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Wei ChenView profile

Professor

• Statistical Genetics
• Computational Biology
• Genomics
• +4 more

Wei Chen serves as Professor of Pediatrics (primary appointment) with secondary appointments in Biostatistics and Human Genetics at the University of Pittsburgh, where he leads the Chen Lab focused on statistical genomics and computational biology. His research develops innovative methods for analyzing high-throughput genomic data to study complex diseases including childhood asthma, age-related macular degeneration, and COPD. Dr. Chen earned his PhD in Biostatistics from the University of Michigan, MS in Mathematics from Michigan State University, and BS in Mathematics from Nanjing University. His expertise bridges mathematical theory with biomedical applications through rigorous statistical modeling. His research program centers on creating computational frameworks for single-cell and spatial multi-omics data integration, with particular emphasis on developing novel algorithms for doublet detection, spatial transcriptomics super-resolution, and longitudinal disease progression modeling. Current projects leverage bulk and single-cell multi-omics (DNA-seq, RNA-seq, methylation, ATAC-seq, Spatial Transcriptomics) to uncover disease mechanisms in pediatric asthma and macular degeneration. Recent publications demonstrate consistent leadership in statistical genomics, with 2024-2025 work advancing spatial transcriptomics transformer models and pediatric asthma endotype characterization published in Nature Communications and JAMA. His research shows increasing focus on multi-modal data integration and clinical translation of genomic findings. Pediatric Discovery Award from Department of Pediatrics (2025) Secretary of Chinese-American Lung Association (2024) Chapter Representative, Pittsburgh Chapter American Association Society (2024) Dr. Chen actively mentors 6 current PhD students and has guided over 20 trainees to successful careers in academia and industry. His lab secures substantial external funding including NIH R01 grants from NEI, NHGRI, NHLBI, NIAMS, and NIDDK, plus support from NSF, Scleroderma Research Foundation, Helmsley Charitable Trust, and UPMC. Recent grants include an NIAMS R01 for scleroderma subgroup identification and DoD funding for pediatric scleroderma multi-omics studies. The Chen Lab operates within the Rangos Research Building at Children's Hospital of Pittsburgh, maintaining a collaborative environment with biostatisticians, geneticists, and clinicians. Current projects integrate spatial transcriptomics with histology imaging and develop AI-driven models for disease progression prediction, supported by active partnerships with NIH institutes and disease-specific foundations.

Jennifer BrownView profile

Professor

• Environmental Statistics
• Survey Design
• Environmental Monitoring
• +6 more

Professor Jennifer Brown is a distinguished academic in the School of Mathematics and Statistics at the University of Canterbury, Faculty of Engineering, where she has been Professor since December 1996. She maintains an international reputation in survey design and environmental monitoring, frequently collaborating with both statisticians and biologists across various application areas. Her work spans environmental statistics, human health and wellbeing, and theoretical statistics. Education: PhD, University of Otago, Dunedin (1996) PG Diploma Science, Massey University, Palmerston North (1992) B.For.Sc (hons.), University of Canterbury, Christchurch (1986) Postgraduate Certificate in Strategic Leadership, University of Canterbury (2016) New Zealand Diploma in Health and Wellbeing, CareerForce, Wellington (2020) Professor Brown's research interests primarily focus on environmental statistics, survey design, and environmental monitoring. She has expertise in collaborative research across multiple disciplines, working with agencies in France, Italy, Spain, USA, Australia, and New Zealand. Her work extends to theoretical statistics, including regression trees and sampling theory, while maintaining strong connections to practical applications in environmental science and health. She has hosted international workshops on survey design and environmental monitoring and is committed to developing leadership in mathematical sciences. Her recent publications demonstrate a strong focus on spatial statistics and sampling methodologies, with particular emphasis on spatially balanced sampling techniques, adaptive sampling designs, and environmental monitoring applications. The research trends indicate a consistent progression toward more sophisticated spatial statistical methods with practical applications in environmental monitoring, resource assessment, and ecological studies, while also expanding into health-related statistical applications. Scientific Awards: Senior Fellow of the Higher Education Academy (UK) NZSA Campbell Award New Zealand Statistical Association Lifetime Member International Statistical Institute Elected member Professor Brown has supervised numerous graduate students across PhD, Masters, and undergraduate levels, with research topics spanning statistical learning, spatial statistics, healthcare applications, and sampling methods. She has been actively involved in research projects related to environmental monitoring, health and wellbeing, and statistical methodology development. Her teaching includes specialized courses in sampling methods and generalized linear models. She is Associate Director of the Biomathematics Research Centre and researcher in the NZ Health Ageing Research Group, maintaining strong connections between theoretical statistical development and practical applications in environmental science and health research.

