جستجوی استاد راهنمای دانشگاهی

Jesse Laeuchliمشاهده پروفایل

مدرس ارشد

• Cyber Security
• Quantum Computing
• Linear Algebra
• +۵ مورد دیگر

Dr. Jesse Laeuchli is a Senior Lecturer in the Department of Computer Science and Engineering at UNSW, specializing in Cyber Security, Quantum Computing, and Numerical Linear Algebra. He holds a PhD from the College of William and Mary, where his dissertation focused on matrix function diagonal estimation. His research bridges theoretical foundations (e.g., graph analytics, differential privacy) with practical applications like quantum remote memory attestation and posthumous data management through tools like BeyondLife. He has conducted security reviews for the Australian Government and led projects funded by the Defense Innovation Network and EU's HERMES initiative. Education : PhD in Numerical Linear Algebra, College of William and Mary Research Interests : Quantum Cybersecurity: Developing protocols using quantum entanglement for remote hardware verification Cyber Defense: Specializing in penetration testing and securing IoT systems Data Privacy: Innovating differential privacy techniques for centrality measures and graph analysis Legacy Systems: Creating open-source solutions for posthumous data management Grants & Awards : $528k IoT Security Grant (CSCRC, 2023) $136k Quantum Security Grant (Defense Innovation Network) Cyber Security CRC Award for pandemic contact tracing efforts Advising & Labs : Supervises research in quantum security and data forensics. Collaborates on projects like BeyondLife, an open-source digital will platform.

Zheng Keمشاهده پروفایل

دانشیار

• Network data analysis
• Text analysis
• Natural Language Processing
• +۴ مورد دیگر

Zheng (Tracy) Ke is an Associate Professor of Statistics at Harvard University's Department of Statistics. Her academic journey includes roles at the University of Chicago and Harvard, with a Ph.D. from Princeton University and a B.S. from Tsinghua University. She specializes in network data analysis, text analysis/NLP, high-dimensional statistics, machine learning, and bioinformatics/statistical genetics. Research Contributions Her research emphasizes network analysis (e.g., SCORE normalization, community detection), text analysis (topic modeling, sentiment analysis), and high-dimensional statistics (covariance estimation, eigenvalue analysis). Notable works include the MADStat project on statisticians' networks and advancements in GWAS data harmonization. Awards & Recognition COPSS Emerging Leader Award (2024) Sloan Research Fellowship (2023) IMS Peter Gavin Hall Prize (2021) Professional Activities She serves as an Associate Editor for the Journal of the American Statistical Association and organizes workshops on statistical network analysis and complex data systems. Her work bridges theoretical statistics with applications in genomics, finance, and social networks.

Harold Williamsمشاهده پروفایل

استادیار

• Representation Theory
• Geometry
• Combinatorics
• +۵ مورد دیگر

Harold Williams is an Assistant Professor in the Department of Mathematics at the University of Southern California, supported by an NSF CAREER Grant (DMS-2143922). His research bridges representation theory, geometry, and combinatorics with theoretical physics. He previously held a NSF Postdoctoral Fellowship (DMS-1502845) at the University of Texas at Austin and was a researcher at the Center for Quantum Mathematics and Physics (QMAP) at UC Davis. Research Interests: Representation Theory Geometric and Categorical Structures in Quantum Field Theory Cluster Algebras and their Applications Symplectic Geometry and Mirror Symmetry Mathematical Physics (Integrable Systems, Gauge Theory) Key Research Trends: His work emphasizes categorical and geometric approaches to algebraic structures, with notable contributions to cluster varieties, Coulomb branches, and Langlands duality. Recent publications explore connections between Kasteleyn operators, spectral networks, and mirror symmetry. Awards and Grants: NSF CAREER Grant (2021–present) NSF Postdoctoral Fellowship (2015–2018) Teaching and Outreach: In Spring 2025, he teaches MATH 407 'Probability Theory' at USC. He organizes the 7th Canada-Mexico-US Meeting on Representation Theory (July 2025) and ran the Los Angeles Workshop on Representations and Geometry (LAWRGe) from 2022–2024. Additional Background: Studied classical guitar at UT Austin School of Music. Previously consulted for Helm.ai on machine learning for autonomous vehicles.

