Ernst Hansen is an Associate Professor at the Department of Mathematical Sciences, University of Copenhagen . He is based at University Park 5, Copenhagen Ø, and his work spans mathematical statistics, probability theory, and applied statistical modeling in public health and finance. Email: erhansen@math.ku.dk Research areas: Public health interventions, Markov chain applications, measure-theoretic probability, and statistical education His publications include textbooks and peer-reviewed articles on topics such as neck/shoulder pain prevention , continuous-time rating transition probabilities , and geometric drift analysis . While his work intersects with causal inference and stochastic processes, no formal awards or student advising records are documented in the provided texts.
Wendy Heller is a Professor of Psychology at the University of Illinois at Urbana-Champaign, affiliated with the Cognitive Neuroscience Group at the Beckman Institute (part-time), Gender and Women's Studies, Biomedical and Translational Sciences, and the Institute for Sustainability, Energy, and Environment. She previously served as Director of Clinical Training and Department Head in the Psychology Department, and as Provost Fellow for diversity initiatives. B.A. in Spanish and Psychology with Honors, University of Pennsylvania M.A. and Ph.D. in Biopsychology, University of Chicago Her research focuses on neural mechanisms of emotion-cognition interactions in psychopathology, particularly anxiety and depression. Using fMRI, EEG, and ERP methods, she investigates brain lateralization, network connectivity, and top-down emotional regulation. A pioneer in inclusive science , she integrates equity considerations into psychology research frameworks. The 2010-2025 publications demonstrate sustained contributions to anxiety/depression neuroscience , with recurring themes in executive dysfunction , emotion regulation , neural plasticity , and transdiagnostic models . Her work bridges clinical psychology and cognitive neuroscience through methodological innovations in machine learning applications to neuroimaging. Scientific recognition includes: Larine Y. Cowan Make a Difference Award (2010) Arnold O. Beckman Research Award (2013) Executive Officer Distinguished Leadership Award (2019) PRESIDENT’S EXECUTIVE LEADERSHIP PROGRAM (2019-20) LAS Impact Award (2021) As director of the Cognitive & Affective Neuroscience of Psychopathology (CANOPY) Lab , she leads research on brain mechanisms in mental health, funded by NIMH and NIDA. Her team employs multi-modal approaches combining behavioral tasks, neuroimaging, and clinical assessments.
Joseph Glaz is a Professor in the Department of Statistics at the University of Connecticut. His work focuses on applied probability and statistical methodology, particularly in scan statistics and related inference techniques. Contact: joseph.glaz@uconn.edu Phone: (860) 486-4193 (Storrs Campus) Research highlights: Develops scan statistics for discrete , continuous , and conditional data frameworks Specializes in change-point detection for normal data mean/variance Innovates robust methods for outlier-prone datasets Extends scan statistics to network and graph structures Contributed to foundational texts like the Handbook of Scan Statistics Publication trends include: Scan statistics for genomic data (Hi-C translocation detection) Nonparametric and Bayesian extensions of scan methods Approximations and inequalities for sequential testing Applications in quality control , medical imaging , and sensor networks
Xavier Puig is an Assistant Professor at the Universitat Politècnica de Catalunya (UPC) , affiliated with the Department of Statistics and Operations Research and the School of Mathematics and Statistics (FME). He is a member of the ADBD - Analysis of Complex Data for Business Decisions and GRBIO - Biostatistics and Bioinformatics Research Group . His research focuses on Bayesian data analysis , with applications in Epidemiology Ecology Public health Political science Industrial quality control Marketing analytics Recent publications reveal a strong trend in Bayesian spatiotemporal modeling for health data, alcohol-migraine interaction studies, and industrial error rate monitoring . His work combines methodological innovation with real-world applications across diverse sectors. Collaborations include researchers from biostatistics, clinical epidemiology, and industrial engineering. He has contributed to 44 indexed journal articles and participated in 49 congress presentations , with recent projects focusing on competitive research and non-competitive industrial collaborations in statistical modeling.
