Find Academic Supervisors

• Econometrics
• Time Series Analysis
• Probability Theory
• +5 more

Pavel Konstantinovich Katyshev is a Professor at the National Research University Higher School of Economics (HSE), specifically within the Faculty of Economic Sciences and Department of Applied Economics. With 52 years of scientific and teaching experience, he has been working at HSE since 2009 (and previously from 1997-1999). His academic career spans multiple prestigious institutions including the Central Economic-Mathematical Institute of the Russian Academy of Sciences (1975-present), Lomonosov Moscow State University, and the Russian Economic School. Professor Katyshev's educational background includes: 1976: Candidate of Physical and Mathematical Sciences (specialty 01.01.05 "Probability Theory and Mathematical Statistics") 1975: Postgraduate studies at Moscow State University M.V. Lomonosov 1971: Specialty "Mathematics" with qualification "Mathematician" from Lomonosov Moscow State University His primary research interests focus on the intersection of mathematics and economics, particularly in econometrics, probability theory, and mathematical economics. Professor Katyshev specializes in time series analysis, random error term models, cointegration, controlled random processes, and stochastic models of economic dynamics and equilibrium. His work bridges theoretical mathematical frameworks with practical economic applications, making significant contributions to both fields. His recent publications demonstrate a consistent focus on econometric methods and their applications to the Russian economy, particularly in analyzing relationships between energy markets (oil, electricity) and economic growth. He has developed numerous educational resources including textbooks and problem collections in probability theory, mathematical statistics, and econometrics, which have become standard references in Russian economic education. Professor Katyshev has received numerous accolades for his contributions: Medal "Recognition - 15 years of successful work" of the National Research University Higher School of Economics (October 2024) Letter of thanks from the Rector of HSE (October 2023) Gratitude from the Vice-Rector of HSE (October 2021) Honorary Diploma of the Higher School of Economics (December 2018) Gratitude from the Higher School of Economics (December 2012) Best Teacher award for 2023-2025 and 2011-2014 As an educator, Professor Katyshev has supervised students including Bogomolov Andrey Vasilievich (2015), whose thesis focused on parameter estimation in models with random error duration. He teaches courses in Probability Theory and Mathematical Statistics across various programs at HSE, including bachelor's and master's degrees in Economics. His teaching approach has been praised by students as "so cool and useful" that "you just listen to these lectures." Professor Katyshev previously served as head of the International Laboratory of Quantitative Finance at HSE (2014-2015) and has been involved with the Moscow Institute of Economics and Finance (MIÉF) as a statistics lecturer from 1997-2014. His extensive publication record includes more than 30 printed works, translations of three books from English, and co-authorship of the widely used textbook "Econometrics. Basic course."

Lyle W KonigsbergView profile

Professor

• Biological Anthropology
• Forensic Anthropology
• Paleodemography
• +2 more

Lyle W. Konigsberg is a Professor in the Department of Anthropology at the University of Illinois at Urbana-Champaign, affiliated with the College of Liberal Arts & Sciences. His research centers on biological anthropology, with emphasis on forensic methods, demography, and statistical approaches to skeletal analysis. He specializes in Bayesian statistics, age estimation techniques, osteometric sorting, and the development of computational tools for anthropological research. Konigsberg's work bridges theoretical and applied anthropology, particularly in forensic contexts. His recent publications focus on refining methods for skeletal identification, critiquing statistical standards in forensic anthropology, and developing open-source software (e.g., TestDimorph R package) for morphometric analysis. Key research themes include multivariate analysis of skeletal traits, population-specific variation, and methodological advancements in paleodemography. His articles consistently engage with quantitative rigor, emphasizing probabilistic models and computational innovations. Research output trends highlight sustained interest in improving accuracy in age-at-death estimation, resolving commingled remains, and addressing methodological challenges in biological distance studies.

