جست‌وجوی استاد و پژوهشگر

Sereina Rinikerمشاهده پروفایل

استاد

• Computational Chemistry
• Molecular Dynamics Simulations
• Cheminformatics
• +۳ مورد دیگر

Sereina Riniker is a Full Professor of Computational Chemistry at the Department of Chemistry and Applied Biosciences , ETH Zurich. Her career at ETH Zurich began in June 2014 as Assistant Professor (tenure track), followed by promotions to Associate Professor (April 2020) and Full Professor (April 2025). The group focuses on developing advanced computational methods combining molecular dynamics simulations with cheminformatics techniques . Born in Switzerland (1985) MSc in Chemistry (ETH Zurich, 2008) PhD in Molecular Dynamics (ETH Zurich, 2013) Research interests include: QM/MM hybrid simulations Machine learning for molecular modeling Free energy calculations Implicit solvent models Conformational analysis Drug discovery applications Recent publications show strong trends in combining quantum mechanical accuracy with machine learning efficiency , including novel approaches like neural network potentials and multiresolution modeling . The group has received multiple grants from the Swiss National Science Foundation (SNF) for their work on free energy calculations and molecular modeling . Awards and honors include: 2024 Hansch Award 2018 ETH Latsis Prize 2017 OpenEye Outstanding Junior Faculty Award 2017 Silver Jubilee Award 2015 Ewald Wicke Prize The Computational Chemistry group maintains strong industrial collaborations, particularly with Novartis and Givaudan AG. Their research combines theoretical method development with practical applications in biological and chemical systems.

• Systems Neuroscience
• Animal Behavior
• Computer Vision
• +۴ مورد دیگر

Mackenzie Mathis is a Tenure Track Assistant Professor and Bertarelli Foundation Chair of Integrative Neuroscience at the Brain Mind Institute (BMI) of EPFL. She leads the UPMWMATHIS Lab, focusing on understanding neural circuits underlying adaptive behavior and developing AI tools like DeepLabCut and CEBRA. Her work bridges machine learning and neuroscience, with expertise in systems neuroscience, animal behavior, and computer vision. Education: PhD in Neuroscience, Harvard University (2017) Research Interests: Her lab explores motor learning, sensorimotor control, and neural dynamics. Key tools include markerless pose estimation (DeepLabCut), latent embedding analysis (CEBRA), and generative AI for behavioral studies. Projects span from mouse behavior assays to ethical AI applications in conservation. Publications: Recent work includes advancements in robust ML systems, pre-trained pose models (SuperAnimal), and neural-latent dynamics analysis. Her lab’s tools are widely adopted in neuroscience and robotics. Awards: Swiss Science Latsis Prize (2024) Eric Kandel Young Neuroscientist Prize (2023) FENS EJN Young Investigator Prize (2022) Vallee Scholar & ELLIS Scholar Advising & Grants: Current PhD students include Célia Benquet, Hossein Mirzaei, and others. Grants include SNSF Starting Grant (1.5M CHF) and CZI funding for open-source tools. Collaborates with robotics and conservation initiatives. Labs & Teams: The UPMWMATHIS Lab at EPFL Biotech Campus integrates computational and experimental neuroscience. Tools like AmadeusGPT and CEBRA are developed here, supported by interdisciplinary teams.

Anne-Marie Kermarrecمشاهده پروفایل

استاد

• Large-scale distributed systems
• Epidemic algorithms
• Peer-to-peer networks
• +۴ مورد دیگر

