جست‌وجوی استاد و پژوهشگر

• Molecular Epidemiology
• Human Aging
• Genetic Epidemiology
• +۵ مورد دیگر

Prof. Dr. Eline Slagboom serves as Full Professor of Molecular Epidemiology at the Department of Medical Statistics and Bioinformatics, Leiden University Medical Center , and holds a Max Planck Fellow position at the Max Planck Institute for Biology of Ageing . Her research group specializes in understanding molecular mechanisms of human aging through clinical studies, biobanks, and omics technologies. PhD in Biology (Genomic Instability and Ageing, Leiden University) MSc in Biology, Biochemistry (Leiden University) Research focuses on metabolic factors and biomarkers for healthy aging, with particular emphasis on genomic and epigenomic profiling , cell biological studies (skin fibroblasts, stem cells), and longitudinal cohort analysis across aging spectrums. Key projects include the Leiden Longevity Study (3,500 participants since 2002) and collaborations with BBMRI-NL, Dutch Hunger Winter Cohort, and GARP study. Recent publications and methodologies center on metabolomic aging clocks , Mendelian randomization approaches , and signaling pathway analysis (mTOR, MAPK/ERK) in longevity contexts. The group also develops computational tools like MiMIR for metabolomic data analysis. Max Planck Fellow (methodological innovation in aging research) Editorial board member of Aging Cell Leadership in BBMRI-NL and Dutch Consortium for Healthy Ageing Co-chair of Welcome Trust aging conferences Collaborations span LUMC Sequencing Analysis Support Core , Leiden Longevity Study , and international biobank networks. Research outputs include standards for human sample usage in aging studies and lifestyle intervention frameworks.

Peter Morseمشاهده پروفایل

استادیار

• Computational Physics
• Theoretical Soft Matter
• Condensed Matter Physics
• +۳ مورد دیگر

Peter Morse is an Assistant Professor of Physics at Seton Hall University's Department of Physics. He joined the faculty in 2024 after completing postdoctoral research at Syracuse University, Duke University, and Princeton University. His research focuses on computational and theoretical soft matter physics, particularly the interplay between geometry, dynamics, and thermodynamics in disordered systems. Education: PhD in Physics from the University of Oregon (2016), MS in Physics from the University of Oregon (2010), and dual BS degrees in Applied and Computational Mathematical Sciences and Physics from the University of Washington (2008). Research interests include glass and jamming transitions, hyperuniformity, packing problems, and the development of computational tools for high-performance simulations. His lab emphasizes Python and C++ programming, with a focus on optimizing algorithms for studying material memory and rheology. Recent work highlights the dynamics of glass formers, hyperuniform point patterns, and computational modeling of soft matter systems. Articles frequently explore structural dynamics in high dimensions and the interplay between geometry and material properties. Students in his lab gain expertise in HPC cluster usage and algorithm development for analyzing disordered systems. Current projects include optimizing code for stealthy hyperuniform systems and studying jamming transitions in minimally structured glass formers.

Mads Stehrمشاهده پروفایل

دانشیار

• Applied Probability Theory
• Lévy-based Spatio-temporal Modeling
• Extreme Value Theory
• +۳ مورد دیگر

Mads Stehr is an Associate Professor in Statistics at the Department of Finance, Copenhagen Business School (CBS), and a member of the Center for Statistics. He holds a PhD in Statistics and Probability Theory from Aarhus University. His primary research focuses on applied probability theory, including Lévy-based modeling, extreme value theory, and numerical integration methods. Education: PhD in Statistics and Probability Theory (Aarhus University) His research interests span Lévy processes, spatial and spatio-temporal modeling, and the application of extreme value theory to stochastic systems. He has contributed significantly to improving numerical integration techniques like the Cavalieri estimator and Newton–Cotes quadratures under non-equidistant sampling conditions. Key publications include studies on extremal clustering in spatial random fields and tail asymptotics of Lévy-driven models. While no scientific awards are explicitly mentioned, his work demonstrates impactful contributions to statistical methodology and stochastic analysis. Teaching responsibilities include courses such as Stokastiske processer og deres statistiske analyse (Stochastic Processes and Their Statistical Analysis) and Matematisk statistisk (Mathematical Statistics). His doctoral thesis, Stereology and Spatio-Temporal Models , further underscores his expertise in integrating statistical methods with complex spatial-temporal systems.

