جست‌وجوی استاد و پژوهشگر

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.

Niels Grønbech-Jensenمشاهده پروفایل

عضو هیئت علمی

• Computational Materials Science
• Molecular Dynamics
• Radiation Damage Modeling
• +۵ مورد دیگر

Niels Grønbech-Jensen is a Distinguished Professor holding dual appointments in the Department of Mechanical and Aerospace Engineering and the Department of Mathematics at the University of California, Davis. He is affiliated with the College of Engineering and actively contributes to both academic and research initiatives. His research focuses on computational materials science, molecular dynamics, radiation damage modeling, nonlinear dynamics, numerical analysis, and superconducting device physics. He has developed novel numerical algorithms for discrete-time simulations of thermodynamic systems, particularly in Langevin dynamics frameworks. Grønbech-Jensen's work bridges classical and quantum interpretations of phenomena such as Josephson junction behavior at low temperatures. He has pioneered methods for accurate configurational and kinetic statistics in simulations, earning recognition through prestigious awards including Fellowship in the American Physical Society (2010), the R&D 100 Award (1999), and the Gordon Bell Prize Honorable Mention (1993). His academic journey includes a Lic. (Ph.D.) from the Technical University of Denmark (1991) and postdoctoral research at Stanford University before joining UC Davis in 1999. His recent publications emphasize refining stochastic Verlet-type integrators and exploring velocity definitions in discrete-time Langevin simulations to ensure thermodynamic consistency. He also investigates void-induced explosive sensitivity in materials and the interplay between strain, temperature, and radiation damage in metals. Grønbech-Jensen leads the COSMOS Systemwide Faculty Executive Director role and has collaborated across disciplines, including computational studies of lipid-DNA complexes and theoretical analyses of scintillator materials. His research group actively publishes in Molecular Physics , Physical Review Letters , and Journal of Statistical Physics .

• Satellite Networks
• Machine Learning
• Neural Networks
• +۳ مورد دیگر

Dr. Olga Kondrateva is a researcher specializing in small satellite networks , with a focus on machine learning applications for data transmission optimization . Her work addresses critical challenges in low-Earth-orbit (LEO) satellite communication , including short contact times , bandwidth limitations , and high packet loss probabilities . Key contributions include novel techniques for joint source-and-channel coding , incremental neural network updates , and mathematical optimization frameworks like Benders decomposition for scalable satellite network planning. Education: PhD (implied by "Dr." prefix) Research Interests Olga’s research bridges machine learning and network optimization in constrained satellite environments. She develops methods to enhance reliability and efficiency of onboard neural networks, prioritizes model parameter updates, and designs communication protocols for Earth observation missions . Her work often leverages vector quantization and linear programming to address unique challenges like intermittent connectivity and dynamic mission requirements . Publication Trends Her recent publications (2022–2024) emphasize progressive neural network updates , fault tolerance , and throughput optimization in LEO satellite networks. Themes include bandwidth-efficient transmission , model compression , and incremental learning to adapt to changing mission needs.

Xiaojuan Qiمشاهده پروفایل

استادیار

• 3D Vision
• Deep Learning
• Artificial Intelligence
• +۴ مورد دیگر

Dr. Xiaojuan Qi is an Assistant Professor in the Department of Electrical and Electronic Engineering at The University of Hong Kong. Her research focuses on 3D vision, deep learning, and AI applications in medical imaging and science. She holds memberships in IEEE and CCF. Education: PhD, The Chinese University of Hong Kong (2018) BEng, Shanghai Jiao Tong University (2014) Postdoctoral Researcher, University of Oxford (prior to HKU) Research Interests: Dr. Qi develops scalable deep learning algorithms for medical and natural image analysis, 3D scene understanding, and neural network behavior in out-of-distribution scenarios. Her work emphasizes label efficiency, geometric reasoning, and cross-modal integration. Lab Contributions: As head of the Computer Vision and Machine Intelligence Lab (CVMI Lab), she explores open-world intelligence, 3D reconstruction, and AI applications in robotics, autonomous systems, and healthcare. Recent lab highlights include 10+ ICCV/CVPR/NeurIPS papers in 2023-2025. Awards: ImageNet Semantic Parsing Challenge (1st Place) Outstanding Reviewer Awards (ICCV 2017/2019) CVPR Doctoral Consortium Travel Award Advising & Grants: Supervises ~20 PhD students (listed in detail). Active in securing grants for embodied AI, medical imaging, and edge computing hardware. Lab Collaborations: Focuses on interdisciplinary projects with Intel, Oxford, and Toronto groups, emphasizing 3D vision, neuromorphic computing, and AI-driven medical solutions.

