Find Academic Supervisors

• Machine Learning
• Healthcare Technology
• Sensor Networks
• +3 more

Michel Deriaz is a Senior Lecturer at the University of Geneva's Research Institute for Statistics and Information Science. He holds a Ph.D. from the same institution. His research focuses on machine learning applications in healthcare technology, sensor systems, and recommendation systems, with a strong emphasis on interdisciplinary projects involving wearable devices, data analysis, and human-centric technologies. Key research interests include: Machine learning for gait analysis and early disease detection Indoor/outdoor localization using IMU and GPS fusion AI-driven recommendation systems for seniors and startups Health monitoring through biometric sensors (e.g., grip strength analysis) Optimization of mobile positioning systems His recent work demonstrates expertise in: Convolutional neural networks for equine lameness detection Transformer-based models for localization during GPS outages Predictive analytics for foreign exchange markets Validation of medical devices for clinical assessment No scientific awards or current grants are listed, though his extensive publication record indicates active collaboration with healthcare providers and tech innovators. His work often bridges theoretical computer science with practical applications in geriatric care, veterinary science, and disaster management systems.

Janne EstillView profile

Researcher

• Mathematical Modelling
• Epidemiology
• HIV Research
• +3 more

Janne Estill is a Researcher at the Institute of Global Health (University of Geneva), affiliated with the Faculty of Medicine. He holds a MSc in Mathematics from the University of Helsinki (2008) and a PhD in Mathematical Modelling of HIV from the University of Bern (2013). His research focuses on developing mathematical models to evaluate disease progression and transmission, particularly for HIV, hepatitis C, and other infectious diseases, with an emphasis on cost-effectiveness analysis of health policies in resource-limited settings. Education: MSc in Mathematics, University of Helsinki, 2008 PhD in Mathematical Modelling of HIV, University of Bern (ISPM), 2013 Research Interests: Mathematical modelling, epidemiology, HIV/AIDS research, cost-effectiveness analysis, infectious disease transmission dynamics, and healthcare policy evaluation. His work applies quantitative methods to improve public health outcomes globally, particularly in sub-Saharan Africa. Publications Trends: Janne’s articles span mathematical modeling of HIV treatment strategies, cost-effectiveness of monitoring tools, and pandemic response (e.g., COVID-19). Recent work incorporates artificial intelligence for bias assessment in clinical trials, showcasing interdisciplinary innovation. Labs/Teams: Active within the Institute of Global Health, collaborating with global health networks and institutions like the Institute of Social and Preventive Medicine (ISPM), Geneva.

• Data Science
• Computational Statistics
• Bayesian Models
• +5 more

Antonietta Mira is a Full Professor of Statistics at the University of Italian Switzerland (USI), where she also serves as the founder and director of the Data Science Lab. She holds a part-time professorship at Università dell'Insubria and has held visiting roles at institutions such as the University of Cambridge and Université Paris-Dauphine. Her academic leadership includes a Vice-Dean role in the Faculty of Economics (2013-2015). She earned a PhD in Computational Statistics from the University of Minnesota (1998), a Master's in Statistics (1996), and a Doctorate in Methodological Statistics from the University of Trento (1995). Her research focuses on Bayesian statistical models, computational statistics, and interdisciplinary applications in healthcare, finance, and social sciences. Mira is a Fellow of the International Society for Bayesian Analysis (ISBA) and the Istituto Lombardo Accademia di Scienze e Lettere. Her work has been recognized with awards for both research and teaching excellence. She leads Swiss National Science Foundation-funded projects and serves on editorial boards of journals like Bayesian Analysis and Statistica Sinica . Her publications span topics such as approximate Bayesian computation (ABC), spatio-temporal modeling of health events, and data science applications in public policy. Mira also engages in public outreach, including scientific exhibitions like Numbed by Numbers! and lectures on data-driven societal issues.

