جستجوی استاد راهنمای دانشگاهی

Ueli Maurerمشاهده پروفایل

استاد

• Information Security
• Cryptography
• Theoretical Computer Science
• +۲ مورد دیگر

Ueli Maurer is a Full Professor of Computer Science at ETH Zurich and heads the Information Security and Cryptography research group. He received his diploma and Ph.D. in Electrical Engineering from ETH Zurich in 1985 and 1990 respectively. After a fellowship at Princeton University, his research focuses on foundational aspects of cryptography and information security. Education Diploma in Electrical Engineering, ETH Zurich (1985) Ph.D. in Electrical Engineering, ETH Zurich (1990) DIMACS Research Fellow, Princeton University (1990-1991) His research spans information security, cryptographic protocols, discrete mathematics, and theoretical computer science. Key areas include provably secure systems design, digital signatures, public-key infrastructures, and trust management. His work bridges theoretical foundations with practical applications in digital security. His publications demonstrate a consistent focus on advancing cryptographic theory and security proofs. Recent works explore anamorphic encryption, blockchain security frameworks, and composable protocol design. Primary trends include privacy-preserving communication, adaptive security models, and information-theoretic approaches to cryptography. Awards & Honors IEEE Fellow ACM Fellow IACR Fellow Member, German Academy of Sciences (Leopoldina) Rademacher Lecturer (2000) Vodafone Innovation Award (2013) RSA Mathematics Award (2016) TCC Test-of-Time Award (2016) He advises doctoral students on cryptographic protocols and security proofs. His research group works on diverse projects including secure multi-party computation and blockchain technologies. He co-founded the Zurich Symposium on Privacy and Security and serves on editorial boards of major cryptography journals.

Mauro Pezzèمشاهده پروفایل

استاد

• Software Engineering
• Software Testing and Analysis
• Self-Adaptive Systems
• +۵ مورد دیگر

Mauro Pezzè is a Full Professor of Software Engineering at the Università della Svizzera italiana (USI) and Università di Milano Bicocca, leading the STAR research group since 2006. He holds a laurea from the University of Pisa and a PhD from Politecnico di Milano. His research focuses on software testing, analysis, self-adaptive systems, and cloud systems. He has held editorial roles, including Editor-in-Chief of ACM Transactions on Software Engineering and Methodologies (TOSEM), and served on numerous program committees. Education: Laurea (Pisa), PhD (Politecnico di Milano). Professional roles include Dean of the Faculty of Informatics at USI (2009-2013), visiting scientist at UC Irvine and Edinburgh, and technical lead for international projects. He co-authored a seminal book on software testing (Wiley, 2007), with over 670 citations. Research Interests: Software Testing, Self-Adaptive Systems, Cloud Computing, AI in SE, Sustainable Software. Projects include work on field-based testing, failure prediction in distributed systems, and neuro-symbolic approaches for test oracles. Grants and Advising: Led STAR Lab projects in self-healing systems, GUI testing, and semantic matching. Advised numerous PhD/postdoc students (e.g., Ciniselli, Di Grazia, Qiu). Collaborations with European tech firms on R&D initiatives. Labs/Teams: STAR Group at USI/Constructor Institute, Bicocca, and Politecnico di Milano. Current members include postdocs and PhD students working on AI-driven testing and cloud reliability.

• Computer Graphics
• Computer Vision
• Virtual Reality
• +۷ مورد دیگر

Prof. Dr. Aljosa Smolic is a Professor and Co-Head of the Immersive Realities Research Lab at Lucerne School of Computer Science and Information Technology, Lucerne University of Applied Sciences and Arts. He joined HSLU in 2022 and became Co-Head in 2023. Previously, he served as SFI Research Professor at Trinity College Dublin (2016-2021) where he led the V-SENSE group in visual computing, combining computer vision, graphics, and media technology. His career includes positions as Senior Research Scientist at Disney Research Zurich (2009-2016) and Scientific Project Manager at Fraunhofer HHI (2001-2009). He holds a PhD from RWTH Aachen University. Research focuses on immersive technologies including AR/VR, volumetric video, light-fields, and deep learning applications in visual computing. His work has resulted in over 50 Disney R&D projects, publications, patents, and technology transfers. Publications emphasize VR evaluation, volumetric video applications, 3D reconstruction, and XR in education, frequently employing deep learning and computer vision techniques. Awards and Recognition: IEEE ICME Star Innovator Award 2020 TCD Campus Company Founders Award 2020 Multiple best paper awards Co-founded Volograms (volumetric video startup) and holds editorial roles including Associate Editor for IEEE Transactions on Image Processing.