James Hendersonمشاهده پروفایل

پژوهشگر

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

James Henderson is a Senior Researcher at Idiap Research Institute where he heads the Natural Language Understanding group. He currently serves as Action Editor for Transactions of the Association for Computational Linguistics (TACL) and was recently awarded an ERC Advanced Grant for his project 'Interpretable Beliefs and Programmable Knowledge with Bayesian Attention in Large Language Models' (BALM). Previously, Henderson held positions as Chargé de Cours at University of Geneva's Department of Computer Science and Principal Scientist at Xerox Research Centre Europe (now Naver Labs Europe). Henderson's research focuses on machine learning methods for natural language processing, with pioneering work on recurrent neural networks for syntactic and semantic parsing. His current investigations include representation learning for language semantics, graph-to-graph deep learning models, entity induction, and variational-Bayesian attention-based representation learning. His research bridges Bayesian inference, transformer architectures, and structured prediction for NLP tasks. His publication portfolio demonstrates consistent contributions to core NLP methodologies, with recent emphasis on transformer optimization, Bayesian neural methods, efficient model architectures, and graph-based language representations. Research frequently appears in top venues including ACL, EMNLP, ICLR, and NeurIPS. Honors: ERC Advanced Grant (2023) Henderson leads the Natural Language Understanding group at Idiap, currently recruiting PhD students and postdoctoral researchers for his ERC project. He obtained his PhD and MSc from University of Pennsylvania and BSc from Massachusetts Institute of Technology, all in computer science.

Volkan Cevherمشاهده پروفایل

دانشیار

• Machine Learning
• Optimization
• Reinforcement Learning
• +۵ 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.

Marcel Nutzمشاهده پروفایل

استاد

• Mathematical Finance
• Optimal Transport
• Game Theory

Marcel Nutz is a Professor of Statistics at Columbia University's Faculty of Arts and Sciences. He holds a PhD in Mathematics from ETH Zurich. Research Focus His primary research areas include: Mathematical Finance: Modeling market dynamics, trading strategies, and asset pricing Optimal Transport: Theoretical foundations and computational methods Game Theory: Strategic interactions in economic and financial contexts Recent Publication Trends (2023-2025) Analysis of 15 most recent articles reveals strong emphasis on: Regularized optimal transport techniques (quadratic/sparse) Convergence properties of computational algorithms (Sinkhorn, quantization) Financial applications: Price impact, volatility modeling, execution games Interdisciplinary connections: Machine learning (variational inference), information theory (rate-distortion) Mathematical foundations: Schrödinger bridges, martingale transport Awards and Honors No scientific awards mentioned in available sources. Academic Activities No information available regarding student advising, research grants, or laboratory affiliations.