Anne-Marie Kermarrec is a Professor at the École Polytechnique Fédérale de Lausanne (EPFL) since 2020, specializing in large-scale distributed systems, epidemic algorithms, and system support for machine learning. She previously served as CEO of Mediego (2015–2020), a startup providing content personalization services, and held a Research Director role at Inria, France (2004–2015). Her academic affiliations include the School of Computer and Communication Sciences (IC), specifically the Institute of Communication Systems (IINFCOM) and the Scalable and Adaptive Distributed Systems Lab (SACS). She also holds leadership roles in education and governance at EPFL, including Vice-President for Doctoral Education and Continuing Education. Her research focuses on distributed systems, federated learning, privacy-preserving technologies, and scalable machine learning architectures. Notable achievements include an ERC Grant (2008), ERC Proof of Concept Grant (2013), ACM Fellowship (2016), and election to the European Academy (2013). She advises multiple PhD students and has authored influential papers on decentralized learning systems, graph watermarking, and federated learning optimizations. Professional roles include membership in the Global Ethics and Partnerships Committee (GEP) and leadership in the Commission Doctorale (CDOCT). Her work bridges theoretical computer science with practical applications, emphasizing ethical AI and system interoperability.

• Robotics
• Industry 4.0
• Predictive Maintenance
• +۵ مورد دیگر

Prof. Hans Wernher van de Venn is a Professor at the ZHAW School of Engineering's Institute of Mechatronic Systems, Zurich, Switzerland. His work focuses on Industry 4.0, robotics, predictive maintenance, and human-robot collaboration. He leads and collaborates on projects such as SmartAssets, Cybathlon 2024, and Robo-Mate, aiming to enhance industrial automation and safety. Research Interests: His research spans robotics systems, digital twins, and sustainable manufacturing. Notable contributions include predictive maintenance algorithms using autoencoders and domain adaptation techniques, as well as advancements in human-robot interaction safety through deep learning. He has also pioneered wearable robotic exoskeletons for industrial applications. Projects & Awards: Over 20 projects highlight his leadership in Industry 4.0 innovation. Recent work includes testing frameworks for digital twins, generative SDK development, and applying ChatGPT for structured industrial data. His publications emphasize practical applications of AI in manufacturing and healthcare robotics. Grants & Labs: Active in collaborative grants focusing on smart factories, maintenance systems, and embedded systems. His team develops solutions at the intersection of mechatronics and software engineering, with a focus on real-world deployment.

Athanasios Mokosمشاهده پروفایل

پژوهشگر

• Computational Physics
• Geochemistry
• Fluid Dynamics
• +۵ مورد دیگر

Dr. Athanasios Mokos is a researcher at the Paul Scherrer Institute (PSI) in Switzerland, specializing in computational physics and fluid dynamics. His work bridges nuclear engineering, geochemistry, and environmental engineering through advanced numerical simulations. Expertise in SPH (Smoothed Particle Hydrodynamics) Focus on reactive transport and multiphase flow Key applications in nuclear reactor safety and subsurface processes Research spans pore-scale modeling of cement-clay interactions, gas transport in geological media, and GPU-accelerated simulations for multiphysics problems. His publications emphasize machine learning integration, environmental remediation, and coastal/coastal structure hydrodynamics. Current trends in his work include Digital Twin frameworks for carbonate precipitation, CRUD analysis in nuclear fuel assemblies, and validation of surface wetting models via lattice Boltzmann methods. His position at PSI involves high-performance computing and multiphase flow simulations, with contributions to desalination membrane design and sediment grain dynamics.

Sergey Litvinovمشاهده پروفایل

پژوهشگر

• Computational Mechanics
• Biomedical Engineering
• Industrial Applications
• +۶ مورد دیگر

Sergey Litvinov is a Researcher at ETH Zürich, affiliated with the Department of Structural Mechanics and Monitoring within the College of Civil, Environmental and Geomatic Engineering. His work focuses on computational and statistical models for biomedical and industrial applications, leveraging tools like C++, PyTorch, and cloud computing. He contributes to hierarchical Bayesian analysis on big data and probabilistic programming, emphasizing data-driven methodologies. Current projects include the DCoMEX initiative (Data-Driven Computational Mechanics at Exascale), aiming to advance large-scale computational frameworks for structural mechanics and monitoring. His research interests span computational mechanics, fluid dynamics, and machine learning applications, with a strong emphasis on hybrid numerical methods and high-performance computing for multiphase systems. He has no listed scientific awards or advisees. His contributions are centered on software development (e.g., Aphros and Mirheo ) for multiphase flow simulations and novel optimization techniques for inverse problems, particularly in biomedical and engineering contexts. Recent articles highlight his work on discrete loss optimization, reinforcement learning for flow control, and data-driven methods for tumor growth modeling and drug delivery systems. He is part of interdisciplinary teams addressing challenges in microfluidics, turbulence modeling, and biomedical applications, with a focus on scalable solutions for complex systems. His projects often involve collaboration with industrial and academic partners on computational models for real-world applications.

