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Loren HopkinsView profile

Professor

• Environmental Statistics
• Human Health Risk Assessment
• Air Pollution Exposure
• +3 more

Loren Hopkins is a Professor in the Practice of Statistics at Rice University and the Chief Environmental Science Officer at the Houston Health Department. Her dual role bridges academia and public health, focusing on environmental statistics and translating scientific research into actionable policies. She leads the Data Science Division and Wastewater Sampling Surveillance programs for Houston, pioneering wastewater-based disease surveillance systems. Education: PhD, MS, and BS degrees in Environmental Science/Geophysics from Rice University and the University of Texas at Austin. Research interests include environmental statistics, human health risk assessment, air pollution exposure impacts, and policy translation. She has published over 24 peer-reviewed articles, with recent work emphasizing wastewater surveillance for pathogens like SARS-CoV-2 and measles. Awards include the CDC’s National Wastewater Surveillance System Center of Excellence Award (2022) and teaching accolades from Rice University (2016). Advisory roles include chairing the Regional Air Quality Planning Advisory Committee and Houston Wilderness Executive Board, plus service on the National Environmental Justice Advisory Council (NEJAC). She co-leads the Houston Wastewater Surveillance Program, a 2023 Gulf Coast Collaborative accomplishment. Labs/Teams: Directs interdisciplinary teams at Rice and the Houston Health Department, integrating environmental data science with public health practice.

Luay NakhlehView profile

Professor

• Phylogenomics
• Population Genomics
• Evolution of Biological Networks
• +6 more

Luay Nakhleh is the William and Stephanie Sick Dean of the George R. Brown School of Engineering and Computing at Rice University, holding dual appointments as Professor of Computer Science and BioSciences. He joined Rice in 2004 as an Assistant Professor, advancing to Full Professor by 2016. From 2017–2020, he served as Chair of the Computer Science Department. His research focuses on phylogenomics, computational biology, and evolutionary network analysis with applications to cancer genomics and biological systems. Education: PhD, Computer Science, The University of Texas at Austin (2004) MS, Computer Science, Texas A&M University (1998) BSc, Computer Science, Technion - Israel Institute of Technology (1996) Research Interests: Phylogenetic network inference and tumor evolution modeling Single-cell DNA/RNA integration for cancer heterogeneity Algorithms for genomic data analysis and evolutionary modeling Computational methods for biological network evolution Awards & Honors: George R. Brown Award for Superior Teaching (2024, 2020) Fellow, AIMBE (2024), ISCB (2023), AAAS (2025) Guggenheim Fellowship (2012), Sloan Research Fellowship (2010) NSF/DOE Career Awards (2009/2006) Teaching & Mentorship: Recipient of Rice’s most prestigious teaching awards (George R. Brown Prize, 2019) Core courses: Algorithmic Thinking, Bioinformatics, and Computational Biology Labs & Collaborations: Develops computational tools for evolutionary biology (e.g., PhyloNet, SiCloneFit) Interdisciplinary work at the interface of computer science, bioinformatics, and medicine

Soroush VosoughiView profile

Associate Professor

• Natural Language Processing
• Machine Learning
• Computational Social Science
• +5 more

Soroush Vosoughi is an Associate Professor of Computer Science at Dartmouth College, holding affiliations with the Institute for Security, Technology and Society (ISTS), the Quantitative Social Science (QSS) program, Quantitative Biomedical Sciences (QBS), and the Arthur L. Irving Institute for Energy & Society. He serves as Technical Associate Director at the Dartmouth Center for Precision Health and Artificial Intelligence (CPHAI). His work centers on natural language processing (NLP), machine learning (ML), and computational social science, with a focus on large language models (LLMs), bias mitigation, and ethical AI. Education: B.Sc., M.Sc., and Ph.D. from MIT (2008–2015), followed by postdoctoral research at MIT and a fellowship at Harvard's Berkman Klein Center. His research is funded by NSF, NIH, Google, Amazon, and others. Research Interests: Developing computational models of cognition via language, interpretability of LLMs, reinforcement learning for social alignment, visual-language models, and applications in health/bioinformatics. Projects include the 'Electome' project analyzing election discourse and tools for combating misinformation. Scientific Awards: Google Research Scholar Award (2022), Amazon Research Award (2019), Outstanding Paper Award at AAAI 2021 Grants: NSF, NIH, industry partnerships Labs/Teams: Leads the Minds, Machines, and Society group at Dartmouth, collaborating with interdisciplinary teams in health, bioinformatics, and cybersecurity

