Professor Grzegorz Pawlina is Chair of Finance at Lancaster University Management School. His research explores corporate finance, investment decisions under uncertainty, and game-theoretic applications to financial strategy. Research Focus: Core interests include real options analysis, debt valuation, and corporate investment dynamics. Recent work examines capital adjustment costs, credit risk modeling, and governance impacts on cash holdings. Publication Trends: Pawlina's scholarship combines theoretical modeling with empirical validation across corporate finance domains. Methodological approaches feature dynamic optimization, structural modeling, and game-theoretic frameworks. Education & Background: Holds a doctorate in Finance from Tilburg University and MSc in Economics from Warsaw School of Economics. Previously held visiting positions at Aarhus University, University of Michigan Ross School of Business, and Warwick Business School.
Riccardo Simionato is a Research Fellow in the Department of Musicology at the University of Oslo (UiO), affiliated with the Faculty of Humanities. He holds a MSc and BSc in Computer Science and Information Engineering from the University of Padova, Italy, and conducted research at Aalto University, Finland (2017–2018). His research focuses on nonlinear audio modeling using deep learning, particularly addressing low-latency interactive solutions for acoustic and electronic musical instruments/devices. Education: 2018: MSc in Computer Science Engineering, University of Padova 2015: BSc in Information Engineering, University of Padova Research Interests: Deep Learning , Audio Modeling , and Sound Synthesis . He explores how deep learning can approximate complex nonlinear phenomena in audio systems, balancing computational efficiency with interpretability. Recent work emphasizes hybrid neural-audio effects, time-variant systems, and physics-informed methods for piano modeling. Publications highlight advancements in optical compressor modeling, piano analysis, and tools for generating audio effect datasets. His work bridges machine learning and music technology, aiming for practical applications in real-time audio processing and electronic instrument emulation. Grants and advising: No specific grants or student advisees listed in the provided text. Collaborations include projects with Prof. Stefano Fasciani and teams at UiO’s Department of Musicology. Labs/Teams: Active within UiO’s Sound and Music Computing research group, focusing on interdisciplinary projects combining computer science and music technology.
Jarek Nabrzyski is the founding and current director of the Center for Research Computing (CRC) at the University of Notre Dame and a concurrent Professor in the Department of Computer Science and Engineering. He co-directs the Blockchain Research Lab with Ian Taylor and leads the Quantum Computing Lab. His research focuses on distributed ledger technologies, quantum computing, and resource management in distributed, cloud, and exascale systems. He has overseen over 50 national and international research projects developing cyberinfrastructure for science and industry, emphasizing collaboration and team-building in complex projects. Key areas of research include blockchain applications in decentralized systems, quantum algorithm optimization for near-term hardware, and cybersecurity protocols for data integrity. He actively engages in interdisciplinary initiatives like the Center for Social Science Research and has pioneered frameworks for verifiable credentials and reproducible scientific data. His work bridges theoretical advancements with practical industry partnerships. Notable contributions include hybrid cross-chain protocols, quantum Poisson solvers, and frameworks for blockchain trust analytics. His recent publications highlight advancements in AI-driven blockchain stabilization, suspicious transaction detection, and scalable quantum computing solutions. Despite no explicit mentions of awards, his leadership roles and project count underscore significant contributions to computational science and infrastructure. As a mentor, he fosters student engagement in cyberinfrastructure through initiatives like the Cyberinfrastructure Center of Excellence. His labs and collaborations drive innovation in quantum computing, blockchain interoperability, and high-performance computing systems. Current efforts include transparent data tracking for aerospace supply chains and secure scientific data reproducibility.
Heath Pearson serves as an Assistant Professor in the Department of Anthropology at Georgetown University with a joint appointment in Cultural Anthropology and Justice & Peace Studies. He holds the position of book and media reviews editor for the journal Current Anthropology. His research centers on cultural anthropology with emphases on prison studies, critical race theory, and political economy. Pearson investigates intersections of confinement, capitalism, and racial politics in American contexts, particularly examining how carceral systems permeate rural communities and shape social relations. His work extends to media studies through analyses of satellite television's role in reproducing law, order, and masculinity. His 2024 monograph "Life beside Bars: Confinement & Capital in an American Prison Town" (Duke University Press) represents a major contribution to understanding prison-town political economies. His featured publications collectively address racialized justice systems, white supremacy in rural America, and the labor dynamics surrounding incarceration, demonstrating consistent engagement with structural inequality and carceral geography. Information regarding student advising and research grants is not documented in the available source materials.
