جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Artificial Intelligence
• Deep Learning
• +۳ مورد دیگر

Grégory Mermoud is an Associate Professor at HES-SO Valais-Wallis, School of Engineering, specializing in Machine Learning, Artificial Intelligence, and large-scale distributed systems. He teaches computer networks, machine learning, and distributed computing. His research focuses on the intersection of ML, simulation, software systems, and data science. Affiliations: HES-SO Valais-Wallis (current), Cisco Systems (former Distinguished Engineer) Gregory holds a BSC in Computer Science and Communication Systems from HES-SO Valais-Wallis. With over a decade of industry experience at Cisco, he led teams developing flagship enterprise products and holds over 200 U.S. patents. His work spans network optimization, predictive modeling, and application-driven routing. Research Interests: Machine Learning applications in networking, Quality of Experience (QoE) prediction, network path optimization, and adaptive systems. His contributions include groundbreaking work on AI-driven network monitoring, QoE prediction models, and SD-WAN optimization. Advising & Grants: Oversaw recruitment of over 50 engineers at Cisco, many from HES-SO. His research has been supported by industry collaborations and academic grants. Labs/Teams: Engaged in collaborative projects with Cisco’s R&D teams and academic institutions, focusing on network intelligence and distributed systems innovation.

Achim Walterمشاهده پروفایل

استاد

• Plant Ecophysiology
• Image-based Phenotyping
• Crop Science
• +۳ مورد دیگر

Achim Walter is a Full Professor and Deputy Head at the Institute of Agricultural Sciences, ETH Zurich, specializing in crop science and plant phenotyping. He holds a habilitation in botany from the University of Düsseldorf and has led research groups at institutions like Forschungszentrum Jülich. PhD, University of Heidelberg (1998-2001) Postdoctoral Fellow, Biosphere 2 Center (2001-2003) Research Group Leader, Forschungszentrum Jülich (2003-2010) His research focuses on non-invasive plant growth analysis , image-based phenotyping , and precision agriculture , with recent work emphasizing remote sensing, deep learning for disease detection, and nitrogen use efficiency. Publications from 2025 highlight advancements in UAV thermal imaging, dendrometer-based irrigation, and phenology modeling. 2017 Golden Owl Award for Excellence in Teaching Honorary Member of SVIAL He teaches courses including Plant Ecophysiology, Alternative Crops, and Introduction to Crop Production, with a strong emphasis on digital agriculture and data-centric farming education initiatives.

• Wave Physics
• Computational Seismology
• Earthquake Seismology
• +۴ مورد دیگر

Dr. Maria Koroni is a senior researcher at the Swiss Seismological Service (SED) within ETH Zürich's Department of Earth Sciences. She specializes in computational seismology, focusing on physics-based numerical modeling of ground motion and hybrid methodologies combining deterministic and stochastic approaches. Her work directly supports seismic hazard assessment for Swiss nuclear facilities through ENSI-funded projects. Education: PhD in Computational Seismology (2018), Utrecht University MSc in Geophysics (2014), Utrecht University BSc in Earth Science (2011), Aristotle University of Thessaloniki Research Interests: Wave physics, earthquake seismology, full-waveform inversion, and high-performance computing. Her current projects involve modular numerical meshes for high-fidelity seismic simulations using 3D velocity models and integrating machine learning into seismic waveform modeling. Publications reflect a strong focus on: Core-mantle boundary studies using adjoint methods Alpine valley ground motion modeling Gender representation in seismology authorship 3D basin structure analysis in Swiss geological contexts Grants include ERC Horizon 2020 funding (2018-2021) and ongoing ENSI support. She teaches Engineering Seismology at ETH Zürich and collaborates with the Institute of Geophysics on advanced seismic instrumentation projects.