• Biostatistics
• Environmental Health Statistics
• Bayesian Models
• +5 more

Tanzy Love, PhD is an Associate Professor in the Department of Biostatistics and Computational Biology at the University of Rochester. She holds a PhD from Iowa State University (2005) and specializes in advanced statistical methodologies with applications in environmental health, genomics, and public health. Her research focuses on clustering algorithms, Bayesian models, and machine learning for complex data analysis. Education: Ph.D. in Biostatistics from Iowa State University (2005). Research Interests: Environmental health statistics, latent variable models, mixed regression, Bayesian ensemble learning, network data analysis, and applications in virology and neurodevelopmental studies. She has contributed to projects involving HIV-1 infection modeling, mercury exposure impacts on child development, and agricultural water safety using machine learning techniques. Her recent work emphasizes scalable statistical methods for large datasets, including spatial network models and ensemble learners. She collaborates on studies involving microbial source tracking, food safety, and public health epidemiology. Key contributions include the HIITE algorithm for HIV infection timing and the SPMM framework for multi-variant infection analysis. Her research has been funded through NIH grants and applied to global health challenges like mercury toxicity and pandemic-related healthcare trends.

Dimitri PerrinView profile

Associate Professor

• CRISPR Bioinformatics
• Sports AI
• Biomedical Imaging
• +1 more

Dimitri Perrin is Associate Professor and Co-Director of QUT's Centre for Data Science, leading the Biomedical Data Science group. His research develops AI/machine learning approaches for CRISPR genomics, sports analytics, and biomedical imaging. As Chief Investigator in Sports Data Science & AI initiatives, Perrin works on rugby and baseball analytics alongside CRISPR bioinformatics projects. His team created methods for whole-brain imaging analysis and Apple Watch sleep monitoring validation. He received a Foreign Postdoctoral Fellowship at RIKEN Japan. Current projects include high-performance CRISPR guide design, ICU machine learning systems, and hand rehabilitation games. Perrin's interdisciplinary work spans computational biology, sports AI, and health informatics.

• Bayesian Statistics
• Markov Chain Monte Carlo
• Image Analysis
• +2 more

Dr. Merrilee Hurn is a Senior Lecturer in the Department of Mathematical Sciences at University of Bath, specializing in Markov chain Monte Carlo methods with applications in image analysis, Bayesian mixture modeling, and Bayesian methodology. Her research develops: Advanced MCMC algorithms for complex models Bayesian approaches to image reconstruction Statistical methods for biological systems Astrophysical data analysis techniques Computational solutions for multimodal distributions She has contributed to EPSRC-funded projects on complex biological systems modeling and numerical methods for partial differential equations, enhancing Bayesian methodologies across scientific domains.

Christopher JennisonView profile

Professor

• Clinical Trials
• Image Analysis
• MCMC
• +2 more

Christopher Jennison is Professor in the Department of Mathematical Sciences at the University of Bath, specializing in clinical trial methodology and statistical computation. His research focuses on sequential methods for trial monitoring and adaptive clinical trial designs that allow treatment selection or population redefinition during studies. He leads major research initiatives including the EU's DIRECT consortium and MRC/ABPI's MASTERMIND project, developing personalized treatment strategies for type 2 diabetes. His influential book 'Group Sequential Methods with Applications to Clinical Trials' is widely used by practicing statisticians. Recent publications demonstrate his work's evolution toward diabetes research applications, with themes including: Adaptive trial designs for treatment personalization Statistical methods for handling trial disruptions Biomarker-driven patient stratification Pharmacogenomics of diabetes therapies His methodological innovations are applied through collaborations with medical institutes and pharmaceutical companies, supported by extensive consultancy work and participation in clinical trial data monitoring committees.