Lorenzo Campos Venutiمشاهده پروفایل

دانشیار مدعو

• Condensed Matter Physics
• Quantum Information Processing
• Quantum Statistical Mechanics
• +۷ مورد دیگر

Lorenzo Campos Venuti is an Adjunct Associate Professor (Research) in the Department of Physics and Astronomy at the University of Southern California (USC), affiliated with the USC Dornsife College of Letters, Arts and Sciences. His research focuses on foundational aspects of quantum mechanics, quantum information processing, and condensed matter physics. He holds a Ph.D. in Condensed Matter Physics from the University of Stuttgart (2003) and an M.S. in Physics from the University of Bologna (1998), followed by postdoctoral training at the Institute for Scientific Interchange (ISI) in Torino, Italy (2006–2011). His research interests include equilibration and thermalization in quantum systems, quantum entanglement dynamics, topological protection of coherence, and the development of numerical algorithms for quantum many-body systems. He explores topics such as the eigenstate thermalization hypothesis, hidden order in quantum states, and long-distance entanglement creation. His work also addresses quantum annealing optimization, error suppression techniques, and the interplay between dissipation and quantum data classification. Recent research trends in his publications emphasize interdisciplinary approaches, combining quantum statistical mechanics with computational methods for open quantum systems. His studies on adiabatic quantum computing and boundary cancellation methods aim to enhance the robustness of quantum algorithms against noise and errors. Venuti has contributed to theoretical frameworks for understanding quantum criticality, non-Markovian dynamics, and the geometry of quantum state manifolds. His grants and advising activities are not detailed in the provided text, but his extensive publication record reflects a strong focus on advancing quantum technologies and foundational quantum physics. He is associated with the USC Dornsife College, leveraging its interdisciplinary environment for collaborative research in theoretical and computational physics.

Kaave Hosseiniمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Additive Combinatorics
• Approximate Algebraic Structure
• +۳ مورد دیگر

Kaave Hosseini is an Assistant Professor in the Department of Computer Science at the University of Rochester. He holds a PhD from UC San Diego and a BSc from Sharif University of Technology. His research focuses on theoretical computer science and additive combinatorics, emphasizing approximate algebraic structures and pseudorandomness. Education: PhD in Computer Science, UC San Diego (advisor: Shachar Lovett) BSc in Mathematics and Computer Science, Sharif University of Technology Research Interests: His work bridges theoretical computer science and mathematics, particularly in: Computational complexity Combinatorial structures Discrepancy theory Algebraic methods in computer science Key Publications: Recent works include advancements in communication complexity, pseudorandomness, and lower bounds. Notable achievements include a Best Paper Award at ICALP 2023. Awards: Best Paper Award at ICALP 2023. Teaching: He has taught courses such as Advanced Algorithms (CSC 484/284) and Analytic Methods in Computer Science (CSC 488/288) at the University of Rochester, and combinatorics and probability courses at Carnegie Mellon University. Professional Activities: Organized the Eastern Great Lakes (EaGL) workshop in Theory of Computation.

• Animal Breeding
• Veterinary Medicine
• Biomedicine
• +۸ مورد دیگر

Christos Dadousis is a Research Fellow in Health and Biomedical Informatics at the University of Surrey's School of Health Sciences. He is affiliated with the Digital Health Expert Group, Cancer Care Expert Group, and the Institute for Sustainability. His research focuses on animal breeding, veterinary medicine, biomedicine, and genetic diversity, with a particular emphasis on genomic selection, inbreeding mitigation, and host-microbiome interactions. Dadousis holds a PhD in Animal Science from the University of Padova and has conducted postdoctoral research at institutions like the Roslin Institute (Edinburgh) and the Universities of Florence and Parma. Education: PhD in Animal Science (University of Padova, Italy), Postdoctoral Research at Roslin Institute (UK) and Universities of Florence/Parma (Italy). Affiliations: Veterinary Health Innovation Engine (VHIVE) research team, Sustainability Fellow at the Institute for Sustainability. His research interests span genetic diversity, statistical genomics, and digital health applications in agriculture. Key projects include genomic studies on poultry microbiota, renal disease in companion animals, and inbreeding mitigation strategies in dairy cattle. Dadousis also contributes to preserving endangered livestock breeds through genomic analysis. Publications highlight his work on GWAS for poultry traits, genomic inbreeding estimation, and FTIR-based milk quality prediction. He actively participates in committees like the European Federation of Animal Science (EAAP) Young Club.

• Neuroscience
• Brain Injury
• Stroke
• +۵ مورد دیگر

Dr. Philip Dean is a Research Fellow and Deputy Chair of the University Ethics Committee at the University of Surrey's School of Psychology, within the Brain and Behaviour section of the Faculty of Health and Medical Sciences. He holds a PhD from the University of Surrey, an MSc in Neuroscience from the University of Edinburgh, and a BSc in Biochemistry & Genetics from the University of Sheffield. His research focuses on clinical neuroscience, particularly risk factors, outcomes, and interventions for brain injury. He investigates interactions between brain injury, healthy ageing, and neurodegenerative disorders using neuroimaging (EEG, MRI/fMRI) and multivariate pattern analysis. Key areas include creatine supplementation for neuroprotection, motor imagery performance, and post-concussion syndrome (PCS) mechanisms. Dr. Dean supervises numerous PhD students in topics like cognitive control, motor disorders, and anxiety. He collaborates with institutions such as the University of São Paulo and Federal University of ABC, Brazil. His work spans over 30 peer-reviewed publications, including studies on PCS structural correlates, EEG method validation, and stroke rehabilitation efficacy.