Ehsan Rahmatizad KhajehpashaView profile

Research Fellow

• Computational Physics
• Materials Science
• Machine Learning
• +3 more

Ehsan Rahmatizad Khajehpasha is a Research Fellow at the University of Basel's Department of Physics, part of the Computational Physics Group led by Prof. Stefan Goedecker. He holds a PhD in Computational Physics from the Institute for Advanced Studies in Basic Sciences (Zanjan, 2016–2021), a Master's in Solid-state Physics from the University of Zanjan (2014–2016), and a Bachelor's in Solid-state Physics from the Iran University of Science and Technology (2010–2014). His research focuses on advancing computational methods for atomistic simulations, including machine learning potentials, charge equilibration techniques, and structure prediction algorithms. Key projects include the development of the FLAME library for atomistic modeling environments and contributions to the Minima Hopping algorithm for materials discovery. Recent work emphasizes entropic contributions in phase transitions and improving the accuracy of density functional theory calculations using surface integral approaches. Publications highlight advancements in computational physics, such as optimizing geometry calculations, accelerating machine learning potentials, and analyzing free energy landscapes in molecular crystals. Collaborations span interdisciplinary topics like bioinformatics (e.g., predicting BRCA gene variants) and agricultural genomics (vernalization gene prediction). He is affiliated with the Philosophisch-Naturwissenschaftliche Fakultät and actively contributes to the Computational Physics Group's projects in materials science and quantum chemistry.

Roman SchmiedView profile

Researcher

• Quantum Optics
• Atomic Physics
• Quantum Information
• +4 more

Roman Schmied is a Researcher in the Department of Physics at the University of Basel, affiliated with both the Quantum Optics Lab (Treutlein group) since 2010 and the Human Optics Lab since 2016. His work bridges quantum physics and applied optics, focusing on entanglement in atomic ensembles, quantum metrology, and myopia research. He earned his Ph.D. from Princeton University (2006) and conducted postdoctoral research at the Max Planck Institute of Quantum Optics (2006–2010). Roles: Senior Scientist at University of Basel (2010–present) Former Substitute Professor at University of Freiburg (2017) Research interests span quantum simulations with trapped ions, Bell correlations in Bose-Einstein condensates, and optical systems for myopia studies. He has pioneered Mathematica tools like SurfacePattern for trap design and contributed to quantum entanglement detection protocols. A skilled educator, he won the Golden Chalk Award for teaching excellence multiple times. Notable achievements include the Paul Ehrenfest Award (2017) for Bell Correlations in a Bose-Einstein Condensate , and ERC-supported collaborations on hybrid atom-optomechanical systems. His work integrates experimental and theoretical approaches, addressing fundamental quantum phenomena and their technological applications.

Virginia Ruiz-VillanuevaView profile

Assistant Professor

• Fluvial Geomorphology
• River Dynamics
• Flood Risks
• +7 more

Virginia Ruiz-Villanueva is an Eccellenza Assistant Professor at the Institute of Earth Surface Dynamics, University of Lausanne. She holds a PhD in Fluvial Geomorphology from the Complutense University of Madrid (2013) and has held postdoctoral positions at the University of Bern, University of Geneva, and ETH Zurich. Her research focuses on fluvial processes, river ecosystem dynamics, and driftwood interactions, with emphasis on flood risks in mountain basins and transdisciplinary approaches integrating physical, ecological, and societal dimensions. Education: BSc Earth Sciences (University of Oviedo, 2006), MSc Geomorphology & Natural Hazards (Complutense University of Madrid, 2008), PhD Fluvial Geomorphology (2013). Postdoctoral roles include Dendrolab (University of Bern), Environmental Sciences (University of Geneva), and senior researcher at VAW/ETH Zurich (2019). Research interests span fluvial geomorphology, hydrogeomorphology, and ecohydraulics. She investigates flood hazards, driftwood dynamics, and cascading processes in mountain rivers across the Swiss Alps, Carpathians, Pyrenees, Himalayas, Chilean Andes, and Tian Shan. Her work combines field studies, numerical modeling, and remote sensing, contributing to sustainable river management strategies. Recent articles emphasize large wood transport modeling, flood mitigation, and innovative applications like driftwood-derived biochar for energy storage. Awards include recognition from SEG, AEQUA, and BSG for contributions to fluvial research. Virginia collaborates internationally, leveraging interdisciplinary methods to bridge geomorphology and environmental policy. She actively promotes gender equity in field-based geosciences and advocates for participatory approaches in hazard management.