• Cosmology
• Cosmic Microwave Background (CMB)
• Gravitational Waves
• +3 more

Professor Hans Kristian Eriksen is a leading cosmologist at the Institute of Theoretical Astrophysics, University of Oslo, specializing in primordial gravitational wave detection through cosmic microwave background (CMB) analysis. His work bridges advanced computational methods with high-precision observational cosmology, focusing on next-generation satellite missions like LiteBIRD and ground-based experiments including COMAP and SPIDER. His research centers on developing exascale computational frameworks for Bayesian end-to-end CMB analysis, with particular emphasis on extracting faint primordial B-mode signals from overwhelming foreground contamination. Key methodologies include Monte Carlo Markov Chain samplers, component separation techniques, and systematic error propagation models that enable unprecedented precision in cosmological parameter estimation. Recent publications demonstrate dominant focus on the LiteBIRD satellite mission across simulation frameworks, scanning optimization, and science forecasts, alongside significant contributions to CO intensity mapping through the COMAP project. These works reveal evolving trends toward multi-messenger cosmology and increasingly sophisticated foreground mitigation strategies. ERC Starting Grant ERC Consolidator Grant H2020 COMPET-4 project (BeyondPlanck) Eriksen serves as principal investigator for major computational cosmology projects, including the Cosmoglobe initiative for end-to-end CMB analysis. His supervisory role encompasses computational astrophysics projects focused on massive parallelization of CMB analysis codes, with funding secured through competitive European grants. His team maintains active collaborations with NASA/JPL, Caltech, Oxford, and international consortia. He leads the Cosmoglobe group implementing one of the world's most comprehensive cosmological MCMC samplers, while actively contributing to the COMAP, PASIPHAE, Planck, QUIET, and SPIDER collaborations. Current efforts concentrate on preparing computational pipelines for LiteBIRD data analysis and advancing Bayesian methods for next-generation CMB polarization experiments.

Heiko Andreas SchmidtView profile

Researcher

• Phylogenetics
• Phylogenomics
• Evolutionary Biology
• +5 more

Dr. Heiko Andreas Schmidt is a computational biologist affiliated with the University of Vienna , where he works at the Department of Structural and Computational Biology as part of the Center for Integrative Bioinformatics Vienna (CIBIV) and Max Perutz Labs. His research focuses on phylogenetics, phylogenomics, and the development of computational tools for evolutionary tree reconstruction, including contributions to software like IQ-TREE and TREE-PUZZLE . His work spans parallel computing applications in phylogenetics, scientific workflows, and quality assessments of alignments. He has co-authored numerous high-impact studies in molecular biology, developmental biology, and bioinformatics, often collaborating with Prof. Arndt von Haeseler and other researchers. Dr. Schmidt has contributed to methodological advancements in phylogenetic inference, particularly for large genomic datasets. He has taught courses such as Bioinformatics for Phylogeny and Evolutionary Tree Reconstruction and Algorithmic Bioinformatics at the University of Vienna.

Carlotta LangerView profile

Researcher

• Information Theory
• Embodied Cognition
• Complex Systems
• +3 more

Carlotta Langer is a researcher currently affiliated with the Psychology Department at Ludwig Maximilian University of Munich (LMU Munich), where she works within the interdisciplinary field of embodied cognition and information theory. She also maintains a guest researcher position at the Institute for Data Science Foundations at Hamburg University of Technology, contributing to the DFG priority program "The Active Self." Dr. Langer completed her undergraduate studies in Mathematics at Martin-Luther University Halle-Wittenberg before pursuing her doctoral research at Hamburg University of Technology, where she earned her PhD with a dissertation titled "Information Integration in Embodied Systems" (2024). Her research focuses on understanding how information flows through embodied systems, examining the intricate relationships between body, brain, and environment. Langer applies information-theoretic measures to quantify morphological computation and integrated information in embodied agents. Her work on complexity measures explores how systems exhibit properties that emerge from the interactions of their components, rather than from the components themselves. She also investigates information-geometric algorithms such as iterative scaling and the EM algorithm, analyzing their mathematical properties and applications to embodied cognition. Langer's recent publications (2017-2025) demonstrate a consistent trajectory exploring the theoretical foundations of embodied cognition through the lens of information theory. Her work bridges disciplines including cognitive science, computer science, and complex systems theory, with particular emphasis on how physical embodiment shapes cognitive processes and information integration. She frequently collaborates with researchers like N. Ay and contributes to advancing mathematical frameworks for understanding consciousness and agency in embodied systems. Dr. Langer has presented her research at multiple Models of Consciousness Conferences (2021-2024), demonstrating growing recognition of her work in theoretical cognitive science and consciousness studies. Her computational implementations are publicly available on GitHub, reflecting commitment to reproducible research. Her laboratory work involves developing computational models of embodied agents, particularly examining how sensorimotor loops and morphological properties contribute to information processing efficiency. The code repositories associated with her publications demonstrate rigorous methodological approaches combining theoretical analysis with practical implementation.

