Find Academic Supervisors

• Software Engineering
• Robotics
• Computer Graphics
• +3 more

Joachim Wirth is a Lecturer at the Lucerne School of Computer Science and Information Technology (HSLU). He holds a Dr. sc. techn. (PhD) from ETH Zurich and a Diplom-Mathematiker from Heidelberg University. His professional experience spans roles as a Senior Software Engineer at companies like Agathon AG and Leica Geosystems, alongside academic appointments at multiple Swiss institutions including ZHAW Winterthur and HTW Rapperswil. His technical competencies include advanced software development (C++, C#, Java), computer mathematics (Matlab, Maple), computer graphics (WebGL, OpenGL), robotics programming (ABB RAPID, KUKA), and microcontroller systems (Raspberry Pi, Arduino). He has also contributed to research projects such as 'XR in Higher Education Mathematics,' focusing on integrating extended reality into STEM education. Prior roles include scientific work at the CIM-Zentrum Muttenz and clinical research projects at University Hospital Basel. His work bridges applied software engineering with academic instruction, emphasizing practical skills in computer science and robotics.

• Artificial Intelligence (AI) in Industry
• Machine Learning
• Natural Language Processing (LLMs)
• +4 more

Prof. Dr. Esther Gelle is a Lecturer in Digital System Design at the FHNW Institute of Business Engineering, part of the FHNW School of Engineering and Environment at the University of Applied Sciences Northwestern Switzerland. Her work focuses on AI applications in industry, machine learning, natural language processing (LLMs), constraint satisfaction problems, and digital business process modeling. She has contributed to advancements in industrial automation systems, IT asset management, and configuration frameworks. Her research spans over two decades, with notable contributions in constraint satisfaction problem solving, industrial standard adaptation (e.g., IEC 61346), and autonomous IT management. She collaborates with industry partners to bridge theoretical concepts with practical applications. Her publications emphasize optimization techniques, system modeling, and automated configuration in engineering contexts. Her articles consistently explore themes like constraint-based problem solving, industrial process control, and the integration of AI into technical systems. While no awards are explicitly listed, her extensive publication record reflects sustained academic and industrial impact. She currently holds no listed students or grants but actively engages in research teams focused on automation and digital transformation. Her affiliation with the Institute of Business Engineering positions her at the intersection of technical systems and business processes, emphasizing practical solutions for modern industrial challenges. She is based at the FHNW Campus Brugg-Windisch, contributing to both educational and applied research initiatives.

Marco ZaffalonView profile

Professor

• Machine Learning
• Causal AI
• Probabilistic Graphical Models
• +3 more

Marco Zaffalon is a Professor at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI) and Scientific Director of the Dalle Molle Institute for Artificial Intelligence (IDSIA). He holds an MSc in Computer Science (cum laude) and a PhD in Applied Mathematics. His research focuses on causal AI, machine learning, and probabilistic models under uncertainty, with applications in defense, environment, medicine, engineering, and business. He leads a 30-member research group in probabilistic machine learning and has secured over 15 million Swiss Francs in competitive grants. Education: MSc in Computer Science (cum laude), PhD in Applied Mathematics. Professional milestones include roles as a software engineer at an Italian insurance group, consultant for SGS-Thomson, post-doctoral fellow at IDSIA, and tenure as a researcher since 2001. Research interests emphasize foundational and applied aspects of probability theory, particularly imprecise probability, and causal reasoning. His work integrates knowledge-based systems, probabilistic graphical models, and robust algorithms for real-world problems. Notable projects include OmniProfiler , Credal Model Averaging , and Learning under Near-Ignorance . Awards & Recognition: President of SIPTA (2019–present) Senior Area Editor for International Journal of Approximate Reasoning (IJAR) Top 2% Scientist (Stanford’s ranking since 2019) Grants & Leadership: Oversaw 15+ million CHF in competitive research funding Founded and directs a 30-member research group He collaborates on interdisciplinary projects, including environmental modeling, medical applications, and defense-related AI systems through IDSIA’s labs and partnerships.