Prof. Mile Šikić is a Full Professor at the Department of Electronic Systems and Information Processing, Faculty of Electrical Engineering and Computing (University of Zagreb). His research spans computational biology, genomics, and machine learning applications in sequencing technologies. Focus on nanopore sequencing analysis, genome assembly, and protein interaction prediction Developed tools like GraphMap , RiNALMo , and Orthobalancer Active in metagenomics, RNA structure prediction, and CUDA-based algorithm acceleration Scientific contributions include: Advances in de novo genome assembly for error-prone long reads Deep learning models for base modification detection Efficient algorithms for sequence alignment and similarity searches Technical implementations cover: GPU-accelerated sequence alignment libraries (e.g., SW# ) Web platforms for comparative protein analysis Simulation tools for epidemic spread on complex networks
Minh Huynh is a Senior Lecturer in the Department of Econometrics & Business Statistics within the Faculty of Business and Economics at Monash University. His work bridges statistical methodology with practical applications in sports science, biomechanics, and health research. Position: Senior Lecturer Institution: Monash University Department: Econometrics & Business Statistics School: Faculty of Business and Economics Dr. Huynh's research focuses on the application of statistical models to real-world problems in sports performance and human health. His primary interests include sports analytics, biomechanics of cricket and football, sleep science, and the impact of lifestyle factors on athletic recovery. He employs advanced quantitative methods to analyze player performance, injury risk, and recovery strategies. His recent publications reveal a strong trend toward interdisciplinary research, particularly in validating new technologies (such as AI-based speed tracking), meta-analyses on sleep and alcohol, and biomechanical assessments in cricket. The articles span high-impact journals in sports science and medicine, indicating a rigorous, peer-reviewed research trajectory with practical implications for coaching and athlete management. While no specific scientific awards are listed in the provided text, his work has been widely disseminated, appearing in 45 research outputs with significant media and academic attention, including coverage by over 100 news outlets and engagement on social platforms. Dr. Huynh collaborates extensively with researchers in sports science and medicine, suggesting active involvement in research teams and potential supervision of students, though no advisees are explicitly named. His research likely involves data-driven projects in sports performance labs or collaborative health studies. He has not indicated any part-time status, retirement, or former staff designation, and remains an active academic contributor.
Jan Stuhler is a Professor of Economics at Charles III University of Madrid's School of Economics and Business, Department of Economics. He maintains significant affiliations with the Swedish Institute for Social Research (Stockholm), Centre for Research and Analysis of Migration (London), Centre for Economic Policy Research, Institute for the Study of Labor (Bonn), and HCEO Global Working Group. His research focuses on Labour and Public Economics, with particular expertise in Immigration and Intergenerational Mobility. Stuhler's work examines labor market integration of immigrants, measurement of social mobility across generations, and the economic impacts of migration policies. His methodological approaches combine microeconometric analysis with large-scale administrative and survey data. His publication record shows consistent contributions to top journals including Quarterly Journal of Economics, Journal of Political Economy (revise and resubmit), Economic Journal, and Journal of Human Resources. Research trends indicate specialization in intergenerational transmission mechanisms and rigorous analysis of immigration's labor market effects, often using German and international datasets. Stuhler serves as co-editor at Labour Economics and has held visiting positions at Harvard University (2017) and Massachusetts Institute of Technology (2021/22), where he lectured on Graduate Labor Economics (14.662). He teaches PhD Reading Groups in Applied Economics at UC3M. His academic trajectory includes a PhD from University College London (2014) under Christian Dustmann and Uta Schönberg, undergraduate studies at University of Bonn with visiting period at UC Berkeley, and current leadership in multiple international research networks focused on labor economics and social policy.
Jinchuan Xing is a Professor at Rutgers, The State University of New Jersey, where he leads the Xing Lab of Genomics. His research focuses on human genomic variation, mobile DNA elements, evolutionary and population genetics, and their implications for human disease. He integrates computational and experimental methods to study genome-wide variation, with applications in disease gene identification and genomic technology development. His research interests include: Mobile DNA element biology Human demographic history and population diversity Disease gene identification using whole-exome and whole-genome sequencing High-altitude adaptation genetics Transposable element regulation and expression piRNA and small RNA pathways His recent publications highlight a strong trend in reproductive genomics, particularly in identifying genetic risk factors for embryo aneuploidy in IVF patients, using advanced sequencing technologies. Other work spans evolutionary genomics in diverse species (bats, moths, Drosophila), structural variation in neurodevelopmental disorders, and proteogenomic discovery. His lab actively develops and applies bioinformatic tools for variant analysis and gene prioritization. Scientific awards and honors are not explicitly mentioned in the provided text. Jinchuan Xing advises several graduate students, including Nan Wang, Siqi, Ellie Lu, and Tongji Xing. His lab has received significant funding, including an R01 grant from NICHD on aneuploidy risk, a grant from the Center for Human Evolutionary Studies, and a Life Sciences Alliance Pilot Seed Funding grant in collaboration with the Department of Statistics. These grants support research in fertility genomics, disease gene discovery, and genomic tool development. The Xing Lab of Genomics is an active research group conducting interdisciplinary studies combining genomics, bioinformatics, and molecular biology. The lab welcomes new members regularly, including master’s and PhD students, and collaborates across departments. Current projects include understanding the genetic basis of meiosis, fertility, and high-altitude adaptation, as well as developing Markovian gene networks for disease gene discovery.