Fredrik D. JohanssonView profile

Associate Professor

• Causal Inference
• Machine Learning
• Healthcare Applications
• +5 more

Fredrik D. Johansson is an Associate Professor of Computer Science & Engineering at Chalmers University of Technology in Sweden, where he serves as Principal Investigator for the Healthy AI Lab. His academic career bridges theoretical machine learning with practical healthcare applications, focusing on causal inference methodologies for real-world decision making. His research interests center on causal inference in machine learning and decision-making with healthcare applications. Johansson's work spans multiple subfields including treatment effect estimation, missing data handling, reinforcement learning for healthcare, and Alzheimer's disease simulation. His research group develops both theoretical frameworks and practical tools for causal machine learning. Analysis of his recent publications reveals a strong emphasis on causal inference methodology with increasing healthcare applications, particularly in Alzheimer's disease research and treatment effect heterogeneity. His work consistently appears in top-tier machine learning venues including NeurIPS, ICML, AISTATS, and JMLR, demonstrating both theoretical rigor and practical relevance to healthcare decision making. As an academic advisor, Johansson currently supervises seven PhD students: Herman Bergström, Alessandro Margueritte, Ahmet Balcioglu, Adam Breitholtz, Newton Mwai Kinyanjui, Anton Matsson, and Lena Stempfle, indicating an active and growing research group. He leads the Healthy AI Lab at Chalmers University of Technology, which focuses on developing machine learning methods for healthcare applications with particular emphasis on causal inference. The lab has produced numerous software tools including Counterfactual Regression, Matching Kernels, and DLOREAN, along with datasets like IHDP-100, Jobs-Binary, and ADCB that have become standard benchmarks in the causal inference community.

Kevin BuchinView profile

Professor

• algorithm engineering
• computational geometry
• spatial networks
• +2 more

Kevin Buchin is a Professor at the Department of Computer Science, Faculty of Computer Science at Technical University of Dortmund, where he leads Chair 11: Algorithm Engineering. His work focuses on bridging theoretical and experimental algorithm development, particularly in computational geometry and spatial data analysis. Professor Buchin's research spans several key areas in algorithm engineering, with particular expertise in computational geometry, spatial networks, algorithms for GIS (Geographic Information Systems), and motion planning algorithms. His work combines theoretical foundations with practical applications, developing algorithms that address real-world challenges in spatial data processing and analysis. He has made significant contributions to trajectory analysis, geometric spanners, Fréchet distance computations, and map construction algorithms. His research often involves interdisciplinary collaborations that connect computer science with geographic applications and movement data analysis. An analysis of Professor Buchin's recent publications reveals a strong focus on trajectory analysis and spatial network algorithms. His work consistently addresses fundamental problems in computational geometry while developing practical solutions for real-world applications. Key trends include advancements in Fréchet distance computations, trajectory clustering techniques, geometric spanner constructions, and map-matching algorithms. His research demonstrates a balanced approach between theoretical algorithm development and practical implementation, often resulting in benchmark suites and publicly available code to support reproducibility and further research. Professor Buchin actively supervises student research projects and theses. For Bachelor's theses, he requires successful completion of "Effiziente Algorithmen (EA)" and DAP2 courses, while Master's thesis students should have completed "Algorithmen und Datenstrukturen (AuD)" and ideally specialized courses and seminars in algorithms/algorithm engineering. His group offers final projects in algorithm engineering, geometric algorithms, geometric graphs, networks, GIS algorithms, and motion planning. He has supervised numerous Master's theses, with examples of completed projects available on his group's website. The Algorithm Engineering research group led by Professor Buchin includes Mart Hagedoorn (MSc), Antonia Kalb (MSc), Dr. Guangping Li, Aleksandr Popov (external, MSc), Dr. Carolin Rehs, and student assistants Jan Erik Swiadek and Torben Scheele. The group develops tools like the "Ruler of the Plane" game that illustrates geometric problems and algorithms, which is also available online. They maintain repositories containing game sources, art, and documentation for extending the games, including the "dots & polygons" game and new levels for the art gallery game. The group also produces educational videos illustrating research results, particularly in trajectory analysis, and offers a Coursera course on geometric algorithms.