• Theory of Machine Learning
• Randomized Algorithms
• Convex Optimization
• +۴ مورد دیگر

Nicholas Harvey is a Professor in the Department of Computer Science at the University of British Columbia (UBC), part of the Faculty of Science. He holds affiliations as an Associate Member in the Department of Mathematics and serves as Director of the Mathematics of Information, Learning, and Data (MILD) research cluster. His research focuses on randomized algorithms, convex optimization, and machine learning theory, with notable contributions to online learning, submodular functions, and algorithmic aspects of discrepancy theory. Harvey's academic journey includes significant work on problems such as prediction with expert advice, learning mixtures of Gaussians, and graph algorithms. He has supervised numerous graduate students, including notable researchers like Huang Fang and Victor Sanches Portella. His work has been recognized with awards such as the Best Paper Award at NeurIPS 2018 and the Excellence in Teaching Award in 2024. His research outputs span theoretical computer science, optimization, and machine learning, with key contributions in algorithms for submodular functions, online convex optimization, and probabilistic methods. He has also contributed to practical applications, such as algorithms for network coding and efficient failure notification systems in distributed networks. Harvey is actively involved in teaching, including pioneering courses on randomized algorithms and machine learning theory. His research group collaborates on projects related to the MILD cluster, focusing on interdisciplinary applications of data science and mathematics.

Tobias Lasserمشاهده پروفایل

استاد مدعو

• Computational Imaging
• Inverse Problems in Tomographic Reconstruction
• X-ray Phase-Contrast and Dark-field Imaging
• +۲ مورد دیگر

Prof. Dr. Tobias Lasser is an Adjunct Professor at the Technical University of Munich (TUM) since 2024, leading the Computational Imaging and Inverse Problems research group. He holds affiliations with the TUM School of Computation, Information and Technology and the Munich Institute of Biomedical Engineering. His academic career includes a PhD (2011) and habilitation (2017) in Computer Science from TUM, along with prior roles as a Postdoctoral Fellow and Akademischer Rat at TUM's Chair for Computer Aided Medical Procedures. His research focuses on computational imaging , inverse problems in tomography , and clinical decision support systems . Key areas include X-ray phase-contrast/dark-field imaging, light field microscopy, and multi-modal medical data analysis. Notable contributions include advancements in sparse-view CT reconstruction, artifact-free deconvolution techniques, and AI-driven diagnostic tools. Recent work emphasizes integrating deep learning with traditional imaging modalities, such as encoder-decoder architectures for anomaly detection and attention-based models for skin lesion classification. His team also explores robotic sample holders for advanced CT setups and open-source frameworks like elsa for tomographic reconstruction. Educations: Diplom-Informatiker (2006), Diplom-Mathematiker (2008), Dr. rer. nat. (2011, summa cum laude), Habilitation (2017) Awards: IEEE editorial award (2023), Best Poster (2021), Supervisory Excellence (2021), Teaching Award (2021) Labs/Teams: Munich Institute of Biomedical Engineering, Computational Imaging Group (TUM)

