جستجوی استاد راهنمای دانشگاهی

Victor Chestopal serves as Professor of Piano and Chairman of the Keyboard Section at the Royal Conservatory of Brussels since 2020, with concurrent professorship at Italy's Accademia Perosi. A globally recognized performer, he has collaborated with orchestras including the Moscow Philharmonic and Helsinki Philharmonic, and performed at venues like Brussels' Palais des Beaux-Arts and Moscow Conservatoire. His educational background includes: Early training at Moscow's Gnessin Music School and Central Music School of the Tchaikovsky Conservatoire Master's degree from Sibelius Academy (2001) Doctor of Music from Sibelius Academy (2010) with thesis "Temporal correlation in the Goldberg Variations" Studies at Imola's Accademia Pianistica (1992-1997) and Weimar's Hochschule für Musik (1995-1997) Chestopal's research integrates performance practice with musicological analysis, focusing on structural interpretation of Baroque and Romantic repertoire. His scholarly work examines temporal relationships in canonical piano works, while his artistic output spans Early Music, Contemporary Theatre, and Jazz Education. As a multilingual poet publishing in Russian, Italian, and French, he bridges musical and literary arts through collections like "Anno" (2014) and "Poeticheskie dnevniki" (2018). His honors include: First Prize at the “Carlo Soliva” International Competition (1990) As an educator since 2007, Chestopal conducts annual masterclasses in Liège and serves on international competition juries. He actively mentors students across European conservatories while leading charity initiatives for organizations including "Save the Children" and "Mary's Meals", often performing benefit concerts for humanitarian causes.

• Piano Performance
• Musicology
• Classical Music
• +۲ مورد دیگر

Raman Arora is an Associate Professor in the Department of Computer Science at Johns Hopkins University, with affiliations to the Mathematical Institute for Data Science (MINDS), the Center for Language and Speech Processing (CLSP), and the Institute for Data-Intensive Engineering and Science (IDIES). His research spans theoretical and practical aspects of machine learning, focusing on robustness, privacy, representation learning, and optimization. Research Interests: Machine Learning Theory Representation Learning (e.g., Deep CCA, Multi-view Learning) Privacy-Preserving Machine Learning (Differential Privacy) Robustness in Deep Learning Online and Reinforcement Learning Stochastic Optimization Algorithms His recent publications, primarily in top-tier venues like NeurIPS, ICML, and ICLR, demonstrate a strong focus on the theoretical foundations of adversarial robustness, multi-task learning, offline reinforcement learning, and differentially private optimization. His work often bridges theory and practice, with applications in speech, language, and data-intensive systems. Scientific Awards and Honors: NSF CAREER Award (2020) ICML Test-of-Time Award Finalist (2023) for Deep CCA Member, Institute for Advanced Study (2019–2020) Visiting Scientist, Simons Institute (2019, 2020, 2022) Advising and Grants: Raman Arora has advised numerous PhD and master’s students, many of whom are now researchers at leading tech companies like Google, Meta, and Microsoft. His research is supported by significant grants from the NSF (including CAREER, BIGDATA, TRIPODS, and CRCNS awards), DARPA, and other agencies, focusing on foundational aspects of machine learning such as inductive biases, privacy, robustness, and computational neuroscience. Laboratory and Research Group: He leads a dynamic research group at Johns Hopkins, comprising current PhD students and postdoctoral researchers working on the intersection of theory and applications in machine learning. The group is actively involved in projects related to adversarial robustness, meta-learning, offline reinforcement learning, and private optimization.

• Machine Learning
• Statistical Signal Processing
• Stochastic Approximation Algorithms
• +۱۶ مورد دیگر

