جست‌وجوی استاد و پژوهشگر

• Information Security
• Software Security
• Security Engineering
• +۴ مورد دیگر

Prof. Marc Rennhard is Director of the Department of Information Technology, Electrical Engineering and Mechatronics at ZHAW School of Engineering. He holds a PhD in anonymous internet communication from ETH Zurich (2004) and has over 20 years of experience in information security. His roles include Head of the Institute of Applied Information Technology (InIT) until 2023 and Head of the Information Security Research Group since 2005. He collaborates with ETH Zurich's Zurich Information Security Center (ZISC). Education: Dr. sc. techn. ETH (2004), Dipl. El.-Ing. ETH (2004), Certified CISSP (2004). Leadership includes projects like FASTscan, OptiPhish, and dokspot. Research focuses on software security, web application vulnerabilities, and automated security testing. Active in networks such as Information Security Society Switzerland (ISSS) and the Digital Society Initiative (DSI) Cybersecurity Advisory Board. Research interests span dynamic security analysis of Android systems, access control vulnerabilities, and machine learning for security. Over 30 publications include work on vulnerability detection, secure data storage (SecureSafe), and telematics security. Leading 15+ industry-funded projects with emphasis on applied cybersecurity solutions.

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.

• Service Design & Management
• Collaboration Engineering
• Digital Learning
• +۷ مورد دیگر

Jan Marco Leimeister is a Professor and Director at the Institute of Business Administration (IBWL) at the University of Kassel. His research focuses on service design & management, collaboration engineering, and digital learning innovations. He leads interdisciplinary projects exploring conversational AI, generative AI applications, and ethical AI system design. Key research areas include service innovation ecosystems, hybrid human-AI collaboration frameworks, and digital transformation strategies. His work emphasizes practical applications in areas like financial fraud detection (via synthetic data ecosystems) and adaptive learning systems for argumentation skill development. He actively contributes to advancing methodologies for structured literature reviews in business research and integrating generative AI into scholarly practices. Recent publications (2024-2025) highlight contributions in AI ethics, metaverse platform analysis, and process mining for IT service management. His work bridges theory and practice, addressing challenges in data fairness, conversational agent branding, and scaling digital service innovations. Leimeister's academic leadership includes directing the IBWL's master's programs in Digital Business and Sustainable Management. He collaborates internationally, emphasizing design science research and technology-driven solutions for modern organizational challenges.

Martin Vechevمشاهده پروفایل

استاد

• Artificial Intelligence
• Programming Languages
• Cybersecurity
• +۷ مورد دیگر

Martin Vechev is a Full Professor of Computer Science at ETH Zurich, leading the Secure, Reliable, and Intelligent Systems Lab. He is also the Founder and Architect of INSAIT, a pioneering research center in computer science and AI in Eastern Europe. Previously, he worked at IBM T.J. Watson Research Center and earned his PhD from the University of Cambridge and B.Sc. from Simon Fraser University. His research focuses on AI and programming languages, with contributions to quantum programming (e.g., Silq), AI-driven code analysis (DeepCode), and security for decentralized systems (ChainSecurity). He has co-founded three deep-tech startups and received numerous awards, including the ACM SIGPLAN Robin Milner Award. His work spans theoretical foundations and practical systems, emphasizing formal methods and security guarantees. Education: PhD in Computer Science, University of Cambridge (2003-2008) B.Sc. in Computer Science, Simon Fraser University (1996-2001) Research Interests: His work bridges AI and programming languages, addressing challenges in secure AI, quantum computing, and automated reasoning. Key areas include: AI for code analysis and security Formal verification of decentralized systems Quantum programming languages Data privacy and adversarial machine learning Key Contributions: His group built widely used systems like Silq (first high-level quantum language) and developed AI-driven tools for detecting security vulnerabilities (DeepCode). His research on watermarking and federated learning privacy has been influential in both academia and industry. Awards: ACM SIGPLAN Robin Milner Young Researcher Award ERC Starting and Consolidator Grants Google/Facebook Faculty Awards IBM Research Excellence Awards Advising & Grants: He leads the SRI Lab at ETH, supervises PhD students in AI and programming languages, and manages grants from ERC, industry partners, and Swiss national initiatives. His startups (DeepCode, ChainSecurity, LatticeFlow) address real-world challenges in AI security, blockchain, and robust machine learning. Labs & Teams: The Secure, Reliable, and Intelligent Systems (SRI) Lab at ETH Zurich focuses on safe AI, programming languages, and security. Collaborations include INSAIT, a joint venture with ETH and EPFL.