Carsten Urbachمشاهده پروفایل

استاد

• Theoretical Physics
• Lattice Quantum Chromodynamics (QCD)
• Quantum Computing
• +۴ مورد دیگر

Carsten Urbach is a Professor of Theoretical Physics at the Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn. His research focuses on lattice quantum chromodynamics (LQCD), quantum computing, and high-performance computing. He holds roles including spokesperson of CRC1639 NuMeriQS and editorial board member of the European Physics Journal A. Educations: PhD in Physics (2005), Diplom in Physics (2002), both from Freie Universität Berlin Research interests span strongly coupled quantum systems, hadron properties, lattice QCD simulations, and algorithm development. Recent work emphasizes Hamiltonian lattice gauge theories, quantum computing applications, and reproducibility in lattice QFT. Key contributions include studies on meson spectroscopy, form factors, and the muon g-2 anomaly using twisted-mass fermions. Awards include the Mario Markus Prize (2022) and Pineapple Award in Physics (2021). Active in collaborative projects like the International Lattice Data Grid 2.0. Leads the Lattice QCD group at Bonn, advancing computational infrastructure and open science practices.

Weihong Guoمشاهده پروفایل

استاد

• Mathematical Image Analysis
• Inverse Problems
• Scientific Computing
• +۳ مورد دیگر

Professor Weihong Guo holds the position of Professor and Chair in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University. His research focuses on Mathematical Image Analysis, Inverse Problems, Scientific Computing, and Computer Vision, with significant contributions to medical imaging and computational mathematics. His work emphasizes developing advanced algorithms for image segmentation, denoising, and reconstruction, often integrating deep learning with traditional variational methods. Notable applications include lung nodule detection, MRI muscle segmentation, and quantum tensor decomposition in biomedical contexts. Guo’s research also extends to graph signal processing, dynamic network analysis, and sparse recovery techniques. He has published extensively in top-tier journals and conferences, demonstrating a strong interdisciplinary approach bridging applied mathematics, computer science, and biomedical engineering.

Michael Tippingمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Data Science
• +۹ مورد دیگر

Michael Tipping is Professor of Machine Learning within the Department of Computer Science at the University of Bath, where he joined in early 2016. He is a member of the Artificial Intelligence & Machine Learning research group and the Bath Institute for the Augmented Human. His work spans both theoretical and applied aspects of machine learning, with particular expertise in probabilistic modeling and Bayesian statistical approaches. Professor Tipping's research interests center on Machine Learning and extend to Data Science and Artificial Intelligence, viewed from a probabilistic modeling and Bayesian statistical perspective. His specific research areas include Sparse Bayesian models (the "relevance vector machine") and related novel learning techniques, probabilistic approaches to tree-based pattern recognition, adaptive analysis of multivariate time series, methods for intelligent statistical automation, new perspectives on deep neural networks, and model-driven data mapping and visualisation techniques. He has maintained an interest in specific application areas including medical diagnostics, automotive modeling, payment fraud detection, sports performance analysis, and interactive entertainment. His publication record demonstrates consistent contributions to the field since the 1990s, with particular impact in probabilistic methods. His most influential works include the Relevance Vector Machine (over 6,300 citations) and Probabilistic Principal Component Analysis (over 2,800 citations), which have become foundational techniques in Bayesian machine learning. His recent work continues to bridge theoretical advances with practical applications across diverse domains, as evidenced by his 2024 publication on underwater environment classification. Professor Tipping has been recognized for his significant contributions to the field, with his publications being cited thousands of times across various platforms. His work on the Relevance Vector Machine has been particularly influential, offering a probabilistic alternative to Support Vector Machines with comparable performance but dramatically fewer basis functions. With extensive industry experience including eight years at Microsoft Research (Cambridge), six years running an independent statistical consultancy, and two years as Director of Science at Cambridge analytics start-up Featurespace, Professor Tipping brings a unique perspective that bridges academic research and real-world applications. His work on the "Drivatar AI" system for Microsoft's Forza Motorsport franchise demonstrates his ability to translate advanced machine learning concepts into consumer-facing technologies.

Lijun Dingمشاهده پروفایل

استادیار

• Optimization
• Semidefinite Programming
• Statistics
• +۲ مورد دیگر

Lijun Ding is an Assistant Professor specializing in optimization and data science at UC San Diego. He received his Ph.D. in Operations Research from Cornell University in 2021 and teaches courses in optimization and data science. His research focuses on continuous optimization methods, particularly semidefinite programming and applications in statistics. Prior to joining UCSD, he was an assistant professor at Texas A&M University and a post-doctoral scholar at the Institute of Foundations of Data Science. Recent publications demonstrate his work in developing efficient algorithms for large-scale optimization problems, with applications in machine learning and statistical signal processing. His articles frequently explore convergence properties of optimization methods and novel formulations for challenging mathematical programs.