Leonel Aguilar Melgarمشاهده پروفایل

مدرس

• Computational Social Science
• Human-Computer Interaction
• Data Science
• +۲ مورد دیگر

Leonel Aguilar Melgar is a Lecturer in the Department of Humanities, Social and Political Sciences at ETH Zürich, where he researches computational social science and human-computer interaction. His work develops AI/ML systems to model social phenomena, with applications in urban dynamics, environmental psychology, and digital health. Aguilar's research bridges data science and social systems through: Agent-based modeling of collective behavior Human-building interaction studies Digital support systems for public health Reproducible experimental frameworks He contributes to open-source projects including Easeml/AutoML for accessible machine learning and computational platforms for social simulation. Aguilar's publications demonstrate innovative approaches to studying wayfinding behaviors, evacuation dynamics, and parenting interventions in low-resource settings. Recent work advances digital twin technology and VR-based experimental methods. Honors include recognition as MIT Technology Review Innovator under 35 for Latin America (2020).

Victor Boussangeمشاهده پروفایل

پژوهشگر

• Ecology and evolution
• Scientific machine learning
• Complex dynamical systems
• +۳ مورد دیگر

Victor Boussange is a Postdoctoral Researcher at the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) and the Department of Environmental Systems Science (D-USYS) at ETH Zurich. His research focuses on integrating scientific machine learning with ecological and evolutionary modeling to understand complex systems dynamics. He holds a PhD in Environmental Sciences from ETH Zurich (2022) and an MSc in Energy and Environmental Sciences from INSA Lyon (2018). Primary affiliation: Dynamic Macroecology Group at WSL Secondary affiliation: D-USYS, ETH Zurich Research Interests: Development of hybrid mechanistic-machine learning frameworks to model ecosystem responses to disturbances, ecological connectivity analysis, and eco-evolutionary dynamics. His work emphasizes interpretable models and scalable computational methods. Publications highlight contributions to high-dimensional PDE solvers (HighDimPDE.jl), inverse modeling frameworks (PiecewiseInference.jl), and ecological connectivity prioritization (JAXScape). His work bridges theoretical ecology with computational tools for real-world applications like biodiversity conservation and climate change impact assessment.

Loïc Pellissierمشاهده پروفایل

دانشیار

• Landscape Ecology
• Climate Change Impact
• Biodiversity Modeling
• +۳ مورد دیگر

Loïc Pellissier is an Associate Professor at the Department of Environmental Systems Science, ETH Zurich, and holds a joint position at the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). His research focuses on understanding and modeling landscape dynamics, biodiversity patterns, and climate change impacts. He employs ecological and evolutionary principles to improve sustainable resource management and biodiversity monitoring technologies. Key areas include eDNA applications, tropical and alpine biodiversity, and ecosystem responses to environmental changes. Education: PhD in Ecology, University of Lausanne (2012) Postdoctoral Research, Arctic Research Center (Roskilde/Aarhus University, Denmark) Research Group Leader, University of Fribourg (2013–2015) Research Interests: Landscape ecology and biodiversity modeling Climate change and ecosystem resilience eDNA technologies for biodiversity assessment Tectonic and climatic drivers of biodiversity hotspots Labs/Teams: Leads research groups at ETH Zurich and WSL, collaborating on projects integrating fieldwork, remote sensing, and computational modeling.

