Patrick T. Brandt is a Professor of Political Science, Public Policy, and Political Economy at the University of Texas at Dallas , affiliated with the School of Economic, Political and Policy Sciences . His work integrates advanced statistical methods with political science, focusing on time series analysis, machine learning, and Bayesian modeling to study political dynamics. His research spans international relations , political economy , terrorist targeting , and conflict forecasting . He specializes in developing novel models for event count time series, including the Bayesian Poisson Vector Autoregression and MS-BVAR packages for R. His NSF-funded projects focus on event data generation and real-time conflict forecasting. Recent publications emphasize domain-specific language models (ConfliBERT variants), graph neural networks for conflict prediction, and machine translation challenges in political text analysis. He maintains the OpenEvent Data Repository and develops software like MSBVAR and PESTS for academic use. Scientific Awards : Robert H. Durr Award for Best Methodology Paper, Midwest Political Science Association (2006)
David R. Raleigh, MD, PhD, is an Assistant Professor in the Departments of Radiation Oncology and Neurological Surgery at the University of California San Francisco (UCSF). He serves as a Principal Investigator at the Brain Tumor Center and Director of the Preclinical Therapeutics Core. Education: BA in Molecular and Cell Biology and Cognitive Science (UC Berkeley, 2004), MD and PhD in Pathology (University of Chicago, 2012), Residency in Radiation Oncology (UCSF, 2017) His research focuses on the molecular mechanisms of brain tumor growth, particularly meningiomas, integrating developmental biology with oncology to identify novel treatments. Methodologies include biochemistry, mouse genetics, genomics, and pharmacology. Dr. Raleigh's recent publications highlight molecular classification of meningiomas, genomics, and targeted therapies. Awards include Phi Beta Kappa, multiple travel grants, and the Robert and Ruth Halperin Endowed Chair in Meningioma Research.
Professor Annette Byrne is a leading academic at RCSI University of Medicine and Health Sciences , where she serves as Professor of Physiology and Head of the Precision Cancer Medicine (PCM) Group. She has held this position since 2019 after progressing through roles as Lecturer (2008), Senior Lecturer (2013), and Associate Professor (2017). Her research focuses on precision medicine approaches for colorectal and brain cancers , integrating multi-modality molecular imaging , Next Generation Sequencing , and patient-derived xenograft models . PhD in Cell Biology (University of York, 1999) John Kerner Fellowship in Gynaecologic Oncology (UCSF, 1999-2001) Scientist at Pharmacyclics Inc. (2001-2003) Senior Scientist at Angion Biomedica Corp. (2003-2005) Principal Investigator at UCD Conway Institute (2005-2008) Her research interest lies in precision cancer medicine , particularly elucidating predictive biomarkers (genomic, transcriptomic, proteomic) and identifying novel therapeutic targets . Key methodologies include radiomics , fluorescence-guided surgery , and systems modeling of apoptosis pathways. She has pioneered Ireland's first Tumour Xenograft Facility and Translational In Vivo Imaging Centre . Recent publications highlight her work on cross-species radiomics , cell-free DNA analysis , and glioblastoma microenvironment subtyping . Her Marie Curie networks (Gliotrain, Glioresolve) and COLOSSUS project have trained 25+ PhD researchers in brain cancer therapeutics. Over €45M in national/international grants Member of Royal Irish Academy (2025) Highly cited in Cancer Discovery , Annals of Oncology , and Nature journals She supervises multiple PhD candidates and leads the RCSI Precision Cancer Medicine Group , which utilizes computational approaches and molecular imaging to improve cancer treatment outcomes. Her GLIORESOLVE and EDIReX projects focus on tumor microenvironment manipulation and distributed PDX infrastructure.