Parsa Rahimi Noshanaghمشاهده پروفایل

پژوهشگر

• Synthetic Data Generation
• Computer Vision
• Generative Models
• +۷ مورد دیگر

Parsa Rahimi Noshanagh is a Doctoral Assistant and PhD student at the School of Engineering , École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the Department of Electrical Engineering and the LIDIAP laboratory (Idiap Research Institute) . He is conducting research in synthetic data generation and its applications in computer vision under the supervision of Prof. Marcel and Prof. Alahi. Master of Science in Electrical Engineering, Sharif University of Technology Currently pursuing Doctoral Program in Electrical Engineering, EPFL His research focuses on enhancing discriminative models such as classifiers using synthetic data generated by generative models (e.g., StyleGANs, Diffusion models, Flows). He explores the concept of Analysis by Synthesis , aiming to make generative models practically useful in real-world applications beyond entertainment. His work investigates realism transfer in 3D face renderings and synthetic data augmentation to improve face recognition accuracy. His recent publications have been accepted at top conferences including ECCVW 2024 (ORAL) , ICASSP 2024 , and IJCB 2023 (ORAL) , demonstrating performance gains in benchmarks like IJB-C, IJB-B, and LFW. These works highlight that synthetic augmentation can rival architectural improvements in model performance. Paper accepted as ORAL presentation at ECCVW 2024 Paper accepted at ICASSP 2024 Paper accepted as ORAL presentation at IJCB 2023 Parsa previously worked as a Lead Research Engineer at MCI before starting his PhD. He has contributed to the release of datasets such as RealDigiFace and plans to release synthetic datasets from AugGen. He does not currently supervise any students but is actively involved in research collaborations with the Biometric Group at Idiap. He is a member of the Doctoral Program in Electrical Engineering (EDEE) at EPFL and contributes to advancing privacy-conscious and resource-efficient machine learning through self-contained synthetic data generation.

Meisam Razaviyaynمشاهده پروفایل

دانشیار

• Optimization Algorithms
• Data Science
• Machine Learning
• +۴ مورد دیگر

Meisam Razaviyayn is an Associate Professor at the University of Southern California (USC) with joint appointments in the Department of Industrial and Systems Engineering , Computer Science , Quantitative and Computational Biology , and Electrical Engineering . He serves as the Associate Director of the USC-Meta Center for Research and Education in AI and Learning and holds the Andrew and Erna Viterbi Early Career Chair . His academic background includes a Ph.D. in Electrical Engineering with a minor in Computer Science from the University of Minnesota, followed by a postdoctoral fellowship at Stanford University. Razaviyayn maintains active collaborations with industry partners like Google Research and Amazon. Current Roles : Faculty Visitor at Google Research Editorial Roles : Associate Editor for SIAM Journal on Optimization and IEEE Transactions on Signal Processing His research focuses on optimization algorithms for modern data science problems, particularly addressing privacy , fairness , and scalability in machine learning systems. Recent work includes differentally private optimization frameworks and fair learning algorithms applicable to large-scale and decentralized data scenarios. Key scientific awards include: 2022 NSF CAREER Award 2022 Northrop Grumman Excellence in Teaching Award 2021 AFOSR Young Investigator Award 2021 3M Nontenured Faculty Award 2020 ICCM Best Paper Award in Mathematics 2019 IEEE Data Science Workshop Best Paper Award He actively advises Ph.D. students and leads research initiatives in robust and private machine learning . His group has received funding from NSF, Google Research, and Amazon for projects related to generative AI training , differentially private algorithms , and distributed optimization . Razaviyayn also contributes to academic service as a conference chair and workshop organizer.