Luigi Ceruloمشاهده پروفایل

دانشیار

• Bioinformatics
• Computational Genomics
• Machine Learning
• +۳ مورد دیگر

Luigi Cerulo is an Associate Professor in Computer Science at the Department of Science and Technology (DST) , University of Sannio in Benevento. He co-directs the bioinformatics research group with Francesco Napolitano, focusing on integrating computational methods into life sciences. His teaching includes advanced topics like Computational Genomics and Statistics and Bioinformatics for Master's programs in Biotechnology and Biology. Academic Rank: Associate Professor Department: Science and Technology, University of Sannio Email: lcerulo@unisannio.it Research Interests: Driven by cross-disciplinary innovation, his work bridges machine learning and bioinformatics. Key areas include reverse engineering gene regulatory networks using deep graph neural networks, predicting ligand-receptor interactions via graph autoencoders, and exploring non-coding RNA functions with self-supervised learning. His team also investigates drug synergy prediction and AI applications in biological data. Recent Publications: His 15 most recent works span 2022 to 2007, emphasizing machine learning in gene network reconstruction (Chang et al., 2020), deep learning for RNA classification (Noviello et al., 2020), and foundational contributions to software engineering tools (Canfora et al., 2007-2008). Research Infrastructure: The group utilizes high-performance systems including a Superdome Flex server (224 CPU cores, 1.5 TB RAM) and a GPU cluster at Biogem Institute. Advising and Grants: He mentors PhD students in bioinformatics and leads projects such as GENOMA E SALUTE (EU-funded) and Non-Coding RNA Explosion (Ministry of Research grant). Collaborations span institutions like the University of Naples and Biogem.

Frank Noéمشاهده پروفایل

دانشیار

• Machine Learning
• Computational Biology
• Molecular Dynamics
• +۳ مورد دیگر

Frank Noé is a W2 Professor (Associate Professor equivalent) of Mathematical Modeling in the Life Sciences at the Free University of Berlin, with promotion to W3 pending. He concurrently serves as an Adjunct Professor in the Department of Chemistry at Rice University. His research develops computational frameworks for molecular kinetics, integrating machine learning with biophysical simulation to decode complex biomolecular processes. Academic qualifications: Dr. rer. nat. in Informatics and Biophysics, Universität Heidelberg (2006, summa cum laude) Master of Science in Computing, Cork Institute of Technology, Ireland (2000-2002) Diplom Ingenieur (BA) in Electrical Engineering, Berufsakademie Stuttgart (1996-1999) Noé pioneers the application of deep learning to molecular simulation, notably through Boltzmann generators and VAMPnets. His work bridges statistical mechanics, biophysics, and data science to model protein dynamics, ligand binding, and cellular signal transmission at unprecedented timescales. This interdisciplinary approach has established new paradigms for analyzing molecular kinetics. Recent publications demonstrate a decisive shift toward AI-driven methodologies, where neural networks extract kinetic features from molecular trajectories. This enables atomistic exploration of processes previously inaccessible to simulation, such as millisecond-scale protein folding and membrane receptor dynamics. Major honors: Simons Fellow at IPAM (2019) ACS Early-Career Award in Theoretical Chemistry (2019) ERC Consolidator Grant (2017) ERC Starting Grant (2012) Eliteprogramm für Postdoktoranden fellowship (2007) Research leadership: Principal Investigator, DFG Collaborative Research Center 958 "Scaffolding of Membranes" (2011-present) Principal Investigator, DFG Collaborative Research Center 740 "From Molecules to Modules" (2009-present) Faculty member, International Max-Planck Graduate School for Computational Biology Organizer, International Workshop on Molecular Kinetics He directs theoretical research within the SFB/TRR 186 consortium on molecular switches, developing open-source software for kinetic analysis while mentoring the next generation of computational biophysicists through DFG-funded graduate programs.