Guido W ImbensView profile

Professor

• Causal Inference
• Econometrics
• Program Evaluation
• +6 more

Guido W Imbens serves as Applied Econometrics Professor at Stanford University's Graduate School of Business and Professor of Economics in the School of Humanities & Sciences. He holds additional affiliation as Senior Fellow at the Stanford Institute for Economic Policy Research, with prior academic appointments at Harvard University, UCLA, and UC Berkeley. Education: Brown University Professor Imbens specializes in causal inference methodology for observational studies, developing foundational techniques in matching, instrumental variables, and regression discontinuity designs. His research bridges theoretical econometrics with practical applications in program evaluation and identification strategies, significantly influencing empirical economics and social science research. Recent publications demonstrate continued innovation in methodological challenges including panel data analysis, clustered standard errors, and historical policy impact assessment, maintaining focus on robust causal identification in complex real-world settings. Scientific Awards: The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel (2021) Fellow of the National Academy of Sciences (2022) Honorary Doctorate from Brown University (2022) Amman Mineral Faculty Fellow (2021-2022) Horace Mann Medal from Brown University (2017) Fellow of the American Academy of Arts and Sciences Fellow of the Econometric Society Stanford GSB Trust Faculty Fellow (2013-2014) Honorary Doctorate from the University of St Gallen Available documentation provides no details regarding graduate student advising, research grants, laboratory affiliations, or collaborative research teams.

• Statistical Physics
• Condensed Matter Physics
• Biological Physics
• +6 more

Jack Lidmar is an Associate Professor in the Department of Physics at KTH Royal Institute of Technology. His research focuses on statistical physics, condensed matter physics, and biological physics, with emphasis on fluctuation phenomena in superconductors, phase transitions, quantum phase transitions, disordered systems, and the development of efficient Monte Carlo simulation methods. He has contributed to studies of superconducting nanostructures, spin glasses, and biological systems such as virus structural mechanics. Lidmar teaches several courses at KTH, including Computational Physics , Statistical Physics , and Quantum Information , and oversees multiple degree projects. His work integrates theoretical modeling with advanced simulation techniques, such as the Accelerated Weight Histogram (AWH) method, to explore rare events and free energy landscapes in materials and biological systems. Recent research highlights include investigations of superconducting nanostrips, phase transitions in two-dimensional materials, and the application of simulation methods to predict material properties like skin permeability. His interdisciplinary approach bridges condensed matter physics with computational biology and engineering applications.