• Computer Graphics
• Geometry Processing
• 3D Reconstruction
• +3 more

Dr. Jing Ren is affiliated with the Department of Computer Science at ETH Zürich, holding a role within the Professorship for Computer Science. Their research focuses on computational geometry, 3D reconstruction, and computer graphics, with notable contributions to shape analysis, non-rigid matching, and fabric modeling. Dr. Ren’s work bridges theoretical advancements with practical applications in textile design, architectural modeling, and medical imaging. They collaborate extensively on projects involving functional maps, optimization algorithms, and geometric morphometrics. Key research interests include: Non-rigid shape correspondence and matching Computational modeling of woven fabrics and textiles 3D face and building reconstruction techniques Efficient spectral and discrete optimization methods Recent publications (2022–2024) emphasize innovations in fabric parameterization, Gaussian noise distribution, and rethinking 3D face reconstruction benchmarks. Their work often employs machine learning and functional map frameworks to solve geometric problems across disciplines. Laboratory and team affiliations are not explicitly detailed in the provided materials, but their research aligns with ETH Zürich’s broader initiatives in computer science and engineering. No grants or advising activities are specified in the current data.

• Computer Graphics
• Computer Science

Rita Sevastjanova serves as a Researcher within the Department of Computer Science at ETH Zurich, specializing in Computer Graphics. She is based at the Stampfenbachstrasse 114 campus in Zürich, Switzerland, where she contributes to the university's visual computing research initiatives. Her primary research domain focuses on: Computer Graphics Computer Science with specific emphasis on visual computing methodologies and applications within academic research contexts. Sevastjanova operates within the Computer Graphics research group at ETH Zurich, collaborating on departmental projects from her office location (STF F 124) and maintaining active scholarly engagement through her ORCID identifier (0000-0002-2629-9579).

• Computer Science
• Computer Graphics
• Visualization
• +1 more

Dr. Baran Gözcü is a Lecturer at the Department of Computer Science at ETH Zürich, Switzerland. His professional affiliation includes the Informatik (Computergraphik) division, located at Universitätstrasse 6 in Zürich. Contact information includes the office CNB G 101 and a direct phone number. Research interests are inferred to focus on computer graphics, visualization, and related computational methodologies within the broader discipline of computer science. Specific subtopics may include rendering techniques, interactive systems, or computational geometry, though explicit research details are not provided in the text. No awards, grants, or published articles are listed in the provided information. The lecturer's homepage can be found at sites.google.com/view/barangozcu for further details.

• Mechanical Engineering
• Computational Mechanics
• Structural Stability
• +3 more

Arefeh Abbasi is a researcher at ETH Zürich, affiliated with the Professorship for Computational Mechanics. Her work focuses on advanced mechanical engineering problems involving structural stability, magnetic actuation, and computational modeling of complex systems. Institution: ETH Zürich Department: Computational Mechanics Email: abbasia@ethz.ch Her research explores buckling mechanics in imperfect shells, bistable magnetic structures, and smart materials. She has developed frameworks for analyzing magneto-elastic systems and refined finite element models for composite and functionally graded plates. Recent publications highlight her contributions to understanding defect interactions in spherical shells, magnetic actuation of bistable beams, and tunable buckling behaviors in smart structures. These works span computational modeling, experimental validation, and reduced-order theoretical frameworks. She employs finite element analysis and numerical simulations to study nonlinear mechanical phenomena, with applications in adaptive structures and human-computer interaction technologies like refreshable braille displays.