Professor Rory Fitzgerald is a leading academic and the founding Director of the European Social Survey European Research Infrastructure Consortium (ESS ERIC), hosted at City, University of London , where he is affiliated with the School of Health & Psychological Sciences and the Centre for Comparative Social Surveys . He has played a pivotal role in establishing ESS as a landmark social science infrastructure in Europe. Education: DPhil in Sociology, City, University of London (2016) MSc (Econ) in European Politics, University of Wales (1996) BSc (Econ) in Politics, University of Wales (1995) Research Interests: Professor Fitzgerald specializes in cross-national survey methodology , with deep expertise in questionnaire design, pre-testing, non-response issues, and the integration of web-based survey panels. His work emphasizes methodological rigor and cross-cultural comparability in large-scale social surveys. He has pioneered innovations in probability-based online panels and mixed-mode data collection across Europe. Publications & Research Trends: His recent publications reflect a strong focus on measurement quality , error source typology , health inequalities , democratic attitudes , and data harmonization . The ESS, under his leadership, supports over 185,000 data users and has generated nearly 4,500 scholarly publications, making it one of the most impactful social science infrastructures in Europe. Scientific Awards: Descartes Prize (2005) for excellence in collaborative scientific research ESS recognized as a landmark research infrastructure by ESFRI (2016) Grants and Enterprise: Fitzgerald coordinated two Horizon 2020 grants totaling over €10 million in 2015 and secured a €5 million grant in 2019 to support ESS sustainability and launch a 12-country harmonized web panel (CRONOS). ESS ERIC operates with an annual central budget of €2.4 million. He also co-organizes a Methods Seminar Series with NatCen, fostering collaboration between academics and practitioners in survey research. Labs and Teams: He leads the ESS Core Scientific Team (CST) and the ESS National Coordinators Forum , working closely with the General Assembly and scientific advisory boards. The ESS infrastructure spans 27 member countries and conducts biennial surveys across up to 34 European nations.
O. Remus Tutunea-Fatan is a Professor in the Department of Mechanical & Materials Engineering at Western University, with cross appointments in Biomedical Engineering and Electrical and Computer Engineering. His work focuses on laser polishing, CNC machining, and surface structuring for drag reduction and biomedical applications. Ph.D. in Mechanical Engineering, The University of Western Ontario M.E.Sc. and B.E.Sc. in Mechanical Engineering, Transilvania University, Romania Research interests include: Advanced CAD/CAM frameworks Laser remelting process optimization Biomedical device design Composite manufacturing techniques Surface topography analysis Artificial intelligence in process control Recent publications indicate expertise in: Laser polishing of metallic surfaces Riblet microstructures for drag reduction AI-driven process monitoring 5-axis machining error compensation Scientific recognition includes: Edward G. Pleva Award for Excellence in Teaching (Western University, 2023) Dr. Terry Base Memorial Teaching Award (2022 co-winner, 2021, 2019) R. Mohan Mathur Award (Faculty of Engineering, 2020) University Students' Council Teaching Honour Roll (2011-2012) Prof. Tutunea-Fatan serves as Associate Chair for Graduate Research Programs (2025-2027) Acting Associate Dean for Undergraduate Studies (2023-2024) Acting Chair, Department of Mechanical and Materials Engineering (2021-2022) He supervises over 40 graduate students and collaborates with industry partners including DuPont Safety and Construction, General Motors, and Active Industrial Solutions Inc.
Ashwin Machanavajjhala is a Professor in the Department of Computer Science at Duke University's Pratt School of Engineering. With over 166 publications spanning from 2001 to 2025, his research has significantly impacted the fields of differential privacy, database systems, and data security. His recent work focuses on practical applications of differential privacy for government data releases, particularly for the US Census Bureau. Machanavajjhala's research primarily centers on differential privacy, with significant contributions to database systems, privacy-preserving data analysis, and statistical disclosure control. His work bridges theoretical foundations with real-world applications, particularly in government statistics and census data protection. He has developed numerous frameworks and algorithms including DPXPlain for explaining differentially private query results, PreFair for generating fair synthetic data, and various components of the US Census Bureau's disclosure avoidance system. His research demonstrates a consistent trajectory from theoretical privacy mechanisms toward practical implementations that balance privacy guarantees with data utility. His recent publications reveal a strong focus on applying differential privacy to census data (SafeTab, PHSafe), developing methods for explaining private query results (DPXPlain), addressing fairness in private data analysis (PreFair), and exploring privacy applications in blockchain technology. His work shows increasing engagement with government agencies, particularly the US Census Bureau, where his research has directly informed disclosure avoidance systems for the 2020 Census. Machanavajjhala has advised numerous PhD students who have become prominent researchers in privacy and databases, including Ryan McKenna, Xi He, Yuchao Tao, and David Pujol. His collaborative network includes leading researchers from major institutions, with frequent collaborations with Gerome Miklau, Michael Hay, and Daniel Kifer. His research has been consistently funded by major grants supporting privacy-preserving data analysis. He leads research on the Tumult Analytics framework, a robust and scalable differential privacy system, and has been instrumental in developing privacy technologies for the US Census Bureau's 2020 data release. His work demonstrates a commitment to making differential privacy practical for real-world statistical agencies and data providers.