• Signal Processing
• Information Theory
• Multimedia Security
• +۵ مورد دیگر

Svyatoslav Voloshynovskyy is a Professor at the University of Geneva's Faculty of Science, Department of Computer Science, with an impressive publication record of 195 works and supervision of 26 graduate projects. His extensive research portfolio demonstrates significant contributions to signal processing, information theory, and multimedia security, evolving into cutting-edge applications in medical imaging and privacy-preserving technologies. Dr. Voloshynovskyy's research interests span multiple interconnected domains at the intersection of theoretical foundations and practical applications. His work has progressed from foundational research in data hiding and watermarking to contemporary applications in privacy-preserving medical imaging. Key research areas include: Information-theoretic approaches to multimedia security Advanced signal processing techniques for image and data analysis Federated learning frameworks for healthcare applications Privacy-preserving machine learning methodologies Medical imaging analysis with focus on PET/CT technologies Theoretical foundations of data hiding and watermarking Analysis of his recent publications (2022-2024) reveals a strategic shift toward healthcare applications, particularly in medical imaging with privacy preservation. His work integrates federated learning, differential privacy, and deep learning to address critical challenges in healthcare data analysis while maintaining patient confidentiality. This represents a natural evolution from his earlier theoretical work in signal processing and information theory to impactful real-world healthcare applications requiring multi-institutional collaboration. With 26 supervised works documented in the University of Geneva archive, Dr. Voloshynskyy has demonstrated significant commitment to academic mentoring and research supervision. His publication pattern suggests successful grant acquisition for projects at the intersection of signal processing, machine learning, and healthcare applications, though specific funding details aren't provided in the available information. His research trajectory indicates leadership in a specialized research group focused on signal processing and machine learning applications, with recent emphasis on medical imaging. The consistent mention of multi-institutional collaborations in his latest publications suggests an active research team working on federated learning approaches for medical image analysis, likely involving partnerships with healthcare institutions across multiple countries.

Luca Schaufelbergerمشاهده پروفایل

پژوهشگر

• Computational Chemistry
• Machine Learning Applications
• Materials Discovery
• +۳ مورد دیگر

Luca Schaufelberger is a PhD researcher at the Digital Chemistry Laboratory, Department of Chemistry and Applied Biosciences (D-CHAB), ETH Zürich. His work focuses on computational methods for materials discovery and environmental chemistry applications. BSc. and MSc. in Interdisciplinary Sciences (Physics and Materials Science), ETH Zürich Master Thesis: Leveraging Machine Learning Uncertainty to Discover Singlet Fission Materials , EPFL Research interests include computational chemistry, machine learning for materials discovery, singlet fission materials, and nanocellulose-based environmental applications. Recent work integrates genetic optimization with uncertainty quantification to accelerate materials design. His scientific contributions span computational molecular design (2023), inverse design of materials (2025), and environmental applications of nanocellulose (2016–2017). Articles highlight interdisciplinary approaches combining chemistry, physics, and machine learning. Scientific Recognition Willi Studer Prize 2021 ETH Zürich Excellence Scholarship & Opportunity Programme Werner Siemens Fellowship ETH Medal for Master Thesis Luca collaborates with the Laboratory for Computational Molecular Design (EPFL) and contributes to the Digital Chemistry Laboratory at ETH Zürich.

Mengtian Cuiمشاهده پروفایل

استاد مهمان

• Trusted Computing
• Formal Methods
• Malware Detection
• +۴ مورد دیگر

Prof. Mengtian Cui is a Visiting Professor at the Key Laboratory of Computer System under the State Ethnic Affairs Commission at Southwest Minzu University, China. She earned her Ph.D. in Computer Software and Theory from the Chinese Academy of Sciences (CAS) in 2010, and has held research fellow positions at Ryerson University (Canada) and the University of Fribourg (Switzerland). Research Interests: Trusted computing, formal methods, malware detection, open-source software defect prediction, machine learning, graph neural networks, and complex systems. Collaboration: Collaborates with Prof. Dr. Claudio J. Tessone on open-source software defect prediction from a complex systems perspective. Publication Trends: Her work spans network analysis (e.g., algorithmic bias in PageRank), graph-based malware detection, named data networking applications for vehicles, feature optimization in defect prediction models, data cleaning techniques, and formal methods for software reliability. These studies integrate machine learning, complex systems theory, and graph neural networks. Laboratory Affiliation: She works at Southwest Minzu University's Key Laboratory of Computer System, focusing on software engineering and cybersecurity research.

Yaniss Guigozمشاهده پروفایل

مدرس

• Geographic Information Systems (GIS)
• Spatial Data Infrastructures (SDI)
• Environmental Informatics
• +۱۰ مورد دیگر