Semyon Malamudمشاهده پروفایل

دانشیار

• Big Data
• Machine Learning
• Liquidity and Market Frictions
• +۸ مورد دیگر

Semyon Malamud is an Associate Professor of Finance at the Swiss Finance Institute at École Polytechnique Fédérale de Lausanne (EPFL), where he has held a tenured position since October 2015. He is also a Research Fellow at the Center for Economic Policy Research (CEPR), a Senior Chair at the Swiss Finance Institute, and has served as a Visiting Assistant Professor at the University of Texas at Austin’s McCombs School of Business and a Visiting Professor at UBC Sauder School of Business in 2020. Additionally, he holds a Research Fellow position at the Bank for International Settlements and was a Lamfalussy Fellow of the European Central Bank in 2015. Education: Ph.D. in Mathematics, Swiss Federal Institute of Technology (ETH Zurich), Switzerland, May 2006. Diploma in Mathematics, Donetsk National University, Donetsk, Ukraine, July 2001. Current Research Interests: Big Data and Machine Learning , focusing on their applications in asset pricing and financial markets. Liquidity and Market Frictions , exploring their impact on market dynamics and investment strategies. International Finance , including exchange rate models and dominant currency debt. Information Economics , studying information percolation and equilibrium-driven market behaviors. Equilibrium Asset Pricing and Optimal Contracting , addressing viable markets and security design. Network Economics and Corporate Finance , analyzing market integration and corporate capital structure. Work in Macroeconomics , particularly in endogenous growth and risk management. Scientific Awards and Grants: INQUIRE Europe Research Grant (2022) Lamfalussy Fellow of the European Central Bank (2015) Joint INQUIRE Europe-INQUIRE UK Prize (2015) CEPR Research Fellow (2015–present) ETF Academy Award (2015) Europlace Institute of Finance Award (2015) Swiss National Science Foundation Starting Grant (CHF 1m, 5 years) Dauphine Amundi Prize in Asset Management (2014 and 2013) Swiss Finance Institute Research Grant (2013–2015) Narrative on Advising and Grants: Malamud’s research has been supported through prestigious grants such as the Swiss National Science Foundation Starting Grant and INQUIRE Europe awards. He has collaborated extensively with scholars like Bryan Kelly, Darrell Duffie, and Julien Hugonnier. As an Associate Editor for top journals including the Journal of Finance and Journal of Financial Econometrics , he contributes to academic rigor in finance. His work on machine learning applications and large factor models reflects cutting-edge trends in financial economics. Labs and Teams: Malamud’s research is anchored within the Swiss Finance Institute (SFI) and its collaborative networks. He actively engages with the CEPR and BIS research communities, contributing to interdisciplinary projects in financial markets and policy.

• Computer Science
• Big Data
• Machine Learning
• +۳ مورد دیگر

Fredrick Egli is a Lecturer and Head of the Bachelor's Program in Information Technology at the School of Information Technology, Lucerne University of Applied Sciences and Arts (HSLU). He is based in Rotkreuz, Switzerland, located at Suurstoffi 1, Room 8.32. Education: Master of Science in Computer Science (2012–2015), ETH Zurich Bachelor of Science in Computer Science (2009–2012), HSR Rapperswil Research & Expertise: Egli specializes in software engineering, big data technologies, and machine learning. His technical competencies include programming languages (Scala, Java, Python, JavaScript), ML frameworks (Spark-MLlib, Scikit-learn), data tools (Spark-SQL, Pandas, JupyterLab), and web development (Svelte, Vue.js, Node.js). He also manages big data infrastructure and dashboards (Splunk, Grafana). Labs/Teams: He leads the Big Data Lab Cluster, integrating practical software development with advanced data technologies.

