جستجوی استاد راهنمای دانشگاهی

Gianira Alfarano is an Assistant Professor (tenure-track) at the University of Rennes 1, affiliated with the Géométrie et Algèbre Effectives team. She holds a Ph.D. in Mathematics from the University of Zurich (2022) and has held postdoctoral positions at TU Eindhoven and University College Dublin. Her research focuses on Algebraic Coding Theory, Finite Fields, Combinatorics, and Finite Geometry. Alfarano has organized sessions at international conferences and won awards such as the ACA-ERA 2022 and a Best Presentation Award. She teaches courses in algorithms, coding theory, and cryptography at Rennes University. Her academic journey includes teaching assistant roles at Zurich and postdoctoral research in network coding and information theory. Alfarano’s work bridges algebraic structures with practical coding solutions, addressing challenges in data storage, security, and error correction. Recent research emphasizes rank-metric codes, q-matroids, and service rate regions in distributed systems. Education: Ph.D. in Mathematics, University of Zurich, 2022 Postdoc: TU Eindhoven (2022–2023), UCD (2023–2024) Awards: Applications of Computer Algebra Early Research Award (ACA-ERA) 2022 Best Presentation Award at Contemporary Algebraic and Geometric Techniques in Coding Theory and Cryptography Alfarano’s publications span journals like SIAM Journal on Applied Algebra and Geometry and conferences such as ISIT. Her invited talks include sessions on minimal codes, service rate regions, and q-analogues in combinatorics. She actively contributes to the academic community through organizing workshops and editorial roles in coding theory.

• Algebraic Coding Theory
• Finite Fields
• Combinatorics
• +۱ مورد دیگر

Prof. Dr. Fabian Schär is a Professor of Distributed Ledger Technology and Fintech at the University of Basel's Faculty of Business and Economics. He serves as Managing Director of the Center for Innovative Finance (CIF), a board member of the Responsible Digital Society, and an Affiliate Professor at the Swiss Finance Institute. His research focuses on blockchain technology, decentralized finance (DeFi), financial privacy, and cryptoassets. Schär co-authored the MIT Press bestseller *Bitcoin, Blockchain, and Cryptoassets* and advises institutions like the Bank for International Settlements (BIS) and the IMF. His work bridges economics and computer science, addressing topics such as blockchain governance, metaverse economics, and regulatory frameworks. **Education**: No explicit details provided in texts, though his academic role implies doctoral qualifications. **Research Interests**: Public blockchains, DeFi protocols, cryptoasset markets, metaverse spatial economics, and regulatory compliance for decentralized systems. His interdisciplinary approach explores technical and socio-economic dimensions of blockchain applications. **Articles Trends**: Recent works analyze metaverse governance, DeFi risk transfer, blockchain privacy, and contagion effects in crypto markets. Early contributions include foundational analyses of Bitcoin mining and stablecoin design. **Awards**: Recognized in the NZZ ranking of Switzerland's most influential economists (2023). His book is a key reference in the field. **Advising & Grants**: Technical advisor to BIS’s CPMI, visiting scholar at IMF, and contributor to OECD/IOSCO working groups. No explicit grants listed, but his involvement in policy discussions implies institutional support. **Labs/Teams**: Leads the CIF, a hub for innovative finance research, and collaborates with decentralized autonomous organizations (DAOs) and central bank initiatives.

• Distributed Ledger Technology
• Blockchain
• Fintech
• +۶ مورد دیگر

Selin Yilmaz is an Assistant Professor (conditional pre-tenure at the rank of Associate Professor) in Energy Transformation Governance at the University of Lausanne (UNIL), affiliated with the Institute of Geography and Sustainability (IGD) within the Faculty of Geosciences and the Environment. She holds a PhD in Philosophy (2017) from the London-Loughborough Centre for Doctoral Training in Energy Demand, combining quantitative socio-technical modeling with interdisciplinary approaches. Her work bridges micro and macro scales, focusing on energy demand dynamics, sustainability transitions, and policy frameworks. She previously spent six years at the University of Geneva as a postdoc and assistant professor. Her research integrates sociology of energy, science and technology studies (STS), and institutional policy analyses. Key methodologies include participatory methods, co-design, and joint governance within Living Labs ecosystems to enhance sustainability experimentation. She explores intersections between energy efficiency/sufficiency, supply-demand interactions, and social license for automated demand-side management (DSM). Recent work emphasizes electricity tariff design, consumer acceptance of smart technologies (heat pumps/EVs), and typologies of energy communities. Her projects address climate-resilient energy systems, including fifth-generation district heating/cooling and borehole thermal storage. Cross-cutting themes include equity considerations, behavioral segmentation, and governance frameworks for energy transitions.