Dr. Yaniss Guigoz is a Lecturer in the GeoHealth group at the Institute of Global Health, University of Geneva, and an expert in Geographic Information Systems (GIS) and Spatial Data Infrastructures (SDI) at UN Environment/GRID-Geneva. He is also affiliated with the enviroSPACE and GeoHealth laboratories at the University of Geneva, where he contributes to research and teaching in geomatics and environmental sciences. He coordinates the Certificate of Advanced Studies (CAS) in 'Geomatics for a Sustainable Environment' at the university. PhD in Environmental Sciences, University of Geneva (2016) Certificate of Specialization in Geomatics, University of Geneva Bachelor/Master in Geography, University of Geneva Dr. Guigoz's research focuses on the development and application of Spatial Data Infrastructures (SDI) and Earth Observation technologies to address environmental and global health challenges. His work spans geospatial data integration, accessibility modeling, climate variables, and sustainable development. He applies GIS to public health projects, such as optimizing emergency obstetric care networks and modeling access to urban green spaces. His research is deeply interdisciplinary, combining environmental science, computer science, and health geography. His recent publications (2017–2025) reflect a strong trend in leveraging Earth Observation data cubes, cloud-based processing, and open geospatial platforms to support sustainable development and environmental monitoring. Topics include shoreline detection, snow cover evolution, air temperature forecasting, and urban green space accessibility, with applications across Europe, Africa, and the South Caucasus. The work frequently involves international collaboration and contributes to UN-led initiatives and SDGs. Dr. Guigoz has been involved in several major international research projects, including FP7 (AFROMAISON, ECOArm2ERA, IASON, EOPOWER), H2020 ECOPOTENTIAL, and SCOPES initiatives (ARPEGEO, Sustainable Caucasus). He has also contributed to capacity-building efforts through training and teaching in GIS and SDI, particularly in Africa. He has held professional positions at the World Health Organization (2000–2010) and has been continuously engaged with UN Environment/GRID-Geneva since 2005 as a GIS officer, webmaster, and SDI specialist. His dual affiliation with the United Nations and the University of Geneva enables impactful translational research bridging science and policy.

Gilles Vilmartمشاهده پروفایل

مدرس ارشد

• Numerical Analysis
• Geometric Numerical Integration
• Multiscale Methods
• +۳ مورد دیگر

Gilles Vilmart is a Senior Lecturer (Maître d'enseignement et de recherche) at the University of Geneva's Department of Mathematics since 2013. His research focuses on numerical analysis of geometric and multiscale methods for deterministic and stochastic differential equations, emphasizing geometric numerical integration and numerical homogenization. He holds a PhD (2008, double doctorate from University of Rennes 1 and Geneva) and a Habilitation (2013, ENS Rennes). Former affiliations include the Swiss Federal Institute of Technology Lausanne (EPFL), University of Rennes 1, and INRIA Rennes. He organizes the numerical analysis seminar with Martin Gander and Bart Vandereycken, and co-founded the CAMAS Consulting platform. He is a member of Swiss mathematical society, Swiccomas, and SMAI (France). Key achievements include the Young Researcher First Prize 2013 from Brittany Region for Mathematics. His work includes developing numerical methods for stochastic and multiscale systems, with software contributions like PIROCK and multirevolution integrators. He advises PhD students and organizes workshops on numerical analysis and complex systems.

• Membrane Proteins
• Structural Biology
• Biophysics
• +۳ مورد دیگر

Juan Francisco Bada Juarez is a Scientist at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Prof. Dal Peraro Group within the Institute of Bioengineering (IBI-SV). His research focuses on structural biology, membrane proteins, and nanopore technology, particularly leveraging lipid-based systems like lipodisq nanoparticles for protein stabilization and drug delivery. He contributes to innovative methods for detergent-free membrane protein extraction and high-resolution structural characterization. His work integrates experimental and computational approaches, including single-molecule detection with biological nanopores and deep learning-assisted analysis. Key projects involve engineering aerolysin-based nanopores for biosensing, dissecting membrane association mechanisms of pore-forming toxins, and developing light-sensitive membrane proteins for dynamic studies. He is based at EPFL’s School of Basic Sciences, contributing to interdisciplinary research at the intersection of biophysics and nanotechnology. Juan’s lab investigates applications of structural biology to antimicrobial peptides, drug delivery systems, and protein engineering, with a focus on maintaining protein functionality in native-like environments. His research has advanced understanding of protein-membrane interactions, enabling breakthroughs in biomolecular detection and therapeutic delivery mechanisms.

Adrian Holzerمشاهده پروفایل

استاد

• Information Systems
• Human-Computer Interaction
• Digital Education
• +۴ مورد دیگر