Dongyang Fanمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning
• Natural Language Processing
• +۵ مورد دیگر

Dongyang Fan is a Researcher at the Swiss Federal Institute of Technology Lausanne (EPFL), affiliated with the School of Computer and Communication Sciences and the Institute of Computer Science. She works in the Machine Learning and Optimization Laboratory (MLO) under EPFL's Doctoral Program in Computer and Communication Sciences, conducting cutting-edge research in artificial intelligence and machine learning. Her research spans Machine Learning, Deep Learning, Natural Language Processing, Optimization, Federated Learning, Mixture of Experts architectures, On-device Learning, and Ethics in AI. She focuses on developing efficient, ethical, and personalized AI systems with practical applications in edge computing and collaborative environments, addressing challenges in model training, data valuation, and real-world deployment constraints. Analysis of her recent publications reveals a concentrated effort on large language models (LLMs), with significant contributions to on-device collaborative learning frameworks, Mixture of Experts optimization, and ethical implications of web data usage. Her work bridges theoretical advancements in model architecture with practical implementations for resource-constrained devices, emphasizing privacy-preserving techniques and fairness-aware design principles. She is an integral member of EPFL's Machine Learning and Optimization Laboratory, contributing to research teams focused on next-generation AI algorithms and their optimization for real-world applications in distributed systems and edge computing environments.

Adel Bibiمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Computer Vision
• AI Safety
• +۴ مورد دیگر

Adel Bibi is a prominent Research Fellow at the University of Oxford 's Department of Engineering Science, with concurrent roles at Kellogg College and the ELLIS Society . He serves as R&D Distinguished Advisor for Softserve , specializing in AI Safety through robustness certification and optimization. Academic Background: PhD in Electrical Engineering (4.0/4.0), KAUST (2020) MSc in Electrical Engineering (4.0/4.0), KAUST (2016) BSc in Electrical Engineering (3.99/4.0), Kuwait University (2014) His research focuses on Trustworthy AI through three main pillars: AI Safety : Specializing in robustness certification, alignment, and security of foundational models in vision/language Continual Learning : Developing efficient frameworks for model updates and domain adaptation Optimization : Creating novel approaches for training stability and resource allocation Recent publications demonstrate expertise in large language model security , interpretable architectures , and robust training , with a particular focus on mitigating risks in agentic systems. Scientific Recognition: Systemic AI Safety Grant (~$250,000) - UK AI Security Institute (2025) Google Gemma 2 Academic Program - $10,000 GCP Credit (2024) Amazon Research Award - Machine Learning Algorithms & Theory (2022) 4 Best Paper Awards (NeurIPS23, ICML23, CVPR22, 2018 Optimization & Big Data) Notable Area Chair Award - NeurIPS23 (top 8.1%) He actively mentors through: 13 PhD/MSc students at Oxford (including 4 graduated PhDs) 3 Unofficial PhD Mentees at KAUST/MIT Supervision of 6 Graduated MSc Students

Haoyu Chenمشاهده پروفایل

استادیار

• Machine Learning
• Human Behaviour Analysis
• Emotion AI
• +۴ مورد دیگر

Haoyu Chen is a Tenure-track Assistant Professor at the Center for Machine Vision and Signal Analysis (CMVS), University of Oulu. He is also a co-founder of the AI startup Aitomore and an AI-based restaurant Aitofresh. His research focuses on Machine Learning, Human Behaviour Analysis, Emotion AI, and Adversarial Learning, with particular emphasis on Hybrid Intelligence and human-AI interaction. Dr. Chen received his Ph.D. from the University of Oulu, Finland, where he was advised by Academy Professor Guoying Zhao. During his PhD studies, he visited CEL, TU Delft, the Netherlands. Prior to that, he received his B.E. degree from China University of Geosciences, China, and Master degree from University of Oulu, Finland. Before joining as faculty, he conducted Postdoc research in CMVS, University of Oulu, with projects on Emotion AI (Academy Finland project) and trustworthy AI (Infotech project). His research spans multiple areas of artificial intelligence with a focus on understanding human behavior through AI systems. Dr. Chen's work particularly emphasizes emotion recognition, 3D pose transfer, micro-gesture analysis, and the development of hybrid intelligence systems where humans and AI mutually enhance each other's capabilities. His recent work has increasingly incorporated large language models for multimodal understanding of human emotions and behaviors, with significant publications in ICML, CVPR, and NeurIPS. Academy Research Fellow funding (622k euros), ranked 1st in AI panel ELLIS member IEEE Finland Jt. Chapter SP/CAS Best Paper Award Dr. Chen actively supervises multiple students including PhD candidates, Master's students, and research interns. His teaching includes courses on Affective Computing and Computer Graphics at the University of Oulu. He also organizes workshops and seminars, including the MiGA workshop series on Micro-gesture Analysis for Hidden Emotion Understanding and the HAECU tutorial on Human-AI mutual promotion for emotion and cognition understanding.

