Find Academic Supervisors

• Manufacturing Engineering
• Sustainable Product Development
• Digital Twin Applications
• +5 more

Simon Züst is a Professor at Lucerne University of Applied Sciences and Arts (HSLU), affiliated with the Institute of Innovation and Technology Management (IIT) within the Lucerne School of Engineering and Architecture. He holds dual roles as the Head of the Research Group and is active in the Competence Center Business Engineering. His academic career includes a Master's and Ph.D. from ETH Zurich in Mechanical Engineering, with a focus on thermal dynamics of machine tools and nanoscale manufacturing. Research expertise spans modeling/simulation of dynamic systems, mechatronics, sustainable product design, and lean manufacturing. Notable projects include the DeCIRRA communication platform, ReRe circular metal sourcing, and Clart-E energy innovation initiatives. He has pioneered digital twin applications for factory optimization and decision support systems in industrial ecosystems. Züst has authored/co-authored over 30 peer-reviewed articles since 2012, with recent work focusing on lifecycle management frameworks, data-driven product service systems, and energy efficiency metrics. His 2013 Zuger Wissenschaftspreis recognized innovative contributions to machine tool energy modeling. Teaching responsibilities include leading mechatronic systems development and product engineering modules. Industry collaborations span over 20 firms in R&D areas like resource-efficient production systems and factory layout optimization. Current research emphasizes circular economy strategies, digitalization in manufacturing, and CO₂ footprint analysis. He is actively involved in innovation ecosystems through projects like WETZI (CO) 3N and the eco Hotel optimization initiative.

• 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.

Nicolas FlammarionView profile

Assistant Professor

• Machine Learning
• Statistics
• Optimization
• +3 more

Nicolas Flammarion is a tenure-track Assistant Professor in Computer Science at the École Polytechnique Fédérale de Lausanne (EPFL). He holds positions in multiple departments including the Theory in Machine Learning (TML) lab under the School of Computer and Communication Sciences (IC). His roles include teaching and doctoral program leadership across disciplines like Communication Systems and Computer Science. Education: PhD in 2017 from École Normale Supérieure (Paris), advised by Alexandre d’Aspremont and Francis Bach. Postdoctoral fellowship at UC Berkeley under Michael I. Jordan. Research focuses on machine learning theory, optimization, and statistical methods. Key areas include adversarial robustness, algorithmic generalization, and optimization dynamics. Notable contributions include work on SGD/GD comparisons, adversarial benchmarking (RobustBench), and safety in AI systems. Awards include the 2018 Fondation Mathématique Jacques Hadamard PhD Prize, 2021 NeurIPS Outstanding Paper Award, and 2024 Trust & Safety Google Research Award. Supervises PhD students in topics like algorithm design and machine learning theory. Labs/Teams: Leads TML lab and contributes to EPFL's doctoral programs in Informatics and Communication Sciences. Active in curriculum development for advanced machine learning topics.

• Product Management
• Supply Chain Management
• Artificial Intelligence
• +3 more

Amin Kaboli is a Lecturer at the Swiss Federal Institute of Technology in Lausanne (EPFL), holding positions in the School of Mechanical and Process Engineering and the School of Computer and Communication Sciences . As Co-Director of the AI Product Management program , he specializes in teaching students to apply AI/ML principles to real-world product development. His academic roles include designing curricula that blend technical engineering with business leadership, such as courses on Sustainable Products and Production Management. He earned a Ph.D. in Manufacturing Systems & Robotics from EPFL and holds a graduate degree in Industrial Engineering. His leadership training at IMD Business School complements his engineering background, enabling him to advise Fortune 500 corporations like Philip Morris International, Nespresso, and Rolex, as well as Swiss startups. His professional experience includes roles as an executive and COO of a startup, alongside work as an executive coach (PCC-certified). Kaboli’s research interests span AI-driven innovation , supply chain optimization , and sustainability . He explores solutions for energy management in smart grids, waste reduction in construction, and fault detection in industrial machinery. His work integrates fuzzy logic, multi-criteria decision-making (MCDM), and hybrid machine learning frameworks to address complex challenges. He teaches courses such as AI Product Management (focus on practical AI/ML applications), Sustainable Products and Supply Chains (sustainability principles and logistics optimization), Production Management (cost-effective manufacturing strategies), and Continuous Improvement of Manufacturing Systems (process optimization and team leadership). No scientific awards or grants are explicitly listed. His contributions include developing frameworks for eco-city solutions and waste management strategies. Kaboli’s office is located at ME A2 408 , EPFL’s Lausanne campus, where he also engages in administrative and educational leadership roles.

