جستجوی استاد راهنمای دانشگاهی

Caglar Gulcehre is a Tenure Track Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL) and Principal Investigator of the CLAIRE (Caglar Gulcehre Laboratory of Artificial Intelligence Research) lab. Previously, he worked as a Staff Research Scientist at Google DeepMind, Microsoft Research, and IBM Research. His research focuses on reinforcement learning , foundation models , LLM alignment , and sequence modeling . Current Position : Assistant Professor, EPFL Lab : CLAIRE Lab Previous Roles : Staff Research Scientist at DeepMind, MSR, IBM Research His work spans reinforcement learning , deep learning , and neural architecture design , with a focus on safety , trustworthy AI , and real-world applications . He has published in top venues including Nature , NeurIPS , ICML , and JMLR . Scientific contributions include: Best paper award at NeurIPS Nonconvex Optimization workshop Honorable mention for best paper at ICML 2019 Co-organizer of seven workshops at NeurIPS, ICML, and ICLR He supervises PhD students in areas related to AI for algorithm discovery , neural architectures , and foundation models , including: Skander Moalla Justin Samuel Deschenaux Liangze Jiang Xiuying Wei Yitao Xu

• Reinforcement Learning
• Foundation Models
• Large Language Models
• +۶ مورد دیگر

Pierre Vandergheynst is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) in the Department of Electrical Engineering, with a courtesy appointment in Computer and Communication Sciences. He serves as EPFL’s Vice-Provost for Education since 2015 and leads the Signal Processing Laboratory 2 (LTS2). His research spans harmonic analysis, sparse approximations, mathematical data processing, and applications in signal/image processing, computer vision, machine learning, and graph-based data analysis. PhD in Mathematical Physics (1998), Université catholique de Louvain Postdoctoral Researcher at EPFL (1998-2001) Assistant Professor at EPFL (2002-2007) His research explores geometry/symmetry in high-dimensional data, redundant dictionaries for dimensionality reduction, and computational harmonic analysis on manifolds. Recent work focuses on protein structure modeling, geometric deep learning, and graph-based signal processing. Key article trends include graph neural networks for protein analysis, geometric deep learning in neuroscience, and structured knowledge priors in neural models. His 2023-2025 publications emphasize interpretable AI, long-range dependencies in graphs, and molecular representation learning. Scientific Awards: IEEE Signal Processing Magazine Best Paper Award (2023) Signal Processing Society Best Paper Award (2022) Apple ARTS Award (2007) De Boelpaepe Prize, Royal Academy of Sciences of Belgium (2009-2010) He has supervised over 30 PhD theses and contributed to foundational work in graph signal processing, compressive sensing, and geometric deep learning. His lab develops tools for data science on non-Euclidean structures, with applications in medicine, astronomy, and wireless systems.

• Data Science
• Machine Learning
• Computational Harmonic Analysis
• +۶ مورد دیگر

Afonso S. Bandeira is a Professor in the Department of Mathematics (D-MATH) at ETH Zurich, where he conducts research at the intersection of mathematics, statistics, and computer science. He maintains strong affiliations with several interdisciplinary research centers including the Institute For Operations Research (IFOR), the Max Planck ETH Center for Learning Systems, the ETH Foundations of Data Science, and the ETH AI Center, with a courtesy appointment at D-ITET. Bandeira actively teaches courses including Mathematics of Signals, Networks, and Learning, and Mathematics of Data Science. His research focuses on High Dimensional Probability, Random Matrices, Mathematical Statistics, Theoretical Computer Science, Combinatorics, and Mathematical Optimization. Bandeira's work often explores the theoretical foundations of data science, examining phase transitions in statistical problems, computational barriers, and the geometry of high-dimensional spaces. He maintains a research group blog called Randomstrasse101 that emphasizes open problems in his field. Analysis of his recent publications reveals a strong focus on matrix and tensor concentration inequalities, synchronization problems, nonconvex optimization landscapes, and computational-statistical tradeoffs in high-dimensional inference. His work bridges theoretical mathematics with practical applications in machine learning and data analysis, particularly examining where computational limitations arise in statistical problems. Bandeira actively mentors students and researchers, currently supervising several doctoral candidates including Daniil Dmitriev, Konstantin Donhauser, Anastasia Kireeva, Kevin Lucca, Chiara Meroni, Gil Kur, Petar Nizic-Nikolac, and Almut Roedder. He emphasizes that students working with him should participate in the DACO seminar and group meetings, and encourages prospective students to have completed his Mathematics of Signals, Networks, and Learning or Mathematics of Data Science courses. He leads a research group focused on the mathematics of data science, with regular group meetings and seminars. Bandeira has developed comprehensive lecture notes including 'A Tour Through the Mathematics of Signals, Learning, and Networks' (2025) and 'Mathematics of Machine Learning' (2021), and previously authored 'Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science' (2015).