Dr. Enrico Opri is an Assistant Professor in the Department of Biomedical Engineering at the University of Michigan , where he directs the Opri Lab. His research focuses on engineering novel methodologies to automate and enhance clinical procedures in neuromodulation for neurological disorders such as Parkinson’s, Tourette syndrome, essential tremor, and epilepsy. Research Focus Neurophysiological activity in basal ganglia-thalamocortical circuits Therapeutic effects of neuromodulation (e.g., Deep Brain Stimulation, cortical stimulation mapping) Identification of neurological biomarkers for improved clinical procedures Advancements in closed-loop neurostimulation systems Article Trends Dr. Opri's recent publications emphasize closed-loop deep brain stimulation (DBS) systems, computational modeling of neural activity, and the identification of biomarkers for neurological disorders. Key subfields include Parkinson’s disease motor dynamics, Tourette syndrome tic detection, essential tremor treatment, and cortico-thalamic coupling mechanisms. His work bridges biomedical engineering and clinical neuroscience, focusing on translating technological innovations into therapeutic applications.
Shaun Truelove is an Associate Research Professor at Johns Hopkins University , affiliated with the Bloomberg School of Public Health and the Department of International Health . He is also a joint member of the Infectious Disease Dynamics group and the International Vaccine Access Center . Research interests focus on infectious disease dynamics , epidemiology , vaccination strategies , and humanitarian/refugee health . His work spans Measles transmission modeling in Zambia COVID-19 impact assessments Diphtheria epidemiology Mobile phone data applications Supplementary immunization activities Scenario modeling for pandemics . Article trends highlight expertise in infectious disease modeling across multiple pathogens (Measles, MERS-CoV, Influenza), with emphasis on Vaccine effectiveness Human mobility patterns Real-time forecasting Public health policy Global health disparities . Current projects include the flepiMoP modeling pipeline for pandemic response, collaborations with the CDC, and field studies in Zambia/India. Contact: shauntruelove@jhu.edu | trueloves@jhu.edu
Zebo Peng is a Professor and Deputy Head of Department at Linköping University's Department of Computer and Information Science (IDA), leading the Software and Systems (SAS) division. His research focuses on embedded systems design, electronic design automation, SoC testing, and real-time systems with emphasis on fault tolerance and hardware/software co-design. He has contributed to projects like the ASTECC initiative, funded by the Swedish Foundation for Strategic Research, addressing adaptive software in edge-cloud continuum systems. Key research interests include cyber-physical systems security, time-sensitive networking (TSN), and optimization techniques using genetic algorithms. Recent work explores thermal-aware design for reliability, security-aware scheduling, and stability guarantees in control systems. His publications span journals like IEEE TPDS and ACM TECS, alongside conference contributions on topics like resource management and fault detection in distributed systems. Prof. Peng collaborates extensively within the SAS division, which bridges academic and industrial research in software engineering and computer systems. His team's projects address challenges in real-time systems, embedded security, and parallel computing architectures.
Dr. Kelly Burkett is an Associate Professor in the Department of Mathematics and Statistics at the University of Ottawa, within the Faculty of Science. She specializes in Statistical Genetics and Genetic Epidemiology, focusing on genealogical relationships, population substructure, and family-based study designs. Her work includes software development for genetic data analysis, such as SMLE and GENLIB. Dr. Burkett holds an MSc and PhD from Simon Fraser University. Research Interests: Dr. Burkett’s research addresses challenges in genetic epidemiology, including maternal effects, gene-environment interactions, and the impact of population substructure on study design. Her methodologies often integrate computational tools to analyze large-scale genetic datasets. She actively collaborates with biomedical researchers, inspiring new questions in statistical genetics and genomics. Publications & Software: Her recent work includes studies on orofacial clefting etiology, software for high-dimensional feature screening, and genealogical simulations in French Canadian populations. Her articles span topics from computational genetics to molecular biology, emphasizing interdisciplinary approaches. Advising & Collaborations: Dr. Burkett has mentored over 20 students and trainees across MSc, PhD, and postdoctoral programs. Current advisees include Yuewen Pan (MSc) and Yuhao Feng (PhD). Past students hold roles in academia, healthcare, and industry. She collaborates on biomedical projects, leveraging statistical methods to address genetic and environmental interactions. Labs & Teams: While no formal lab is explicitly mentioned, her software contributions (e.g., hapassoc, sampletrees) and collaborative projects suggest involvement in computational biology and genetics research networks at the University of Ottawa and beyond.