Robert BamlerView profile

Professor

• Deep Generative Models
• Machine Learning
• Probabilistic Inference
• +3 more

Robert Bamler is a Professor of Data Science and Machine Learning at the University of Tübingen, Germany , and a member of the Cluster of Excellence "Machine Learning: New Perspectives for Science" and the Tübingen AI Center . Current affiliation: University of Tübingen (since November 2020) Prior roles: Postdoctoral Scholar at UC Irvine (with Stephan Mandt), Machine Learning Researcher at Disney Research (Pittsburgh/Los Angeles) Education: PhD in Theoretical Statistical and Quantum Physics from University of Cologne (2016), advised by Achim Rosch Research Focus : Algorithm development for deep generative models (including large language models) Resource-efficient inference and model compression Probabilistic machine learning applications in natural sciences Theoretical foundations in statistics, information theory, and physics Scientific Contributions : German Telekom Foundation PhD scholarship awardee Co-founder of the BamlerLab reading group on probabilistic modeling and compression Key Collaborations : Stephan Mandt (UC Irvine) Bernhard Schölkopf (MPI-IS) Cluster of Excellence "Machine Learning" (Tübingen) Teaching : Lecturer for "Data Compression With and Without Deep Probabilistic Models" (2021-2025) Organizer of weekly reading groups on function-space inference and compression algorithms

Ingmar R. PruchaView profile

Professor

• Theoretical Econometrics
• Applied Econometrics
• Spatial Econometrics
• +4 more

Ingmar R. Prucha is a Distinguished University Professor in the Department of Economics at the University of Maryland, specializing in theoretical and applied econometrics with a focus on spatial and network dependencies. He holds a PhD in Mathematical Economics from the University of Technology, Vienna (1977), and a post-graduate degree from the Institute for Advanced Studies in Vienna. Key affiliations: University of Maryland (Distinguished University Professor) Editorial roles: Associate Editor of Econometric Theory, Journal of Econometrics, Regional Science and Urban Economics, and member of Letters in Spatial and Resource Sciences editorial board His research spans spatial econometrics, dynamic panel data models with cross-sectional interactions, robust estimation methods, and productivity analysis. He has developed frameworks for spatial autoregressive models, generalized moments estimators, and peer effects modeling. Recent publications focus on network-generated dependence testing higher-order spatial interactions in simultaneous equations sequential exogeneity in dynamic panels random group effects in peer effect estimation He has contributed extensively to Stata software commands for spatial econometric analysis. While no explicit awards are listed, his long-term editorial roles and methodological innovations suggest significant recognition in econometric theory and spatial modeling. His teaching includes advanced econometrics courses (ECON624, ECON722) and mentoring in productivity/estimation research.

Fabian ZanderView profile

Associate Professor

• Hypersonics
• Optical Diagnostics
• Flight Testing Measurements
• +6 more

Associate Professor Fabian Zander is affiliated with the School of Engineering at the University of Southern Queensland. His expertise spans hypersonics, optical diagnostics, and flight testing measurements. He holds a BEng from the University of Queensland (2004) and a PhD (2013), followed by postdoctoral research at the University of Stuttgart (2014–2018). He currently leads the Institute for Advanced Engineering and Space Sciences' hypersonics research initiatives. Research Focus: Prof. Zander’s work focuses on hypersonic propulsion systems, re-entry aerothermodynamics, and space debris analysis. He develops advanced optical diagnostics techniques and leads airborne observation campaigns for spacecraft re-entry studies, such as the Hayabusa2 mission. His team operates the USQ hypersonic wind tunnel facility for experimental testing of thermal protection systems and aerodynamic configurations. Key Contributions: His research bridges laboratory experiments (e.g., plasma wind tunnel testing) with numerical simulations, addressing challenges in hypersonic flow control, material ablation, and trajectory analysis. He collaborates internationally on projects like the τ-Herculid meteor shower studies and OSIRIS-REx re-entry seismic measurements. Advisory & Facilities: Supervises doctoral research on hypersonic vehicle design, space debris dispersion modeling, and detonation engines. His lab develops innovative diagnostic tools like laser-induced diaphragm rupture systems and focused laser differential interferometry for hypersonic flow analysis.