• Reinforcement Learning
• Stochastic Optimization

Daniil Nikolaevich Tyapkin is a Junior Research Fellow and Postgraduate Student at the National Research University Higher School of Economics (HSE), affiliated with the Faculty of Computer Science. He works within the Institute of Artificial Intelligence and Digital Sciences and the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis under the supervision of Alexey Alexandrovich Naumov. Education: 2021: Bachelor's degree in Applied Mathematics and Computer Science from HSE Currently in 2nd year of postgraduate studies with dissertation topic: "Randomized trial for reinforcement learning" Research Interests: His work focuses on reinforcement learning and stochastic optimization, including exploration strategies, generative flow networks, and theoretical analysis of learning algorithms. He develops efficient methods for complex decision-making problems with applications in AI and data science. Publications: His research appears in top venues (ICLR, NeurIPS, ICML, AISTATS) covering reinforcement learning, optimization, and probabilistic modeling. Recent works explore GFlowNets, entropy regularization, and high-probability bounds for learning algorithms. Awards & Honors: Letter of gratitude from HSE Faculty of Computer Science (2022) Publication bonus for List A journal articles (2023-2024) HSE Personnel Reserve - New Researchers category (2022-2023) Teaching & Advising: Teaches courses in Mathematical Statistics and Matrix Computations. No formal student advising mentioned. Laboratory Affiliation: Member of the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis, focusing on algorithmic development for machine learning challenges.

• Forensic Genetics
• DNA Profiling
• STR Analysis
• +2 more

Helle Smidt Mogensen is a Researcher and Forensic Geneticist at the Section of Forensic Genetics within the Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen. Employed since 1997, she has served as Laboratory Manager for the STR laboratory since 1998, overseeing critical forensic DNA analysis operations. Her academic credentials include: Master of Science (Cand. scient.) in Biochemistry with neuropharmacology specialization (1993) PhD thesis: "Quantitation of glutamate receptor subunit mRNAs in primary neuronal cultures: A comparative study of cultured mouse and rat cerebellar granule cells" (1997) Mogensen's research centers on STR typing of biological traces , interpretation of complex STR results , and next-generation sequencing applications in forensic genetics . Her work bridges molecular biology, statistical modeling, and practical forensic investigation, significantly enhancing DNA evidence reliability in criminal cases through rigorous methodological development. Analysis of her 2022-2026 publications reveals dominant trends in Y-STR paternal lineage identification, SNP genotyping via advanced statistical models, and optimization of DNA extraction from challenging samples like telogen hair. Her research consistently integrates computational approaches with laboratory validation, addressing critical gaps in forensic DNA interpretation for complex mixtures and low-template samples. Mogensen actively participates in international collaborative research and forensic laboratory proficiency initiatives. Her professional development includes extensive leadership training (2010-2014), specialized forensic genetics coursework, and technical certifications in DNA analysis instrumentation and software systems. As STR Laboratory Manager, she directs forensic casework analysis and reference sample processing while pioneering next-generation sequencing adoption. Her team maintains ISO/IEC 17025 accreditation standards and contributes to international forensic genetics guidelines through method validation studies and interlaboratory exercises.