Chiara Sabatti is a Professor of Biomedical Data Science and Statistics at Stanford University, with affiliations to the Stanford Center for Computational, Evolutionary and Human Genomics (CEHG), Bio-X, and the Stanford Cancer Institute. She serves as Associate Director for Stanford Data Science and has led the development of the Data Science Major curriculum since 2012. Research Focus: Statistical models for high-throughput genomics data, causal inference in genetic studies, false discovery rate control, and knockoff methods for variable selection. Key Leadership: Associate Chair for Education and Training (2020-present), Vice Chair of Biomedical Data Science (2018-2019). Her work bridges statistical genetics with data science education, emphasizing robustness and interpretability in scientific findings. Recent publications highlight innovations in genome-wide association studies (GWAS), causal variant localization, and cost-effective sequencing techniques for underrepresented populations. Current projects include developing knockoff-based methods to address population structure and multi-resolution hypothesis testing. Scientific Awards: Institute of Mathematical Statistics (IMS) Fellow (2022) NSF CAREER Award (2003-2008) She mentors doctoral and graduate students in Biomedical Data Science, collaborates with the Data Studio on interdisciplinary projects, and actively recruits curious researchers to her lab. Her outreach efforts focus on expanding data science education and increasing research participation from underrepresented groups.
Dr. Masahiro Ono is a Reader in Immunology at Imperial College London's Department of Life Sciences, within the Faculty of Natural Sciences. He leads research on T-cell regulation, focusing on autoimmunity, infections, and cancer. His lab pioneered the Tocky system, using Fluorescent Timer proteins to study T-cell activity dynamics in vivo. Dr. Ono holds affiliations with the CRUK Convergence Science Centre, Infection and Immunity, and Integrative Systems Biology. His academic journey includes an MD from Kyoto University (1993-1999) and a PhD in regulatory T cells (2002-2006). He was awarded a HFSP Fellowship (2009) and BBSRC David Phillips Fellowship (2012), establishing his UCL lab before joining Imperial in 2015. Research Interests : Dr. Ono's work bridges immunology, genomics, and systems biology. His lab explores T-cell activation mechanisms, tumor immunology, and bioinformatics tools for single-cell analysis. Key innovations include the Tocky system for real-time cell kinetics tracking and integrative approaches like GatingTree for cytometry data analysis. Awards : HFSP Fellowship (2009) BBSRC David Phillips Fellowship (2012) Grants & Advising : Dr. Ono's grants have supported projects on viral latency, tumor microenvironment modulation, and immune checkpoint therapies. His lab actively collaborates on translational research, though specific grant details are not listed here. Labs & Teams : His lab at Imperial focuses on Tocky-based technologies and interdisciplinary convergence science through the CRUK Centre. Collaborations span virology, oncology, and bioengineering to address unmet clinical needs in immunotherapy.