Markus MeiererView profile

Assistant Professor

• Marketing Analytics
• Customer Relationship Management
• Customer Lifetime Value
• +7 more

Markus Meierer is an Assistant Professor at the Institute of Management, Geneva School of Economics and Management (GSEM), University of Geneva. He holds a Ph.D. in Business and Economics from the University of Trier, Germany, and previously served as a Senior Associate at the University of Zurich, where he led the 'Networks & Consumers' project in the University Research Priority Program 'Social Networks'. His research lies at the intersection of marketing, statistics, computational linguistics, and computer science, with a focus on customer purchase behavior, customer lifetime value, and marketing analytics. He employs advanced statistical and machine learning methods to model customer attrition, transaction behavior, and spending patterns, particularly in non-contractual settings. Meierer's recent work emphasizes uplift modeling for marketing campaigns, integration of user-generated content (images and text) for brand positioning, and the development of open-source tools like CLVTools and REndo in R. His publications appear in leading journals such as Marketing Science and Journal of Statistical Software . Rigor and Relevance Research Award, Swiss Academy of Marketing Science He teaches executive courses in Marketing Management and Applied Programming with R and Python. He actively disseminates his research through academic conferences and seminars globally and contributes to the research community via software development and open science practices. His work has practical applications in customer base analysis, marketing strategy, and decision support systems.

• Modelling and simulation of multi-physics systems
• Machine learning and data analysis
• High performance computing
• +3 more

Dr. Tobias Kaufmann is the Acting Head of the FHNW Institute of Mathematics and Natural Sciences and a Lecturer in Mathematics at FHNW's School of Engineering and Environment. He teaches in bachelor's programs including Mechanical Engineering, Electrical and Information Technology, Computer Sciences, and Energy and Environmental Technology. His research focuses on multi-physics system modeling, machine learning, high-performance computing, ultrasonics, radar, and vacuum measurement technologies. He holds a PhD in theoretical physics from the University of Zurich and completed postdoctoral research at ETH Zurich and the University of California, Irvine. Education Background: Tobias Kaufmann earned a Diploma in Theoretical Physics and a Teaching Diploma from the University of Zurich. He also holds a CAS in Higher Education from the University of Applied Sciences of Eastern Switzerland. His professional career spans roles as a Professor at the University of Applied Sciences of Eastern Switzerland, a Data Scientist at Geopraevent, a Scientist at ABB Corporate Research, and a Principal Investigator at ETH Zurich's Department of Physics. Research Interests: His work bridges computational physics and engineering applications, emphasizing: Multi-physics system modeling and simulation Machine learning for data analysis and predictive systems High-performance computing for large-scale simulations Ultrasonic and radar sensing technologies Continuum mechanics and electrodynamics Notable Projects: As project leader, he has managed initiatives like the Innosuisse-funded 'Optimized Side Seamer for Fleximed tube bodies' with Neopac AG and SF-funded projects on particle-based CFD simulations, smart power tools, food industry classifiers, and cable car dynamics simulators. Collaborations include Hilti AG, Winterhalter AG, and Bartholet AG. Technical Contributions: His inventions include non-intrusive liquid level measurement systems (patented with ABB) and acoustic signal propagation methods. He has also contributed to Google's AI research, including text prediction and communication template systems.

Benedikt HelgasonView profile

Senior Lecturer

• Biomechanics
• Finite Element Analysis
• Orthopedic Surgery
• +4 more

PD Dr. Benedikt Helgason is a Senior Lecturer at the Department of Health Sciences and Technology, ETH Zürich. He works at the Institute for Biomechanics (Laboratory for Orthopaedic Technology), focusing on computational modeling of bone mechanics and medical device innovation. His research spans: Finite element analysis of bone-implant interactions Fracture risk prediction algorithms Computational modeling Medical device design and testing Machine learning applications in biomechanics Recent work demonstrates expertise in generating super-resolution 3D models from clinical CT scans, optimizing spinal fusion cages through topology design, and developing frameworks for early health technology assessments. He investigates prophylactic intramedullary nailing for hip fracture prevention and evaluates neural network models for screw stability prediction. Key collaborations include the AGES-Reykjavik study cohort for aging research and experimental validation of finite element models using high-speed x-ray techniques. His team develops validated, non-linear explicit FE models for screw-bone interactions and explores bioresorbable magnesium-fiber reinforced bone cements. Contact: bhelgason@ethz.ch

Shanghua GaoView profile

Research Fellow

• Foundation Models
• Artificial Intelligence
• Computer Vision
• +5 more

Shanghua Gao serves as a Research Fellow at Harvard Medical School's Department of Biomedical Informatics, Harvard University, where he develops foundation models and universal representation learning methods to advance artificial general intelligence across computer vision, biomedical applications, and time series analysis. His research focuses on creating generalizable AI techniques including AI agents, generative modeling, and self-supervised learning frameworks. Gao emphasizes resource-efficient architectures that maintain high performance while minimizing computational demands, with notable contributions in salient object detection and biomedical AI systems. His work bridges theoretical innovation and practical deployment, with several projects transitioning into real-world applications. Analysis of his recent publications reveals a strategic shift toward foundation models for biomedical reasoning (e.g., TxAgent for therapeutic applications) and unified time-series frameworks (UniTS), while maintaining expertise in efficient computer vision systems. His research consistently prioritizes model generalizability across domains, evident in cross-disciplinary applications from medical diagnostics to creative AI systems.

