جست‌وجوی استاد و پژوهشگر

Alexander Eichlerمشاهده پروفایل

مدرس ارشد

• nanomechanical sensing
• spin physics
• quantum mechanics
• +۴ مورد دیگر

Dr. Alexander Eichler is a Senior Lecturer (Privatdozent) and Senior Scientist at ETH Zürich's Department of Physics. His research focuses on nanomechanical sensing, spin-mechanics coupling, and parametric phase logic devices. He holds a permanent position at ETH since 2019 and became a Privatdozent in 2022. Education: Doctoral studies at University of Basel (2006–2009) Teaching: Teaches Classical and Quantum Parametric Phenomena (Autumn 2025). His work explores cutting-edge topics like nuclear spin detection, parametric instability landscapes, and topological classification of nonlinear systems. Research highlights include developing membrane-based scanning force microscopy and creating Boltzmann machines using coupled parametric oscillators. Awards: KITE award for teaching innovation (2022) Swiss Physical Society ABB Award (2012) Marie-Curie IEF Fellowship (2010) Current projects involve nanoscale MRI, stochastic dynamics in coupled oscillators, and parametric phenomena in graphene resonators. His lab develops advanced microscopy techniques and quantum-classical hybrid systems.

• Digital Communications
• Information Theory
• Signal Processing
• +۳ مورد دیگر

Prof. Amos Lapidoth is a Full Professor at ETH Zürich's Department of Information Technology and Electrical Engineering, leading the Signal and Information Processing Lab. He holds a B.A. (summa cum laude) in Mathematics and B.Sc. (summa cum laude) in Electrical Engineering from the Technion, along with an M.Sc. from Technion and Ph.D. from Stanford University. Previously, he was an Associate Professor at MIT and held the KDD Career Development Chair in Communications and Technology. His research focuses on Digital Communications and Information Theory , with contributions to coding theory, channel modeling, and signal processing. He has conducted R&D in wireless communications at the IDF Signal Corps Labs, earning two Creative Thinking Awards. His awards include the NSF CAREER Award and Rothschild Fellowship. Recent work explores state-dependent channels with helpers, feedback-assisted communication, and identification over noisy channels. His lab develops theoretical frameworks for reliable communication in complex systems, with applications in coding, modulation, and network design. Labs/Teams: Head of Signal and Information Processing Lab at ETH Zurich Key Contributions: Pioneering work on helper channels, zero-error capacity, and Rényi entropy applications

• Network Science
• Nonlinear Dynamics
• Machine Learning
• +۴ مورد دیگر

Michelle Girvan is a Professor in the Department of Physics at the University of Maryland, College Park (UMD), with affiliations in the Institute for Physical Science and Technology and the Institute for Research in Electronics and Applied Physics. Her research focuses on network science applications in biological, social, and technological systems, integrating machine learning and nonlinear dynamics. She holds a B.S. from MIT (1999) and a Ph.D. from Cornell University (2003), followed by a postdoctoral fellowship at the Santa Fe Institute. Notable awards include the UMD Distinguished Scholar-Teacher (2022) and Fellow of the American Physical Society. Her work spans interdisciplinary topics such as information cascades, epidemiology, and genetic regulatory networks. Recent projects emphasize hybrid forecasting models combining machine learning with knowledge-based systems. Girvan leads the Girvan Networks Lab, advising over 20 graduate students and postdocs. She teaches courses like Physics 165 (Programming for Physical Sciences) and Physics 615 (Nonlinear Dynamics). Her $3M NSF grant supports training in network biology, leveraging computational methods to address complex systems challenges. Scientific contributions include studies on reservoir computing for dynamical systems prediction, jet lag asymmetry modeling, and network robustness in neuronal systems. She serves on the Santa Fe Institute's External Faculty and Science Steering Committee, promoting interdisciplinary collaboration in network science.