Alexis Battle is an Associate Professor at Johns Hopkins University with appointments in Biomedical Engineering , Computer Science , and Genetic Medicine (secondary). She directs the Malone Center for Engineering in Healthcare and serves as Deputy Director of the Data Science and AI Institute . Educated at Stanford University (PhD in Computer Science, 2013), Battle transitioned to academia after leadership roles at Google. Research Focus: Battle’s work bridges genomics and machine learning , emphasizing the impact of genetic variation on human health. Her lab develops tools like Watershed to predict functional effects of rare variants, aiming to enhance rare disease diagnosis. Key themes include non-coding DNA analysis , personalized genomics , and systems biology , with applications in cardiovascular disease and neurodegenerative disorders . Publications & Awards: Over 60 peer-reviewed articles in journals like Nature , Science , and Genome Biology , with recent emphasis on single-cell transcriptomics , multiomics integration , and telomere biology . Recipient of the President’s Frontier Award (2022), Microsoft Investigator Fellowship (2019), and Searle Scholar (2016). Scientific Awards: 2022 President’s Frontier Award 2019 Microsoft Investigator Fellowship 2019 Johns Hopkins Discovery Award 2017 Johns Hopkins Catalyst Award 2016 Searle Scholar Advising & Funding: Mentors 11 PhD students, 3 undergraduates, and postdoctoral fellows. Her research is funded by NIH, Searle Scholars, and institutional grants. The Battle Lab collaborates on projects like the GTEx Consortium , focusing on gene regulation and clinical genomics .

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

Dr. Konstantin (Kostia) M. Zuev serves as Teaching Professor in the Computing + Mathematical Sciences Department at California Institute of Technology , where he has made significant contributions to network science and computational statistics since 2016. His dual PhDs in Mathematics (Moscow State University, 2008) and Civil Engineering (HKUST, 2009) underpin his interdisciplinary research spanning differential geometry, stochastic simulation, and network dynamics. Education PhD in Mathematics, Lomonosov Moscow State University (2008) PhD in Civil Engineering, Hong Kong University of Science & Technology (2009) His research focuses on network science , particularly course-prerequisite networks and complex financial systems , with recent work extending to network navigability in cosmological models and rare event simulation. Over his career, he has developed innovative Bayesian inference methods and geometric preferential attachment theories while maintaining active collaborations across mathematics, physics, and biomedical domains. Recent publications highlight network analysis in education ( 2023 ), hyperbolic graph theory ( 2024 ), and pandemic-informed cancer mortality studies ( 2023 ). His 15 most recent articles demonstrate methodological innovations across disciplines including statistics, physics, finance, and cosmology. Scientific recognition includes Humboldt Research Fellowship (2021) Carver Mead Seed Fund Grant (2023) ASCIT Teaching Award (2018, 2023) Northrop Grumman Teaching Excellence Prize (2019) As Graduate Option Representative for Information and Data Sciences at Caltech and faculty advisor for multiple student organizations including the Caltech Karate Club and Caltech Chess Club , he actively bridges academic rigor with community engagement through outreach initiatives like the virtual math education channel and university math circles for K-12 students.

• Network Science
• Applied Probability
• Computational Statistics
• +۴ مورد دیگر

Aapo Hyvärinen is a Professor of Computer Science at the University of Helsinki , affiliated with the Helsinki Institute for Information Technology and the Helsinki Probabilistic Machine Learning Lab . He previously held the position of Professor of Machine Learning at the Gatsby Computational Neuroscience Unit, University College London (2016-2019). Education : Undergraduate Mathematics at University of Helsinki, Vienna, and Paris; Ph.D. in Information Science from Helsinki University of Technology (1997) His research focuses on machine learning and computational neuroscience , particularly: Independent Component Analysis (ICA) Natural Image Statistics Causal Representation Learning Neural Signal Processing Applications to brain imaging (MEG, CryoEM) Recent publications emphasize causal discovery , identifiable machine learning , and nonlinear ICA . Key projects include: VETURI (AI for health) DIGIMIND (AI in mental health) CIFAR grants (2022-2025) Scientific awards : Highly Cited Researcher (2010) He serves as Action Editor for the Journal of Machine Learning Research and Neural Computation , and has held Area Chair roles at NeurIPS, ICML, ICLR, AISTATS, and UAI conferences. His work bridges theoretical machine learning with neuroscience and philosophical implications of artificial intelligence .

• Machine Learning
• Computational Neuroscience
• Artificial Intelligence
• +۷ مورد دیگر