Wai-Kwong Kwokمشاهده پروفایل

استاد مدعو

• Topological Materials
• Nanoscale superconductors and magnets
• Vortex matter in superconductors
• +۲ مورد دیگر

Wai-Kwong Kwok is an Argonne Distinguished Fellow and Senior Physicist at Argonne National Laboratory, where he leads the Superconductivity & Magnetism Group. He concurrently serves as an Adjunct Professor at the University of Notre Dame and holds a Fellowship with the Northwestern Argonne Institute for Science and Engineering (NAISE). His 30+ year career at Argonne began with a postdoctoral appointment in 1987. His educational background: Ph.D. in Condensed Matter Physics, Purdue University B.A. in Physics and Mathematics, Kenyon College Kwok's research centers on quantum phenomena in superconducting and magnetic materials, with emphasis on topological materials, nanoscale superconductors/magnets, vortex matter dynamics, and correlated electron systems. His work bridges fundamental condensed matter physics with applications in quantum computing and energy technologies, particularly through engineered material heterostructures. Analysis of his recent publications reveals dominant themes in iron-based magnetic superconductors (e.g., RbEuFe 4 As 4 ), topological superconductivity in doped insulators (Sr 0.1 Bi 2 Se 3 ), and hybrid systems combining superconductors with artificial spin ices. His group pioneers methods to manipulate vortex matter through nanoscale patterning and magnetic frustration, with direct implications for superconducting device engineering. His major recognitions include: American Physical Society Outstanding Referee Award (2019) Fellow of the American Physical Society (1999) University of Chicago Outstanding Accomplishment Award (1998) Department of Energy Award for Outstanding Accomplishment in Materials Science (1997) Department of Energy Award for Outstanding Accomplishment in Materials Science (1990) Kwok has mentored 12 PhD students, postdocs, and diploma students since 1990, plus over 25 undergraduates through the DOE-sponsored SERS program. His leadership extends to major research initiatives: Co-Associate Director for the DOE Energy Frontier Research Center on Emergent Superconductivity (2014-2018) Theme Leader for Vortex/Critical Current research in the same EFRC (2009-2014) Contributor to DOE-BES Basic Research Needs Report on Quantum Materials (2016) He directs Argonne's Superconductivity & Magnetism Group, leveraging national facilities including the Advanced Photon Source and Center for Nanoscale Materials to investigate quantum material behavior at atomic scales, with ongoing work focused on topological superconductivity and vortex engineering for quantum applications.

Raviraj Adveمشاهده پروفایل

استاد

• Wireless Communications
• Radar Signal Processing
• Cooperative Networks
• +۷ مورد دیگر

Professor Raviraj Adve is affiliated with the University of Toronto , working in the Department of Electrical and Computer Engineering and cross-listed with the Electromagnetics Group . He holds a PhD in Electrical Engineering from Syracuse University (1996) and previously worked at Research Associates for Defense Conversion Inc. (1997–2000). Education: B.Tech, Indian Institute of Technology Bombay (1990) PhD, Syracuse University (1996) Research Interests span two main areas: Wireless Communications (heterogeneous networks, massive MIMO, cooperative communication, resource allocation) and Radar Signal Processing (waveform diversity, space-time adaptive processing, HF radar systems). His work emphasizes practical algorithm development for real-world constraints. Scientific Contributions include 15 recent articles covering cooperative networks , radar clutter modeling , low-complexity MIMO algorithms , and molecular communication . Notable awards: IEEE Fellow (2017), Young Radar Engineer of the Year (2009), and multiple teaching awards. Leadership Roles: Vice-chair of IEEE Radar Systems Panel, Associate Editor for IEEE Trans. on Aerospace and Electronic Systems , and editorial board member for IET Trans. on Radar, Sonar and Navigation .