Adrian Ioanaمشاهده پروفایل

استاد

• Operator Algebras
• Functional Analysis
• Ergodic Theory
• +۴ مورد دیگر

Adrian Ioana is a Professor in the Department of Mathematics at the University of California, San Diego (UCSD). He holds a Ph.D. from UCLA (2007) under Sorin Popa and has held prestigious fellowships including the Clay Research Fellowship (2008–2011) and Alfred P. Sloan Research Fellowship (2014). His research focuses on operator algebras, ergodic theory, and group theory, with expertise in von Neumann algebras and their classification. He has organized major conferences such as the 2024 Banff Workshop on Group Operator Algebras and co-edits journals like Journal of Operator Theory and Journal of Group Theory . Research interests include von Neumann algebras, ergodic theory (orbit equivalence), group stability, and logic (descriptive set theory). Notable achievements include rigidity results for group von Neumann algebras and contributions to the classification of II₁ factors. Awards include the European Math Society Prize and being an invited speaker at the 2018 International Congress of Mathematicians. Recent work explores wreath-like group products, non-isomorphism in von Neumann algebras, and asymptotic freeness in ultraproducts. He actively teaches graduate courses and mentors students in functional analysis and related fields.

Birgit Kleinschmitمشاهده پروفایل

استاد

• Remote Sensing
• Climate Change
• Environmental Science
• +۴ مورد دیگر

Prof. Birgit Kleinschmit is a Professor and Head of the Department of Geoinformation in Environmental Planning at Technische Universität Berlin. Her research focuses on remote sensing applications for analyzing human-environment systems, particularly in the context of climate and land-use changes. She leads projects on spatio-temporal modeling of landscapes, drought impacts, and wildfire dynamics using AI and satellite data. Member of the Scientific Advisory Board for Forest Policy (Bundesministerium) Co-Speaker of the DFG Urban Water Interfaces Research Group Recipient of the 2024 '100 Most Influential Minds in Berlin Science' award Her work integrates multisensor data (e.g., Sentinel-2, SAR) with machine learning to assess forest health, wildfire susceptibility, and urban evapotranspiration. Recent studies include drought responses in Central Europe, wildfire driver analysis, and sustainable land-use strategies. Publications emphasize environmental monitoring innovations, with over 20 peer-reviewed articles since 2022. Active in policy advising through Geo.X Research Network and federal committees.

Alby Duarte Rochaمشاهده پروفایل

پژوهشگر

• Remote Sensing
• Machine Learning
• Environmental Planning
• +۳ مورد دیگر

Dr. Alby Duarte Rocha is a Researcher at the Technical University of Berlin's Department of Geoinformation Processing for Landscape and Environmental Planning. His work focuses on remote sensing applications, machine learning, and environmental modeling. He holds a Doctorate in Remote Sensing from the University of Twente (2019), a Master in Geodesy from Federal University of Paraná (2007), and a Bachelor in Statistics (1998). Research interests include predicting plant traits using hyperspectral data, spatiotemporal models for vegetation parameters, and urban evapotranspiration modeling. His studies address climate change impacts, urban heat mitigation, and environmental justice through interdisciplinary approaches integrating AI and remote sensing. Key publications (2021–2025) highlight contributions to soil moisture modeling, wildfire analysis, and urban cooling disparities using satellite and machine learning techniques. His work supports sustainable urban planning and ecosystem monitoring.

Tobias Breitenمشاهده پروفایل

استاد

• Model Reduction
• Control Theory
• Optimal Control
• +۳ مورد دیگر

Prof. Dr. Tobias Breiten is a Professor of Mathematics at Technische Universität Berlin, affiliated with Faculty II - Mathematics and Natural Sciences and the Institute of Mathematics. His research focuses on model reduction, control theory, and numerical methods for complex systems. Education: Diploma in Technomathematik (2009, TU Kaiserslautern), PhD in Mathematics (2013, Otto-von-Guericke University Magdeburg), Habilitation (2018, University of Graz). Roles: Associate Professor (2019-2020, University of Graz), Junior Professor (2020-2022, TU Berlin), Full Professor since 2022. Research interests include structure-preserving model reduction, nonlinear observer design, and optimal control of port-Hamiltonian systems. He leads projects on topics like thermoacoustic spectra and nonlinear control perspectives. Notable publications include works on H2 optimal rational approximation, balanced truncation methods, and Mortensen observer theory. His work often bridges numerical analysis and systems theory.