• Energy Transformation Governance
• Socio-technical Modeling
• Sustainability Studies
• +۳ مورد دیگر

Dr. Gracia Brückmann is a Post-Doctoral Researcher at the University of Bern's Institute of Political Science and the Oeschger Centre for Climate Change Research (OCCR). She leads research within the Swiss Federal Office of Energy (SFOE)-funded projects SWEET EDGE and SURE, focusing on decentralized renewable energy systems and policy frameworks for sustainable transitions. Her current institutional affiliations include: Post-Doctoral Researcher, Institute of Political Science, University of Bern (2021-present) Affiliated Researcher, Oeschger Centre for Climate Change Research (2021-present) Visiting Fellow, London School of Economics (2023) Former Researcher, ETH Zurich's International Political Economy Group (2017-2021) Brückmann's research centers on the social dimensions of energy transitions, with three interconnected pillars: (1) public opinion on climate policies including cross-border emission accounting, (2) behavioral barriers to electric vehicle adoption such as resale anxiety and charging infrastructure preferences, and (3) citizen engagement in renewable energy projects like tenant photovoltaics. Her work uniquely combines experimental methods with real-world policy analysis to address the intention-behavior gap in sustainable technology adoption. Her publication trajectory since 2020 reveals increasing focus on policy implementation challenges, with recent work exposing limitations in common interventions like test drives for EVs and identifying design principles for successful citizen energy projects. The research demonstrates consistent methodological rigor while maintaining strong relevance to Swiss energy strategy. Brückmann actively contributes to academic discourse through teaching the Master Seminar 'Energy and Mobility Policies for Decarbonisation' at the University of Bern and advising Master's theses. Her funded research provides students with opportunities to engage in current energy policy evaluation projects. Her work is supported by active SFOE grants and disseminated through high-impact publications in Environmental Research Letters, Transportation Research, and Energy Policy, with findings regularly covered in international media including The Guardian and Yale Environment Review.

• Climate Change Policy
• Renewable Energy
• Electric Vehicle Adoption
• +۴ مورد دیگر

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.

• Modelling and simulation of multi-physics systems
• Machine learning and data analysis
• High performance computing
• +۳ مورد دیگر

Prof. Roy Smith is a Professor at ETH Zurich specializing in control systems, with a focus on model predictive control (MPC), system identification, and robust control. His research spans energy systems, aerospace applications, and data-driven methods. He collaborates with leading institutions like the Swiss Federal Institute of Technology (ETH Zurich) and has co-authored numerous high-impact papers in top journals like IEEE Transactions on Automatic Control and Automatica. Key research interests include: distributed control of interconnected systems, kernel-based system identification, and real-time optimization for energy networks. His work bridges theoretical advancements with practical applications in buildings, renewable energy, and aerospace. Recent publications highlight contributions to MPC design under uncertainty, closed-loop identification techniques, and stochastic control systems. He has pioneered methods for energy hub networks and developed tools for data-driven predictive control in smart buildings. Academic collaborations include projects with Prof. John Lygeros (ETH Zurich) and Prof. Florian Dörfler (ETH Zurich), focusing on multi-agent coordination and energy systems. His research also extends to aerospace applications such as autonomous kite power systems and spacecraft pose estimation.