Claire Donnat is an Assistant Professor in the Department of Statistics at the University of Chicago, specializing in statistical and machine learning methods for graph-structured and high-dimensional data. Her work bridges theoretical innovation with applications in biomedical research, environmental science, and public health. Education: B.S. and M.S. in Applied Mathematics from Ecole Polytechnique; Ph.D. in Statistics from Stanford University (2020). Her research focuses on three methodological directions: (1) statistical foundations for graph neural networks (GNNs), (2) structured estimation with graph constraints, and (3) multimodal data integration with uncertainty quantification. Key applications include thermotolerance in photosynthetic microbes, family network analysis for child welfare, and spatial transcriptomics. The 15 most recent publications highlight her work in GNNs, CCA, tensor modeling, and epidemic analysis, with keywords spanning statistics, machine learning, and network science. Her methodological contributions address challenges in sparsity, graph topology, and heterogeneous data fusion. Scientific Awards: Facebook Research Award (2021), C3.AI COVID Grand Challenge winner (2020), Lumiata hackathon winner (2020), Stanford Centennial Award (2019), and others. Claire's research group actively recruits postdocs and students for projects involving graph-based modeling, data integration, and biomedical applications. She also provides research consulting in statistical methodology and graph modeling for life sciences.

• Statistics
• Machine Learning
• Graph Neural Networks (GNNs)
• +۷ مورد دیگر

S. Yaser Samadi is an Associate Professor in the Department of Mathematics at the School of Mathematical and Statistical Sciences, Southern Illinois University Carbondale. He holds a Ph.D. in Statistics from the University of Georgia (2014) and maintains an active research program in advanced statistical methodologies. Education: Ph.D. in Statistics, University of Georgia, 2014 Research Interests: Dr. Samadi specializes in multivariate time series analysis, high-dimensional statistical inference, and tensor data analysis. His work addresses critical challenges in big data, symbolic data, and dimension reduction for time series through Bayesian analysis and sequential methods for dependent and independent data, yielding robust models for complex data structures. Publication Trends: His recent publications (2014-2023) emphasize time series analysis, dimension reduction, and innovative approaches for interval-valued and matrix-valued data. Key contributions include envelope models for vector autoregression, copula-based count data modeling, and sequential analysis techniques, bridging theoretical statistics with econometrics and data science applications. Scientific Awards: Outstanding Teacher of the Year, School of Mathematical and Statistical Sciences (2021) Advising: Dr. Samadi has mentored four Ph.D. students to completion: Rukayya Ibrahim (Assistant Professor, Penn State Harrisburg), Wiranthe Herath (Assistant Professor, Drake University), Tharindu De Alwis (Postdoctoral Fellow, WPI), and Hadi Safari Katesari (Teaching Assistant Professor, Stevens Institute of Technology). His Master's students Samira Zaroudi (CUNY) and Reginald Ziedzor (Amplify) have also achieved notable career placements.

• Multivariate Time Series Analysis
• High Dimensional Statistical Inference
• Tensor Data Analysis
• +۵ مورد دیگر

Diego Garlaschelli is Professor of Theoretical Physics at the IMT School for Advanced Studies in Lucca, Italy, and at the Lorentz Institute for Theoretical Physics, University of Leiden, the Netherlands. He leads the NETWORKS research unit at IMT and the Econophysics and Network Theory group at Leiden. He is also an external faculty member at the Complexity Science Hub in Vienna and an associate member of the Enrico Fermi Research Center in Rome. His affiliations reflect a strong international and interdisciplinary research profile in network science and statistical physics. He holds a master's degree in theoretical physics from the University of Rome III (2001) and a PhD in Physics from the University of Siena (2005). His postdoctoral experience includes positions at the Australian National University, the University of Siena, the University of Oxford, and the Sant’Anna School of Advanced Studies in Pisa. Garlaschelli’s research spans network theory, statistical physics, econophysics, financial complexity, ecological networks, and social dynamics. He applies maximum entropy models, information theory, and random graph frameworks to understand complex real-world systems. His teaching includes courses in Network Theory, Econophysics, and Complex Systems at both PhD and MSc levels. The 15 most recent publications highlight a consistent focus on network reconstruction, ensemble inequivalence, renormalization, and applications to financial and socio-economic systems. Key themes include statistical inference in networks, resilience, and multi-scale modeling, with publications in top journals such as Nature Reviews Physics , Physics Reports , Science , and Physical Review Letters . His scientific awards include the Best Paper Award at the 6th International Workshop on Self-Organizing Systems (2012) and the Jan Kijne Prize (2013) as supervisor. He has secured multiple grants from NWO, the European Union, and the Royal Society, and has supervised over 40 students at PhD, master’s, and bachelor’s levels. He also mentors postdocs and visiting scientists. Garlaschelli leads and organizes major international workshops and schools in network science and complex systems. He serves on scientific committees and is an active referee for journals like Nature and Physical Review Letters , as well as funding agencies including the ERC and NWO.