Alexander IlicView profile

Lecturer

• Artificial Intelligence
• Computer Vision
• Entrepreneurship
• +3 more

Roles & Affiliations: PD Dr. Alexander Ilic is a Lecturer at the Department of Computer Science and Executive Director of the ETH AI Center at ETH Zürich. He co-founded the ETH AI Center and previously led Magic Leap Switzerland, focusing on R&D in Computer Vision and Advanced Photonics. He holds a PhD from ETH Zurich and a habilitation from the University of St. Gallen. Education: PhD in Computer Science, ETH Zurich Habilitation in Entrepreneurship, University of St. Gallen MSc in Computer Science, TU Munich Research Interests: Alexander’s work spans Artificial Intelligence, Entrepreneurship, and Technology Investing. He pioneered AI-driven start-ups like Dacuda (acquired by Magic Leap) and developed cutting-edge sensors and imaging systems. His research emphasizes real-time systems, computer vision applications, and wearable technology. Notable Achievements: Co-founder of Dacuda and Magic Leap Switzerland 两次获得“Entrepreneur of the Year”（2011年和2012年） Swiss Economic Award Over 50+ patents in imaging, AR, and sensor technology Courses Taught: Data Science Lab Technology Investing Patenting Digital Innovations Technology and Entrepreneurship Labs & Leadership: Leads the ETH AI Center, driving AI innovation and interdisciplinary projects. His teams focus on applied AI in real-time systems and cross-reality devices.

• Aerothermodynamics
• Mechanical Engineering
• Process Engineering
• +3 more

Dr. Ndaona Chokani is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich. Their research focuses on aerothermodynamics, mechanical engineering, and process engineering, with a recent emphasis on interdisciplinary applications in computer vision and image processing. Affiliated with the Professur f. Aerothermodynamik, their work spans theoretical and applied domains. While explicit educational background details are not provided, their academic career includes contributions to fields such as fluid dynamics and process systems engineering. Research interests are inferred from departmental affiliation and recent publications, emphasizing computational methods in mechanical systems and image quality assessment. Recent publications highlight work in perceptual quality metrics for images and videos, immersive systems (VR/360 content), and machine learning applications in 3D pose estimation and generative models. Key areas include diffusion models for low-light imaging, spatial-temporal geometric networks, and benchmarking frameworks for image harmonization. No awards or grants are explicitly listed in the provided text. Their research often involves collaborations on datasets like Salient360! and tools for gaze data analysis in 3D environments. Future work appears to focus on improving perceptual metrics and expanding applications in healthcare and immersive technologies.

• Informatics Education
• Algorithms
• Computer Science Education
• +3 more

Regula Lacher is a Researcher at ETH Zürich’s Department of Computer Science, affiliated with the Professorship for Algorithms and Didactics. Her work focuses on advancing computer science education through innovative pedagogical methods, emphasizing algorithm design, problem-solving techniques, and the integration of computational thinking into K-12 curricula. She specializes in developing tangible, non-digital teaching tools and game-based approaches to enhance abstract concept comprehension. Her research explores how informatics education can improve general educational outcomes, particularly through constructive induction and genetic Socratic methodologies. She has contributed to curriculum design frameworks for both Swiss Maturität and international K-12 systems, advocating for cross-disciplinary learning and teacher training programs. Publications span topics like robotics-based programming, cybersecurity education, and multilingual educational materials. Key themes include bridging concrete and abstract learning, promoting equity through accessible resources, and leveraging historical pedagogical tools like LOGO in modern classrooms. Her work addresses challenges in standardizing informatics education while maintaining adaptability for diverse cultural and linguistic contexts, as seen in her French and Italian language publications.

• Robotics
• Human-Computer Interaction
• Machine Learning
• +3 more

Dr. Yi-Chi Liao is a Lecturer in the Department of Computer Science at ETH Zürich, specializing in intelligent interactive systems. Their research focuses on human-robot interaction, wearable technology, and optimization techniques for user interface design. Key areas include developing datasets for naturalistic handover behaviors with robotic limbs, human-in-the-loop optimization methods, and computational workflows for designing input devices. Research Interests: Explores the intersection of robotics, machine learning, and human-centered design. Recent work emphasizes Bayesian optimization techniques, affordance theory, and tactile feedback systems. Projects like the 3HANDS dataset and ThirdHand wearable robotic arm demonstrate innovation in human augmentation and interaction design. Advising: Supervises doctoral student Peizhuo Li in the D-INFK program. Research outputs span 2015–2025, with notable contributions to haptic interfaces, multi-objective optimization, and wearable computing. Active in conferences and journals addressing HCI, robotics, and design automation.