• Portfolio Management
• Risk Management
• Machine Learning
• +7 more

Marc Weibel is a Senior Lecturer in the Center for Financial Data Science and Econometrics at the School of Management and Law, Zurich University of Applied Sciences (ZHAW), a position he has held continuously since August 2021. Previously, he served as Senior Lecturer in Financial Mathematics at ZHAW from January 2010 to July 2017 and concurrently as Chief Investment Officer at ENISO Partners AG from September 2017 to November 2022. He maintains active professional memberships in the Bachelier Society and European Finance Association, with an ORCID identifier (0000-0002-0819-9224) for scholarly attribution. His academic credentials include a PhD in Mathematics / Financial Mathematics from the University of Technology Sydney (2016-2019), an Advanced Certificate in Portfolio and Risk Management from Symmys (2012), a Master of Advanced Studies in Economics and Finance from the University of Geneva (2002-2004), and a Master in Economics from the University of Neuchâtel (1997-2001). Weibel's research focuses on the intersection of quantitative finance and data science, with primary emphasis on Portfolio Management, Risk Management, Machine Learning, and ESG Investing. His work integrates advanced mathematical techniques with practical financial applications, particularly in scenario-based optimization and alternative data utilization. He has published extensively on ESG factor integration, portfolio risk control, and financial data quality, often leveraging R and Python programming for empirical analysis. Analysis of his 2022-2024 publications reveals a pronounced shift toward sustainable finance, featuring multiple studies on ESG data validation, climate risk tools, and factor-tilt investment strategies. His scholarly output demonstrates consistent innovation in optimization algorithms (ADMM, model predictive control) and machine learning applications (NLP for greenwashing detection, reinforcement learning for SMEs), bridging theoretical finance with Swiss market implementation challenges. Marc Weibel has led significant applied research projects including the Adaptive AI-Driven Platform for P2P Lending Decisions and Liquid Instruments-Based Replication of Financial Indices. His collaborative work spans ESG-compliant product development, climate commitment tracking, and NLP-based analysis of corporate communication. Project roles reflect deep engagement with Swiss financial institutions through the Risk- and Finance-Lab. As a core team member of ZHAW's Risk- and Finance-Lab, he contributes to industry-academia partnerships focused on real-time financial decision systems. His project portfolio emphasizes practical solutions for Swiss asset owners, including ESG product customization and reinforcement learning frameworks for SME risk management.

• Neural Machine Translation
• Computational Linguistics
• Natural Language Processing
• +6 more

Antonio Toral is a leading researcher at the University of Groningen, focusing on Neural Machine Translation (NMT) , human evaluation , and parallel corpus curation . His work spans low-resource language modeling, lexical diversity enhancement, and multilingual figurative language detection. Affiliations: University of Groningen, MaCoCu Project, CREAMT Consortium Key projects: MaCoCu (Massive collection of under-resourced language data) CREAMT (Creativity in literary translation) Research interests include: Improving NMT naturalness and lexical richness Document-level evaluation of machine translations Character-level modeling and downsampling techniques Reproducibility challenges in human NLP evaluation Cross-lingual formality transfer without parallel data His recent articles (2021–2025) demonstrate expertise in: Reinforcement learning for naturalness preservation Statistical analysis of translationese effects Dependency-based reordering models Pivot translation for Catalan→Chinese Domain-specific corpus creation for EU Digital Service Infrastructures

• Natural Language Processing
• Machine Learning
• Grammatical Error Correction
• +5 more