Ryan Williamsمشاهده پروفایل

استاد

• Algorithm Design
• Computational Complexity
• Theoretical Computer Science
• +۴ مورد دیگر

Ryan Williams is a Professor of Electrical Engineering and Computer Science at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Department of Electrical Engineering and Computer Science. Previously, he held a faculty position at Stanford University from 2011 to 2016. He obtained his PhD in Computer Science from Carnegie Mellon University under Manuel Blum and completed his undergraduate studies at Cornell University. His research focuses on computational complexity theory, exploring the boundaries of efficient computation and connections between algorithm design and complexity lower bounds. He teaches advanced courses such as Automata, Computability, and Complexity Theory at MIT. Education: PhD in Computer Science, Carnegie Mellon University (Advisor: Manuel Blum) Bachelor's Degree in Computer Science, Cornell University Research Interests: His work addresses fundamental questions in theoretical computer science, including the P vs. PSPACE problem, circuit lower bounds, and the development of algorithms with provable efficiency. He investigates connections between algorithmic techniques and complexity-theoretic limitations, aiming to establish barriers to solving computational problems efficiently. Publications and Trends: Ryan Williams' recent work spans topics like space-bounded computation, probabilistic polynomial sparsity, circuit lower bounds, and algorithms for compression and graph problems. His research often bridges theoretical insights with practical algorithm design, emphasizing the interplay between computational models and their limitations. Advising and Grants: Current advisees include Rahul Ilango, Ce Jin, and Ted Pyne. He has mentored numerous PhD students who have contributed to areas like fine-grained complexity and circuit analysis. While specific grants are not detailed, his research aligns with foundational studies in theoretical computer science. Labs and Teams: Williams is affiliated with MIT CSAIL, where he collaborates on projects exploring computational complexity and algorithmic foundations.

Michele Coti Zelatiمشاهده پروفایل

استاد

• Partial Differential Equations
• Infinite-Dimensional Dynamical Systems
• Fluid Dynamics
• +۴ مورد دیگر

Michele Coti Zelati is a Royal Society University Research Fellow and Professor of Mathematics at Imperial College London's Department of Mathematics within the Faculty of Natural Sciences. Her research focuses on partial differential equations, particularly infinite-dimensional dynamical systems, fluid dynamics, and nonlocal equations, supported by grants from the EPSRC Horizon Europe Guarantee and Royal Society. She contributes to editorial boards of journals including the Journal of Functional Analysis and Proceedings of the Royal Society of Edinburgh Section A: Mathematics. Current affiliation: Imperial College London (Faculty of Natural Sciences, Department of Mathematics) Previous roles: Chapman Fellow (Imperial), Brin Postdoctoral Fellow (University of Maryland) Key research areas include: Enhanced dissipation and mixing mechanisms Stability of shear and stratified flows Stochastic effects in fluid dynamics Dynamo action and magnetic field amplification Asymptotic behavior of PDEs Scientific awards: Royal Society University Research Fellowship EPSRC Horizon Europe Guarantee grant (EP/X020886/1) Former ERC-StG STABLE-CHAOS grant She teaches courses on partial differential equations, statistical mechanics in fluid flows, and Landau damping, with historical teaching roles spanning stochastic differential equations to advanced calculus. Her work appears in top journals like Archive for Rational Mechanics and Analysis, Communications in Mathematical Physics, and Annals of PDE.

Ayfer Ozgurمشاهده پروفایل

دانشیار

• Information Theory
• Wireless Communication
• Networks
• +۷ مورد دیگر

Ayfer Ozgur is an Associate Professor of Electrical Engineering at Stanford University and a Chambers Faculty Scholar in the School of Engineering. She holds a Ph.D. from EPFL (2009) and B.S./M.S. degrees from Middle East Technical University. Her research focuses on information theory, wireless communication, distributed estimation, and machine learning, with notable contributions to federated learning, privacy-aware systems, and energy-efficient networks. Education: Ph.D., EPFL (2009); M.S., Middle East Technical University (2005); B.S. in Electrical Engineering and Physics (2001). Research Interests: Information Theory and Geometry Wireless Networks and Communication Federated Learning and Privacy Energy Harvesting Systems Distributed Estimation and Learning Publications Trends: Ozgur's work emphasizes theoretical foundations of decentralized data science, privacy-utility trade-offs, and geometric approaches to information theory. Recent articles explore over-the-air statistical estimation, federated learning with secure aggregation, and optimal compression under privacy constraints. Awards: Recipient of the IEEE CTTC Early Achievement Award (2018), Okawa Foundation Award (2018), and NSF Career Award (2013). Advising & Grants: Supervises doctoral and master's students in distributed systems and machine learning. Current NSF projects include 'Foundations of Decentralized Data Science' and 'Geometric Network Information Theory.' Labs/Teams: Leads a research group with active members in theoretical and applied communication systems, collaborating with industry partners like Google and META.