Adrian Perrigمشاهده پروفایل

استاد

• Network Security
• Cybersecurity
• Distributed Systems
• +۳ مورد دیگر

Adrian Perrig is a Full Professor at the Department of Computer Science at ETH Zürich. He leads research in network security, distributed systems, and internet architecture, focusing on projects like the SCION secure internet architecture and its commercialization through Anapaya Systems. His work emphasizes secure communication, denial-of-service defense, and public key infrastructure (PKI) innovations. Affiliations: ETH Zürich, Institute for Information Security Key Contributions: SCION, SAGE, RHINE, F-PKI Research interests include path-aware networks, cryptographic protocols, and resilient systems. His publications span over 295 results since 2005, with notable awards including the Best Paper Award (CoNEXT 2021) and ANRP 2023. He has contributed to foundational work in secure routing, DNS security, and GPU attestation. Scientific awards include Best Paper Awards at CoNEXT and ACM SIGCOMM, as well as recognition for applied networking research. His work bridges academia and industry, addressing challenges in global network security and scalability.

Bernhard Thomaszewskiمشاهده پروفایل

مدرس

• Computer Graphics
• Robotics
• Computational Mechanics
• +۳ مورد دیگر

Bernhard Thomaszewski is a Lecturer at the Department of Computer Science at ETH Zürich. His research focuses on computational mechanics, robotics, and computer graphics, with an emphasis on simulation-based design and material modeling. He explores topics such as deformable contact, flexible materials, and robotic mechanisms. His work bridges theoretical foundations and practical applications, including medical imaging, garment simulation, and biomechanical systems. Notable research interests include the development of novel algorithms for real-time simulation, optimization-driven design of mechanical systems, and integration of machine learning with physical models. He has contributed to advancements in finite element modeling, differentiable simulation, and topology optimization for robotic and biomedical applications. His recent projects highlight interdisciplinary collaboration, addressing challenges in areas like orthodontic treatment prediction, automated pipeline design, and neural network-driven material characterization. While no specific grants or awards are explicitly listed, his prolific publication record underscores his impactful contributions to computational engineering and computer science.

Dusan Licinaمشاهده پروفایل

دانشیار

• Sustainable and healthy built environment
• Indoor air pollutant dynamics
• Human exposure assessment
• +۳ مورد دیگر

Dusan Licina is an Associate Professor at the École polytechnique fédérale de Lausanne (EPFL) and director of the Human-Oriented Built Environment Laboratory (HOBEL) within the Smart Living Lab initiative. His research bridges building science, environmental engineering, and human health. Education: BSc/MSc in Mechanical Engineering (University of Belgrade), joint PhD (National University of Singapore & Technical University of Denmark), postdoc (University of California Berkeley) Key research areas: Indoor air quality, human exposure science, building ventilation, IoT sensing, energy-efficient housing His 15 most cited works focus on particle dynamics, ozone chemistry, and occupant-driven building systems. Notable trends include human-clothing interactions in pollution transport, nanocluster aerosol formation, and real-time CO2 monitoring behavior. Awarded Yaglou award (ISIAQ) for sustainable healthy buildings research Ralph G. Nevin’s award (ASHRAE) for human exposure studies and serving on the Indoor Air editorial board. Current PhD students Dong Hui, Liu Tianqi, Loizou Maria, Medina Taylor Oran explore: Occupant behavior in energy systems Smart sensor networks Radon measurement technologies Work-from-home environmental quality His laboratory develops Human-Centric Sensing Platforms and Occupant-Centric Building Design methodologies through experimental chambers and computational models.