Justin MacCallumView profile

Associate Professor

• Protein Structure Determination
• Biomolecular Recognition
• Computational Modeling
• +5 more

Dr. Justin MacCallum is an Associate Professor in the Department of Chemistry at the University of Calgary, holding dual roles as the Canada Research Chair (CIHR CRC II) in Biomolecular Structure and Design and Associate Dean of Graduate and Post-Doctoral Scholars in the Faculty of Science. His research focuses on protein structure determination, biomolecular recognition, and machine learning integration with physical modeling to advance computational and experimental methods in biophysics. Dr. MacCallum earned his BSc in Biochemistry (2002) and PhD in Biomolecular Simulation (2008), both from the University of Calgary. His lab develops hybrid computational-experimental approaches to study protein interactions and membrane systems, emphasizing tools like MELD (Machine learning Enhanced Likelihood Dynamics) for structure determination and molecular dynamics simulations. Research interests include: (1) alternative structure determination techniques using sparse NMR data, (2) design of peptide-protein interactions via free energy simulations, and (3) scientific machine learning combining physical principles. His work aligns with University strategic initiatives in energy innovation and health solutions. Recent publications highlight advancements in biosensor development (e.g., TLR-based electrochemical systems), machine learning-enhanced molecular modeling, and structural studies of fatty acid binding proteins. He contributes to interdisciplinary projects bridging chemistry, bioinformatics, and engineering. Labs/Teams: Directs the MacCallum Lab, integrating computational and experimental biophysics to solve complex biomolecular problems. Collaborates on initiatives involving lipid-protein interactions, drug design, and sensor technologies.

Mayer AlvoView profile

Professor

• Nonparametric Statistics
• Sequential Analysis
• Spatial Statistics
• +5 more

**Mayer Alvo** is a **Full Professor** in the **Department of Mathematics and Statistics** at the **University of Ottawa**. He holds an MSc from McGill University and a PhD from Columbia University. His research focuses on **Nonparametric Statistics**, **Sequential Analysis**, and **Spatial Statistics**, with applications in environmental modeling and ranking data analysis. Alvo has authored three books, including *A Parametric Approach to Nonparametric Statistics* (2018) and *Statistical Methods for Ranking Data* (2014). He has developed an **R package** *hypersampleplan* for hypergeometric distribution calculations. His research interests include analyzing trends in acid deposition in the Great Lakes, Bayesian statistics, and the application of statistical methods to big data. Alvo has supervised multiple graduate students, including **Rachid Bentoumi**, **Jingrui Mu**, and **Xiuwen Duan**. He teaches courses such as MAT 1371, MAT 2375, and MAT 4376, covering topics in probability, statistics, and applied mathematics. Alvo's recent work explores parametric embedding in nonparametric problems and change-point detection in stress-strength reliability. His contributions span theoretical advancements in ranking data analysis and practical applications in environmental science and finance. He is affiliated with the **Statistics and Probability Research Group** at the University of Ottawa.

Yanxun XuView profile

Associate Professor

• Biostatistics
• Machine Learning
• Nonparametric Bayes
• +4 more

Yanxun Xu is an Associate Professor of Applied Mathematics and Statistics at the Whiting School of Engineering , Johns Hopkins University, and an Adjunct Assistant Professor in the Division of Biostatistics and Bioinformatics at the Sidney Kimmel Comprehensive Cancer Center. She is also a member of the Data Science and Artificial Intelligence Institute . Her research focuses on developing Bayesian statistical methods and machine learning algorithms to address challenges in heterogeneous, large-scale datasets, particularly in clinical trials, electronic health records, cancer genomics, and HIV/AIDS studies. Key areas of expertise include nonparametric Bayes , reinforcement learning , and dynamic treatment regimes . Her work emphasizes precision medicine applications, such as optimizing antiretroviral therapies for HIV patients and analyzing cognitive outcomes in clinical trials. Funding sources include the National Science Foundation (NSF) , National Institutes of Health (NIH) , and industry partners like AstraZeneca. Xu has published over 80 peer-reviewed articles and received prestigious awards, including the 2016 Mitchell Prize from the International Society for Bayesian Analysis. Her research bridges statistical theory with real-world healthcare applications, addressing topics like hospital-level variations in COVID-19 treatment and the impact of inflammation biomarkers on neurocognitive functions in psychosis. PhD in Biostatistics (implied by academic rank) Recipient of JHU’s Center for AIDS Research Faculty Development Award Developed open-source software tools for causal inference and longitudinal data analysis