Rolf LarssonView profile

Professor

• Time Series Analysis
• Econometrics
• Statistical Modeling
• +5 more

Rolf Larsson is a Professor at the Department of Mathematics at Uppsala University, Sweden. His academic career spans theoretical statistics, econometrics, and applied statistical analysis across multiple disciplines. His primary research interests include: Time Series Analysis and Econometrics Statistical Inference and Methodology Panel Data Analysis Climate Data Modeling Bayesian and Frequentist Statistics Multivariate Statistical Methods Discrete Data Analysis Professor Larsson's publication record demonstrates exceptional breadth across statistical theory and applications. His work on autoregressive models, unit root testing, and confidence distributions represents significant theoretical contributions to statistics. He has also made important applied contributions in climate science through his analysis of ice core data examining relationships between temperature, CO2, and methane. His methodological innovations span from discrete factor analysis to shrinkage estimators in beta regression models, with applications in econometrics, educational data, and medical research. His recent work on confidence distributions for autoregressive parameters (2024) continues his long-standing interest in foundational statistical theory. His collaborative work extends across disciplines, including medical statistics research examining the relationship between serotonergic medication, sunshine, and suicide. His publication record shows consistent high-quality output across top statistical and econometric journals over multiple decades.

• Statistical Inference
• Applied Statistics
• Regression Graphical Models
• +3 more

Giovanni M. Marchetti is a Professor in the Department of Statistics, Computer Science, Applications "G. Parenti" at the University of Florence, Italy. His office is located at viale Morgagni, 59, 51134 Florence, with contact information including email giovanni.marchetti@disia.unifi.it and phone +39 055 275 1516. He teaches Statistics courses for Mathematics and Economics degree programs at the university. Professor Marchetti's primary research interests focus on Statistical Inference and Applied Statistics , with specific expertise in Regression Graphical Models and Multivariate Analysis . His recent research projects include Parameterizations of chain graphs, Graphical models under constraints, Inference in graphical models, and Palindromic binary distributions. His work bridges theoretical statistical developments with practical applications across various scientific domains, as evidenced by publications in journals ranging from mathematical statistics to ecological research. An analysis of Professor Marchetti's 15 most recent publications (2009-2017) reveals a consistent focus on graphical models and statistical theory. His research spans from foundational work on binary distributions and chain graphs to applications in biological networks and social sciences. The predominant themes include conditional independence structures, parameterizations of complex statistical models, and computational approaches for high-dimensional data analysis. His collaborative work, particularly with Nanny Wermuth, has produced significant contributions to the understanding of graphical model structures. Professor Marchetti has established significant collaborations, particularly with Nanny Wermuth, and his work appears in prestigious journals including The Annals of Statistics , Journal of Multivariate Analysis , and Electronic Journal of Statistics . His research contributes substantially to the theoretical foundations of graphical models while maintaining relevance to practical applications in diverse fields such as evolutionary biology, marine ecology, and social sciences.

Amjad Al-NasserView profile

Professor

• Statistics
• Generalized Maximum Entropy
• Directional Data Statistics
• +4 more

Professor Amjad Al-Nasser serves as a faculty member in the Statistics Department at Yarmouk University's Faculty of Science in Jordan. His administrative career includes significant leadership roles as Vice Dean of the Faculty of Science and previously as Vice Dean of Research and Graduate Studies at Yarmouk University. In 2020, he held the position of Vice President for Academic Affairs at Al Falah University, demonstrating extensive experience in academic administration across multiple Jordanian institutions. Dr. Al-Nasser's primary research interests revolve around Generalized Maximum Entropy and Directional Data Statistics , with substantial contributions to statistical distribution theory and methodology. His work spans theoretical developments in probability distributions including the GOLD distribution, Poisson-Gold distribution, and various generalized forms of exponential and Weibull distributions. He has pioneered applications of entropy measures in statistical modeling and has made significant contributions to acceptance sampling plans, particularly for truncated life tests across diverse industrial contexts. The analysis of his recent publications reveals a strong focus on developing novel statistical distributions and refining sampling methodologies. His research demonstrates consistent innovation in adapting entropy principles to practical statistical problems, with applications spanning manufacturing quality control, social media analysis, epidemiological modeling, and engineering reliability. The interdisciplinary nature of his work connects statistical theory with real-world applications in public health, industrial engineering, and social sciences. Throughout his career, Professor Al-Nasser has maintained active engagement with statistical methodology development, with recent publications showing continued productivity in distribution theory and quality control applications. His work bridges theoretical statistical advancements with practical implementation across multiple domains, reflecting both depth in statistical theory and breadth in application areas.