Hristo Georgiev PapazovView profile

Researcher

• Machine Learning Theory
• Deep Learning
• Optimization Algorithms
• +3 more

Hristo Georgiev Papazov is a Doctoral Assistant at the Theory of Machine Learning (TML) Laboratory under the Institute of Information and Communication Systems (IINFCOM) at EPFL. His primary affiliation is within the School of Computer and Communication Sciences (IC) at the prestigious École Polytechnique Fédérale de Lausanne. Additionally, he is enrolled in the Doctoral Program in Computer Science (EDIC) within the School of Engineering (ETU). His research interests span theoretical computer science, machine learning fundamentals, and applied deep learning in healthcare contexts. Notable focuses include algorithmic analysis of neural networks, optimization dynamics, and clinical text classification systems using SNOMED CT standards. His work also intersects with mathematical analysis, particularly in extending classical complex analysis techniques to elliptic frameworks. Papazov has contributed to publications addressing momentum-based training in linear networks, dimensionality effects in online algorithms, and interdisciplinary applications of machine learning in medical coding. His current position involves both research in the TML lab and doctoral studies, reflecting a dual commitment to advancing theoretical understanding and practical applications in computer science.

• Computer Science Education
• Programming Pedagogy
• Visual Programming
• +3 more

Dr. Sverrir Thorgeirsson is a Professor of Computer Science at ETH Zürich, affiliated with the Department of Computer Science under the School of Computer and Communication Sciences. His work focuses on innovative educational technologies and pedagogical frameworks to enhance programming education. His research interests include visual programming languages (e.g., Algot), cognitive aspects of learning (e.g., cognitive load measurement via EEG), and strategies to improve abstraction skills and software testing comprehension in students. He emphasizes bridging the syntax-semantics gap in programming through hands-on, interactive tools. Recent publications highlight his contributions to visual programming environments, failure-based learning, and the design of pedagogical frameworks. He actively explores how cognitive science principles can optimize educational outcomes in introductory and advanced CS courses. No scientific awards were explicitly listed. His advising and grants sections remain under development, pending additional data.

• High-Performance Computing
• Performance Modeling
• Parallel Computing
• +3 more

Alexandru Calotoiu is a Researcher in the Department of Computer Science at ETH Zürich, affiliated with the Professorship for Scalable Parallel Computing. His work focuses on performance modeling, high-performance computing (HPC), serverless systems, and cloud computing. He leads research in empirical performance modeling for complex applications, optimization of parallel algorithms, and scalable cloud architectures. Key research areas include noise-resilient performance models, serverless computing frameworks, and compositional parallel programming. He has contributed to benchmarking tools like SeBS and developed techniques for loop scheduling, static analysis, and resource disaggregation in HPC environments. His publications from 2023–2025 emphasize serverless systems (e.g., FaaSKeeper, Cppless), performance embeddings for optimization, and specialized supercomputing for climate science. These studies address scalability, reproducibility, and cross-platform performance portability in data-centric workloads. No scientific awards are explicitly listed, but his work has been presented at leading conferences such as ISCA and IEEE/ACM events. He collaborates on projects like rFaaS (RDMA-enabled serverless platforms) and Process-as-a-Service frameworks. His research bridges theoretical models with practical implementations in distributed systems and cloud infrastructure.

Moritz GoldView profile

Researcher

• Sustainable Food Processing
• Biowaste Management
• Environmental Engineering
• +3 more

Dr. Moritz Gold is a researcher affiliated with the Professorship for Sustainable Food Processing at ETH Zurich's Department of Health Sciences and Technology. His work focuses on scalable approaches to recycle biowaste into marketable products, aiming to reduce environmental impact, offset waste management costs, and improve public health. He is currently a visiting researcher at the Singapore-ETH Center. His research integrates microbiology, environmental engineering, and agricultural science to optimize black soldier fly (BSF) larvae processing of organic wastes. Key areas include biowaste pretreatment, microbial community dynamics in rearing residues, and the nutritional value of BSF-derived products in animal feed. Collaborations include studies with institutions like Eawag and Huazhong Agricultural University, exploring topics such as faecal sludge utilization and heavy metal transfer in food chains. Gold's publications emphasize practical applications of insect-based biowaste recycling, including feed substitution for poultry and nutritional safety assessments. His work also addresses global sanitation challenges, such as dewatering faecal sludge for fuel production in Sub-Saharan Africa. Experimental methods in his lab include in vitro digestion modeling and electron beam sterilization techniques. Notable contributions include demonstrating the potential of BSF larvae to process aflatoxin-contaminated substrates and the impact of residue microbes on rearing performance. His interdisciplinary approach bridges laboratory research with industrial-scale applications, contributing to circular economy principles in food systems.

