence of 3D scanning, real-time depth sensors, and 3D printing technologies, polygonal meshes have become the de-factor standard for 3D surface representation. This course will cover the mathematical foundations for studying 3D surfaces from a discrete differential geometric standpoint and present the full geometry processing pipeline: from 3D data capture, mesh smoothing, surface reconstruction, parameterization, registration, shape analysis (correspondence, symmetry, matching), data-driven synthesis, interactive manipulation, to 3D printing. We will also illustrate this course with important applications and recent AI advances in this field, especially with new developments in 3D deep learning, deep generative models for 3D objects, and differentiable rendering. In analogy to image processing for which inputs are 2D images and video, 3D Geometry processing involves the treatment of 3D depth maps, point clouds, polygonal meshes and volumetric data and involves many techniques from linear algebra, differential geometry, signal processing, and numerical optimization.

Vision and language encode complementary information and have long been studied together. With the advent of Large Language Models, vision-language models are now more popular than ever and represent one of the most active areas of modern computer vision. This course will cover learning methods and joint models for image and text modalities and will address a wide range of problems including vision-language pretraining, text-based image search, image and video captioning, visual question answering, visual dialog, text-to-image synthesis as well as vision-language navigation and manipulation.

The course covers the advanced topics in Reinforcement Learning that have been briefly introduced in ROB702. Participants will read the current state-of-the-art relevant literature and prepare presentations to the other students. In the sequel participants will explore how the presented methods work in simplified computing environments to get a deeper understanding of the challenges that are being discussed.

This is a graduate course in a new branch of machine learning: federated learning (FL). In FL, machine learning models are trained on mobile devices with an explicit effort to preserve the privacy of users’ data. FL combines supervised machine learning, privacy, distributed and edge computing, optimization, communication compression, and systems. This is a new and fast-growing field with few theoretical results and early production systems (e.g., Tensor Flow Federated and FedML). This course aims for students to become familiar with field’s key developments and practices.

In the past decades, interesting advances were made in machine learning, philosophy, and statistics for tackling long-standing causality problems, including how to discover causal knowledge from observational data, known as causal discovery, and how to infer the effect of interventions. Furthermore, it has recently been shown that the causal perspective may facilitate understanding and solving various machine learning / artificial intelligence problems such as transfer learning, semi-supervised learning, out-of-distribution prediction, disentanglement, and adversarial vulnerability. This course is concerned with understanding causality, learning causality from observational data, and using causality to tackle a large class of learning problems. The course will include topics like graphical models, causal inference, causal discovery, and counterfactual reasoning. It will also discuss how we can learn causal representations, perform transfer learning, and understand deep generative models.

This course is an introduction to the core ideas and theories of statistical learning theory, and their uses in designing and analyzing machine learning systems. Statistical learning theory studies how to fit predictive models to training data, usually by solving an optimization problem, in such a way that the model will predict well, on average, on new data.

The course focuses on building foundations and introducing recent advances in dimensionality reduction and manifold learning, important topics in machine learning. This course builds upon fundamental concepts in machine learning and additionally assumes familiarity with concepts in optimization and mathematics. The course covers advanced topics in spectral, probabilistic, and neural network-based dimensionality reduction and manifold learning. Students will be engaged through course-work, assignments, and projects.

Training the largest Machine Learning (ML) programs requires petaFLOPs (1015) to exaFLOPs (1018) of computing operations, as well as multiple terabytes (1012) of hardware accelerator memory. Accordingly, 100s to 1000s of these accelerators are needed to satisfy both the computing and memory requirements of the large-scale ML. This course covers systems architecture design, communication strategies and algorithmic modifications required to execute ML training in a parallel and distributed fashion across many network-connected hardware accelerators. In the first part of the course, students will learn a comprehensive set of principles, representations, and performance metrics for parallelizing ML programs and learning algorithms, as well as learn how to compare and evaluate different parallel ML strategies composed out of basic parallel ML “aspects”. In the second part of the course, students will apply these skills to read and critique peer-reviewed literature on parallel and distributed ML systems.

This course will provide students with a deep dive into key issues related to trustworthy and responsible machine learning. Students will learn about adversarial/poisoning and privacy/confidentiality attacks against machine learning systems and defense mechanisms to mitigate them. The course will approach adversarial machine learning through an optimization and game-theoretic framework. The emphasis on privacy-preserving computation for data science and machine learning would be through a formal mathematical notion of privacy called differential privacy. The course will also cover other ethical issues in machine learning such as fairness, safe unbiased and responsible content generation, watermarking for content authentication and ownership verification (provenance), and attacks such as deep fakes, their detection and verification of robustness of relevant defenses (multimedia forensics).

This course focuses on recent advances in deep learning for natural language generation. It builds upon concepts from Natural Language Processing and assumes familiarity with fundamental concepts such as Transformers, Machine Translation, and Text Summarization.

This course focuses on recent research in Natural Language Processing and on developing skills for performing research to advance the state of the art in Natural Language Processing.

This course focuses on recent topics in Natural Language Processing and on developing skills for performing research to advance the state of the art in Natural Language Processing. Specifically, this course will cover fundamentals of LLMs such as Transformers architecture, methods on training and evaluating LLMs via distributed training and efficiency methods, and application in multilinguality, translation and multimodality.

This course provides a comprehensive introduction to Speech Processing. It focuses on developing knowledge about the state of the art in a wide range of Speech Processing tasks, and readiness for performing research to advance the state of the art in these topics. Topics include speech production, speech signal analysis, automatic speech recognition, speech synthesis, neural speech recognition and synthesis, and recent topics in foundation models and speech processing.

This course focuses on recent topics in Natural Language Processing and on developing skills for performing research to advance the state of the art in Natural Language Processing.

This course explores the cutting-edge techniques and methodologies in the field of speech processing. The course covers advanced topics such as Automatic Speech Recognition, Language Modeling and Decoding, Speech Synthesis, Speaker Identification, Speech Diarization, Paralinguistic analysis, Speech Translation & Summarization, Multilinguality and low-resource languages and Spoken Dialog Systems. Students will delve into modern models and frameworks for the different speech tasks. The course emphasizes both theoretical understanding and practical implementation, fostering skills necessary for innovative research and development in speech technologies.

The course introduces students to advanced topics in natural language processing concerning the robustness and trustworthiness of language models (LMs), specifically world knowledge in LMs, safety and inclusivity of LMs, and inner workings of LMs. The course prepares them to perform research that advances the state of the art in these research areas.

PhD thesis research exposes students to cutting-edge and unsolved research problems, requiring them to propose new solutions and significantly contribute to the body of knowledge. Students pursue an independent research study, under the guidance of a supervisor/s. PhD thesis research helps train graduates to become leaders in their chosen area of research through partly supervised study, eventually transforming them into researchers who can work independently or interdependently to carry out cutting-edge research.

This course will prepare students to produce professional-quality research and solve a practical research challenge in an organization based on an innovative, sustainable, and entrepreneurial research topic. This course will provide exposure to a variety of special topics, research integrity, ethics, organizational challenges, and needs related to various disciplines. Students will design and implement a research project suitable for conference presentation or journal submission relevant to their field of interest, in addition to peer-reviewing a paper. The instructor, and guest lecturers, as appropriate, will present topics necessary to develop well-rounded researchers, innovators, and entrepreneurs in the AI disciplines.

PhD Internship (up to four months)