Kexin Rongمشاهده پروفایل

استادیار

• Database systems
• Machine learning
• Data analytics
• +۳ مورد دیگر

Kexin Rong is an Assistant Professor in the School of Computer Science at Georgia Institute of Technology. She leads the D2I Lab, which is part of the Georgia Tech database group, and maintains an affiliation with VMware Research Group as an affiliated researcher. Dr. Rong received her Ph.D. in Computer Science from Stanford University in 2021 (advised by Peter Bailis and Philip Levis) and her B.S. in Computer Science from California Institute of Technology in 2015. Her academic journey includes recognition as an EECS Rising Star by UC Berkeley and an Honorable Mention for the SIGMOD Jim Gray Doctoral Dissertation Award. Her research focuses on democratizing data science through systems that simplify large-scale data analytics for non-experts. She synthesizes techniques from data management, machine learning, and human-computer interaction to improve both computational and human efficiency. Current research directions include analytics over unstructured data (Video Labeling with SketchQL and Image Understanding with VCR) and data infrastructure for AI (Data preprocessing with LOTUS and DiffPrep, Checkpointing for DL training with Inshrinkerator). Her recent publications show a strong trend toward building user-friendly systems that bridge complex data analytics and non-expert users, with significant impact demonstrated by adoption of systems like ASAP by industry players including Datadog and TimescaleDB for time series visualizations. Amazon Research Award recipient (2024) SIGMOD 2024 Distinguished PC Award Multiple NSF awards (2023-2024) Best Paper Finalist at IISWC 2024 Catherine M. and James E. Allchin Early Career Professorship Dr. Rong actively mentors students, with recent PhD graduates including Peng Li (VLDB'23 Best Paper Award winner), Hantian Zhang, and Renzhi Wu now at leading tech companies. She teaches core database courses including CS6400 Database Systems Concepts and Design and CS4440 Emerging Database Technologies. The D2I Lab under her direction is committed to providing an inclusive environment that welcomes applicants from diverse backgrounds, especially those underrepresented in computer science.

Volker Schomerusمشاهده پروفایل

استاد

• String Theory
• Quantum Field Theory
• Mathematical Physics
• +۲ مورد دیگر

Volker Schomerus is a Professor of Mathematical Physics at the University of Hamburg and a Lead Scientist at DESY (Deutsches Elektronen-Synchrotron). His research focuses on the unification of string theory, quantum field theory, and mathematical physics, particularly through geometric quantization of spacetime and solving quantum theory challenges via geometric techniques. He coordinates the European RTN GATIS initiative and previously led the DESY Theory Group (2007–2010). Research Interests : Schomerus explores string theory's role in fundamental physics, including quantum geometry, holographic dualities, and integrable models. His work bridges particle physics, conformal field theory, and statistical mechanics, with emphasis on bootstrap methods, thermal correlators, and defect CFTs. Publications : Recent articles (2020–2025) demonstrate advances in multipoint conformal bootstrap, holographic interfaces, and thermal field theory. Key themes include integrability in CFT, lightcone limits, and applications of Gaudin models to higher-dimensional quantum systems. Academic Leadership : Heads research groups at DESY and Universität Hamburg, fostering collaborations in theoretical high-energy physics. No awards or students are explicitly listed in the sources.

• Adversarial Machine Learning
• Anomaly Detection
• Graph Convolutional Network
• +۷ مورد دیگر

Tianjin Huang is a University Researcher in the Mathematics and Computer Science school at Eindhoven University of Technology, focusing on deep learning, adversarial training, and sparsity. Their work spans traffic prediction, fairness in AI, and robustness evaluation. Specializes in Adversarial Machine Learning and Spatiotemporal Graph Modeling Develops novel frameworks for Traffic Prediction under real-world data challenges Explores Sparse Neural Networks for efficiency and performance Recent research trends include 4-bit training stability for LLMs, confusional spectral regularization for fairness, and principal eigenvalue methods for certified robustness. Their 2025 publications demonstrate cutting-edge work in adversarial training, graph neural networks, and low-bit optimization. Scientific recognition includes the Best Paper Award of LoG 2022 for collaborative work on sparse neural network training. They collaborate with institutions across Europe and Asia, focusing on trustworthy AI systems.