Sharmistha Guha is an Assistant Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. Her research focuses on Bayesian methods for analyzing complex, high-dimensional data, with applications in neuroscience, network security, and social sciences. She develops techniques for supervised network data analysis, causal inference, and data privacy preservation. Education: Ph.D. in Statistical Science (Duke University, implied by dissertation recognition). Research Interests: Bayesian high-dimensional regression, object-oriented regression, causal inference in randomized trials, probabilistic record linkage, Bayesian nonparametric mixture models, and integration of heterogeneous data types like networks and functional data. Her work addresses challenges in neuroimaging (dMRI/fMRI) and big data analytics. Recent Contributions: Her articles span Bayesian methodologies for network analysis, differential privacy in treatment effect estimation, and applications in environmental health (e.g., PM2.5 exposure studies). Notable trends include leveraging Bayesian hierarchical models for complex data structures and advancing causal inference frameworks. Awards: 2022 Blackwell-Rosenbluth Award (ISBA) 2021 Savage Award Honorable Mention 2024 NSF-DMS Grant 2413721 Grants & Advising: Secured NSF funding for heterogeneous data integration research. Mentored students like Jose Rodriguez-Acosta (NSF GRFP Fellow) and Jacob Pagel (NIH-funded CoSIBS participant). Lab/Team: Leads a research group integrating statistical method development with domain collaborations in neuroscience and network security. Active in organizing workshops and serving on panels to foster academic engagement.
Scott T. Doyle is an Associate Professor in the Department of Pathology and Anatomical Sciences at the Jacobs School of Medicine & Biomedical Sciences, University at Buffalo. His research integrates biomedical imaging, artificial intelligence, and computational pathology to develop quantitative tools for clinical diagnostics and anatomical modeling. Education: PhD in Biomedical Engineering, Rutgers, The State University of New Jersey (2011) BS in Biomedical Engineering, Rutgers, The State University of New Jersey (2006) Optical Microscopy & Imaging in the Biomedical Sciences, Marine Biological Laboratory (2014) hES Stem Cell Culture Training, WNYSTEM (2014) R Bioconductor Training, Roswell Park Cancer Institute (2016) Dr. Doyle’s research focuses on developing AI-driven algorithms for biomedical image analysis, particularly in digital pathology and 3D anatomical modeling. His work spans tumor segmentation, risk prediction in oral and thyroid cancers, and integration of virtual and physical anatomy in medical education. He applies machine learning, deep learning, and computational modeling to enhance diagnostic accuracy and patient outcomes. His recent publications reflect a strong trend in applying artificial intelligence to histopathology, with emphasis on active learning, 3D reconstruction, and multi-institutional data fusion. Key areas include oral cavity cancer recurrence prediction, thyroid cancer subtyping, and computational modeling of surgical margins and anatomical structures. Scientific Service and Recognition: Reviewer for NIH SPORE grants Peer reviewer for journals including Medical Image Analysis , BMC Bioinformatics , IEEE Transactions on Biomedical Engineering Program Committee and Session Chair, SPIE Medical Imaging: Digital Pathology (2016–present) Member, Graduate Program Steering Committee, Pathology & Anatomical Sciences Mentor, McNair Scholarship and CSTEP programs for underrepresented students Dr. Doyle has secured significant research funding as Principal Investigator on NIH and CTSI grants, including a $2M+ NIH grant for predicting oral cancer recurrence. He has also contributed to educational innovation through hybrid anatomy curriculum development and AI training for pathologists. He leads the 'Atoms to Anatomy' research initiative and is active in strategic planning at the Jacobs School. Laboratories and Collaborative Teams: Dr. Doyle collaborates with the Center for Computational Research (CCR) and is involved in the Structural Sciences Learning Center (SSLC). He has led projects with teams at Ibris, Inc., Veterans Affairs Hospital, and Mount Sinai School of Medicine.
Michael Nothnagel is a Professor at the University of Cologne, where he leads the Department of Statistical Genetics and Bioinformatics within the Cologne Center for Genomics (CCG). His work spans statistical genetics, genetic epidemiology, and forensic genetics, focusing on methodological development and large-scale genomic data analysis. His research interests encompass theoretical and applied statistical genetics, with emphasis on human genetic diversity, disease etiology, and forensic applications. Key areas include Y-chromosomal phylogeography, genome-wide association studies for complex diseases, development of statistical methods for variant interpretation, and forensic marker optimization. His group leverages next-generation sequencing data and specialized forensic markers to address questions in population history, disease mechanisms, and identification systems. Recent publications reveal a strong focus on computational approaches to genetic analysis, including spatial frequency interpolation for haplogroup mapping, polygenic risk score applications for behavioral traits, and advanced methods for variant classification. His work demonstrates consistent integration of statistical theory with practical applications in medical and forensic genetics, often through international collaborations like the VISAGE Consortium. Nothnagel maintains active involvement in the Cologne Center for Genomics, contributing to seminars and collaborative projects including the upcoming 34th International Genetic Epidemiology Society meeting. His research group operates at the intersection of computational biology and medicine, with particular strengths in handling complex genomic datasets and developing novel analytical frameworks for genetic epidemiology.