• Network Theory
• Statistical Physics
• Econophysics
• +۷ مورد دیگر

Professor Yongcheol Shin is a faculty member in the Department of Economics at the University of York. His academic background includes a BA (Hanyang University), MA (Hanyang University), and PhD (Michigan State University). He specializes in applied and theoretical econometrics, focusing on financial and macroeconomic modeling. Education: BA (Hanyang University), MA (Hanyang University), PhD (Michigan State University) His research spans econometric theory and applications in finance and macroeconomics. Key areas include nonlinear panel data modeling, cointegrating VAR models, regime-switching models, and statistical hypothesis testing for time series. Recent work addresses interactive effects in panel data and multilevel factor models. Recent publications (2023) focus on panel data analysis, canonical correlation, and unit root testing. These studies reflect his expertise in econometric methodology and its application to financial economics, macroeconomics, and trade dynamics. Scientific awards include: 2018 Maekyung-KAEA Economist Award He leads the ESRC-funded project 'New Cross-Sectionally Dependent Panel Data Methods for the Analysis of Macroeconomic and Financial Networks' (2020-2024), collaborating with researchers like J. Chen and W. Wang.

• Econometrics
• Applied Econometrics
• Financial Econometrics
• +۶ مورد دیگر

Antti Poso is a Professor of Drug Design at the University of Eastern Finland (Kuopio), affiliated with the School of Pharmacy under the Faculty of Health Sciences. His research focuses on computer-aided molecular design, particularly targeting anti-cancer drugs and anti-microbials. Key projects include the EDCMET project (2019–2024) and the GeneCellNano Flagship (2020–2028). He leads the Molecular Modeling and Drug Design Research Group, specializing in QSAR analysis, kinase inhibition profiling, and systems-level drug response modeling. Recent work includes studies on SARS-CoV-2 inhibitors, endocrine disruptors, and bacterial pathogenesis. His findings bridge chemical structure with biological outcomes, leveraging computational tools like CCA and molecular dynamics simulations. Collaborations span medicinal chemistry, pharmacology, and systems biology, contributing to both academic and applied drug discovery efforts. Education: Not explicitly stated in texts; assumed to hold advanced degrees in pharmacy or chemistry. Research Themes: Drug design, molecular modeling, QSAR, computational biology, and anti-infective agents. Key Contributions: Over 150+ publications, including influential works on chemoinformatics-driven drug response analysis and structure-based inhibitor design. Publications highlight advancements in kinase inhibitors, anti-microbial strategies, and viral hijacking mechanisms. His work emphasizes translating computational insights into therapeutic solutions for cancer, infectious diseases, and metabolic disorders.

• Drug Design
• Molecular Modeling
• Anti-Cancer Drugs
• +۳ مورد دیگر

Garnet K. Chan is the Bren Professor of Chemistry and Director of the Rudolph A. Marcus Center for Theoretical Chemistry at the California Institute of Technology. He received his B.S. from the University of Cambridge in 1996 and his M.A. and Ph.D. from the University of Cambridge in 2000. Dr. Chan's research lies at the interface of theoretical chemistry, condensed matter physics, and quantum information theory, focusing on quantum many-particle phenomena and the numerical methods to simulate them. His group has developed numerous methodologies including density matrix renormalization and tensor network algorithms, canonical transformation-based down-foldings, local quantum chemistry methods, quantum embeddings, and new quantum Monte Carlo algorithms. His work addresses problems that appear naively exponentially hard but where understanding of physics, particularly entanglement structure, allows for calculations of polynomial cost. Analysis of his recent publications reveals a strong focus on quantum simulation techniques, particularly tensor network methods applied to strongly correlated systems. His research spans fundamental theoretical developments to practical applications in quantum computing, molecular simulation, and materials science, with increasing integration of machine learning techniques and GPU acceleration in computational chemistry frameworks. Dr. Chan leads an active research group at Caltech dedicated to simulating chemical and physical systems at the level of many-particle quantum mechanics. His group has welcomed numerous researchers including Kasra Hejazi, Zuxin Jin, Zhihao Cui, Ke Liao, Henrik Larsson, and Wenyuan Liu. He teaches courses in Physical Chemistry (Ch 21 abc) and Advanced Quantum Chemistry (Ch 225), contributing significantly to theoretical chemistry education at Caltech.