• High Dimensional Probability
• Random Matrices
• Mathematical Statistics
• +۳ مورد دیگر

Prof. Dennis Komm is an Associate Professor at ETH Zurich's Department of Computer Science, leading the group for Algorithms and Didactics. He chairs the Center for Computer Science Education (ABZ) and serves on committees such as the Swiss Maturity Board (Schweizerische Maturitätskommission) and the STEM Commission of the Swiss Academies. Previously, he held roles at RWTH Aachen University (Master's, 2008), ETH Zurich (PhD, 2012), University of Zurich (external lecturer, 2014–2020), and PH Graubünden (including department head and professor of 'Fachdidaktik Informatik'). Education: He completed a Master's in Computer Science at RWTH Aachen (2008), a PhD at ETH Zurich (2012), and studies in Information Technology at Queensland University of Technology (2006). His academic journey includes visiting roles at King's College, Stanford, and Comenius University. He has taught extensively across institutions, emphasizing Python and LOGO-based approaches for beginners. Research focuses on algorithm design, approximation algorithms, reoptimization, and advice complexity in theoretical CS. His work in education explores computational thinking, programming pedagogy (especially for K–12), and interdisciplinary approaches (e.g., robotics in math). Recent trends in his articles highlight advancements in online algorithms, optimization under dynamic conditions, and initiatives to integrate CS into Swiss school curricula sustainably. He actively promotes CS education through platforms like WebTigerPython and collaborates on projects such as CyberQuest and MINTerlink. His outreach includes organizing conferences (e.g., STIU 2025) and workshops on programming and cybersecurity for teachers and students. Despite no listed scientific awards, his contributions to education and theoretical CS are recognized through editorial roles in journals like Informatics in Education and contributions to the TigerJython Group. Grant-related advising includes co-supervising doctoral theses on robotics, USOs, and programming didactics. He advocates for equitable educational opportunities via the Passerelle exam and the Swiss Beaver Competition. His team's work spans teacher training, didactic certifications, and bridging university-school collaborations through initiatives like MINTerlink. Labs and teams: Head of ABZ (ETH's CS education center), collaborator with the Computational Robotics Lab, and part of the TigerJython Group. He also co-organizes the Colloquium on Mathematics, Computer Science, and Education with ETH's Mathematics Department.

• Algorithm Design and Analysis
• Computer Science Education
• Theoretical Computer Science
• +۳ مورد دیگر