• Materials Science
• Spectroscopy Techniques
• Biomedical Applications
• +3 more

Yuriy Markushin is an Associate Professor at Delaware State University, affiliated with the Division of Physics, Engineering, Mathematics, and Computer Sciences, and the Optical Science Center for Applied Research (OSCAR). His work focuses on advanced spectroscopic techniques, nanotechnology, and biomedical applications. He has pioneered innovations in laser-induced breakdown spectroscopy (LIBS) for biomarker detection and material analysis. Research Interests: Materials characterization via ATR-FTIR, Raman, and LIBS Nanoparticle uptake in plants and biomedical applications Machine learning integration with spectroscopic data Airborne thermography for geological analysis Grants & Collaborations: MUREP Space Technology Artemis Research (M-STAR): $899,000 (2023-2026) NASA's Space Education Excellence for Delaware (SEED): $900,000 (2023-2025) Teaching: He instructs courses such as General Physics, Statistical Mechanics, Principles of Lasers, and Biosensors. His educational outreach includes developing the Mission to Mars program at Dover Middle School, engaging 10 students with PhD researchers.

• Natural Product Discovery
• Synthetic Biology
• Bioproduction Optimization
• +3 more

Dr Hong Gao is a Senior Lecturer in Biology at Teesside University, affiliated with the School of Science, Engineering and Design and the SHLS Life Sciences Centre for Biodiscovery. She holds a PhD in Biochemical Engineering from Tsinghua University (2005) and a Bachelor's degree in Chemical Engineering from the same institution (2000). Her career includes roles as Assistant and Associate Professor at the Chinese Academy of Sciences, and postdoctoral research at the University of East Anglia, St Andrews, Edinburgh, and York, supported by a Marie-Curie Fellowship. Research Interests : Natural product discovery and biosynthesis Synthetic biology Bioproduction optimization Metabolic engineering of Streptomyces and other actinomycetes Antibiotic and anticancer agent development Her work focuses on engineering microbial systems for enhanced production of valuable compounds, particularly secondary metabolites with pharmaceutical applications. Recent research highlights include optimizing marinomycin synthesis and developing serine integrase tools for genetic manipulation in Streptomyces species. Grants & Projects : Platform Cell Factory (UKRI BBSRC, 2021–2025) EQATA: Equitable Access to Antibiotics in Africa (2020–2022) THYME: Redox Balancing for Ethanol Production (2019–2021) Awards : Marie-Curie International Incoming Fellowship (2011). Dr Gao teaches Food Biotechnology and contributes to interdisciplinary research bridging chemical engineering, microbiology, and synthetic biology.

Russell BarlowView profile

Researcher

• Language Documentation
• Historical Linguistics
• Linguistic Typology
• +5 more

Russell Barlow is a Postdoctoral Researcher at the Department of Linguistic and Cultural Evolution within the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany. His work focuses on endangered languages of Papua New Guinea, particularly within the Keram language family. His educational background includes: PhD in Linguistics (2018), University of Hawai‘i at Mānoa MA in Linguistics (2016), University of Hawai‘i at Mānoa BA in Indo-European Studies (2013), CUNY Baccalaureate BA in Film Studies (2006), Wesleyan University Barlow's research centers on language documentation, historical linguistics, and linguistic typology with a geographical focus on the Pacific region. His fieldwork-intensive approach emphasizes preserving endangered Keram languages like Ulwa, Pondi, and Tomoip. He investigates numeral systems, language contact phenomena between Papuan and Austronesian languages, and morphosyntactic structures including antipassive constructions. His methodology combines traditional fieldwork with computational approaches to language evolution. His publication record shows consistent output in top linguistics journals with recent work examining numeral colexification patterns, areal loanwords in New Britain, and detailed grammatical descriptions of endangered languages. The publications demonstrate interdisciplinary connections between linguistic typology, cultural evolution, and cognitive science. As an active member of the Comparative Oceanic Linguistics (CoOL) research group, Barlow has secured significant research funding through Fulbright and DAAD programs. His fieldwork spans Indonesia (Gorontalo State University), Papua New Guinea, and Germany (MPI-SHH in Jena prior to Leipzig). His research infrastructure includes extensive language archives at SOAS, PARADISEC, and Kaipuleohone, ensuring long-term preservation of endangered language materials. Current projects involve analyzing esoteric numeral systems and cross-linguistic patterns in infix preservation.

