جستجوی استاد راهنمای دانشگاهی

Gersende Fortمشاهده پروفایل

پژوهشگر

• Stochastic Approximation
• Bayesian Statistics
• Machine Learning
• +۳ مورد دیگر

Gersende Fort is a CNRS Senior Researcher affiliated with the Institut de Mathématiques de Toulouse (IMT) at the University of Toulouse. Her research focuses on stochastic approximation methods, Bayesian statistics, optimization algorithms, and computational statistics. She has presented at major conferences such as ICASSP 2025 (Suzhou & Hyderabad) and the French-German-Spanish conference on Optimization (Gijon, 2024), often collaborating with researchers like Eric Moulines and Hoi To Wai. Recent work includes developing sampling techniques for nonsmooth log-concave densities, hierarchical Bayesian models for epidemiological analysis (e.g., COVID-19 reproduction number estimation), and federated learning algorithms. She leads the MAD project funded by the French National Research Agency (ANR), advancing scalable optimization methods. Her publications span technical reports on stochastic proximal-gradient algorithms, fluid-limit-based MCMC tuning, and PLS classification in microarray data analysis. Fort actively engages in academic outreach, including the 'AI and Society' Summit in Paris (2025) and a workshop on mathematics of machine learning. She also contributed to educational initiatives like the Women and Mathematics event in Lavelanet (2024). Her research bridges theoretical foundations with applications in health, optimization, and machine learning.

• Privacy
• Natural Language Processing
• Machine Learning
• +۳ مورد دیگر

Christof Bless is a Researcher and Doctoral Student at the Lucerne School of Computer Science and Information Technology (University of Lucerne). His work focuses on privacy technologies, natural language processing, and knowledge graph applications. He contributes to projects like Privacy Simplified Reloaded, Music DNA, and Argument Representation Learning (ARL). His research interests span privacy policy comprehension tools, semantic annotation frameworks, and reputation systems leveraging blockchain technology. He has developed specialized NLP tools such as SciKGTeX for scientific publications and contributed to benchmark datasets like PrivacyGLUE. Recent work emphasizes visualizing data consent mechanisms and exploring cross-lingual semantic analysis. His publications demonstrate interdisciplinary approaches combining computer science with legal/ethical considerations in data governance. Labs/Teams: Active in the HSLU research group working on privacy-preserving technologies and educational AI applications.

• 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.

Florian Tramèrمشاهده پروفایل

استادیار

• Computer Security
• Machine Learning
• Privacy-Preserving Technologies
• +۲ مورد دیگر

Florian Tramèr is an Assistant Professor of Computer Science at ETH Zurich, leading the SPY Lab. His research focuses on computer security, privacy, and adversarial machine learning, particularly studying vulnerabilities in deep learning systems. He holds a PhD from Stanford University and previously worked at Google Brain. Education: Bachelor's and Master's from EPFL (Switzerland), PhD from Stanford University. Research Interests: Adversarial machine learning, privacy-preserving technologies, cryptography, and evaluating AI safety through frameworks like AgentDojo. His work addresses societal impacts of rapidly deployed machine learning systems. Awards: Two Best Paper Awards at ICML 2024, CAIS SafeBench competition win, Google/Amazon Research Awards. His research has been featured in Nature , Science , and The Economist . Labs/Teams: SPY Lab (ETH Zurich), part of the Information Security Institute and ZISC. Associated with ETHZ AI Center. Advises PhD students on AI safety and security topics.

• Cybersecurity
• Blockchain Technology
• Privacy-Preserving Systems
• +۵ مورد دیگر

Dr. Kari Kostiainen is a Lecturer at the Department of Computer Science, ETH Zürich. His affiliation includes the Institute for Information Security (Institut f. Informationssicherheit) and he is associated with the Security of Wireless Networks course in the Autumn Semester 2025. His research focuses on cybersecurity, blockchain technology, privacy-preserving systems, and trusted execution environments. Kari's work addresses challenges in phishing detection, cryptocurrency regulation, and secure communication protocols. Key research interests include: Phishing prevention and organizational cybersecurity Blockchain scalability and privacy in digital currencies Trusted execution environments (TEEs) and enclave security Privacy-preserving technologies for decentralized systems Secure access control mechanisms and user-friendly security setups His recent publications emphasize advancements in CBDC design, censorship-resistant payment systems, and lightweight blockchain client privacy. Notable contributions include Platypus (privacy-preserving CBDC framework) and Tee-based mining pool optimizations. Dr. Kari Kostiainen can be contacted at kari.kostiainen@inf.ethz.ch . His office is located at CNB F 103.2, ETH Zürich.

