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Ashish RajView profile

Professor

• Medical Imaging
• Neuroscience
• Graph Theory
• +7 more

Ashish Raj, PhD, is a Professor in the Department of Radiology at the University of California, San Francisco (UCSF), School of Medicine. His interdisciplinary research focuses on applying computational methods and graph theory to biomedical applications, particularly in neuroimaging and brain connectivity. He leads research teams in NIH-funded projects on neurodegenerative diseases, including Alzheimer's and Parkinson's, leveraging structural and functional connectome analysis. Raj has over 70 peer-reviewed publications, two US patents, and prestigious awards like the NIH EUREKA Award and BRAIN Initiative grants. Education: PhD in Electrical and Computer Engineering from Cornell University (2005) and BE in Electrical Engineering from the University of Auckland (1998). Research interests include modeling brain network dynamics, tau and amyloid pathology progression, and developing algorithms for accelerated MRI and motion correction. His work bridges computer science, neuroradiology, and clinical neuroscience, aiming to advance precision medicine in neurology and radiology. Key achievements include pioneering mathematical models of brain connectivity networks and developing novel imaging techniques like tractography and Q-ball imaging. His lab explores network diffusion models to predict disease progression and has contributed to understanding microglial roles in tauopathy. Grants and collaborations span NIH-funded projects on network-based disease modeling, with a focus on translational applications. He directs the Brain Networks Laboratory, advancing computational neurology and multimodal data integration.

Carlos YeroView profile

Assistant Professor

• Short-range nuclear physics
• Nuclear instrumentation
• Spectrometers
• +4 more

Dr. Carlos Yero is an Assistant Professor in the Department of Physics at Catholic University of America's School of Arts and Sciences. He specializes in experimental nuclear physics, with research focusing on short-range nuclear phenomena, deuteron structure, and color transparency effects. Dr. Yero earned his Ph.D. from Florida International University in 2020, where his dissertation centered on deuteron electro-disintegration experiments using the Super High Momentum Spectrometer (SHMS) at Jefferson Lab. His current research examines short-range nuclear correlations through deuteron electro-disintegration and nuclear transparency phenomena. Dr. Yero maintains active affiliations with Jefferson Lab, where he coordinates experiments and mentors graduate students from Florida International University and Old Dominion University. He received a National Science Foundation research grant in 2022 to advance his investigations into short-range nuclear physics. Recent publications demonstrate Dr. Yero's focus on nuclear structure measurements, quantum chromodynamics phenomena, and precision instrumentation development. His work frequently appears in high-impact nuclear and particle physics journals, covering topics from parton fragmentation functions to gluonic gravitational form factors. National Science Foundation Research Grant (2022)

Kyle A CaudleView profile

Professor

• Forecasting
• Time Series Analysis
• Multilinear Algebra
• +3 more

Kyle A. Caudle is a Professor of Mathematics at the South Dakota School of Mines and Technology, holding a Ph.D. from George Mason University, an M.S. from Salve Regina University, and a B.A. from Western State College. His research spans forecasting, tensor analysis, and anomaly detection, with applications in engineering and data science. Caudle develops computational tools for time series forecasting, tensor decomposition, and graph representation learning. He created software packages like Flow Field, rTensor2, and LTAR, published on CRAN. His interdisciplinary projects include collaborations with NIST and Naval Surface Warfare Centers on surface ship maintenance and anomaly detection. Publications emphasize multilinear algebra, temporal forecasting, and machine learning. Recent work advances tensor factorization for high-dimensional data, hierarchical graph networks, and deep generative models. Awards include the 2015 Peter Holmes Prize for innovative statistics teaching and accreditation as a Professional Statistician (ASA, 2013). He mentors graduate students and co-developed the Ph.D. in Data Science.

Peter ZizlerView profile

Professor

• Linear Algebra
• Wavelet Theory
• Business Mathematical Modelling
• +3 more

Peter Zizler is a Professor in the Department of Mathematics & Computing at Mount Royal University. He teaches calculus, linear algebra, and courses in General Education. His research focuses on Linear Algebra, Wavelet Theory, business mathematical modeling, and statistical crime analysis. He holds a PhD from the University of Calgary. His research interests include advanced mathematical techniques such as singular value decomposition, wavelet transforms, and their applications in data science, signal processing, and education. He explores topics like statistical normalization of grades, sports analytics using SVD, and logic-based pedagogy in general education courses. His publications span over two decades, addressing diverse areas from non-stationary filtering to interdisciplinary applications in chemistry and quantum physics. Notable recent work includes leveraging linear algebra for Anscombe’s Quartet construction and analyzing FIFA 2022 data through SVD. Peter has no listed scientific awards or grants. His academic contributions primarily center on teaching and applied mathematical research without direct mention of lab affiliations or student advisement.