Dr. Ali Çarkoğlu is Professor of Political Science at Koc University in Istanbul, Turkey, specializing in comparative politics, electoral systems, and political communication. Holding a PhD from SUNY Binghamton (1994), he has previously taught at Boğaziçi and Sabancı Universities. Doctoral Degree: State University of New York-Binghamton (1994) Master's Degree: Rutgers University (1989) Bachelor's Degree: Boğaziçi University (1986) His research focuses on political polarization in multiparty systems, misinformation dynamics in authoritarian regimes, and the emotional mobilization effects of populist rhetoric. Recent studies examine partisan bias in pandemic-era conspiracy theories and electoral alliance challenges in Turkey's 2023 presidential election. Notable scientific achievements include the 2013 Wapor Best Article Award. He has developed innovative network-based approaches for measuring party polarization and conducted experimental analyses of voting advice applications' impact in constrained media environments.
Dr. Xianghong Jasmine Zhou is a Professor in the Department of Pathology and Laboratory Medicine at the University of California, Los Angeles (UCLA) School of Medicine . Her work focuses on integrating genomic , epigenetic , and bioinformatic approaches for non-invasive cancer detection and monitoring through cell-free DNA analysis . Principal Investigator for multiple NIH grants (R01CA246329, U01CA230705, etc.) Developed software tools ( cfSNV , cfTools , CancerDetector ) for cell-free DNA mutation and methylation analysis Co-developed CancerLocator and HCC EV ECG score for tumor-of-origin prediction and hepatocellular carcinoma monitoring Research Interests: Specializes in 3D genome modeling , epigenetic regulation , and machine learning for liquid biopsy applications. Her work bridges computational biology with clinical translation , particularly in cancer diagnostics and treatment response assessment . Scientific Contributions: She has pioneered deep learning and population-based modeling approaches for cell-free DNA deconvolution , isoform-level functional annotation , and multi-omics integration . Her 2016 Cell paper on alternative splicing has been highly influential, with over 266 mentions.
D.S. Fahmeed Hyder is a Professor of Biomedical Engineering at Yale University, with additional appointments in Radiology & Biomedical Imaging. He holds a Ph.D. from Yale University and leads the Hyder Lab, which focuses on advancing quantitative and translational imaging technologies using magnetic resonance methods to study brain function and dysfunction at the laminar level. His research integrates multidisciplinary approaches, including molecular imaging, neurophysiology, and material science. Research Interests: Neurovascular and neurometabolic coupling mechanisms Molecular imaging probes for disease diagnostics Functional MRI (fMRI) and metabolic imaging in health and disease Imaging applications for Alzheimer’s, stroke, and cancer Publications: Dr. Hyder’s recent work emphasizes cutting-edge imaging techniques, such as pH-sensitive biosensors, high-resolution fMRI, and multimodal optical imaging. His research highlights include studies on neurovascular dysfunction in Alzheimer’s models, therapeutic interventions for brain injury, and molecular imaging of tumor microenvironments. Awards: Niels Lassen Award (2003), for cerebral blood flow research Early Career Faculty Award (1998), NSF & NIH Pilot Awards from JDRF & Yale-UCL Collaborative (2008, 2013) Labs/Teams: The Hyder Lab collaborates with institutions like UCL and the James S. McDonnell Foundation, advancing translational imaging solutions for clinical applications.