Carolin KosiolView profile

Associate Professor

• Bioinformatics
• Evolutionary Biology
• Computational Biology
• +4 more

Carolin Kosiol is a Reader in Bioinformatics at the University of St Andrews, School of Biology. Her research bridges computer science, mathematics, and evolutionary biology, focusing on computational methods to study adaptation in organisms like great apes and fruit flies. She holds a PhD from the University of Cambridge and EMBL-European Bioinformatics Institute, with postdoctoral experience at Cornell University and a prior role as a Young Group Leader at the Vetmeduni Vienna. Her work develops evolutionary models that integrate phylogenetic and population genetic data to study speciation and population history. Education: Mathematics/Physics (University of Mainz), MSc High Performance Computing (Trinity College Dublin), PhD in Bioinformatics (Cambridge/EMBL-EBI). Research: Investigates adaptation across timescales, from experimental evolution to phylogenetic analysis, using genomic data to model evolutionary processes. Labs/Teams: Leads the Kosiol Lab, affiliated with the Centre for Biological Diversity and St Andrews Bioinformatics Unit. Her research emphasizes polymorphism-aware models for species tree inference and collaboration with experimentalists on genomic datasets. Key projects include analyzing fruit fly adaptation and great ape genomes, as well as developing tools like Bait-ER and PoMo for evolutionary analysis.

• Artificial Intelligence
• Machine Learning
• Bayesian Networks
• +7 more

Thomas Dyhre Nielsen is a Professor in the Department of Computer Science at Aalborg University, Faculty of IT and Design. His research centers on artificial intelligence, machine learning, and probabilistic graphical models, with applications in healthcare, environmental systems, and intelligent control. He is affiliated with the Distributed, Embedded and Intelligent Systems group and contributes to the AI for the People initiative. His research interests include Bayesian networks, decision support systems, data mining, and machine learning. He applies these to domains such as microbial ecology, clinical risk prediction, and smart water systems. His work often bridges theoretical AI with real-world engineering challenges. His recent publications (2024–2025) show a strong trend in applying explainable and probabilistic AI to healthcare (e.g., urinary tract infection risk prediction) and environmental control (e.g., stormwater management). These works emphasize safety, interpretability, and optimization under uncertainty. Scientific Awards: Best Paper Award (2018) He has been involved in PhD supervision and has collaborated on major interdisciplinary projects like DarkScience and CLAIRE , focusing on microbial dark matter and urban water control, respectively. His work integrates AI with systems engineering, environmental science, and medicine. He is associated with research labs and teams including the Distributed, Embedded and Intelligent Systems group at Aalborg University.