Isabelle Guyonمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Computer Vision
• +۳ مورد دیگر

Isabelle Guyon is a Full Professor at University Paris-Saclay and a Research Scientist at Google DeepMind since 2022. She is also a community lead for the open-source Codalab competitions project and President of ChaLearn, a non-profit organizing machine learning challenges. Her career spans academia, industry, and non-profit leadership, with affiliations at INRIA and the LISN laboratory. PhD in Paris under Gerard Dreyfus and Leon Personnaz Bell Labs researcher (1990s) Graduate of ESPCI Her research focuses on machine learning challenges and benchmarks across diverse domains including causality , computer vision , deep learning , and high energy physics . Recent projects explore power grid optimization , LLM-assisted paper reviewing , and systematic bias mitigation in AI applications. Key awards include the BBVA Frontier in Knowledge Award (2020), ICMI Ten-Year Impact Award (2023), and IEEE Best Paper Award (2023). She is also an ELLISS fellow and AMIA fellow .

Tomer Korenمشاهده پروفایل

دانشیار

• Machine Learning
• Optimization

Tomer Koren is an Associate Professor at the Blavatnik School of Computer Science , Tel Aviv University, and a Senior Research Scientist at Google Research, Tel Aviv. His research focuses on machine learning and optimization , particularly in stochastic optimization, online learning, and reinforcement learning. Recent work includes advancements in learning rate annealing, bandit optimization, continual learning, and private online learning, with publications at top venues such as ICML, NeurIPS, and COLT. Key subfields span gradient methods , robust optimization , and privacy-preserving algorithms . Scientific achievements include the Outstanding Paper Award at ALT 2025 and Spotlight presentations at ICML 2025 and 2024 . He received his PhD from the Technion—Israel Institute of Technology , advised by Prof. Elad Hazan. Collaborations include work with researchers at Google and Tel Aviv University, with a focus on theoretical foundations and practical algorithm design. His current affiliations and research integrate both academia and industrial innovation.

• Human-Centered Computing
• Gaze Estimation
• Embodied Visual Intelligence
• +۶ مورد دیگر

Xucong Zhang is a tenured Assistant Professor at the Intelligent Systems Department , Faculty of EEMCS , Delft University of Technology (TU Delft) . He also serves as a Guest Scientist at the Max Planck Institute for Intelligent Systems and is an EL LIS Scholar . He joined TU Delft in 2021 and was granted a permanent position in 2023. PhD from Max Planck Institute for Informatics (2013–2018) Postdoc at ETH Zurich (2018–2021) Research Interests : Xucong’s work centers on human-centered computing , combining gaze estimation and holistic human motion modeling (gaze, face, hand, full-body) to advance human-AI interaction , human-robot collaboration , and avatar generation . His healthcare research integrates computer vision with medical applications to support clinical practice. Publications Trends : Xucong’s recent work focuses on gaze estimation (e.g., UniGaze , PrivateGaze ), 3D kinematics (e.g., GazeNeRF ), and human-robot interaction (e.g., GazeHTA , SpeechCAT ). His research spans multi-camera systems , adversarial learning , and domain adaptation . Best Paper Honorable Mention, UIST 2017 Best Paper Honorable Mention, UIST 2016 EL LIS Scholar title (2023) Teaching & Funding : He leads the BSc Image Processing course and co-teaches MSc Computer Vision by Deep Learning . Xucong has secured 1.5M EUR in funding, including a DFG UGaze Programme grant (2026–2029) and sole PI for the Honda Research Institute + Netherlands TKI grant (2023–2028). Labs & Collaborations : Xucong coordinates the INSYGHT Lab at TU Delft and collaborates with institutions like Honda Research Institute Japan and ETH Zurich . His lab’s Cutting-Edge Multi-Camera System supports multi-modal data capture for social interaction analysis.