• Geometry processing
• Discrete differential geometry
• Physical simulation
• +2 more

Marcel Padilla is Lecturer at ETH Zurich's Department of Computer Science, researching geometric algorithms for physical simulation and visualization. As Feodor-Lynen Fellow, he develops computational methods for fluid dynamics and astrophysical phenomena. Research integrates discrete exterior calculus with computer graphics to model complex systems like solar coronae and vortex dynamics. Recent publications present novel frameworks for rigid body motion estimation and magnetic filament simulation. Teaches geometry processing and mathematical visualization.

Dimosthenis PasadakisView profile

Research Fellow

• Graph Theory and Algorithms
• Data Science
• Machine Learning
• +3 more

Dimosthenis Pasadakis is a Research Fellow at the Institute of Computing (CI) of the Università della Svizzera italiana (USI), where he serves as the Principal Investigator (PI) of the Huawei Research Center Zürich-funded project on Directed Acyclic Graph Partitioning for Scheduling Tasks. His research focuses on graph learning, combinatorial optimization, and large-scale algorithms for clustering, anomaly detection, and data analysis. He also holds the role of Chief Operating Officer (COO) at Panua Technologies Sagl, a Lugano-based software company specializing in high-end solutions for graph analytics and optimization. His academic journey includes a PhD successfully defended in 2023, supported by the Swiss National Science Foundation (SNSF) project on Balanced Graph Partition Refinement. He has organized minisymposia at SIAM LA 24 and PASC 24, and his work has been recognized with awards such as the IEEE HPEC Outstanding Paper Award (2024) and the IEEE SDS24 Best Poster Award (2024). Key research interests include spectral clustering, graph-based fraud detection, and high-performance computing. He actively collaborates with industry through grants and maintains an academic-industrial bridge through Panua Technologies.

Markus PüschelView profile

Professor

• Program Generation
• Fourier Analysis
• Signal Processing
• +6 more

Markus Püschel is a Professor of Computer Science at ETH Zurich, leading the Advanced Computing Laboratory. He previously served as Head of the Department of Computer Science at ETH from 2013 to 2016. Before joining ETH in 2010, he was a Professor at Carnegie Mellon University (CMU) and retains adjunct status there. He holds a PhD in Computer Science (1998) and a Mathematics Diploma (1995) from the University of Karlsruhe. His research focuses on program generation for performance, Fourier analysis, signal processing, machine learning, and compiler optimization. He pioneered the SPIRAL system for generating optimized signal processing libraries and has contributed to algebraic signal processing theory. His work bridges mathematical foundations with practical high-performance computing, emphasizing automated code generation and domain-specific languages. Research trends in his articles include causal inference on directed acyclic graphs, quantized neural network inference, and efficient compiler techniques. His work on graph signal processing and causal analysis has led to novel methods in time-series data and DAG learning. Awards: IEEE Fellow (2020), Golden Owl Teaching Award (2015), NSF Discovery Grant (2008), and multiple best paper awards. Grants/Projects: Co-PI for Making Program Analysis Fast (SNF), SPIRAL GPU projects, and collaborations with industry partners like Intel and AMD. He advises over 40 PhD and master’s students, many of whom contribute to impactful projects in compilers, machine learning, and signal processing. His lab also co-founded the Swiss Data Science Center.

• Computer Graphics
• Rendering Techniques
• 3D Visualization
• +3 more

Markus Portmann is a Researcher in the Department of Computer Science at ETH Zürich, specifically within the Computer Graphics group. His affiliation is listed under the Professorship for Computer Science (Computer Graphics). He is based at the Universitätstrasse 6, 8092 Zürich, Switzerland, and can be reached via email at markus.portmann@inf.ethz.ch. While specific research interests are not detailed in the provided text, his affiliation with the Computer Graphics department suggests a focus on areas such as rendering algorithms, 3D modeling, virtual environments, and visualization technologies. No information on academic awards, grants, or advised students is explicitly mentioned in the text.