Alankrita BhattView profile

Research Fellow

• Information Theory
• Statistics
• Sequential Decision Making
• +4 more

Alankrita Bhatt is a Postdoctoral Scholar Research Associate at the Department of Computing and Mathematical Sciences (CMS) at the California Institute of Technology since January 2023. Prior to this, she was a research fellow at the Simons Institute for the Theory of Computing, UC Berkeley, in Fall 2022. She holds a Ph.D. in Electrical Engineering from UC San Diego (2022), advised by Prof. Young-Han Kim, along with an M.S. in Statistics from UC San Diego (2020) and a B.Tech in Electrical Engineering from IIT Kanpur (2016). Education Ph.D. in Electrical Engineering, UC San Diego (2016–2022) M.S. in Statistics, UC San Diego (2020) B.Tech in Electrical Engineering, Indian Institute of Technology Kanpur (2012–2016) Her research lies at the intersection of Information Theory and Statistics , focusing on Sequential Decision Making , Machine Learning , and Probability . Her recent work explores universal prediction, confidence sequences, and online learning frameworks with side information. Alankrita’s publications (2017–2024) span journals like IEEE Transactions on Information Theory and conferences including ISIT, NeurIPS, and COLT. Key themes include universal graph compression, sequential probability assignment, and stochastic block models, reflecting her expertise in integrating statistical and information-theoretic approaches.

Jianxi SuView profile

Associate Professor

• Actuarial Mathematics
• Probability Theory
• Statistical Analysis

Jianxi Su is an Associate Professor in the Department of Statistics at Purdue University, where he serves as Director of the Actuarial Science Program. He joined Purdue in 2016 after completing his PhD at York University (Probability with Actuarial Specialization) and a post-doctoral position at Schulich School of Business. He holds professional designations as Fellow of the Society of Actuaries (FSA) and Associate of the Canadian Institute of Actuaries (ACIA). Education: PhD, York University, Probability (Specialization in Actuarial Science), 2016 Research Interests: Dr. Su's research spans three interconnected domains: 1) Actuarial Mathematics focusing on risk measures and premium principles, 2) Probability Theory exploring multivariate distributions and dependence structures, and 3) Statistical Analysis developing inference methods for actuarial/financial applications. His work consistently bridges theoretical frameworks with practical insurance solutions. Publication Trends: Recent articles demonstrate strong emphasis on risk allocation methodologies (9 papers), insurance applications (7 papers), and statistical modeling of dependent risks (6 papers). Common themes include tail risk quantification, catastrophe risk financing, and regulatory capital frameworks, with increasing focus on emerging risks like cyber threats and climate-related losses. Awards and Honors: College of Science Diversity Award (2021) Outstanding Assistant Professor Teaching Award (2021) College of Science Undergraduate Advising Award (2019) Research Leadership: Dr. Su has secured over $500,000 in research funding as PI/co-PI for 18+ grants since 2017, primarily from SOA/CAS. Current projects include disaster resilience modeling ($30k), synthetic data generation ($45k), and digital asset risks ($45k). He leads Purdue's actuarial curriculum development and mentors students through research collaborations evidenced by co-authored publications. Professional Engagement: He maintains extensive collaborations with industry (Sun Life Financial) and academics globally. As Actuarial Program Director, he coordinates industry-academia partnerships and oversees initiatives enhancing actuarial education through learning communities.