Miao Qiaoمشاهده پروفایل

مدرس

• Data Science
• Graph Analytics
• Brain Network Analytics
• +۲ مورد دیگر

Miao Qiao is a faculty member at the School of Computer Science , University of Auckland , where she has worked since 2018. Her research bridges theoretical and applied aspects of data science and management , focusing on vector data management , graph search and analytics , and brain network analytics . Research Interests : Data Science, Graph Analytics, Brain Network Analytics Grants : Royal Society of New Zealand (NZD 300,000, 2018–2024): Subgraph Matching: Theory and Practice The Ministry of Business, Innovation and Employment (NZD 3,000,000, 2020–2024): Advanced Graph Analytics for Human Brain Connectivity Professional Services : Review Board Member, PVLDB 2021 Program Committee Member: PODS 2023, ADC 2018, IJCAI 2019–2021, CIKM 2019, AAAI 2020–2021 Invited Reviewer: SCG 2019, TODS 2015–2019, TKDE 2018–2021, KDD 2016, ICDM 2013–2014, WISE 2013, VLDBJ 2013–2021, NIPS 2021 Program Chair: ISWC Doctoral Consortium 2019, ADC 2021 Teaching : Database Systems (2017–2023) Algorithms for Massive Data (2018–2019) Systems Analysis and Modeling (2016–2018) Algorithms (2017–2018) Scientific Awards : PREMIA Best Student Paper Honourable Mention Award VLDB 2013 Travel Fellowship Shanghai Jiao Tong University 1st Class Scholarship ACM/ICPC 3rd Place, Singapore Computer World Scholarship Singapore Technology Engineering Scholarship ACM/ICPC 1st Place, Korea Silver medal, National Olympiad in Informatics, China

Cortes Martinez, Jordiمشاهده پروفایل

استاد

• Biostatistics
• Clinical Trials
• Sports Science
• +۲ مورد دیگر

Jordi Cortes Martinez is a Professor in the Department of Statistics and Operations Research at Universitat Politècnica de Catalunya (UPC), affiliated with the Institute of Health Research and Innovation (INQUIS) and the GRBIO research group (Bio-statistics and Bioinformatics). His expertise focuses on Clinical Trials , Sports Science , R Programming , and Synthetic Data Analysis . Research Themes : Methodological development of composite endpoints in clinical trials, application of R Shiny for sports analytics, synthetic data quality metrics, and statistical modeling for healthcare outcomes. Recent Publications (2025): Design of trials with composite endpoints , R Shiny in sports , and External load analysis in tennis , reflecting ongoing work in clinical trial design, sports data science, and synthetic data validation. Collaborations : Active participation in BIOSTATNET networks and multidisciplinary projects with neurologists, pharmacologists, and sports scientists. His work emphasizes open science , with contributions to R packages and reproducible research frameworks. No current student advising or explicit retirement status is mentioned. Email: jordi.cortes-martinez@upc.edu

Sargur N. Srihariمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Sargur N. Srihari is a SUNY Distinguished Professor in the Department of Computer Science and Engineering at the University at Buffalo, State University of New York, with adjunct appointments in Biostatistics and Electrical Engineering. He concurrently holds the Satish Dhawan Visiting Chair Professorship at the Indian Institute of Science, Bangalore. Previously, he served as the Rukmini Gopalakrishnachar Chair Professor at IISc (2018) and was a visiting professor at the University of Bari, Italy. His research focuses on artificial intelligence , machine learning , deep learning , and probabilistic graphical models , with applications in document analysis, computational forensics, and handwriting recognition. He founded the Center of Excellence for Document Analysis and Recognition (CEDAR) in 1991, pioneering systems for handwritten address interpretation and forensic evidence analysis that are globally utilized. His publications emphasize deep learning architectures , explainable AI , and applications in forensics and healthcare , with trends toward attention mechanisms, Bayesian methods, and hybrid feature learning for pattern recognition tasks. Scientific Awards: Outstanding Achievements Award, IAPR/ICDAR (2011) Distinguished Alumnus, Ohio State University (1999) Fellow, International Association for Pattern Recognition (1996) Life Fellow, IEEE (1995) Fellow, IETE, India (1992) Excellence in Graduate Mentoring Award, University at Buffalo (2018) He has advised 40 doctoral students and secured sustained funding, including 20+ years of support from the US Postal Service for CEDAR. His grants enabled breakthroughs in postal automation and forensic testimony standards. He established CEDAR as a leading lab for document analysis, developed forensic software used worldwide, and co-founded major conferences (ICDAR, ICFHR, IWCF).