Thomas Courtadeمشاهده پروفایل

دانشیار

• Information Theory
• Data Science
• Network Communication
• +۵ مورد دیگر

Thomas Courtade is an Associate Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He joined Berkeley in 2014 after a postdoctoral fellowship at Stanford University, supported by the NSF Center for Science of Information. His research focuses on information theory, data science, and their intersections with machine learning and privacy-preserving algorithms. Education: Ph.D. in Electrical Engineering, University of California, Los Angeles (2012) M.Sc. in Electrical Engineering, University of California, Los Angeles (2008) B.Sc. in Electrical Engineering, Michigan Technological University (2007, summa cum laude) Research Interests: Information Theory and its applications to network communication Privacy-preserving data analysis and differential privacy Statistical estimation under heterogeneous privacy constraints Optimization in distributed systems and market design Functional inequalities (Brascamp-Lieb, Poincaré-Korn) Machine learning with emphasis on model robustness and efficiency Awards and Fellowships: Electrical Engineering Award for Outstanding Teaching (2020) Hellman Fellow (2016) Advising and Grants: Supervised no listed students (student names not provided in text) Recipient of NSF CAREER Award (2018) Labs and Collaborations: Berkeley Laboratory for Information and System Sciences (BLISS) Center for Theoretical Foundations of Learning, Inference, and Mathematics (CLIMB)

• Quantum Nanophotonics
• Plasmonics
• Nonlinear Optics
• +۸ مورد دیگر

Prof. Alexandra Boltasseva is the Ron and Dotty Garvin Tonjes Distinguished Professor of Electrical and Computer Engineering at Purdue University, with a courtesy appointment in Materials Engineering. She leads the Quantum Nanophotonics group at the Birck Nanotechnology Center, focusing on plasmonics, metasurfaces, epsilon-near-zero (ENZ) materials, and machine learning-assisted photonics. Her work bridges fundamental research and applications in quantum technologies, nanolasers, and integrated photonic devices. College of Engineering, Purdue University Birck Nanotechnology Center Quantum Science Center Her research interests span quantum nanophotonics , plasmonics with 2D materials , nonlinear optics , and machine learning for photonic design . Recent projects explore photonic time crystals, ENZ-enhanced quantum emitters, and refractory plasmonic materials like MXenes and titanium nitride. The 15 most recent publications highlight trends in quantum light sources (e.g., single-photon emitters in silicon nitride), machine learning applications to optimize nanostructures, and nonlinear photonic materials . Key subfields include 2D materials, photonic time crystals, ENZ optics, and plasmonic enhancement of optoelectronic performance. Scientific accolades include: Optica R. W. Wood Prize (2023) Clarivate Highly Cited Researcher (2024, 2021, 2020, 2017) National Academy of Inventors Fellow (2020) SPIE Fellow (2015) Dimitris N. Chorafas Foundation Award (2021) She advises PhD students like Soham Saha, Shaimaa Azzam, and Deesha Shah, who have received prestigious awards. Her team collaborates on quantum photonics and nanoscale energy distributions of plasmonic hot-carriers.