Naoaki Okazaki is a Professor in the Department of Computer Science at Tokyo Institute of Technology's Graduate School of Information Science and Engineering, with joint research affiliations at AIST (National Institute of Advanced Industrial Science and Technology). He serves as a leading researcher in Natural Language Processing with particular expertise in grammatical error correction, bias evaluation in language models, and Asian language processing. His research interests span multiple critical areas of contemporary NLP including: Natural language processing for educational applications Bias evaluation and mitigation in pre-trained language models Machine translation, especially for Japanese and Korean Subword tokenization techniques and vocabulary optimization Multimodal learning and vision-language models Development of evaluation metrics for NLP tasks Professor Okazaki's publication record demonstrates consistent high-impact contributions to top NLP conferences (ACL, EMNLP, NAACL) from 2021-2025. His recent work shows increasing focus on the challenges posed by large language models, including membership inference attacks, prompt sensitivity in bias evaluation, and developing native Japanese resources rather than relying on translation approaches. His research group regularly achieves state-of-the-art or competitive results across multiple NLP tasks. As Program Chair for ACL 2023, Professor Okazaki contributed to improving conference peer review processes and increasing transparency in academic decision-making. His leadership extends to mentoring numerous graduate students who frequently appear as co-authors on his publications.

James HendersonView profile

Researcher

• Natural Language Processing
• Machine Learning
• Deep Learning
• +3 more

James Henderson is a Senior Researcher at Idiap Research Institute where he heads the Natural Language Understanding group. He currently serves as Action Editor for Transactions of the Association for Computational Linguistics (TACL) and was recently awarded an ERC Advanced Grant for his project 'Interpretable Beliefs and Programmable Knowledge with Bayesian Attention in Large Language Models' (BALM). Previously, Henderson held positions as Chargé de Cours at University of Geneva's Department of Computer Science and Principal Scientist at Xerox Research Centre Europe (now Naver Labs Europe). Henderson's research focuses on machine learning methods for natural language processing, with pioneering work on recurrent neural networks for syntactic and semantic parsing. His current investigations include representation learning for language semantics, graph-to-graph deep learning models, entity induction, and variational-Bayesian attention-based representation learning. His research bridges Bayesian inference, transformer architectures, and structured prediction for NLP tasks. His publication portfolio demonstrates consistent contributions to core NLP methodologies, with recent emphasis on transformer optimization, Bayesian neural methods, efficient model architectures, and graph-based language representations. Research frequently appears in top venues including ACL, EMNLP, ICLR, and NeurIPS. Honors: ERC Advanced Grant (2023) Henderson leads the Natural Language Understanding group at Idiap, currently recruiting PhD students and postdoctoral researchers for his ERC project. He obtained his PhD and MSc from University of Pennsylvania and BSc from Massachusetts Institute of Technology, all in computer science.

Volkan CevherView profile

Associate Professor

• Machine Learning
• Optimization
• Reinforcement Learning
• +5 more

Volkan Cevher is an Associate Professor at the Swiss Federal Institute of Technology Lausanne (EPFL), where he leads the Laboratory for Information and Inference Systems (LIONS). He also serves as an Amazon Scholar and previously held positions as a Research Scientist at the University of Maryland (2006-2007) and Rice University (2008-2009), as well as a Faculty Fellow at Rice University (2010-2020). Dr. Cevher received his B.Sc. (valedictorian) in electrical engineering from Bilkent University in Ankara, Turkey, in 1999 and his Ph.D. in electrical and computer engineering from the Georgia Institute of Technology in 2005. His research spans machine learning, optimization theory, signal processing, and their applications. He focuses on developing theoretically grounded algorithms for robust machine learning, with particular emphasis on non-convex optimization, adversarial training, and distributed learning. His work addresses fundamental challenges in optimization landscapes for neural networks and min-max problems like GANs and robust reinforcement learning. Dr. Cevher's recent publications demonstrate strong trends in optimization for machine learning, with significant contributions to adversarial robustness, federated learning, and efficient training methods. His work bridges theoretical foundations with practical applications across multiple domains including NLP, computer vision, and reinforcement learning. Dr. Cevher has received numerous prestigious awards including: IEEE Fellow (2024) ELLIS fellow ICML AdvML Best Paper Award (2023) Google Faculty Research award (2018) IEEE Signal Processing Society Best Paper Award (2016) ERC Consolidator Grant (2016) ERC Starting Grant (2011) As head of the Laboratory for Information and Inference Systems at EPFL, Dr. Cevher leads a research group focused on advancing the theoretical foundations of machine learning and optimization. His lab has secured significant funding through competitive grants including multiple ERC grants. He actively mentors PhD students and postdoctoral researchers, with many of his advisees going on to successful careers in academia and industry. The Laboratory for Information and Inference Systems (LIONS) at EPFL is a leading research group in optimization and machine learning, known for developing theoretically sound algorithms with practical impact. The lab collaborates extensively with industry partners and other academic institutions worldwide.