David Ryan Koes is an Associate Professor in the Department of Computational and Systems Biology at the University of Pittsburgh, affiliated with the Joint CMU-Pitt PhD Program in Computational Biology. His research focuses on computational drug discovery, deep learning, and discrete algorithms, aiming to develop novel methods for rapid drug development and open-source software tools. He holds an office at 3064 Biomedical Science Tower 3 and 748 Murdoch Building. Key roles include Associate Director of the CPCB program and leadership in initiatives like CompBio Academy. His software contributions include libmolgrid, gnina, and 3Dmol.js, which advance molecular modeling and visualization. Research interests emphasize AI-driven drug discovery, including molecular docking, pharmacophore modeling, and generative models for molecule design. Recent work involves deep learning for protein structure prediction and pharmacophore elucidation. He has secured NIH grants (e.g., R35GM140753) and collaborations with institutions like CMU and industry partners. Advising over 25 students in computational biology, biotech, and data science programs, Koes bridges academia and industry through projects like Pharmit and the Teach-Discover-Treat initiative. His lab's work spans from foundational ML research to applied drug discovery, with a focus on open science and reproducibility.
Kate Cooper is an Associate Professor and Undergraduate Program Chair for BIOI, CYBR, and ITIN in the School of Interdisciplinary Informatics at the University of Nebraska at Omaha (UNO), College of Information Science & Technology. She has been a member of the UNO Bioinformatics Research Group since 2005 and officially joined the faculty in 2015. Her work bridges computational science and biomedical applications, with a strong focus on network modeling and data interpretation. Research Interests: Dr. Cooper's research centers on applying network science to biomedical data, particularly in modeling gene expression and protein-protein interaction networks. She investigates how graph-theoretic properties can reveal functional insights in biological systems. Her recent focus includes consumer health informatics, exploring how diet impacts the microbiome to prevent disease. She also examines the use of graphs in modeling infectious disease spread and food product label co-occurrence. Teaching and Service: She is passionate about bioinformatics education, modular curriculum design, and promoting reproducibility in research. She actively contributes to academic governance as Chair of multiple committees, including the College of IS&T Advisory Committee and the Bioinformatics Undergraduate Curriculum Committee. Education: BS in Bioinformatics, University of Nebraska at Omaha, 2007 PhD in Pathology & Microbiology (Bioinformatics Specialty Track), University of Nebraska Medical Center, 2013 Scientific Awards: No awards listed in the provided text. Advising and Grants: While no students or grants are explicitly listed, Dr. Cooper plays a significant leadership role in curriculum development and academic service. Her long-standing involvement with the Bioinformatics Research Group since 2005 suggests sustained research engagement and potential grant activity, though specific funding sources are not mentioned. Labs and Teams: She is a core member of the UNO Bioinformatics Research Group, where she collaborates on interdisciplinary projects involving network analysis, high-performance computing, and health informatics applications.
Matthew Page is an Associate Professor and NHMRC Emerging Leadership Investigator Fellow at Monash University's School of Public Health and Preventive Medicine, leading research on methods for evidence synthesis. He holds roles such as Deputy Head of the Methods in Evidence Synthesis Unit and co-chair of the PRISMA Executive. His work focuses on improving systematic review quality, including co-developing the PRISMA 2020 statement and the RoB 2 tool for assessing bias in clinical trials. Page has led numerous studies addressing transparency, reproducibility, and bias in health research. He has held editorial roles at journals like the Cochrane Database of Systematic Reviews and the Journal of Clinical Epidemiology. His research spans interventions for musculoskeletal conditions, ADHD, and postpartum hemorrhage, with over 179 peer-reviewed publications. Awards include the Cochrane Bill Silverman Prize and multiple early-career recognitions. Education: PhD in epidemiology (2011-2015), visiting postdoctoral fellow at University of Bristol (2015-2017). Research Interests : Systematic review methodology, risk of bias assessment, reporting guidelines (PRISMA), health economics, and improving research transparency. His work bridges methodological innovation with applied clinical research, influencing global standards for evidence synthesis. Awards : NHMRC Fellowships, Cochrane Bill Silverman Prize (2018), Australasian Epidemiological Association awards (2011-2013). Grants & Projects : Leads projects on enhancing systematic review quality, updating PRISMA guidelines, and improving reporting/peer review practices. Active in initiatives like the PRISMA Executive and Cochrane Methods Group. Labs/Teams : Methods in Evidence Synthesis Unit at Monash University, collaborating internationally on guideline development and bias assessment frameworks.