Vladimir M. Shalaevمشاهده پروفایل

استاد

• Nanophotonics
• Plasmonics
• Optical Metamaterials
• +۷ مورد دیگر

Vladimir M. Shalaev is the Robert and Anne Burnett Distinguished Professor of Electrical and Computer Engineering at Purdue University , with additional appointments in Biomedical Engineering and Physics. He serves as Scientific Director for Nanophotonics at Purdue's Birck Nanotechnology Center and co-directs the Purdue Quantum Science and Engineering Institute . His research spans nanophotonics, plasmonics, and optical metamaterials. Education: Ph.D. in Physics and Mathematics, Siberian Federal University (Russia), 1983 Shalaev pioneers optical metamaterials with negative refractive index and spaser technology for nanoscale light sources. His group explores photonic time crystals , 2D quantum photonics , and machine learning-assisted optical design . Recent work focuses on refractory plasmonics and quantum emitter engineering in silicon nitride and transition metal dichalcogenides. The 15 most recent articles highlight his leadership in quantum nanophotonics , with trends including 2D material integration , machine learning for photonics , and epsilon-near-zero (ENZ) optics . Key subfields span quantum light sources , plasmonic-enhanced nonlinear optics , photonic time crystals , and advanced nanofabrication techniques . Scientific Recognition : 2025: Elected to National Academy of Engineering and American Academy of Arts and Sciences 2015-2024: Clarivate Highly Cited Researcher (Physics) 2015: IEEE Photonics Society William Streifer Award 2012: UNESCO Medal for Nanosciences Multiple journal-specific 'Most Cited Author' distinctions Advising focus on quantum photonics and plasmonic materials , Shalaev's team has secured 40+ US patents and maintains a Google Scholar h-index of 128 with 72,000+ citations. His research drives Purdue's quantum technology initiative , with applications in secure communications and nanoscale sensing .

Sourya Royمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Pseudorandomness
• Coding Theory
• +۴ مورد دیگر

Sourya Roy is an Assistant Professor in the Department of Computer Science at the University of Iowa. Prior to his academic career, he worked as a Data Scientist at Foursquare. He earned his PhD in Computer Science from the University of California, Riverside in 2022, advised by Silas Richelson and Amey Bhangale. Education: PhD in Computer Science (University of California, Riverside, 2022) His research spans theoretical and applied domains, focusing on pseudorandomness, coding theory, and cryptography in theoretical computer science, while also contributing to computer vision and machine learning applications. Recent publications emphasize expander graph theory, coding algorithms, and spatio-temporal modeling. Dr. Roy is actively seeking PhD students to collaborate on theoretical computer science projects. His publications highlight expertise in pseudorandomness, expander graphs, and scalable unsupervised learning techniques. Key conferences include FOCS, ECCV, and IEEE Transactions on Information Theory.

Marten van Dijkمشاهده پروفایل

استاد

• Computer Security
• Cryptography
• Secure Processor Design
• +۷ مورد دیگر

Dr. Marten van Dijk is a Full Professor in the Computer Security department at Vrije Universiteit Amsterdam (VU) since 2022 and a Group Leader for Computer Security at CWI since 2020. He also holds a Gratis Full Research Professor position at the University of Connecticut's ECE Department since 2020. Previously, he served as Associate and Full Professor at the University of Connecticut and held research roles at MIT CSAIL, RSA Laboratories, and Philips Research. PhD in Mathematics (1997, Eindhoven University of Technology) M.S. in Mathematics (Cum Laude, 1993) M.S. in Computer Science (Cum Laude, 1991) His research focuses on foundational computer security problems using cryptographic principles, including secure processor design, oblivious computation, and privacy-preserving machine learning. Notable contributions span Physical Unclonable Functions (PUFs), Aegis secure processor architecture, and oblivious RAM protocols. 15+ publications in 2023-2025 address topics like PUF cryptanalysis, differential privacy in federated learning, and Byzantine fault tolerance Key journals: IEEE Transactions on Computers, Journal of Cryptology, ACM CCS Conference Award highlights include: IEEE Fellow (2022) for secure processor design and encrypted computation IEEE Technical Achievement Award (2023) Intel Test of Time Award (2022) ACM CCS Best Paper (2013) A. Richard Newton Technical Impact Award (2015) His technical leadership spans hardware security (blu-ray error correction codes), cryptographic protocol design, and machine learning privacy frameworks. Current projects focus on secure processors with hardware-enforced isolation and differential privacy optimization.

Divesh Aggarwalمشاهده پروفایل

دانشیار

• Lattices
• Pseudorandomness
• Computational Complexity
• +۳ مورد دیگر

Divesh Aggarwal is an Associate Professor at the Department of Computer Science , National University of Singapore , and a Principal Investigator at the Centre for Quantum Technologies . He received his PhD from ETH Zurich under Prof. Ueli Maurer , focusing on theoretical cryptography. His research spans lattices, pseudorandomness, computational complexity, cryptography, and coding theory , with a focus on quantum-safe cryptographic systems. Research Themes: Lattice-based cryptography, quantum algorithms, computational complexity, non-malleable codes, and randomness extraction. Advising: Actively mentors graduate students in theoretical computer science and quantum computing. Professional Service: Serves on program committees for top conferences (CRYPTO, STOC, TCC, etc.) and editorial boards. Group Activities: Organizes a weekly research seminar covering diverse topics in theoretical computer science, including lattice algorithms, quantum computing, and cryptographic protocols.