Patricia Römerمشاهده پروفایل

پژوهشگر

• Phase Retrieval
• Inverse Problems
• Imaging Science
• +۳ مورد دیگر

Patricia Römer is a doctoral student and scientific staff member at the Department of Mathematics, Technical University of Munich (TUM), School of Computation, Information and Technology, with dual affiliation at the Helmholtz Center Munich. Her research focuses on developing algorithms for phase retrieval in imaging applications with Poisson noise. Her key research interests include: Phase retrieval and inverse problems Computational imaging under Poisson noise Optimization algorithms for low-dose imaging Mathematical recovery guarantees Ptychography and background removal techniques Recent publications (2021–2024) demonstrate consistent advancement in phase retrieval methodologies, particularly for real-world imaging scenarios involving quantization and noise constraints, bridging theoretical mathematics with practical computational solutions. Scientific awards: No awards documented As a doctoral student, she does not advise students. No grants are referenced. She operates within TUM's Data Science research group and maintains active collaboration with the Helmholtz Center Munich on imaging projects.

Oscar Fernandezمشاهده پروفایل

استاد

• Geometric Mechanics
• Mathematical Demography
• Nonholonomic Mechanics
• +۳ مورد دیگر

Oscar E. Fernandez is a Professor of Mathematics at Wellesley College, where he has been employed since 2011. He specializes in geometric mechanics (particularly nonholonomic mechanics) and mathematical demography, with recent explorations into quantum nonholonomic mechanics and calculus of variations in demographic modeling. Education: B.A. and B.S. from the University of Chicago, Ph.D. in Applied and Interdisciplinary Mathematics from the University of Michigan-Ann Arbor (2009) His research bridges mathematics with physics and demography, focusing on systems subjected to velocity constraints and lifespan inequality analysis. He advocates for making mathematics accessible through systems thinking and founded the Wellesley Emerging Scholars Initiative (WESI), recognized as a Bright Spot in Hispanic Education by the White House (2015). Fernandez has authored four books: Everyday Calculus (2014), The Calculus of Happiness (2017), Calculus Simplified (2019), and the upcoming Calculus 2 Simplified (2025). These works emphasize practical relevance and accessibility, connecting mathematics to health, finance, and daily life. Scientific Contributions: Developed novel approaches to quantize nonholonomic systems Created high-accuracy life expectancy estimation methods Advanced nonholonomic mechanics integration techniques Explored entropy metrics in life table analysis Honors: White House Initiative Bright Spot in Hispanic Education (2015) MAA-Tensor SUMMA funding recipient As Faculty Director of Wellesley’s Pforzheimer Learning and Teaching Center (2018-2021), he championed equitable education practices and developed interactive math applets to enhance engagement.

Radu-Lucian LUPŞAمشاهده پروفایل

مدرس

• Image Processing
• Computer Graphics
• Graph Algorithms
• +۳ مورد دیگر

Radu-Lucian LUPŞA serves as a Lecturer at Babeş-Bolyai University's Faculty of Mathematics and Computer Science in Romania. His academic career spans over two decades with a consistent focus on computer science, particularly in image processing and graph algorithms. His teaching responsibilities include courses on Graph Algorithms and Parallel and Distributed Programming, demonstrating his expertise across theoretical and applied computer science domains. LUPŞA's research interests center around image processing, with significant contributions to compression methods, dithering algorithms, and multiresolution analysis. His work bridges theoretical computer science with practical applications in graphics and healthcare optimization. The evolution of his research shows a progression from fundamental image compression techniques in the late 1990s to more specialized applications in medical imaging and transportation logistics by the late 2000s. Analysis of his publications reveals strong expertise in computational geometry, algorithm design, and optimization problems. His research demonstrates consistent application of mathematical rigor to practical computing challenges, particularly in the areas of image representation and processing. The interdisciplinary nature of his work is evident in publications spanning computer graphics, healthcare logistics, and mathematical optimization. LUPŞA completed his PhD in 2006 with the dissertation titled Contributions to the analysis, processing and representation of images , which serves as a foundation for his subsequent research trajectory. His academic output shows a period of high productivity between 1996-2008 with publications in respected venues including IEEE conferences and academic journals. As an educator, LUPŞA has developed comprehensive teaching materials for graph algorithms, including lecture notes, practical assignments, and evaluation methods. His teaching approach emphasizes both theoretical foundations and practical implementation, as evidenced by the detailed laboratory assignments and programming tasks he has designed for students.