• Optimization
• Statistics
• Operations Research
• +۷ مورد دیگر

Yifan Hu is an Assistant Professor in the Department of Statistics at Rutgers University (New Brunswick, NJ). His research focuses on optimal decision-making under uncertainty, with expertise spanning optimization, statistics, and operations research. He works on problems arising from reinforcement learning, large language models, causal inference, and operations management. Dr. Hu received his PhD in Operations Research from the University of Illinois at Urbana-Champaign in 2022, advised by Prof. Xin Chen and Prof. Niao He. Prior to his PhD, he earned a Bachelor of Mathematics from Nankai University. He also completed postdoctoral work with Prof. Daniel Kuhn from EPFL and Prof. Andreas Krause from ETH Zurich. Dr. Hu's research centers on developing optimization methods to solve complex problems with provable guarantees. His work addresses challenges such as nonconvexity, noisy observations due to distribution shifts, interactions between multiple decision makers, and limited data samples. He has developed approaches for structured nonconvex optimization problems that can be solved efficiently to global optimality, despite their apparent computational intractability. His research spans several key areas including global optimality in structured nonconvex optimization, large-scale causal inference, contextual bilevel optimization, and robust reinforcement learning with large language models. Dr. Hu has published numerous papers in top-tier venues including NeurIPS, ICML, ICLR, AISTATS, and journals like SIAM Journal on Optimization and Operations Research. His recent work shows a strong trend toward integrating optimization theory with practical applications in machine learning, causal inference, and decision-making systems. He has made significant contributions to understanding the landscape of nonconvex optimization problems, developing efficient algorithms for causal discovery, and creating robust frameworks for reinforcement learning and language model alignment. Dr. Hu serves as an area chair for ICLR and as a reviewer for numerous statistics, optimization, and operations research journals (e.g., JASA, JMLR, OR, MS, MSOM, MP, SIAM OPT, Math OR) as well as machine learning conference proceedings (e.g., NeurIPS, ICML, ICLR, AISTATS, COLT). He is actively seeking motivated PhD students and research interns with backgrounds in mathematics, statistics, operations research, or computer science. Outside of academia, Dr. Hu enjoys skiing, swimming, hiking, and playing tennis.

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.

Karl Schmeddersمشاهده پروفایل

استاد

• Sustainability
• Climate Change Economics
• Asset Pricing
• +۴ مورد دیگر

Karl Schmedders is a Professor of Finance at IMD Business School, specializing in sustainability and climate change economics. He has held prior roles as Professor at the University of Zurich and Associate Professor at Northwestern University's Kellogg School of Management. His research bridges computational finance with environmental challenges, focusing on transition risks, carbon pricing, and ESG equity impacts. Education: PhD (Operations Research), Stanford University MS (Operations Research), Stanford University Research Interests: Schmedders explores the intersection of finance and sustainability. He emphasizes the 'just transition' to ensure equitable climate action, particularly addressing social and governance (S and G in ESG) dimensions. His work applies numerical methods to complex economic models, analyzing asset pricing dynamics and replicating portfolios in insurance sectors. Awards: Multiple teaching awards including Kellogg-HKUST Best Faculty (2017) Walter J. Gores Award (Stanford, 1996) Professional Activities: Directs IMD's Strategic Finance program and advises global firms like ABB and Julius Bär. Serves on boards of Swiss firms LPX Group and SYLVA AG. Active in executive education and venture capital consulting. Labs & Teams: Leads IMD's online certification in structured investment products with Leonteq and contributes to executive education programs (EMBA, AMC).