• Theoretical Chemistry
• Condensed Matter Physics
• Quantum Information Theory
• +۴ مورد دیگر

Mehmet Koyutürk serves as the Andrew R. Jennings Professor in the Department of Computer and Data Sciences at Case Western Reserve University's Case School of Engineering, with additional affiliation as a Member of the Cancer Genomics and Epigenomics Program at the Case Comprehensive Cancer Center. His computational research bridges algorithm development with biological applications, focusing on network-structured data analysis to address complex biomedical challenges. Dr. Koyutürk earned his Ph.D. in Computer Science from Purdue University following B.S. and M.S. degrees in Electrical Engineering and Computer Engineering from Bilkent University. His primary research domains include high-throughput biological data analysis, systems/network biology methodologies, data mining algorithms, and scientific computing optimization, with particular emphasis on phosphorylation networks, genomic interactions, and multi-omics integration. Recent publication trends reveal expanding applications of his network science expertise into Alzheimer's disease phosphoproteomics, bipolar disorder biomarker discovery, and intimate partner violence analysis, while maintaining core contributions to graph neural networks and biological link prediction. His group actively develops open-source analytical tools like RokaiXplorer for phospho-proteomic data accessibility. Scientific Recognition Andrew R. Jennings Professorship Dr. Koyutürk leads multiple NIH-funded initiatives including R01-LM012980 for phosphoproteomics analysis, U01-CA198941 (BD2K program) for big network integration, and R01-LM011247 for GWAS enhancement, complemented by NSF CAREER Award CCF-0953195. He serves on the steering committee for CWRU's Systems Biology and Bioinformatics graduate programs and as Associate Editor for IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), with extensive collaboration through Mark Chance's Center for Proteomics and Bioinformatics. His laboratory specializes in developing scalable algorithms for biological network analysis, currently advancing projects on kinase-substrate association prediction, co-phosphorylation network characterization in cancer, and network-based approaches to intimate partner violence data mining, with strong emphasis on translating computational methods into biomedical insights through open-source software dissemination.

• Systems Biology
• Computational Genomics
• Network Biology
• +۴ مورد دیگر

Rafał Latała is a distinguished Professor at the Institute of Mathematics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, where he has held a full professorship since 2013. He is also a Corresponding Member of the Polish Academy of Sciences since 2016 and an AMS Fellow since 2013. His academic career spans over 25 years at the University of Warsaw, progressing from Instructor (1994-1997) to Assistant Professor (1997-2003), Associate Professor (2003-2012), and finally to his current position as Professor. Additionally, he held a part-time professorship at the Institute of Mathematics of the Polish Academy of Sciences from 2009-2012. His educational background includes a PhD in Mathematics from the University of Warsaw (1997) with a dissertation on estimation of moments of sums of independent random variables under the supervision of Professor Stanisław Kwapien, a Habilitation degree in Mathematics (2002), and the title of Professor awarded by the President of Poland (2009). He completed his MSc in Mathematics at the University of Warsaw in 1994. Latała's research focuses on the intersection of probability theory and geometric analysis, with particular expertise in convex geometry, functional analysis, asymptotic geometric analysis, and the theory of log-concave measures. His work bridges theoretical mathematics with applications in high-dimensional statistics and random matrix theory. He has made significant contributions to understanding moment inequalities, concentration phenomena, and the geometric structure of high-dimensional random objects. His recent work demonstrates increasing sophistication in handling complex relationships between different norms of random vectors and matrices. His publication record shows a consistent focus on probabilistic methods in geometric settings, with recent articles demonstrating advanced techniques for analyzing random matrices, log-concave measures, and canonical processes. The research trajectory reveals increasingly sophisticated methods for bounding norms and moments in high-dimensional spaces, with applications spanning theoretical mathematics to statistical learning theory. Kolmogorov Lecture 2024 Prize of the Foundation for Polish Science in mathematics, physics, and engineering sciences 2023 Orlicz Lecture 2023 Institute of Mathematics of the Polish Academy of Sciences Prize 2014 AMS Fellow since 2013 Foundation for Polish Science Grant Mistrz 2007-2011 Prime Minister Award for Habilitation Thesis 2003 Invited Speaker at International Congress of Mathematicians 2002 Latała has supervised five PhD students to completion (Rafal Meller, Marta Strzelecka, Jakub Wojtaszczyk, Radoslaw Adamczak, and Rafal Lochowski) and four MSc students (Maciej Bartczak, Dariusz Matlak, Tomasz Tkocz, and Marcin Lis). His editorial service includes positions at Probability Surveys (2024-26), The Annals of Probability (2015-20), and Studia Mathematica (2006-present). He has organized numerous international conferences including the High Dimensional Probability X conference in 2023 and served on various professional committees including the Central Commission for Academic Degrees and Titles.