Reinhard Heckel is a Tenured Associate Professor (equivalent to Professor) of Machine Learning at the Department of Computer Engineering, Technical University of Munich (TUM), and Adjunct Faculty in Electrical and Computer Engineering at Rice University. He was previously an Assistant Professor at Rice (2017–2019), a postdoc in the Berkeley Artificial Intelligence Research (BAIR) Lab at UC Berkeley, and a researcher at IBM Research Zurich. Education: PhD, 2014 – ETH Zurich Visiting PhD student – Department of Statistics, Stanford University Research Interests: His work centers on machine learning and information processing with three major thrusts: (1) developing algorithms and theoretical foundations for deep learning, especially for accelerated magnetic resonance imaging ; (2) establishing rigorous mathematical and empirical underpinnings for modern machine-learning systems; and (3) leveraging DNA as a digital information-storage medium , including error-correction coding and system design for DNA-based storage. Across more than 100 peer-reviewed papers since 2017, Heckel’s research exhibits a strong interdisciplinary blend of computational imaging , machine-learning theory , and molecular data storage . Recent 2024–2025 publications show intensive focus on robust MRI reconstruction using diffusion priors, evaluation of bias in large web-text corpora, and state-of-the-art error-correcting codes for DNA storage channels. A forthcoming book, Deep Learning for Computational Imaging (Oxford University Press), consolidates his contributions to the field. Outreach & Media: Keynote and panel talks at DLD, TUM, and major ML conferences Op-eds in Frankfurter Allgemeine on ChatGPT and DNA storage Science features on Netflix, BBC, and German television (Galileo, “Gut zu Wissen”) Research Environment: At TUM he leads a group investigating theoretical and applied aspects of deep learning, compressed sensing, and coding for DNA storage. Open-source repositories on GitHub (e.g., dna_data_storage , supplement_deep_decoder ) provide code and data supplements accompanying his publications.

• Machine Learning
• Deep Learning
• Computational Imaging
• +۵ مورد دیگر

Rasmus Kyng is an Assistant Professor in the Department of Computer Science at ETH Zurich, where he has been since 2019. His research focuses on fast algorithms for graph problems, convex optimization, and their applications in machine learning. He has received grants from the Swiss National Science Foundation, including project grants and a starting grant. Education: B.A. in Computer Science from the University of Cambridge (2011), PhD in Computer Science from Yale University (2017), advised by Daniel A. Spielman. Postdoctoral positions included Harvard University (2018–2019) and a research fellowship at the Simons Institute, UC Berkeley (2017). Research Interests: Development of nearly linear-time algorithms for fundamental graph problems (e.g., maximum flow, minimum-cost flow), dynamic graph algorithms, discrepancy theory, and fine-grained complexity. His work bridges numerical linear algebra and combinatorial optimization, emphasizing practical implementations such as the Laplacians.jl package. Awards: FOCS Best Paper Award (2022), Inaugural ICBS Frontiers of Science Award (2022), Machtey Award (Best Student Paper, FOCS 2017). Teaching: Advanced Graph Algorithms and Optimization (ETH Zurich, 2020–2023), Algorithms, Probability, and Computing (ETH Zurich, 2020–2022). Supervised numerous PhD students and mentored postdocs in theoretical computer science. Labs/Teams: Co-leads a research group with Maximilian Probst Gutenberg, focusing on dynamic graph algorithms and optimization. Collaborations include work on sparsification, spectral graph theory, and machine learning applications.

• Theoretical Computer Science
• Algorithms
• Convex Optimization
• +۴ مورد دیگر

Sebastijan Dumancic is an Assistant Professor at Delft University of Technology, focusing on neuro-symbolic AI through program synthesis and probabilistic programming. He leads the RAIL lab and collaborates with institutions like Harvard, MIT, and CNRS. His research bridges symbolic AI and machine learning, applying program synthesis to scientific discovery, transportation, and robotics. He holds an FWO-funded PhD from KU Leuven and has participated in initiatives like ELLIS and the Symbolic Computation and Machine Learning Initiative. Program synthesis Probabilistic programming Neuro-symbolic AI Constraint-based learning His recent articles highlight advancements in program synthesis, neuro-symbolic integration, and constraint satisfaction. Projects like Find2Fix and Intelligent Greenhouse Horticulture (funded by NWO) demonstrate practical applications. ELLIS Membership University Teaching Qualification He supervises numerous MSc and PhD students in projects involving logic programming, program synthesis, and probabilistic modeling. Active in workshops and symposia, he contributes to neuro-symbolic AI and scientific discovery.