Mengshuo Jiaمشاهده پروفایل

پژوهشگر

• AI4Science
• Uncertainty Modeling
• Probabilistic Analysis
• +۴ مورد دیگر

Dr. Mengshuo Jia is a Senior Scientist and Principal Investigator at ETH Zürich, affiliated with the Power Systems Laboratory (PSL) within the Department of Electrical Engineering. He holds a Ph.D. from Tsinghua University (2016–2021) and a B.Eng. from North China Electric Power University (2012–2016). His roles include Guest Lecturer for 'Optimization in Energy Systems' and Associate Editor for IEEE Systems Journal and IET Renewable Power Generation. Education: Ph.D. in Electrical Engineering, Tsinghua University (2016–2021) B.Eng. in Electrical Engineering, North China Electric Power University (2012–2016) Research focuses on AI4Science , Uncertainty Modeling , Probabilistic Analysis , Stochastic Optimization , and Data-Driven Power Systems . Notable contributions include the RePower LLM-driven research platform and the DALINE toolbox for power flow linearization. His work bridges AI advancements with energy system challenges, enhancing autonomous research and grid optimization. Recent publications emphasize LLM applications in energy systems, small modular reactor integration, and hydrogen supply chain optimization. Over 10 peer-reviewed articles since 2022 highlight interdisciplinary innovation. Awards: 2023 ESI Hot Paper (top 0.1%) and Highly Cited Paper (top 1%) 2023 China First Prize of High-influence Papers 2022 Springer Thesis Award Advising and grants include leadership in Swiss National Science Foundation projects and editorial roles in top-tier journals. Collaborations emphasize data-driven methodologies and privacy-preserving distributed algorithms. Labs: Active in the Power Systems Laboratory (PSL) at ETH Zurich, advancing research in energy system optimization and AI integration.

• Augmented Reality
• Game Technology
• Medical Imaging
• +۴ مورد دیگر

Dr. Fabio Zünd is a Lecturer at the Department of Computer Science, ETH Zurich, affiliated with the Game Technology Center (GTC). He is based in Zurich, Switzerland, and can be reached at fabio.zund@inf.ethz.ch . His work focuses on innovative applications of Augmented Reality (AR), Game Technology, and Human-Computer Interaction (HCI), with notable contributions to medical AR integration, facial image anonymization, and interactive storytelling systems. Research interests include AR-driven medical education, personalized clinical imaging tools, and the development of immersive technologies that bridge real and virtual worlds. His projects often address challenges in user-centered design, such as scent-based gaming interfaces and emotion-aware interaction techniques. Recent work highlights include the VerA system for clinical facial image anonymization, scent prediction in video games, and latent diffusion models for art generation. He explores interdisciplinary applications in healthcare, education, and entertainment through collaborations with institutions like the ETH Library and industry partners. No scientific awards are explicitly mentioned in the provided information. His advising record remains unspecified, though he contributes to ETH’s educational programs through courses like the Seminar on Game Technology . Ongoing projects involve the Game Technology Center’s exploration of tangible interaction systems and wearable input devices such as the DigiGlo glove technology.

Gerhard Schrattمشاهده پروفایل

استاد

• Molecular Neuroscience
• Synaptic Plasticity
• Non-coding RNAs
• +۱ مورد دیگر

Gerhard Schratt is Full Professor and Head of the Institute for Neuroscience at ETH Zürich. His laboratory investigates molecular mechanisms of synapse development and plasticity, with emphasis on non-coding RNA regulation in neurological disorders. Research integrates molecular neurobiology with computational approaches to study microRNA functions in neuronal development, synaptic homeostasis, and stress responses. Current projects examine RNA-based therapies for neurodegenerative conditions and computational pathology platforms for digital histology. Professor Schratt's group develops AI frameworks for neurological assessment including multimodal dementia diagnosis, voice-based cognitive testing with privacy protection, and digital pathology for kidney disease. Collaborative projects with the Chan Zuckerberg Initiative explore mitochondrial regulation by microRNAs. He teaches molecular neurophysiology and neuroscience courses, supervising doctoral candidates in systems neuroscience approaches.

Fanghui Liuمشاهده پروفایل

استادیار

• Machine Learning
• Kernel Methods
• Learning Theory
• +۳ مورد دیگر

Dr. Fanghui Liu is an Assistant Professor at the Department of Computer Science, University of Warwick, UK, and a faculty member of the Centre for Discrete Mathematics and its Applications (DIMAP). Previously, he was a postdoctoral researcher at EPFL (2021-2023) and KU Leuven (2019-2021). He holds a B.E. in Automation from Harbin Institute of Technology (2014) and a Ph.D. in Machine Learning from Shanghai Jiao Tong University (2019). His research focuses on the theoretical foundations of modern machine learning, including kernel methods, adversarial robustness, neural architecture search, and large language models. Notable contributions include work on adversarial training, robust NAS benchmarks, and theoretical analysis of fine-tuning techniques. He has received grants from the Royal Society, Alan Turing Institute, and DAAD. Awards include the DAAD AInet Fellowship (2024) and recognition as a Notable Reviewer for ICLR 2023. His research has been published in top conferences like ICML, ICLR, and NeurIPS, with a focus on bridging theory and practice in machine learning.