Romain JacobView profile

Lecturer

• Computer Networks
• Real-Time Systems
• Wireless Communication
• +5 more

Romain Jacob is a Lecturer at the Department of Information Technology and Electrical Engineering at ETH Zürich, where he also holds a postdoctoral researcher position in Prof. Laurent Vanbever's group. His research focuses on computer networks, real-time systems, and sustainable networking, with an emphasis on reproducible experimental design and open science advocacy. He completed his PhD under Prof. Lothar Thiele at ETH Zürich in 2019, followed by a visiting scholar position at UC Berkeley in 2014. His academic background includes a Master of Science in Engineering of Complex Systems from École Normale Supérieure de Cachan (now ENS Paris-Saclay). Key research interests include: Time-triggered wireless architectures for cyber-physical systems Energy-efficient networking and sustainability Reproducibility frameworks like TriScale Real-time communication protocols Notable contributions include the Best Paper Award at HotCarbon'24 for work on router energy optimization. He actively promotes open science through initiatives like the ORCID Researcher Advisory Council and serves as Editor-in-Chief for JSys. His teaching includes a master's lecture on Sustainable Networking, emphasizing practical applications and open educational resources. He collaborates with the Swiss Reproducibility Network (SwissRN) to advance rigorous research practices.

• Photonics
• Plasmonics
• Nanotechnology
• +5 more

Mario Kupresak is part of the Photonics and Communication research group at ETH Zürich. As a Researcher in this field, he focuses on advanced topics in plasmonics, nanotechnology, and electromagnetic modeling. His work emphasizes the nonlocal hydrodynamic response of plasmonic nanostructures at deep-nanometer scales, semiconductor device modeling at high frequencies, and computational methods for nanoscale systems. His research interests include plasmonic nanostructures , nonlocal effects , computational electromagnetics , and high-frequency semiconductor physics . He has explored applications such as single-molecule fluorescence enhancement and antenna array design through iterative algorithms. Publications span from 2017 to 2024, with a focus on advancing methodologies for characterizing deep-nanometer plasmonic systems and benchmarking computational tools. His contributions include reviews on integral equation-based techniques and unified theoretical frameworks for nonlocal hydrodynamic responses. Currently affiliated with the Institute of Electrodynamics (IEF), he is based at ETH Zürich's Gloriastrasse 35, office ETZ H67.

Michele MagnoView profile

Senior Lecturer

• Smart Sensing
• Low-Power Machine Learning
• Wireless Sensor Networks
• +4 more

Michele Magno is a Senior Lecturer and Privatdozent at ETH Zürich's Department of Information Technology and Electrical Engineering (D-ITET), leading the D-ITET Center for Project-based Learning (pbl.ee.ethz.ch). He holds a PhD in Electronic Engineering from the University of Bologna (2010) and has held visiting roles at institutions like the University of Nice and Mid Sweden University. His research focuses on low-power systems, wearable devices, energy harvesting, and IoT applications. Magno has authored over 350 peer-reviewed papers, with a Google H-index of 49. Notable awards include the 2024 Best Paper Award at ECCV and multiple best poster/demo recognitions at IEEE conferences. His industrial collaborations include projects with STMicroelectronics, Texas Instruments, and Logitech. Teaching contributions include courses on embedded systems, FPGA programming, and machine learning on microcontrollers. Magno's innovations span smart sensors for wind turbines, bio-medical monitoring, and autonomous racing systems, with patents in touch communication and energy-neutral devices. Recent work emphasizes ultra-low-power solutions for AI-integrated wearables, energy-efficient IoT nodes, and real-time embedded vision systems. His labs and teams pioneer technologies like TinyssimoRadar for in-ear gesture recognition and WakeMod for ultra-low-power IoT connectivity.