• Control Systems
• Model Predictive Control (MPC)
• System Identification
• +۶ مورد دیگر

Professor Lorenzo Sommaruga holds the position of Professor in Semantic and Multimedia Systems at the Department of Innovative Technologies (DTI) , University of Applied Sciences and Arts of Southern Switzerland (SUPSI). He is affiliated with the Institute of Information Systems and Networking (ISIN). With a background in Computer Science (University of Milan, 1989) and a PhD in Psychology focusing on Distributed Artificial Intelligence from Nottingham University (1993), he has extensive academic and professional experience. Prof. Sommaruga's professional roles include leadership of the Semantic and Multimedia Lab , coordination of the Master's program in Mobile Internet Applications (2009–2012), and responsibility for numerous EU and national research projects. His expertise spans XML technologies , Linked Open Data , e-learning systems , ambient intelligence , and mixed reality . Research Highlights: Key projects include the GIOCOnDa LOD platform for smart regions, the SEE A Game entrepreneurship initiative, and the RiParTI 2.0 smart home charging system. His work emphasizes usability and accessibility in semantic web applications, with frameworks like Semantic DB , IRCS Water , and AWI . Teaching: He leads courses such as Web Applications and Applicazioni web , focusing on practical, industry-relevant skills. His pedagogical contributions extend to adult educator training via initiatives like ICT per Educatori di Adulti . Grants & Projects: Over 49 funded projects, including EU programs on entrepreneurship, smart cities, and digital factories. His work bridges theoretical research with applied solutions in domains like water management and industrial automation. Labs & Teams: Active in the Semantic and Multimedia Lab , fostering interdisciplinary collaboration between computer science, psychology, and education.

• Semantic Web
• Linked Open Data
• e-Learning Systems
• +۵ مورد دیگر

Jeremias Lenzi is a Research Fellow in Economic Theory at the Faculty of Business and Economics, University of Basel, Switzerland. His office is located at Peter Merian-Weg 6, 4052 Basel. Dr. Lenzi's research focuses on blockchain technology, coordination games, governance and consensus protocols, and decentralized markets. His work explores the intersection of economic theory with emerging decentralized technologies, particularly examining how governance mechanisms function in blockchain ecosystems and decentralized market structures. His current research interests align with the growing field of decentralized finance and economic coordination mechanisms in distributed systems. Dr. Lenzi is currently working on research titled "Decentralized Governance by Bayesian Persuasion," which appears to be in progress according to the available information. Primary Research Areas: Blockchain applications in economic systems, Game theory applications to decentralized networks, Governance structures in consensus protocols His work contributes to the University of Basel's growing research presence in blockchain technology, complementing the work of faculty members like Prof. Dr. Fabian Schär (Blockchain, Cryptoassets, Decentralized Finance) and Prof. Dr. Aleksander Berentsen (DLT, Blockchain and Cryptoassets).

• Blockchain technology
• Coordination Games
• Governance and consensus protocols
• +۱ مورد دیگر

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.

• Portfolio Management
• Risk Management
• Machine Learning
• +۷ مورد دیگر

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.

• Natural Language Processing
• Machine Learning
• Grammatical Error Correction
• +۵ مورد دیگر

Talia Gillis is an Associate Professor of Law at Columbia University and a visiting guest professor at ETH Zurich during the spring semester 2025. Her research bridges law and economics, focusing on consumer markets, financial regulation, and the societal impacts of AI and fintech. Education: S.J.D. and Ph.D. in Business Economics from Harvard, B.C.L. from Oxford, LL.B. in Law and Economics from Hebrew University Her work examines how technological and legal changes affect consumer welfare, particularly in household financial behavior. She has explored regulatory tools like financial disclosures, fiduciary duties, and algorithmic fairness, with a current emphasis on AI's distributional consequences. Recent publications address price discrimination, fair lending algorithms, and personalized law. She has received grants from Columbia University's Richman Center and Harvard's Empirical Law and Finance programs. Scientific Awards: 2022 AALS Scholarly Papers Competition Winner 2022 Junior Faculty Grant Richard Paul Richman Center Grant Harvard Olin Fellowships Weidenfeld Scholarship (Oxford) Gillis clerked for Deputy Chief Justice Hanan Melcer of Israel's Supreme Court and is affiliated with Columbia Law School's research centers. She teaches Contracts and contributes to debates on AI ethics, behavioral economics, and financial regulation.