Laurent Condatمشاهده پروفایل

پژوهشگر

• Optimization
• Convex Relaxation
• Stochastic Algorithms
• +۵ مورد دیگر

Laurent Condat is a Senior Research Scientist at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia, where he conducts research in optimization algorithms and their applications. He is affiliated with the College of Engineering, Department of Computer Science, and has previously held research positions at CNRS in France, working at GREYC in Caen and GIPSA-Lab in Grenoble. Dr. Condat received his PhD in 2006 from Grenoble Institute of Technology, followed by a 2-year postdoc in Munich, Germany. He was recruited as a permanent researcher by CNRS in 2008 and has been on leave from CNRS since November 2019 to work at KAUST. In February 2025, he was promoted to 'chargé de recherche hors classe' (senior research scientist) by CNRS. His research focuses on deterministic and stochastic optimization algorithms, convex relaxations, and applications to machine learning, signal and image processing. His work spans theoretical foundations of optimization methods to practical implementations for distributed and federated learning systems. He has developed several influential algorithms including RandProx, TAMUNA, and LoCoDL that address communication efficiency in distributed optimization. His recent publications demonstrate strong trends in communication-efficient distributed optimization, with particular emphasis on federated learning, compression techniques, and local training methods. His work bridges theoretical optimization with practical machine learning applications, showing consistent innovation in algorithmic design for large-scale problems. Best reviewer award at AISTATS 2025 Meritorious Service Award from Mathematical Programming Stanford's list of world's top 2% most influential scientists Dr. Condat has co-supervised PhD students including Daniele Picone and Julien Baderot. He serves as an Associate Editor for IEEE Transactions on Signal Processing and has presented his work at numerous international conferences including plenary talks at major optimization workshops. His research is supported through KAUST funding and collaborative projects with researchers worldwide.

Alexander Dammمشاهده پروفایل

استاد

• Remote Sensing
• Earth Observation
• Imaging Spectroscopy
• +۷ مورد دیگر

Alexander Damm is a Professor and head of the Remote Sensing of Water Systems (RSWS) group, holding a joint appointment between the Department of Geography at the University of Zurich (UZH) and the Swiss Federal Institute of Aquatic Science and Technology (Eawag). His research integrates advanced Earth observation technologies with environmental science to study water systems under changing climatic and anthropogenic pressures. University: University of Zurich Institutional Affiliation: Swiss Federal Institute of Aquatic Science and Technology (Eawag) Department: Department of Geography Alexander Damm obtained his MSc and PhD in remote sensing from Humboldt-University Berlin. Since 2008, he has been contributing to UZH’s leadership in imaging spectroscopy and Earth observation science. MSc, Remote Sensing, Humboldt-University Berlin PhD, Remote Sensing, Humboldt-University Berlin His research focuses on the fundamentals of remote sensing as applied to terrestrial and aquatic ecosystems, particularly in studying water dynamics and environmental change impacts. He specializes in sun-induced chlorophyll fluorescence (SIF), imaging spectroscopy, and the development of methods to monitor ecosystem productivity, drought responses, and biogeochemical cycles. His work bridges physics, ecology, and environmental engineering to improve understanding of plant-water relations and ecosystem resilience. The recent publications highlight a strong trend in using airborne and satellite-based spectroscopy to assess vegetation health, water stress, forest dynamics, and atmospheric constituents. Key themes include SIF retrieval, drought monitoring, canopy structure modeling, and air quality estimation. These works span applications from croplands and forests to tundra and inland waters, reflecting a broad interdisciplinary approach grounded in quantitative remote sensing. Alexander Damm is involved in several high-profile research initiatives, including: ESA’s FLuorescence EXplorer (FLEX) mission SNSF projects: FLUO4ECO, Spatial-sustainable-finance, DeltAs MeteoSwiss: UrbanNature EU Horizon: NextGenCarbon He leads the RSWS group, which develops and applies cutting-edge remote sensing methodologies for water system monitoring. The team collaborates across disciplines and institutions, focusing on integrating field measurements, airborne campaigns, and satellite data for environmental assessment. Damm’s leadership in projects like FLEX and HyPlant underscores his role in advancing spectroscopic remote sensing for global ecosystem monitoring.