Erwin RieglerView profile

Lecturer

• Information Theory
• Signal Processing
• Communications
• +3 more

Dr. Erwin Riegler is a Lecturer at the Department of Information Technology and Electrical Engineering (D-ITET) at ETH Zurich. His research focuses on information theory, signal processing, and their applications in communication systems, with particular expertise in analog compression, matrix completion, and uncertainty principles. He has contributed to theoretical frameworks for rate-distortion analysis, lossless compression techniques, and the design of efficient algorithms for MIMO systems. Key research areas include: Information-theoretic limits of data compression Signal processing in analog systems Algorithmic approaches for sparse signal recovery Optimization techniques in MIMO communication channels Recent work emphasizes interdisciplinary applications, such as neural networks for measure theory and probabilistic methods for signal separation. No scientific awards are explicitly mentioned in the provided texts. His academic contributions span over 30 peer-reviewed articles since 2001, covering topics from string theory to modern communication systems.

• Medical Education
• Interprofessional Collaboration
• Health Sciences
• +3 more

Stefan Gysin, MD, PhD, MME, is a faculty member at the University of Lucerne’s Faculty of Health Sciences and Medicine . As Head of the Joint Medical Master’s Program (University of Lucerne and Zurich), he focuses on curriculum design, academic quality assurance, and interprofessional education. His work includes coordinating master’s theses, advising students, and organizing academic events.

• Wireless Communication
• Machine Learning
• Digital Signal Processing
• +6 more

Prof. Christoph Studer is a Full Professor of Integrated Information Processing at ETH Zurich's Department of Information Technology and Electrical Engineering. He leads the Integrated Systems Laboratory and directs SwissChips. His research focuses on wireless communication, machine learning, signal processing, and hardware-efficient algorithms, with applications in B5G systems, sensing-communication integration, and low-power signal processing. He holds a Ph.D. and M.S. from ETH Zurich (2009 and 2006) and has held academic positions at Cornell University before returning to ETH in 2020. Notable honors include the NSF CAREER Award (2017), ETH Medal for Doctoral Dissertation (2011), and multiple teaching awards. Education: M.S. and Ph.D. in Information Technology and Electrical Engineering, ETH Zurich (2006, 2009) Visiting Researcher, Stanford University (2005) Research Interests: Develops algorithms and hardware for high-throughput, low-power wireless systems. Key areas include: B5G multi-antenna systems and simultaneous sensing-communication (SISCO) Analog-to-feature (A2F) conversion for low-power signal classification Hardware-software co-design for efficient microchip integration Publications: Focus on channel charting, jammer mitigation, and deep learning for communication. Recent work includes CSI2Vec, jammer-resilient synchronization, and distributed MIMO systems. Awards: US NSF CAREER Award (2017) Michael Tien Teaching Award (2016) ETH Medal for Doctoral Thesis (2011) Labs/Teams: Leads the Integrated Systems Laboratory at ETH and directs SwissChips, a national initiative for integrated circuit development.

Dominik StämpfliView profile

Lecturer

• Clinical Pharmacy
• Pharmacoepidemiology
• Pharmacovigilance
• +5 more

Dr. Dominik Stämpfli is a Lecturer at the Department of Chemistry and Applied Biosciences at ETH Zürich, with a combined appointment as a clinical pharmacist at Kantonsspital Baden . His research focuses on Clinical Pharmacy and Pharmacoepidemiology , emphasizing drug-related problem prevention, opioid deprescribing strategies, and public health interventions. He holds roles in Swiss pharmacy education, including membership in the Commission for the Federal Examinations in Pharmacy and the Study Programme Commission CAS Community Pharmacy . Education includes a pharmacy degree from the University of Basel (2008–2013) and a doctorate in pharmaceutical care (2014–2018). His postdoctoral work at Basel expanded into clinical pharmacy roles, including managing transferable skills programs (2018–2019). His research spans pharmacovigilance, medication safety, and interprofessional collaboration in healthcare. Key research themes include: Optimizing opioid deprescribing protocols to reduce rehospitalization risks Developing tools like the PAR-Risk Score for hospital readmission prediction Pharmacy-led interventions for hepatitis C screening and influenza vaccination Evaluating medication discrepancies and their clinical/economic impacts He is actively involved in: Teaching Therapeutic Skills , Clinical Trainings , and Interprofessional Patient Pathways at ETH Professional organizations such as the Pharmaceutical Care Network Europe and Swiss Academy of Pharmaceutical Sciences His work bridges academic research and clinical practice, with a focus on improving patient safety through pharmacist-led initiatives.