• Program Synthesis
• Probabilistic Programming
• Neuro-Symbolic AI
• +۲ مورد دیگر

Christoph Hertrich is a tenure-track professor for Applied Discrete Mathematics at University of Technology Nuremberg, where he conducts research at the intersection of discrete mathematics, theoretical computer science, and machine learning. His work particularly focuses on applying polyhedral geometry and combinatorial optimization techniques to neural network theory, with significant contributions to understanding the computational complexity and expressivity of neural networks. Hertrich received his BSc and MSc degrees from TU Kaiserslautern (2013-2018) working with Sven O. Krumke, followed by his PhD at TU Berlin (2018-2022) under the supervision of Martin Skutella. His doctoral thesis, titled "Facets of Neural Network Complexity," laid foundational work for his current research direction. Prior to joining UTN, he held postdoctoral positions at Université libre de Bruxelles (2023-2024) with a Marie Skłodowska-Curie fellowship under Samuel Fiorini, and at LSE London (2022-2023) with László Végh. He also served as a substitute professor for discrete mathematics at Goethe-Universität Frankfurt during the winter semester of 2023/24. Hertrich's research interests center on the mathematical foundations of neural networks, with particular emphasis on polyhedral geometry approaches. His work explores computational complexity questions related to neural network training and architecture, expressivity bounds, and connections to combinatorial optimization problems. He has made significant contributions to understanding the relationship between neural network depth and function representation, the complexity of counting linear regions in ReLU networks, and the application of extended formulations to neural network theory. His approach combines rigorous theoretical analysis with practical implications for neural network design and optimization. His recent publication record reveals a strong trend toward establishing fundamental theoretical limits and connections between deep learning and discrete mathematics. A significant portion of his work examines computational complexity of various neural network problems, often proving hardness results or establishing bounds on expressivity. He has also developed novel connections between polyhedral combinatorics and neural network architecture, demonstrating how techniques from operations research can inform deep learning theory. Marie Skłodowska-Curie fellowship Since February 2025, Hertrich has been supervising PhD student Moritz Stargalla at UTN. His research has been supported by prestigious fellowships including a Marie Skłodowska-Curie fellowship during his postdoctoral period in Brussels. He is organizing a workshop on "Polyhedral Geometry for Neural Networks" in March 2026 in Nuremberg, highlighting his leadership in this emerging interdisciplinary field.

• Discrete Mathematics
• Theoretical Computer Science
• Machine Learning
• +۳ مورد دیگر

Andrea Del Prete is an Associate Professor in the Industrial Engineering Department at the University of Trento (Italy) since 2022. His research focuses on robot control, reinforcement learning, trajectory optimization, and numerical algorithms for dynamic systems. He leads the Interdepartmental Robotics Lab (IDRA) and has previously held roles as a tenure-track assistant professor at the University of Trento (2019-2021), a research scientist at the Max-Planck Institute for Intelligent Systems (2018), and an associated researcher at LAAS-CNRS (2014-2017) working with the HRP-2 humanoid robot. Earlier, he conducted PhD and post-doc research at the Italian Institute of Technology (2010-2013) on iCub robot control. PhD in Robotics (2013) - Italian Institute of Technology MEng in Computer Engineering (2009) - University of Bologna BSc in Computer Engineering (2006) - University of Bologna Dr. Del Prete specializes in merging learning and model-based techniques for safe robot control, particularly in legged systems. His work bridges trajectory optimization (TO) with reinforcement learning (RL) to overcome local minima challenges (CACTO/CACTO-SL algorithms) and develops robust controllers for humanoid and quadrupedal robots in unstructured environments. He explores viability kernels in MPC, safety certificates, and bi-level optimization for co-designing hardware/control policies. Key application areas include mountain rescue robotics (ALPINE platform), aerial maneuver recovery, and energy-efficient legged locomotion. His recent publications (2023-2025) emphasize numerical optimization algorithms, multi-contact locomotion, and hybrid control frameworks. Topics span from analytical integral optimization (2025) to climbing robots for mountain operations (2025), demonstrating a trajectory from theoretical algorithm development to real-world robotic applications. Research keywords include robotics, numerical optimization, and machine learning, with sub-fields like MPC for dynamic systems, humanoid control, and terrain adaptation. As an educator, he teaches advanced courses on: Optimization and Learning for Robot Control (48-hour master's course) Optimization-based Control of Legged Robots (12-hour PhD course) Task-Space Inverse Dynamics (3-hour PhD course) Current PhD advisees include Mohammad Hasan Yeganegi (generalization bounds for imitation learning), Pietro Noah Crestaz (numerically-efficient RL), Veronica Campana (ergodic control for defect detection), Elisa Alboni (data-efficient model-based RL), and Gianni Lunardi (MPC for legged locomotion).