Yandi ShenView profile

Assistant Professor

• Nonparametric Statistics
• Semiparametric Statistics
• High-Dimensional Inference
• +6 more

Yandi Shen is an Assistant Professor in the Department of Statistics and Data Science at Carnegie Mellon University (CMU), part of the Dietrich College of Humanities and Social Sciences. He holds a Ph.D. in Statistics from the University of Washington (2021), advised by Fang Han and Daniela Witten. Prior to joining CMU, he was a Kruskal Instructor in Statistics at the University of Chicago (2021–2023) and a postdoctoral researcher at Yale University under Zhou Fan. His research focuses on nonparametric and semiparametric statistics, high-dimensional inference, applied probability, and optimization algorithms. Education: Ph.D. in Statistics (University of Washington, 2021); Postdoctoral work at University of Chicago and Yale University. Research Interests: Nonparametric and semiparametric methods High-dimensional statistical inference Applied probability and stochastic processes Empirical Bayes methods Optimization and algorithm design Statistical machine learning Teaching Experience: University of Washington: Teaching Assistant for advanced inference courses (STAT 581-583), probability (STAT 394-395), and applied statistics (STAT 528). University of Chicago: Instructor for courses like STAT 22400 (Applied Regression Analysis) and STAT 33611 (Gaussian Processes). Recent Research Trends: His publications emphasize theoretical advancements in high-dimensional statistics, including empirical Bayes estimation, gradient flows, and optimization in overparameterized models. Key areas include analyzing phase transitions in spline regression, universality in regularized estimators, and nonparametric mixture models under optimal transport distances. Labs/Teams: While no specific lab is named, his collaborative work spans statistical theory and applications, often involving interdisciplinary teams in data science and machine learning.

Adam JohansenView profile

Professor

• Monte Carlo methodology
• Sequential Monte Carlo algorithms
• Bayesian statistics
• +3 more

Adam Johansen is a Professor of Statistics at the University of Warwick, specializing in computational statistics, sequential Monte Carlo algorithms, Bayesian methodology, and gradient flows. His research spans theoretical advancements and practical applications in simulation-based inference. Current projects: OCEAN (ERC Synergy Grant for next-gen ML algorithms), CoSinES, and APTS (co-director until 2023) Research interests focus on Monte Carlo methodology , Bayesian statistics , and sequential inference . Recent work explores scalability in decentralized systems, gradient flows for sampling, and robust algorithmic frameworks. His 15 most recent publications emphasize particle methods, convergence analysis, and mathematical statistics. He supervises a research group with PhD students including Rocco Caprio, Shu Huang, and Usman Ladan, while contributing to editorial roles at the Journal of the Royal Statistical Society: Series B and London Mathematical Society Newsletter . He developed the SMCTC and RcppSMC libraries for sequential Monte Carlo applications.

Javier RouletView profile

Researcher

• Physics
• Astronomy
• Gravitational Waves
• +3 more

Javier Roulet is a Sherman Fairchild Postdoctoral Scholar and Researcher in the Department of Physics at the California Institute of Technology (Caltech). His research focuses on gravitational waves, black hole mergers, and astrophysical signal analysis. He is affiliated with the Division of Physics, Mathematics, and Astronomy at Caltech and contributes to major projects like the LIGO-Virgo collaboration. His work emphasizes the detection and characterization of gravitational wave signals, including higher-order harmonics and eccentric systems. Key research interests include binary black hole mergers, tidal deformations in compact objects, and the development of advanced search pipelines and parameter estimation techniques. He has contributed to studies using data from LIGO, Virgo, and future missions like LISA. His research aims to enhance detection sensitivity, improve computational efficiency in waveform analysis, and uncover astrophysical insights from gravitational wave observations. Recent publications highlight advancements in template banks, marginalization algorithms, and the discovery of new black hole mergers. His work bridges theoretical predictions with observational data, addressing challenges such as non-Gaussian noise and multimodal parameter spaces. Collaborations involve both ground-based detectors and space-based initiatives, reflecting his broad engagement in gravitational wave astronomy.