Adrian Holzer is a Full Professor at the Institute of Information Management within the Faculty of Economics at the University of Neuchâtel, Switzerland. His research focuses on information systems, human-computer interaction, digital education, and computational thinking. Holzer's work explores the intersection of technology and behavior change through digital nudges, gamification, and AI-enhanced learning environments. University: University of Neuchâtel School: Faculty of Economics Department: Institute of Information Management Email: adrian.holzer@unine.ch Research Interests Adrian Holzer investigates the design and impact of digital systems on learning, behavior change, and privacy. His work spans: Interactive learning technologies and digital detox strategies AI-enhanced educational tools and chatbots Privacy literacy and ethical digital design Behavioral interventions using digital nudges Cultural data analysis (e.g., Arabic poetry structures) Collaborative knowledge sharing in NGOs and humanitarian contexts Publication Trends Over the past decade, Holzer's research has focused on designing human-centered digital interventions for education, health, and sustainability. Key trends include: Developing AI-driven learning companions and gamified systems Exploring ethical design of digital nudges for behavior change Advancing computational thinking skills through digital tools Creating privacy-aware educational platforms Applying data visualization to complex domains like linguistics and environmental behavior Investigating social dynamics in online communities

Teodora Vukovicمشاهده پروفایل

پژوهشگر ارشد

• Computational Linguistics
• Multimodal Analysis
• Human-Computer Interaction
• +۵ مورد دیگر

Dr. Teodora Vuković is a computational linguist at the University of Zurich's Faculty of Arts, specializing in multimodal analysis of human interaction and language technology. As Principal Investigator of the CAPIRE and LCP projects, she leads development of automatic tools for analyzing speech, gestures, and facial expressions to identify person-specific patterns while implementing data anonymization techniques.

• Quantum Computing
• Spin Qubits
• Semiconductor Nanostructures
• +۴ مورد دیگر

Dr. Miguel José Carballido is a Postdoctoral Researcher at the University of Basel, working within the Department of Physics in the Faculty of Science. He is a key member of the Quantum Coherence Lab led by Professor Dominik M. Zumbühl, which is affiliated with several prestigious research initiatives including the Swiss Nanoscience Institute (SNI), NCCR SPIN of the Swiss NSF, and the Basel QC2 Center for Quantum Computing and Quantum Coherence. Dr. Carballido's research focuses on quantum computing and condensed matter physics, with particular expertise in spin qubits, semiconductor nanostructures, and quantum transport phenomena. His work explores the fundamental properties of electron and hole spins in semiconductor devices, with applications to quantum information processing. He investigates spin-orbit interactions in various materials systems including GaAs quantum wells, Ge/Si nanowires, and silicon finFET transistors, aiming to develop practical quantum computing hardware that can operate at higher temperatures. His recent publications demonstrate a strong trend toward integrating machine learning techniques with quantum device control and measurement. This interdisciplinary approach combines expertise in quantum physics with cutting-edge computational methods to address challenges in quantum device tuning, measurement optimization, and quantum system characterization. His work contributes to the broader effort of making quantum computing more accessible and practical by improving qubit coherence, developing autonomous control systems, and exploring novel materials platforms. As a member of the Zumbühl Group, Dr. Carballido collaborates extensively with researchers across multiple institutions including the Schönenberger group, Loss and Klinovaja group, and international partners at institutions such as UC Santa Barbara, ETH Zurich, Princeton University, Harvard University, and the University of Chicago. These collaborations span theoretical modeling, materials science, device fabrication, and experimental characterization of quantum systems.

Jannis Vamvasمشاهده پروفایل

مدرس

• Natural Language Processing
• Multilingual Language Models
• Machine Translation
• +۵ مورد دیگر

Jannis Vamvas is a Lecturer and active researcher in the Department of Computational Linguistics at the University of Zurich, Switzerland. With a PhD thesis titled Model-Based Evaluation of Multilinguality , he specializes in deep learning for natural language processing, with particular emphasis on multilingual language models and their evaluation. Academic Roles Current Lecturer for Large Language Models , Text Generation , and Mathematical Foundations at UZH Postdoctoral Researcher (MUTAMUR Project, 2023) PhD in Computational Linguistics (2019-2023, supervised by Rico Sennrich, Lena A. Jäger, and Martin Volk) Education M.Sc. in Computational Linguistics (major) and Computer Science (minor) at LMU Munich (2017-2019) B.A. in Computer Science and Philosophy from University of Basel (2011-2015) His research focuses on multilingual language models, particularly SwissBERT for Swiss languages, and methods for evaluating translation quality through approaches like Contrastive Conditioning and Translation Cross-Likelihood. He explores how to adapt models to local data, detect translation errors, and improve document translation through source-primed conversation techniques. Recent publications highlight his work on: Translation direction detection Hallucination mitigation in machine translation Minimum Bayes Risk decoding Swiss multilingual language modeling Semantic difference recognition Scientific recognition includes: EAMT 2024 Highly Commended Thesis Best Paper Award at SwissText 2025 Best Scientific Paper Award at SwissText 2024 He teaches advanced courses on Large Language Models, Text Generation, and Mathematical Foundations, and has served as organizer of the Computational Linguistics Colloquium at UZH. Current projects include the InvestigaDiff initiative and machine translation for Romansh idioms.