• Robot Control
• Reinforcement Learning
• Trajectory Optimization
• +۷ مورد دیگر

Dietmar Maringer is Professor of Computational Economics and Finance at the University of Basel's Faculty of Business and Economics (WWZ), where he leads research at the intersection of finance, computational methods, and artificial intelligence. His work focuses on risk management, portfolio optimization, algorithmic trading, and financial simulations. His research interests span computational finance, artificial intelligence in finance, data analysis, risk management, portfolio optimization, algorithmic and high-frequency trading, financial networks, complex adaptive systems, and market simulations. He applies advanced computational and heuristic optimization techniques to solve real-world financial problems, contributing significantly to quantitative finance and financial engineering. His recent publications demonstrate a consistent focus on applying evolutionary algorithms, reinforcement learning, and numerical optimization to portfolio management, market impact modeling, and financial forecasting. The research integrates econometrics, machine learning, and financial theory, emphasizing practical implementation and robust risk-aware decision-making. Several best-paper awards Maringer has served as Chair of the Portfolio Optimization Section of the IEEE Computational Economics and Finance Technical Committee from 2008 to 2018 and is frequently involved in organizing and program committees of international conferences. He has advised or collaborated with numerous researchers, though specific student names are not listed. His research has been supported through academic affiliations and likely institutional or conference-based grants, though explicit funding sources are not detailed. He is affiliated with several research groups, including IEEE Computational Economics and Finance TC, COMISEF, ERCIM, Centre for Innovative Finance, and the European Financial Management Association, reflecting a broad collaborative network in computational finance and economics.

• Computational Economics
• Computational Finance
• Artificial Intelligence in Finance
• +۸ مورد دیگر

Lenka Zdeborová is an Associate Professor at EPFL, jointly affiliated with the School of Basic Sciences and School of Computer and Communication Sciences. She leads the Laboratory of Statistical Physics of Computational Systems, where her research bridges statistical physics, machine learning, and computational biology. Education: PhD in Physics, Université Paris-Cité (2012) MSc in Fundamental Physics, École Normale Supérieure (2009) BSc in Physics, École Normale Supérieure de Lyon (2007) Her work focuses on phase transitions in learning algorithms, high-dimensional statistics, and neural network theory. Current projects investigate fundamental limits of machine learning, dynamics of graph neural networks, and applications to biological systems. Recent publications explore attention mechanisms in transformers, neural network depth advantages, and Bayes-optimal learning. Methodological innovations include cavity methods for hypergraphs and analysis of high-dimensional inference problems. Supervises doctoral students researching statistical physics approaches to machine learning and optimization. Teaches graduate courses in data science and machine learning for physicists.