Anna Nikolei is a research assistant at the University of Kassel in the department of Psychological Research Methods since February 2025. She holds an M.Sc. in Psychology and a B.Sc. in both Mathematics and Psychology from the University of Münster. Education: M.Sc. Psychology (2024), B.Sc. Mathematics (2024), B.Sc. Psychology (2021) Contact: anna.nikolei@uni-kassel.de | +49 561 804-1873 | Room 1303, Dutch Street 36-38, Kassel Her research focuses on statistical inference after model selection and temporal dynamics in experimental longitudinal data . She actively contributes to methodological advancements in multilevel modeling and post-selective inference. Anna's recent publications examine mixed-effects models for time-varying effects and simulation-based validation of inferential techniques in applied linear modeling. Her work bridges methodological rigor with practical applications in psychological experimentation.
Jonathan A. Kelner is a Professor of Applied Mathematics at the Massachusetts Institute of Technology (MIT) and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) . His research bridges pure mathematics and algorithms, focusing on spectral graph theory, combinatorial optimization, and distributed computing.
Daniel S. Berger is a Principal Researcher at Microsoft's Azure Systems Research Group in Redmond, focusing on the efficiency, sustainability, and reliability of cloud platforms . He is also an Affiliate Assistant Professor at the Paul G. Allen School of Computer Science at the University of Washington, where he teaches graduate classes. His research spans systems stack innovations for sustainability , including work on memory tiering , repair operations , and cooling systems (Zissou). He leverages system prototyping , simulation , and statistical modeling in his work, often collaborating with PhD students and postdocs from institutions like Columbia, University of Toronto, CMU, and Princeton. Recent publications highlight his leadership in CXL-based memory management , carbon-efficient cloud design , and latency-aware caching . His tools, such as Belatedly and FOO , have demonstrated significant improvements in cache performance and latency optimization. Best Paper Awards: USENIX OSDI 2023, HotCarbon 2023, ACM SOSP 2021. Distinguished Paper: ASPLOS 2023. His work has been integrated into Apache Traffic Server and Microsoft production systems , with open-source tools and datasets released for reproducibility. Collaborations include hardware and OS development teams within Azure and academia.
PD Dr. Ursula Berger is a Senior Biostatistician and Epidemiologist at Ludwig-Maximilians University Munich's Department of Medical Informatics, Biostatistics and Epidemiology (IBE). She holds a Venia Legendi for Biostatistics and Epidemiology from LMU and has extensive experience in health research, including positions at the University of Glasgow, Universität Bielefeld, and as a consultant for IGES in Berlin. Her research focuses on the evaluation of health programs and interventions, health inequality research, and risk factor assessment using models for complex data structures. Dr. Berger specializes in spatial epidemiology, statistical methods development, and the application of advanced biostatistical techniques to public health problems. She has made significant contributions to understanding health disparities, environmental health impacts, and pandemic response strategies. Dr. Berger has supervised numerous doctoral and master's theses across diverse topics including spatial epidemiology, non-communicable diseases, environmental exposures, and health systems research. Her recent publications demonstrate expertise in statistical modeling of pandemic data, health inequalities, and methodological approaches to epidemiological challenges. Her scientific recognition includes the Harald Mückter Teaching Innovation Award 2025 from the LMU Medical Faculty for her commitment to teaching and promotion of statistical competence in medical education. She has developed innovative educational tools including epiLEARNER, an interactive e-learning program for epidemiology and statistics. As an educator, Dr. Berger coordinates and teaches advanced courses including Quantitative Methods, Survival Analysis, Multilevel Analysis, and Statistical Methods for Spatial Epidemiology. She has played a key role in restructuring epidemiology and biostatistics curriculum for medical students through the MeCuM Science program and has coordinated the development of a national catalogue of learning objectives in biostatistics.