Peter HollandView profile

Professor

• Genome Evolution
• Animal Diversity
• Evolutionary Genomics
• +3 more

Professor Peter Holland FRS is the Linacre Professor of Zoology at the University of Oxford's Department of Biology. His research focuses on genomic evolution, particularly how genomic changes (e.g., duplications, novel genes) drive animal diversity. He leads the Darwin Tree of Life project, sequencing genomes of UK/Irish species, with over 1,000 genomes from Wytham Woods analyzed to date. He co-leads a £5M BBSRC grant on genome duplication in eukaryotes. Teaching includes 'Diversity of Life,' 'Genome Evolution,' and field courses at Oxford. Awards include Fellowship of the Royal Society (FRS). He advises the BBC and served on the Royal Society Newton Fellowship panel. His lab explores genomic data's ecological and evolutionary implications.

• Robust Methods
• Nonparametric Methods
• Uncertainty Quantification
• +7 more

Omer Ozturk is a Professor of Statistics at The Ohio State University (OSU), affiliated with the Department of Statistics. He joined the faculty in 1996 and has held editorial roles at journals including Environmental and Ecological Statistics, and Communications in Statistics. His research focuses on robust and nonparametric statistical methods, particularly in developing efficient sampling designs that minimize costs while maximizing information through auxiliary variables and ranking techniques. He has been funded by the NSA and NSF and actively collaborates with the U.S. Census Bureau as a Summer at Census Scholar. Education: PhD in Statistics from Penn State University (1994). Research Interests: Omer’s work emphasizes statistical inference under relaxed distributional assumptions, including robust methods, nonparametric techniques, and uncertainty quantification. He specializes in finite population sampling designs, such as ranked set sampling and judgment post-stratification, which leverage auxiliary information to enhance efficiency. His contributions span meta-analysis, spatial statistics, and Bayesian mixture modeling, with applications in epidemiology, environmental science, and agriculture. Articles Overview: His recent work addresses meta-analysis of survival times, spatially balanced sampling, and Bayesian modeling with ranked set samples. He developed the R package 'metamedian' for median-based meta-analysis and explored trade-offs in spatial sampling efficiency. His research consistently emphasizes practical applications in reducing sampling costs while improving statistical precision. Awards: ASA Fellow (2010). Advising & Grants: Omer has received grants from NSA and NSF, and his work frequently involves collaboration with institutions like the U.S. Census Bureau. Although specific student advisees are not listed, his research outputs suggest involvement in training graduate students in statistical methodology and applications. Labs/Teams: While no specific lab names are mentioned, his collaborations span statistical methodologies in environmental and medical research contexts, leveraging interdisciplinary teams for applied problems.

Ken ChenView profile

Associate Professor

• Bioinformatics
• Cancer Genomics
• Computational Biology
• +6 more

Dr. Ken Chen is an Associate Professor in the Department of Bioinformatics and Computational Biology at the University of Texas MD Anderson Cancer Center, with an adjunct role at Rice University. His research focuses on developing computational tools for cancer genomics, single-cell analysis, and AI-driven precision medicine. He holds leadership roles, including Director of Bioinformatics at the Sheikh Khalifa Bin Zayed Al Nahyan Institute for Personalized Cancer Therapy. Education: B.E. from Tsinghua University (1996), Ph.D. in Electrical and Computer Engineering from the University of Illinois Urbana-Champaign (2004), and postdoctoral training at UC San Diego. Prior experience includes roles at Washington University School of Medicine and Microsoft Research. Research Interests: Cancer genomics, single-cell/spatial transcriptomics, tumor heterogeneity, AI applications in oncology, and bioinformatics tool development (e.g., BreakDancer, Monopogen, METAFlux). His lab collaborates widely, with grants from NIH, CPRIT, and the Chan Zuckerberg Initiative. Notable Achievements: Developed widely used computational tools for structural variant detection and tumor phylogeny analysis. Awards include the Andrew Sabin Family Fellow (2016) and recognition in the ICGC-TCGA DREAM Challenge (2014). Lab Activities: The Chen Lab trains postdocs, PhD students, and undergraduates in computational biology and translational research. Current projects include CAR-NK cell therapy optimization and spatial omics analysis.