• Statistical Physics
• Machine Learning
• Computational Biology
• +۳ مورد دیگر

Nicolas Zufferey is a Full Professor of Operations Management at the University of Geneva, Switzerland, where he has served since 2008. He leads research in optimization methods for complex systems, focusing on applications in supply chain management, production planning, inventory control, and transportation logistics. His affiliations include the Research Institute of Management and collaborations with CIRRELT (Transportation & Logistics) and GERAD (Decision Analysis). Education: PhD in Operations Research (EPFL, 2002), MSc/BSc in Mathematics (EPFL) Prior Experience: Postdoc at University of Calgary (2003–2004), Assistant Professor at Université Laval (2004–2007) Research Interests: His work emphasizes developing advanced metaheuristics (e.g., VNS, Tabu Search, PSO) for challenging optimization problems. Key domains include: Multi-objective scheduling with resource constraints Inventory deployment under uncertainty Network design for supply chains and transportation systems Publications: Over 150 peer-reviewed articles across journals like European Journal of Operational Research , Transportation Research , and INFORMS Journal on Computing . Recent work addresses electric vehicle routing, drone integration in delivery systems, and robust decision-making under uncertainty. Collaborations: Engaged with 35+ universities and 27 private companies globally. Active in applying operations research to industrial problems (e.g., Swiss railways, luxury watch production, pharmaceutical networks).

• Operations Management
• Supply Chain Management
• Optimization
• +۴ مورد دیگر

PD Dr. Kaspar Riesen is the Head of the Pattern Recognition Group at the Institute of Computer Science, University of Bern. His research focuses on graph-based methods for pattern recognition, with applications in document analysis, environmental modeling, and healthcare. Key interests include graph matching, neural networks, and spatio-temporal modeling. His work spans structural pattern recognition, graph embeddings, and keyword spotting in historical documents. Recent projects involve river network analysis using graph regression and hypoglycemia prediction via LSTM-GNN hybrid models. Publications emphasize graph theory advancements, such as normalized graph compression and geometric similarity learning. Collaborations include developing specialized algorithms for automated error detection and improving decision-making in simulated sports. Labs/Teams: Pattern Recognition Group (PRG) at the University of Bern.

• Pattern Recognition
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

Prof. Dr. Philipp Baumann is a Professor at the University of Bern, affiliated with the Group for Business Analytics, Operations Research and Quantitative Methods. His research focuses on optimization algorithms, clustering techniques, and operations research applications. He has contributed to advancements in constraint-based clustering, fault-tolerant facility location, and fair-capacitated clustering, leveraging mathematical programming and heuristic methods. His work bridges theoretical optimization and practical applications in industries like printing and healthcare. Research interests include combinatorial optimization, algorithm design for machine learning, and large-scale data mining. Notable contributions include the MPFCC algorithm for fair clustering and FT-KMEANS for fault-tolerant systems. His recent publications emphasize scalable solutions for real-world scheduling and resource allocation challenges. No scientific awards are explicitly mentioned. He advises students on thesis guidelines (Bachelor and Master levels) but no specific advisees are listed. His research group collaborates on projects involving mathematical programming in operations and finance, with applications to production scheduling and portfolio optimization.

• Operations Research
• Optimization
• Machine Learning
• +۵ مورد دیگر

Narmina Baghirova is a Researcher at the Department of Informatics within the Interfaculty Informatics Department at the University of Fribourg. She holds a bachelor’s degree in Mathematics from the University of Primorska (Slovenia), a master’s in Mathematical Sciences from the same institution, and has completed exchange semesters at Universidad del País Vasco (Spain) and Eötvös Loránd University (Hungary). She is currently pursuing a PhD under Prof. Bernard Ries and Dr. David Schindl. Her research focuses on Algorithmic Graph Theory, Computational Complexity, and Combinatorial Optimization. Key areas include k-community structure detection in graphs, proportionally dense subgraphs (PDS), and parameterized complexity analysis. Recent work explores efficiently solvable cases for these problems and their applications in social network modeling. Her publications (2022–2025) span theoretical computer science, discrete mathematics, and bioinformatics, addressing graph partitioning, phylogenetic algorithms, and structural graph properties. She has contributed to both journal articles and conference proceedings. Baghirova collaborates with international researchers and is affiliated with the Department of Informatics at the University of Fribourg, where she contributes to research on computational complexity and graph-theoretic problems.

• Algorithmic Graph Theory
• Computational Complexity
• Structural Graph Theory
• +۳ مورد دیگر