Joel Waldfogelمشاهده پروفایل

استاد

• Industrial Organization
• Law and Economics
• Digital Markets
• +۳ مورد دیگر

Joel Waldfogel is a Professor and Frederick R. Kappel Chair in Applied Economics at the University of Minnesota's Carlson School of Management . He previously held positions at the Wharton School (University of Pennsylvania) and Yale University, and served as Associate Dean for MBA and MS programs at the Carlson School from 2017–2023. His academic journey began with a BA in Economics from Brandeis University (1984) and a PhD in Economics from Stanford University (1990). Education : PhD in Economics, Stanford University (1990) BA in Economics, Brandeis University (1984) Research Interests span industrial organization, law and economics, digital markets, intellectual property, and media economics. He focuses on platform economics, market efficiency in digital environments, and welfare implications of technological change, particularly in creative industries. Recent research trends include: Platform bias and regulatory frameworks (e.g., Digital Markets Act) Welfare impacts of gender-inclusive intellectual property creation Legal challenges from AI-generated content and copyright adaptation Consumer welfare in digital product markets Market structure in media and cultural industries Scientific Awards : Kaminstein Scholar at U.S. Copyright Office (2021–2022) Publications include over 80 articles in top journals like the American Economic Review and Journal of Political Economy , as well as books such as Digital Renaissance and The Tyranny of the Market . His work addresses platform power, digital regulation, and the economics of cultural goods.

Mattia Bianchiمشاهده پروفایل

مدرس

• Control Theory
• Game Theory
• Machine Learning
• +۳ مورد دیگر

Mattia Bianchi is a Lecturer at the Department of Information Technology and Electrical Engineering, ETH Zurich, Switzerland. He is affiliated with the Automatic Control Laboratory under Prof. Florian Dörfler, with office location at ETL I 34, Physikstrasse 3, Zurich. His research focuses on developing distributed, efficient, and robust methods for decision and control problems in complex network systems, including power grids and cognitive radio networks. Bachelor’s degree in Information and Communication Engineering (2016), University of L’Aquila, Italy Master’s degree in Systems Engineering (2018), University of L’Aquila, Italy PhD in Systems and Control (2018–2023), TU Delft, The Netherlands Postdoctoral researcher (2023–present), ETH Zurich, Switzerland His methodological approach integrates operator theory, learning algorithms, game theory, and data-driven control. Key research themes include uncovering common structures in optimization and control algorithms, with applications in distributed feedback optimization, Nash equilibrium seeking, and stabilization of constrained systems. Current work explores partial-decision information frameworks and linear convergence guarantees. For detailed information on his publications, visit his Google Scholar profile . Mattia actively supervises Master’s theses and semester projects, inviting candidates to contact him with their academic credentials.