Emmett Wymanمشاهده پروفایل

استادیار

• Harmonic Analysis
• Spectral Geometry
• Geometric Analysis
• +۳ مورد دیگر

Emmett Wyman is an Assistant Professor in the Department of Mathematics and Statistics at Binghamton University, specializing in harmonic analysis and spectral geometry. His research focuses on Laplace-Beltrami eigenfunctions of large eigenvalue, exploring how their asymptotics relate to the geometric and dynamical structure of manifolds, with applications to quantum physics and number theory. His work spans multiple domains including: Microlocal analysis of eigenfunctions Geometric uncertainty principles VC-dimension in finite fields Kuznecov sum formulas Manifold reconstruction via spectral data Recent research examines: Fractal dimensions in data analysis Fourier restriction problems Wave propagation on Riemannian manifolds Geometric incidence theory Spectral asymptotics with curvature constraints Current projects analyze multi-linear forms in geometric settings, spectral localization on manifolds, and combinatorial aspects of eigenfunction restrictions.

• Computational topology
• Topological data analysis
• Machine learning
• +۷ مورد دیگر

Francesco Vaccarino is an Associate Professor in the Department of Mathematical Sciences "GL Lagrange" (DISMA) at the Polytechnic University of Turin and a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. He is actively involved in research, teaching, and doctoral supervision across multiple programs, including Mathematical Sciences, Applied Mathematics, and Artificial Intelligence. Research Interests: Computational topology Topological data analysis (TDA) Machine learning and geometric learning Algebraic and geometric methods in data science Complex networks and higher-order interactions His recent publications reflect a strong interdisciplinary focus, combining topology with machine learning, neuroscience, and physics. Key themes include persistent homology, simplicial network models, and topological visualization of complex systems. He has made significant contributions to understanding brain networks, scientific collaboration patterns, and phase transitions through topological methods. Scientific Awards: The Best Science Visualizations of the Year (Wired.com, 2014) Advising and Grants: He supervises multiple PhD students in Mathematical Sciences and Artificial Intelligence. He has led research projects funded by competitive tenders (e.g., XTERA-FOOD) and commercial contracts (e.g., with FPT Industrial SPA and Stellantis), focusing on AI, digital transformation, and industrial data analysis. He has served as Scientific Director for corporate training programs in AI and data science. Conferences and Leadership: He has been Program Chair for several international events including AMTCS7, TOPDRIM, Toponets15, and ECCS 2013, promoting the application of topology in data science. Labs and Affiliations: He is a core member of SmartData@PoliTO, a leading data science laboratory fostering interdisciplinary research in big data and AI.

• Analytical Chemistry
• Environmental Analysis
• Materials Science
• +۳ مورد دیگر

Ana Ballester Cadet is a Researcher in the Department of Analytical Chemistry at Universitat Jaume I, Spain. She holds a Ph.D. in Analytical Chemistry (2013) from the same institution, specializing in semiconductor nanostructures and anisotropic confinement effects. Her research interests span analytical method development, environmental analysis, and materials science, with a focus on sustainable sensors, chromatographic techniques, and AI-driven academic tools. She has contributed to over 20 peer-reviewed articles since 2010, emphasizing interdisciplinary applications in environmental monitoring, biomedical analysis, and green chemistry. Education: Doctorate in Analytical Chemistry (2013), Universitat Jaume I. Her work integrates innovative analytical methodologies with practical applications, such as PDMS-based sensors for environmental and biomedical diagnostics. Recent trends in her publications highlight advancements in non-separative screening strategies, cost-effective sensor design, and AI tools for academic research. She has pioneered tools like the HEXAGON framework for evaluating analytical methods and explored multifunctional polymer composites for real-time environmental monitoring. Key Themes: Miniaturized liquid chromatography systems, sustainable analytical methods, and nanomaterial-based sensors. She has no listed scientific awards but maintains an active research agenda with a focus on bridging chemistry and technology for environmental and health applications. No grants or labs are explicitly noted in the provided data.