Anastasia KisilView profile

Research Fellow

• Aeroacoustics
• Diffraction Theory
• Metamaterials
• +3 more

Anastasia Kisil is a Royal Society Dorothy Hodgkin and Dame Kathleen Ollerenshaw Research Fellow in Applied Mathematics at the University of Manchester. Her work focuses on analytical and semi-analytical methods in diffraction theory and metamaterials, with expertise in Wiener-Hopf techniques, aeroacoustics, and canonical scattering problems. She holds affiliations within the School of Mathematics and Department of Applied Mathematics. Her research interests span the mathematical analysis of wave phenomena, including elastic wave scattering, acoustic diffraction, and metamaterial design. Notable contributions include advancements in iterative Wiener-Hopf methods for solving complex boundary value problems and their applications to real-world systems like porous extensions in aeroacoustics. Publications highlight her work on anti-plane elastic wave scattering, matrix factorization, and wave diffraction in structured media. Articles often explore the interplay between theoretical developments and numerical simulations, emphasizing practical applications in engineering and materials science. Kisil’s methodological innovations aim to bridge gaps between classical mathematical techniques and modern computational approaches. Her work has been disseminated through peer-reviewed journals such as the Journal of Fluid Mechanics , SIAM Journal on Applied Mathematics , and Proceedings of the Royal Society A . Media engagement includes contributions to podcasts and articles on metamaterial theory and wave scattering.

Alexander ModellView profile

Research Fellow

• AI Interpretability
• Machine Learning
• Statistics
• +5 more

Alexander Modell is a Research Fellow in Statistics and Machine Learning at the Department of Mathematics, Imperial College London, holding the Chapman Research Fellowship. His work focuses on AI safety through interpretability research, studying how large language models represent information and reason about the world. He earned his PhD from the University of Bristol with the thesis "Spectral embedding of large graphs and dynamic networks" under Patrick Rubin-Delanchy's supervision, followed by postdoctoral research with Nick Heard on the NeST programme. Modell's research bridges AI interpretability and neuroscience, using reproducible neural network experiments to uncover knowledge representation mechanisms. He investigates mathematical foundations of machine learning with emphasis on clustering, manifold learning, and analysis of dynamic networks. His work aims to detect AI misalignment pre-deployment through "artificial neurosurgeries" to modify system behavior. Recent publications reveal strong trends in representation learning for large language models and dynamic networks, combining statistical thresholding, spectral methods, and geometric approaches to address AI safety challenges and network analysis problems. Scientific recognition includes: Chapman Research Fellowship Notable Paper Award at AISTATS 2023 (top 32 of 496 accepted papers) Modell actively contributes to the research community through invited talks at ISNPS, PopNets, and NeurIPS workshops, with grant support from his Chapman Fellowship enabling foundational work in AI interpretability and statistical machine learning theory.

• Dynamic Network Analysis
• Bayesian Statistics
• Latent Variable Models
• +6 more

Francesco Sanna Passino is an Assistant Professor in Statistics at Imperial College London's Department of Mathematics, within the Faculty of Natural Sciences. His research focuses on statistical analysis of dynamic networks, Bayesian methods, latent variable models, and applications in cybersecurity, social networks, and transportation systems. He holds a PhD in Statistics from Imperial College London (2021), an MSc in Statistics from Imperial College London (2017), and dual BSc degrees in Statistics from the University of Glasgow (2016) and the University of Bologna (2016). Research interests span dynamic network analysis, event-time data, latent factor models, and model-based clustering. Notable applications include statistical cybersecurity (e.g., attack pattern detection), social network dynamics, and infrastructure systems like bike-sharing. His work integrates advanced statistical techniques such as latent variable models, point processes, and spectral embedding. Publications emphasize methodological innovations in network time series, synthetic data generation, and graph-based processes. Collaborations include Imperial's Artificial Intelligence Network. No awards are explicitly listed, but his research aligns with cutting-edge topics like AI-driven financial trend detection and privacy-preserving synthetic data. He advises no listed students and has no documented grants in the provided texts. His work is housed in the Huxley Building on the South Kensington Campus. For further details, visit his Imperial webpage or Google Scholar profile .

• Statistics
• Applied Mathematics
• Econometrics
• +6 more

Almut Veraart is a **Professor of Statistics** and **Head of the Statistics Section** at the **Department of Mathematics**, **Faculty of Natural Sciences**, Imperial College London. She co-directs the **Center for Doctoral Training on Mathematics for our Future Climate (MFC CDT)**. Her affiliations include the **Grantham Institute**, **Mathematics of Planet Earth**, and **Stochastic Analysis** groups. Her research focuses on statistical methodology for stochastic processes, financial mathematics, climate modeling, and energy systems. **Research Interests**: Her work spans ambit stochastics, stochastic volatility models, extreme value theory, and applications to energy markets and environmental systems. Notable contributions include trawl processes for high-dimensional time series, stochastic modeling of electricity prices, and quantitative energy finance. **Professional Roles**: In addition to her academic leadership roles, she contributes to Imperial's broader research initiatives in climate science and sustainable energy. Her publications address topics like volatility modeling, deep learning applications in finance, and spatio-temporal statistics. **Labs/Teams**: Her research is closely tied to Imperial's **Grantham Institute** and **Mathematics of Planet Earth** initiatives, focusing on interdisciplinary challenges in climate and energy systems.

Jesse LaeuchliView profile

Senior Lecturer

• Cyber Security
• Quantum Computing
• Linear Algebra
• +5 more

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 KeView profile

Associate Professor

• Network data analysis
• Text analysis
• Natural Language Processing
• +4 more

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 WilliamsView profile

Assistant Professor

• Representation Theory
• Geometry
• Combinatorics
• +5 more

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 VenutiView profile

Adjunct Associate Professor

• Condensed Matter Physics
• Quantum Information Processing
• Quantum Statistical Mechanics
• +7 more

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 HosseiniView profile

Assistant Professor

• Theoretical Computer Science
• Additive Combinatorics
• Approximate Algebraic Structure
• +3 more

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
• +8 more

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
• +5 more

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.