Manos Athanassoulisمشاهده پروفایل

دانشیار

• Data Systems
• Cloud Data Management
• Database Systems
• +۷ مورد دیگر

Manos Athanassoulis is an Associate Professor in the Department of Computer Science at the College of Arts and Sciences, Boston University. He is the Founder and Director of the BU Data-intensive Systems and Computing (DiSC) lab and a member of the BU MiDAS group. His research focuses on data systems, particularly cloud data management, hybrid transactional/analytical workloads, and integration with emerging hardware such as non-volatile memory and heterogeneous computing. His educational background includes a PhD from EPFL (2014), an MSc in Computer Systems Technology, and a BSc in Informatics and Telecommunications from the University of Athens, Greece. Prior to BU, he was a Postdoctoral Researcher and Research Associate at Harvard University, supported by a SNSF Postdoc Mobility Fellowship. His research interests span data systems, database architectures, LSM trees, indexing, storage systems, and performance optimization. He explores how novel hardware can be leveraged to improve data management efficiency and scalability, especially in cloud environments. His recent publications (2021–2025) predominantly focus on LSM trees, covering topics such as compaction policies, Bloom filter tuning, DPU offloading, adversarial resilience, and sustainable caching. Earlier works include foundational contributions on access methods (RUM Conjecture) and optimal key-value stores (Monkey). The trend shows a consistent focus on data system efficiency, adaptability, and robustness under varying workloads and hardware constraints. Scientific Awards: NSF CAREER Award (2022) Facebook Faculty Research Award (2020) NSF CRII Award (2019) Best of VLDB 2017 and Best of SIGMOD 2017 SIGMOD Most Reproducible Paper Award (2017) Multiple ACM SIGMOD Distinguished PC Member recognitions (2018–2025) VLDB 2023 Best Demo Award RedHat Collaboratory Research Incubation Awards (multiple, 2021–2023) SNSF Postdoc Mobility Fellowship (2015–16) IBM PhD Fellowship (2011–12) Dr. Athanassoulis has advised numerous students and collaborators, evident from his co-authorship on works with researchers such as Niv Dayan, Stratos Idreos, and A. Ailamaki. His grants include major awards from NSF, Facebook, and RedHat, supporting research in robust data systems, hardware-software co-design, and learned cost models. He has also been recognized for teaching excellence at Harvard University. He leads the DiSC lab at Boston University, which focuses on data-intensive computing and systems research. The lab explores next-generation data architectures, particularly in cloud and hardware-aware environments. Collaborations with the BU MiDAS group enhance interdisciplinary research in data science and AI.

Tova Miloمشاهده پروفایل

استاد

• Databases
• Big Data Management
• Machine Learning for Data Management
• +۷ مورد دیگر

Professor Tova Milo is the Chair for Information Management at the School of Computer Science, Tel Aviv University, leading the prominent Databases Lab (DB Group). Her research spans databases, big data management, crowd-based data sourcing, and business process querying, with significant contributions to data integration and semi-structured data systems. Her research interests focus on innovative approaches to data management challenges through machine learning integration, crowd computing, and business process analysis. Key projects include Business Process Querying (BPQ) for analyzing BPEL specifications, MoDaS for crowd-based data management, and PROX for data provenance summarization. Her work bridges theoretical foundations with practical applications in fraud detection, recommendation systems, and data cleaning. Analysis of her recent publications reveals strong trends in human-in-the-loop data management systems, with growing emphasis on crowd integration for data cleaning and knowledge acquisition. Her research increasingly combines traditional database theory with machine learning techniques for scalable big data processing, while maintaining focus on business process modeling and provenance tracking. ACM PODS Alberto O. Mendelzon Test-of-Time Award (2010) ERC Advanced Investigators grant MoDaS (2011) The Weizmann Prize for Exact Sciences (2017) VLDB Women in Database Research award (2017) IEEE TCDE Impact award (2022) ISF Breakthrough Research Grant (2022) Doctorate Honoris Causa, University of Zurich (2023) ACM Fellow Member of Academia Europaea Professor Milo has advised over 30 graduate students including PhD candidates like Yael Amsterdamer and Ohad Greenshpan, and numerous MSc students working on projects including MoDaS, BPQ, and EDOS. Her research has been supported by major grants including the ERC Advanced Investigators grant and ISF Breakthrough Research Grant. She actively collaborates with industry partners including IBM and Microsoft, particularly in business process management standards. She directs Tel Aviv University's Databases Lab, which maintains the DB Group with multiple research streams including business process querying (BPQ), crowd-based data management (MoDaS), and self-adaptive data dissemination (EDOS/COLT). The lab operates from the Schreiber Building (M-20) and maintains strong international collaborations, particularly with European institutions through the ERC-funded MoDaS project.