• Law and Economics
• Consumer Finance
• Financial Regulation
• +۶ مورد دیگر

Christoph Koch is a Full Professor of Computer Science at École Polytechnique Fédérale de Lausanne (EPFL), leading the Data Analysis Theory and Applications Laboratory (DATA). His academic journey includes previous roles as Associate Professor at Cornell University (2007-2010) and Saarland University (2005-2007), with academic foundations from TU Vienna (PhD 2001, Habilitation 2004) and research at CERN. Education: PhD in Artificial Intelligence (TU Vienna & CERN, 2001), Habilitation (TU Vienna, 2004) Appointments: EPFL (2010-present), Cornell University (2007-2010), Saarland University (2005-2007), TU Vienna (2003-2005) His research focuses on database theory and systems , with recent work spanning query optimization , transaction processing , and parallel data management . His publications address topics like robustness under SQL isolation levels , quantifier elimination for query optimization , and Datalog extensions . Articles trend toward high-dimensional data cubes , transactional concurrency , and embedded DSLs for performance-critical systems . Scientific accolades include: Best Paper Awards: PODS (2002) ICALP (2005) SIGMOD (2011) VLDB (2014) GPCE (2017) ERC Consolidator Grant (2011) Google Research Award (2009) ACM SIGLOG Alonzo Church Award (2021) He has advised numerous PhD students including Immanuel Trummer (now at Cornell), Milos Nikolic (Edinburgh), and Lionel Parreaux (HKUST). His lab contributes to query engine architectures , parallel processing systems , and quantum computing applications in database optimization.

• Database Theory
• Query Optimization
• Data Management Systems
• +۴ مورد دیگر

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.

• Machine Learning
• Optimization
• Reinforcement Learning
• +۵ مورد دیگر

Prof. Taiji Suzuki is an Associate Professor at the University of Tokyo in the Department of Mathematical Informatics . He also serves as Team Leader of the "Deep Learning Theory" team at AIP-RIKEN , Japan. With a PhD in Information Science and Technology from the University of Tokyo (2009), he has held academic positions at the University of Tokyo (2009-2013) and Tokyo Institute of Technology (2013-2017) before returning to the University of Tokyo in 2017. University of Tokyo (2004: BEng in Mathematical Engineering) University of Tokyo (2006: MSc in Information Science and Technology) University of Tokyo (2009: PhD in Information Science and Technology) His research interests span statistical learning theory , deep learning , kernel methods , sparse estimation , and stochastic optimization . He investigates how neural networks adapt to function smoothness, avoid the curse of dimensionality, and achieve global optimization through mean-field dynamics. His work bridges theoretical guarantees (minimax optimality, convergence analysis) with practical implementations (transformers, graph neural networks, diffusion models). The 15 most recent articles focus on transformers' representation power, graph neural networks' limitations, diffusion models' convergence, and optimization theories for deep learning. Key themes include information-theoretic bounds , feature learning dynamics , and mean-field analysis . He explores applications in AI for medicine, federated learning, and biomedical modeling. Awards & Recognition: Outstanding Paper Award, ICLR 2021 MEXT Young Scientists’ Prize Outstanding Achievement Award, Japan Statistical Society 2017 Outstanding Achievement Award, Japan Society for Industrial and Applied Mathematics 2016 Best Paper Award, IBISML 2012 Best Paper Candidate, ICDM 2019 He has served as Area Chair for NeurIPS, ICML, ICLR, AISTATS, and as Program Chair for ACML. His scientific contributions include convergence theories for stochastic gradient methods, minimax analysis of deep learning vs kernel methods, and novel frameworks for distributional optimization in diffusion models.

• Machine Learning
• Statistical Learning Theory
• Deep Learning
• +۶ مورد دیگر

Dr. Berfin Simsek is a doctoral researcher at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Computer and Communication Sciences. Supervised by Clément Hongler and Wulfram Gerstner, her work focuses on the theoretical properties of deep learning models, including neural network loss landscapes, symmetry-induced saddles, global minima manifold geometry, and optimization dynamics in overparameterized regimes. Education Double major in Electrical-Electronics Engineering and Mathematics at Koç University, Istanbul. International Mathematical Olympiad competitor prior to her Ph.D. studies. Research Themes Her research investigates the combinatorial structure of neural network loss surfaces, leveraging permutation symmetry to analyze critical manifolds. She examines how network width and overparameterization affect landscape smoothness, with applications to training failure modes in mildly overparameterized systems. Her work also encompasses generalization theory, random features, kernel methods, and out-of-distribution learning, the latter during an internship at Meta AI.

• Deep Learning
• Neural Networks
• Machine Learning
• +۵ مورد دیگر