William Oestreichمشاهده پروفایل

پژوهشگر ارشد

• Behavioral ecology
• Collective behavior
• Information ecology
• +۷ مورد دیگر

William Oestreich is a Group Leader at the Department of Evolutionary Biology and Environmental Studies, University of Zurich. He holds concurrent postdoctoral fellowships from the US NSF and David & Lucile Packard Foundation, affiliating with Monterey Bay Aquarium Research Institute and the University of Washington's School of Aquatic and Fishery Sciences. Current Positions: Group Leader at University of Zurich; US NSF Postdoctoral Fellow Past Positions: Packard Foundation Postdoctoral Fellow (2022-2024); PhD in Biology (Stanford University, 2017-2022) His research bridges behavioral ecology , bioacoustics , and conservation science , focusing on collective behavior in marine ecosystems and human impacts on animal sociality. Key areas include blue whale migration , acoustic monitoring , and dynamic resource tracking . Recent publications highlight his work on AI integration in ecology, marine acoustic sensing, and global ocean conservation strategies. His 2024 Trends in Ecology & Evolution paper explores animal acoustics as ecological indicators, while 2025 studies address collective science frameworks for ocean protections. Scientific Awards: US NSF Postdoctoral Fellow, Packard Foundation Postdoctoral Fellow Key Collaborations: Stanford University, MBARI, Woods Hole Oceanographic Institution Oestreich mentors students like KM Aiu and develops tools for analyzing animal-borne sensor data , advancing understanding of whale maneuverability and bioacoustic patterns . His work informs adaptive conservation policies for marine megafauna.

Vassily Hatzimanikatisمشاهده پروفایل

دانشیار

• Computational Systems Biology
• Metabolic Engineering
• Biotechnology
• +۳ مورد دیگر

Dr. Vassily Hatzimanikatis is an Associate Professor of Chemical Engineering and Bioengineering at the Swiss Federal Institute of Technology Lausanne (EPFL), holding primary appointments in the School of Basic Sciences (SB) and cross-appointments in the School of Life Sciences (SV). He directs research at the Laboratory of Computational Systems Biotechnology (LCSB) within the Institute of Chemical Sciences and Engineering (ISIC), with additional roles in the Swiss Center for Scientific Computing (SCGC) and Institute of Bioengineering (IBI-SV). His educational background includes a PhD in Chemical and Biological Engineering from the California Institute of Technology (1991-1997) and a Diploma (Master) in Chemical Engineering from the University of Patras, Greece (1986-1991). Prior to EPFL, he served as a research group leader at ETH Zurich and held positions at DuPont, Cargill, and Northwestern University. Hatzimanikatis' research centers on computational systems biology and metabolic engineering, with emphasis on kinetic modeling of metabolic networks, complexity reduction techniques, and strain design for bioproduction. His work bridges theoretical frameworks with industrial applications in pharmaceuticals and biotechnology, focusing on genome-scale models that integrate thermodynamics, expression constraints, and machine learning approaches. Analysis of his 2023-2025 publications reveals three dominant trends: 1) Integration of generative AI with kinetic parameter inference for metabolic networks, 2) Development of model reduction techniques for analyzing complex microbial communities and host-pathogen interactions, and 3) Thermodynamics-driven strain design for optimizing bioproduction while minimizing cellular burden. His recent work increasingly incorporates CRISPR genomics and machine learning to address challenges in pathogen metabolism and sustainable chemical production. His scientific recognition includes: Fellow of the American Institute for Medical and Biological Engineering (2010) DuPont Young Professor Award (2001-2004) Jay Bailey Young Investigator Award in Metabolic Engineering (2000) ACS Elmar Gaden Award (2011) Hatzimanikatis has advised 43 PhD students (8 current, 35 past) across chemical engineering and bioengineering disciplines. His teaching portfolio includes core courses in chemical engineering fundamentals, bioreactor modeling, and systems biology principles. Research funding derives from both industrial partnerships (notably DuPont and Cargill) and Swiss national science foundations, supporting his laboratory's development of computational tools like NICEpath and ATLASx. The Laboratory of Computational Systems Biotechnology (LCSB) operates as a multidisciplinary hub where computer scientists, engineers, and biologists collaborate on developing genome-scale kinetic models, metabolic network reconstructions, and AI-driven design frameworks for biotechnology applications ranging from malaria treatment to sustainable bioplastics production.