• Security of Deep Learning Systems
• Side-Channel Attacks
• Privacy-Preserving Machine Learning
• +۳ مورد دیگر

Dr. Yuanyuan Yuan is a Researcher at the Department of Computer Science, ETH Zurich, Switzerland, based at CNB H 104.1, Universitätstrasse 6, 8092 Zurich. Her work bridges computer security and machine learning with a focus on practical vulnerabilities in deployed AI systems. Her research centers on exposing and mitigating security flaws in deep learning deployments, particularly targeting trusted execution environments (TEEs) and on-device inference systems. Key contributions include pioneering side-channel attacks against TEE-shielded neural networks (CipherSteal, HyperTheft), bit-flip attack surfaces in DNN executables (BitShield), and novel testing methodologies for neural network robustness. She investigates cache/timing side channels, ciphertext analysis, privacy leakage in partitioned ML, and concept-based explainability. Analysis of her 2023-2025 publications reveals a cohesive focus on offensive security research for AI infrastructures, with consistent contributions to top venues in security and machine learning. Her work demonstrates expertise in low-level system interactions (memory, cryptography) applied to ML security, spanning attack vectors, defensive mechanisms, and validation frameworks. The research trajectory shows increasing sophistication in exploiting hardware-software interfaces while developing practical hardening techniques for real-world deployments.

• Media History
• Television History
• Transmedia Narratives
• +۳ مورد دیگر

Massimo Scaglioni is a Full Professor of Media History at the Catholic University of the Sacred Heart in Milan, affiliated with the Faculty of Foreign Languages and Department of Communication and Performing Arts. He concurrently serves as a Lecturer in Transmedia Narratives and Television Communication at Università della Svizzera italiana (USI) in Lugano. Scaglioni directs research at Ce.R.T.A. (Research Centre for Television and Audiovisual Media) and leads the "Fare Tv" Master program at ALMED, while contributing to editorial boards of "View," "Comunicazioni Sociali," "Bianco e Nero," and "Series" journals. His research centers on institutional, economic, and social media history with emphasis on Italian radio and television within international contexts. He pioneers historical methodology application to audiovisual sources, analyzes technological-cultural convergence phenomena, and investigates transmedia narratives. His television text analysis specializes in American and European serial forms, while audience studies explore measurable viewership patterns, fandom dynamics, and evolving spectatorship practices in digital environments. Scaglioni's conference presentations from 2004-2011 reveal consistent engagement with Italian media history, public service broadcasting evolution, and audience behavior transformations. His work demonstrates how media institutions negotiate technological convergence, with recurring themes of national identity construction through broadcasting, transmedia storytelling frameworks, and the tension between traditional broadcaster control and participatory audience practices in digital convergence. As Didactic Director of ALMED's "Fare Tv" Master program, Scaglioni shapes graduate curricula in television analysis and management. His 2012 visiting professorship at Carleton University was funded by the Centre for European Studies - European Union Centre for Excellence grant, supporting transatlantic academic exchange in European media studies. Scaglioni's research leadership manifests through Ce.R.T.A. at Università Cattolica, which investigates television's historical and contemporary evolution, and the "Fare Tv" Master program that trains media professionals in television production, management, and communication strategies within Italy's dynamic broadcasting landscape.

Tim Ruben Davidsonمشاهده پروفایل

پژوهشگر

• deep generative models
• agentic systems
• synthetic data
• +۱ مورد دیگر

Tim Ruben Davidson is a Researcher at the Digital Life Lab (DLAB) within the School of Computer and Communication Sciences at EPFL. His current role is Doctoral Assistant, pursuing a doctoral program in Computer and Communication Sciences. His research focuses on advanced machine learning topics including deep generative models, agentic systems, synthetic data applications, and representation learning. His work bridges theoretical advancements in AI with practical implications, particularly in understanding AI agency, synthetic data generation, and latent space structures. Key research trends in his articles include exploring AI self-awareness, evaluating AI-driven peer review systems, and optimizing generative models through geometric and topological approaches. He has contributed to foundational studies on hyperspherical VAEs and reparameterization techniques on Lie groups, advancing mathematical foundations of neural networks. His work is published in top-tier venues and reflects interdisciplinary engagement between computer science, mathematics, and ethical AI considerations.