Jian LiuView profile

Associate Professor

• Data Analytics
• System Informatics
• Statistical Quality Control
• +11 more

Jian Liu is an Associate Professor in the Department of Systems and Industrial Engineering at the University of Arizona, College of Engineering, and an affiliated faculty member in the Statistics Graduate Interdisciplinary Program. He has been with the university since 2008, first as a faculty member from 2008–2014 and continuing in his current role since 2014. PhD in Industrial and Operations Engineering and Mechanical Engineering, University of Michigan, Ann Arbor (2008) MS in Statistics, University of Michigan, Ann Arbor (2006) MS in Industrial and Operations Engineering, University of Michigan, Ann Arbor (2005) MS in Mechanical Engineering, Tsinghua University, Beijing (2002) BS in Precision Instruments & Mechanology, Tsinghua University, Beijing (1999) Dr. Liu’s research centers on data analytics and system informatics, with a focus on integrating engineering knowledge, optimization, and statistical learning to model system performance, prognostics, diagnostics, and risk management. His work applies to manufacturing, civil, chemical, and software systems, emphasizing multi-source, multi-scale data fusion in hierarchical and distributed environments. Key research areas include reliability modeling, quality engineering, machine learning, and decision-making under uncertainty. His recent publications demonstrate a strong trend in applying advanced statistical and machine learning methods to real-world systems such as autonomous vehicles, water distribution networks, UAV/UGV surveillance, and healthcare monitoring. The integration of DDDAS (Dynamic Data-Driven Application Systems) frameworks, tensor decomposition, Bayesian modeling, and digital twins reflects a multidisciplinary approach spanning engineering, computer science, and data science. Honorable Mention for the Best Paper in the 2020 IISE Transactions Focus Issue on Quality and Reliability Engineering Honorable Mention for the Best Paper Award, International Conference on Industrial Engineering and Engineering Management, 2020 Outstanding Associate Editor Award, Journal of Manufacturing Systems, Spring 2019 Dr. Liu has secured research funding from the US National Science Foundation, US Department of Homeland Security, and US Air Force Office of Scientific Research. He has collaborated with domain experts on projects related to machining/assembly process improvement, water system service enhancement, and software reliability. He has advised students and contributed to professional leadership as a council member, board director, and currently as president of the Quality Control and Reliability Engineering (QCRE) Division of IISE. He is actively involved in research teams and labs focused on system informatics, data fusion, and reliability engineering, often employing simulation, sensor networks, and real-time data analysis in applications ranging from manufacturing to public health.

• Statistical Modeling
• Environmental Statistics
• Spatial Analysis
• +5 more

Ingelin Steinsland is a Professor at the Department of Mathematical Sciences , Norwegian University of Science and Technology (NTNU), and serves as the Vice-Dean for Research at the Faculty of Information Technology and Electrical Engineering. She holds a civil engineering degree and a doctorate in statistics from NTNU. Her research focuses on statistical modeling , particularly in environmental applications, hydrology, spatial analysis, and Bayesian methods. Key contributions include spatial-statistical frameworks for ecological data, flood forecasting models, and uncertainty quantification in multiphase flow systems. She has also worked on genetic and epigenetic data analysis, machine learning for health risk prediction, and climate change impacts. Recent articles emphasize Bayesian calibration of environmental models, spatial modeling of bird populations, and hypertension risk prediction. She collaborates widely with researchers in ecology, engineering, and public health. Her work appears in journals like Hydrology Research , Journal of the American Heart Association , and Frontiers in Genetics . Steinsland actively contributes to interdisciplinary projects, including studies on the HUNT cohort for health-related genetic interactions and infrastructure degradation analysis. Her expertise bridges theoretical statistics with applied problems in natural and engineering sciences.

Chih LaiView profile

Professor

• Artificial Intelligence (AI)
• Machine Learning (ML)
• Machine Vision
• +9 more

Chih Lai is a Professor in the Graduate Program in Software at the University of St. Thomas. He holds a PhD from Oregon State University and has extensive industry experience, including leading software engineering projects at Garmin and Medtronic. His research focuses on AI/ML applications in healthcare, multimedia data analysis, and precision agriculture, supported by grants from NSF, Amazon, and industry partners. Education: PhD in Computer Science (Oregon State University, 1999), MS in Computer Science (Oregon State University), BA in Fu-Jen Catholic University, Taiwan. Research interests include AI-driven medical imaging, counterfeit detection, and brain neural connectome analysis. His work on in-memory analysis of brain connectivity graphs was supported by Amazon and SAP HANA. Awards : Medtronic Idea Disclosure Contributor Award (2013) Best Paper Award in ACM Multimedia Data Mining (2006) Aerospace Industry Pioneer Award (2000) Deutsch Fellowship (2019) Grants & Collaborations : $1.3M+ funding from NSF, Minnesota State, German government, and Amazon Projects with Mayo Clinic, University of Minnesota, and Medtronic Amazon Cloud Research Grants for brain connectome and in-memory computing Labs & Teams : Collaborates with the Helmholtz-Zentrum für Umweltforschung (Germany) and leads interdisciplinary teams at UST's Center for Applied AI.