• Machine Learning
• Deep Learning
• Time Series Analysis
• +۳ مورد دیگر

Fabian Gröger is a Research Associate, Doctoral Student, and Lecturer at Lucerne University of Applied Sciences and Arts (HSLU), affiliated with the Lucerne School of Computer Science and Information Technology. He is a Scientific Collaborator at the Algorithmic Business (ABIZ) Research Lab and teaches in the CAS Machine Learning program. His roles include Data Scientist, Research Assistant, and Teaching Assistant in machine learning and artificial intelligence programs. Education: Ongoing PhD in Biomedical Engineering, University of Basel Master of Science in Data Science/ML, HSLU Bachelor of Science in Computer Science, HSLU His research focuses on Machine Learning , Deep Learning , Audio Data , and Self-Supervised Learning , with applications in digital dermatology, bioaerosol monitoring, and medical anomaly detection. His work emphasizes scalable foundation models, data quality audits, and reducing annotation needs in healthcare AI. Recent publications highlight contributions to hyperbolic space applications in medical AI, audio representation learning for hearables, and benchmarking strategies like CleanPatrick. His work spans conferences like MICCAI, NeurIPS, and ECCV. Awards: Outstanding Reviewer (JEADV, 2024) Best Lightning Talk (AI Medicine Symposium, 2024) Best Paper (MICCAI 2023) Best Master Thesis (HSLU, 2023) Gröger leads projects such as SelfClean for Audio and AI for All , and collaborates with industries like Prepress Media AG. He advises on algorithms for small-hotel revenue management and exhibits at events like the Museum Exposition on Food & AI. His lab affiliations include the ABIZ Research Lab and the Digital Dermatology group, where he explores ethical AI, clinical decision support systems, and data-centric healthcare solutions.

• Artificial Intelligence
• Natural Language Processing
• Blockchain Technology
• +۲ مورد دیگر

Dr. Luca Mazzola is a Lecturer at Lucerne School of Computer Science and Information Technology (HSLU-I) and senior research associate in the Information Systems Research Lab. With a PhD in Technologies for Human Communication (USI, 2014) and MSc in Computer Engineering (Politecnico di Milano, 2004), his research spans artificial intelligence, natural language processing, blockchain applications, and machine learning in distributed systems. Mazzola leads multiple research projects including PrivacyGLUE (privacy policy analysis), blockchain-based healthcare management systems, and personalized event matching frameworks. His recent publications focus on NLP applications for privacy compliance, federated learning architectures, and explainable AI for concept recognition. Mazzola teaches courses in Data Science Fundamentals and Natural Language Processing, emphasizing practical applications of ML and AI technologies. Research collaborations include bilateral projects with South Africa's SARCHi NRF and EU Blockchain Observatory. His technical innovations include developing P2P data marketplaces, blockchain authentication systems, and educational recommendation engines using knowledge graphs.

Aygul Zagidullinaمشاهده پروفایل

مدرس ارشد

• Data Science
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Aygul Zagidullina is a Senior Lecturer and Researcher at the Lucerne University of Applied Sciences and Arts, specifically affiliated with the Lucerne School of Computer Science and Information Technology. She serves as Head of Applied Data Intelligence in Continuing and Executive Education, contributes to teaching and research in Data Science, Machine Learning, and Artificial Intelligence, and co-organizes academic initiatives like Women in AI and the Women in Data Science (WiDS) conference in Zürich. Ph.D. in Quantitative Methods (specialization: High-Dimensional Covariance Matrix Estimation) Diploma in Applied Mathematics (B.Sc. + M.Sc.) Postgraduate Machine Intelligence Program in Computer Science Certificate in Didactics for Higher Education Her research focuses on data-driven solutions at the intersection of Statistics, Computer Science, and Finance, with applications in portfolio optimization, NLP bias mitigation, and image quality assessment. She emphasizes the societal impact of AI and its integration into industry workflows. Recent projects include hydropower digital twins, AI for procurement documentation, and LLM training via user feedback. She authored a Springer textbook on High-Dimensional Covariance Matrix Estimation in 2021 and has co-authored peer-reviewed articles in the Journal of Financial Econometrics (2021, 2024). Her work spans theoretical rigor and practical implementation across finance, energy, and healthcare domains. Aygul contributes to academic leadership through roles such as Module Responsible in AI Competition (Kaggle Challenge), Co-Head of the CAS Machine Learning and Data Science for Medicine and Health programs, and Thesis Supervisor in AI & ML. She collaborates with industry partners (e.g., Paretolabs, Konplan) and leads research at the AI Robotics Research Lab.

• Product Management
• Supply Chain Management
• Artificial Intelligence
• +۳ مورد دیگر

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.