• Probability Theory
• Convex Geometry
• Functional Analysis
• +۳ مورد دیگر

Dr. Gary Glover is a Professor of Radiology (Radiological Sciences Lab) at Stanford University , with courtesy appointments in Psychology and Electrical Engineering. His work focuses on the physics and mathematics of MRI, particularly rapid scanning methods using spiral k-space trajectories for functional brain imaging and multimodal neuroimaging (fMRI/EEG/fPET/fNIRS) combined with neuromodulation techniques like TMS and transcranial ultrasound. Academic Appointments: Radiology, Psychology, Electrical Engineering Professional Affiliations: Bio-X, Stanford Cancer Institute, Wu Tsai Neurosciences Institute Research Interests include: Development of blood oxygen level-dependent (BOLD) and viscoelastic contrast in MRI Functional MR Elastography for brain activation mapping Optimization of MR-ARFI for transcranial ultrasound guidance Automated spinal cord segmentation (EPISeg) using machine learning Scientific Awards : National Academy of Engineering (2013) Gold Medal, ISMRM (2000) Steinmetz Award, General Electric (1985) Lauterbur Lecture, ISMRM (2018) Recent Publications analyze: Fast fMRI sampling and spurious signal correction Dissociated patterns in default mode network anti-correlations Neural correlates of collaborative behavior in triadic fMRI Salience network contributions to depression pathophysiology

• Magnetic Resonance Imaging (MRI)
• Functional MRI (fMRI)
• Biomedical Imaging Physics
• +۶ مورد دیگر

Aurélie Labbe is a Full Professor in the Department of Decision Sciences at HEC Montréal, holding the prestigious FRQ-IVADO Chair in Data Science. Appointed as Co-Scientific Director – Academic Partnerships at IVADO in October 2023, she plays a key leadership role in establishing connections between IVADO and partner universities. Her academic journey includes a PhD in Statistics from the University of Waterloo, a Master's degree in Statistics from the University of Montreal, and dual Bachelor's degrees in Applied Mathematics and Social Sciences from Paris-Dauphine University and Pure Mathematics from Versailles-St Quentin University. Her research spans multiple interdisciplinary domains with a focus on developing advanced statistical and machine learning methodologies for big data analysis. Labbe's work bridges theoretical statistics with practical applications across diverse fields including genomics, neuroscience, transportation systems, and health informatics. She has made significant contributions to kernel methods, matrix factorization techniques, random forest applications, and spatiotemporal data analysis, with publications appearing in top journals across multiple disciplines. Analyzing her recent publications reveals a clear trend toward methodological innovation applied to complex real-world problems. Her work demonstrates expertise in handling high-dimensional data from diverse sources including neuroimaging, transportation networks, and genomic studies. The interdisciplinary nature of her research connects statistical theory with applications in healthcare, transportation safety, and biological sciences, reflecting her ability to develop methods that address domain-specific challenges while advancing statistical methodology. Holder of the FRQ-IVADO Chair in Data Science Member of the Center for Mathematical Research Training Professor Labbe actively mentors the next generation of data scientists, supervising numerous doctoral and master's students. Her supervision portfolio includes 1 doctoral thesis (2023), 4 master's theses (2022-2024), and 32 supervised projects spanning 2019-2025. Her students' work covers diverse applications including transportation safety, healthcare analytics, financial modeling, and environmental analysis. Through her leadership of the FRQ-IVADO Chair in Data Science, she coordinates research activities that integrate mathematical, statistical, and computer science expertise with domain knowledge from various data-generating fields. As Co-Scientific Director at IVADO, Professor Labbe leads efforts to establish connections with faculties and departments across five partner universities, integrating them into IVADO's research and knowledge transfer activities. Her leadership role positions her at the forefront of advancing data science research and applications in Quebec's academic ecosystem.

• Data Science
• Statistical Learning
• Machine Learning
• +۷ مورد دیگر