Anestis Touloumisمشاهده پروفایل

مدرس ارشد

• Biostatistics
• Categorical Data Analysis
• High-Dimensional Statistics
• +۱۳ مورد دیگر

Anestis Touloumis is a Principal Lecturer in the School of Architecture, Technology and Engineering at the University of Brighton, affiliated with the Computing and Mathematical Sciences Research Excellence Group. He holds a PhD in Statistics from the University of Florida and has held postdoctoral positions at the European Bioinformatics Institute and the University of Cambridge before joining Brighton in 2015. Education: PhD in Statistics, University of Florida (2006–2011) MSc in Applied Statistics, University of Piraeus (2003–2006) BSc in Mathematics, Aristotle University of Thessaloniki (1998–2003) His research centers on biostatistics, high-dimensional statistics, and statistical genetics, with a focus on developing methods for correlated responses, covariance matrix estimation, and hypothesis testing in high-dimensional settings. His work is applied in omics, health studies, and social sciences, and he actively disseminates methods through R packages on CRAN and Bioconductor. He teaches a range of statistical modules using R and SAS, emphasizing conceptual understanding and critical thinking. The recent research articles reflect strong trends in biostatistical methodology, particularly in high-dimensional inference, longitudinal and correlated data analysis, and statistical computing. There is a consistent emphasis on developing robust, open-source tools for real-world applications in healthcare and data science. Scientific Awards and Professional Service: Treasurer, British and Irish Region of the International Biometric Society (2020–present) Committee Member, British and Irish Region of the International Biometric Society (2019–present) External Examiner, MSc in Applied Statistics, Birkbeck University of London (2020–present) Member, International Biometric Society He has been active in research supervision and mentoring, with a stated interest in supervising postgraduate students in machine learning, categorical data, and high-dimensional statistics, often involving software development. He has contributed to research grants, notably as a Co-Investigator on the ERDF-funded Digital Research & Innovation Value Accelerator (DRIVA) project (2018–2021). He is involved in multiple research collaborations and has delivered invited talks and peer reviews for leading journals. His work bridges theoretical statistics and practical applications across disciplines.

• Tuberculosis Research
• Antimicrobial Resistance
• Molecular Epidemiology
• +۳ مورد دیگر

Halima Said is a Lecturer in the Department of Clinical Microbiology and Infectious Diseases at the University of the Witwatersrand. She holds a PhD from the University of Pretoria. Her research focuses on tuberculosis (TB) epidemiology, drug resistance mechanisms, and molecular diagnostics, particularly in high-burden settings like South Africa. Her work integrates genomic analysis, clinical microbiology, and public health surveillance to address challenges in TB control and treatment. Key research interests include genetic diversity of Mycobacterium tuberculosis strains, molecular clustering of drug-resistant TB, and optimizing diagnostic methods for TB in resource-limited environments. She has extensively studied TB transmission dynamics in correctional facilities, rural areas, and HIV/AIDS co-infection contexts. Her studies often employ whole-genome sequencing and high-resolution melting curve analysis to understand drug resistance evolution and genetic markers. Notable contributions include evaluating novel preservation media for sputum transport and developing molecular strategies for MDR-TB surveillance. Her publications span journals such as New Microbes and New Infections , BMC Microbiology , and PLOS ONE . While no scientific awards are explicitly mentioned, her work reflects significant academic engagement with global health challenges. Dr. Said collaborates on grants focused on TB strain characterization, diagnostic innovation, and public health intervention strategies. Her research bridges laboratory science and clinical practice, emphasizing real-world applications in high-prevalence regions.