Jad Silbakمشاهده پروفایل

پژوهشگر ارشد

• Cryptography
• Privacy
• Complexity Theory
• +۲ مورد دیگر

Jad Silbak is a Postdoctoral Researcher at Northeastern University , affiliated with the Department of Computer Science and funded by the Khoury College Distinguished Postdoctoral Fellowship . Prior to this, he held a postdoctoral position at Tel Aviv University under Benny Applebaum. His research lies at the intersection of Cryptography , Privacy , and Complexity Theory , focusing on: Secure computation protocols and oblivious transfer Differentially private algorithms and communication complexity Error-correcting/detecting codes under computational constraints Non-malleable codes and entropy-based cryptographic constructions Derandomization techniques for bounded models Publications highlight advancements in code design for computationally bounded channels, cryptographic lower bounds, and privacy-preserving protocols. He has presented at top-tier venues including CRYPTO, STOC, and FOCS. Scientific awards include: Khoury College Distinguished Postdoctoral Fellowship He has served as a teaching assistant at Tel Aviv University and the University of Haifa for courses in algorithms and computational models.

John Daneshمشاهده پروفایل

استاد

• Epidemiology
• Cardiovascular Disease
• Genetic Epidemiology
• +۳ مورد دیگر

John Danesh is Professor of Epidemiology and Medicine and Head of the Department of Public Health and Primary Care at the University of Cambridge. He concurrently serves as a Faculty Member at the Wellcome Sanger Institute, Honorary Consultant at Cambridge University Hospital NHS Foundation Trust (Addenbrooke's Hospital), Professorial Fellow at Jesus College, Cambridge, Founder-Director of the Cardiovascular Epidemiology Unit, and Director of Health Data Research UK-Cambridge. His research spans cardiovascular epidemiology, genetic determinants of disease, and health data science. He pioneers the integration of genomics, metabolomics, and large-scale population data to unravel disease mechanisms, with emphasis on polygenic risk scores, metabolite-disease associations, and international collaborations like the BELIEVE cohort in Bangladesh. His work bridges molecular biology with public health applications for precision prevention. Analysis of his 2022-2025 publications reveals dominant themes in multi-omics cardiovascular risk prediction, including genetic architecture of coronary disease, iron homeostasis pathways, and regional risk variations in South Asian populations. Key methodologies involve individual-participant-data meta-analyses, Mendelian randomization, and computational modeling of biological networks. Scientific Awards: No specific awards listed in source materials Professor Danesh leads major grant-funded initiatives including the Cardiovascular Epidemiology Unit and Health Data Research UK-Cambridge. His advisory roles in national health data infrastructure and longitudinal research environments demonstrate significant influence in shaping large-scale epidemiological studies and translational data science applications. He directs the Cambridge Mathematics of Information in Healthcare (CMIH) hub, fostering cross-disciplinary teams that apply advanced computational methods to healthcare data. His laboratory focuses on genetic and metabolic drivers of cardiovascular disease through international cohort studies and innovative risk prediction modeling.

Daniel Mayerمشاهده پروفایل

پژوهشگر

• Quantum Physics
• Ultracold Atomic Gases
• Nonequilibrium Thermodynamics
• +۳ مورد دیگر

Daniel Mayer is a researcher affiliated with the Department of Physics at RPTU Kaiserslautern-Landau. His work focuses on quantum physics, particularly in ultracold atomic gases , nonequilibrium thermodynamics , and quantum simulation . Current position: Researcher in the Widera research group Email: dmayer@rhrk.uni-kl.de Research highlights include: Quantum thermometry using single atoms Spin dynamics in Bose-Einstein condensates Non-equilibrium processes in few-body systems Precision spectroscopy of rubidium atoms His publications (2015–2023) demonstrate expertise in quantum optics , atomic physics , and statistical mechanics . He has contributed to advancements in single-atom manipulation and quantum sensing technologies.