Liat Shenhav is an Assistant Professor at the Institute for Systems Genetics and the Department of Microbiology at New York University School of Medicine, with affiliations in the Department of Computer Science at the Courant Institute and the Center for Data Science. She leads the Shenhav Lab, which focuses on computational biology and AI-driven solutions for women’s and children’s health, particularly fertility, pregnancy, and lactation. Her work integrates systems biology, machine learning, and multi-omics data to address challenges in maternal and infant health. Education: PhD in Computational Biology from UCLA (2020). Research Interests: Microbiome dynamics, human milk composition, AI in healthcare, trajectory analysis, and biomarker discovery. Her lab develops cutting-edge computational methods like FEAST, CTF, and DVT-Net to analyze microbiome and medical imaging data. Current projects include studying the role of breastfeeding in shaping infant microbiomes, early detection of preeclampsia via retinal imaging, and understanding reproductive tract microbiota’s impact on fertility. Awards include the NIH New Innovator Award (DP2, 2024) for breakthrough research on breastfeeding and respiratory health. Collaborations span obstetrics, pediatrics, ophthalmology, and computational biology. Labs/Teams: Shenhav Lab at NYU Langone, collaborations with Courant Institute, and global health partners like the CHILD Cohort Study.
Dr. Caroline Muellenbroich is a Senior Lecturer at the School of Physics & Astronomy, University of Glasgow, where she develops advanced microscopy techniques for cardiac and neuro imaging. She joined the university in 2018 and is based at the Advanced Research Centre. Her work bridges physics, engineering, and biomedical sciences to create innovative imaging solutions. Dr. Muellenbroich received her physics education at the University of Heidelberg, Germany, and earned her PhD from the Institute of Photonics, University of Strathclyde, Glasgow in 2012. Her doctoral research focused on adaptive optics in advanced microscopy techniques. She then pursued postdoctoral research at the Biophotonics group at the European Laboratory for Nonlinear Spectroscopy (LENS) in Florence, Italy, where she implemented confocal light-sheet microscopy for whole mouse brain imaging and functional calcium imaging in Zebrafish. From 2016-2018, she worked as a researcher with the Italian National Institute of Optics, part of the Italian National Research Council. Dr. Muellenbroich's research focuses on developing and applying advanced optical imaging techniques, particularly light-sheet microscopy, for biomedical applications. Her work spans neuroscience and cardiology, with a strong emphasis on whole-brain imaging in model organisms and cardiac electrophysiology studies. She has made significant contributions to improving imaging fidelity, developing artifact removal techniques, and creating open-source microscope hardware. Her research bridges fundamental physics with practical biomedical applications, enabling new discoveries in brain function and cardiac physiology. Analysis of her recent publications reveals a strong focus on light-sheet microscopy applications in neuroscience and cardiology. Her work addresses technical challenges in whole-brain imaging, artifact reduction, and the development of novel optical approaches for studying brain activity and cardiac function. She has made important contributions to the field through both technical innovations in microscopy hardware and novel applications of these techniques to biological problems. Dr. Muellenbroich is actively involved in developing open-source approaches to microscope hardware, as evidenced by her 2022 Nature Methods publication "CAD we share? Publishing reproducible microscope hardware." Her research has been supported by various grants, though specific funding sources are not detailed in the available information. She leads research efforts in developing advanced microscopy techniques for cardiac and neuro imaging, working with interdisciplinary teams that include physicists, engineers, biologists, and medical researchers. Her laboratory likely focuses on pushing the boundaries of optical imaging to address challenging biomedical questions in brain function and cardiac physiology.
Antoine Geissbuhler is a Full Professor at the University of Geneva's Faculty of Medicine, Department of Radiology and Medical Informatics. He serves as Dean of the Faculty of Medicine since July 2023, following his role as Vice-Rector for Digital Strategy and Innovation. Additional leadership positions include Director of the HUG Innovation Center and Chief Physician of the eHealth and Telemedicine Department at Geneva University Hospitals (HUG), recognized as a WHO collaborating center. He also presides over BioAlps, Western Switzerland's life sciences cluster. Geissbuhler's research focuses on designing and implementing IT tools in healthcare, with emphasis on telemedicine networks and digital health strategies. His work spans global contexts including Africa, Asia, and Latin America through the RAFT project connecting hundreds of healthcare professionals across 20 countries. Key domains include maternal health monitoring, shared medication plans, AI chatbots for patient education, and digital solutions for non-communicable diseases. His publications reveal strong trends in implementation science, particularly frameworks for reporting digital health interventions (iCHECK-DH) and learning from implementation failures. WHO Collaborating Center designation for eHealth department President of BioAlps life sciences cluster (since 2022) Architect of Swiss eHealth strategy implementation Developer of RAFT telemedicine network As an educator, Geissbuhler has pioneered telemedicine training across resource-limited settings through the RAFT network. His leadership extends to digital health policy, including contributions to global standards for health information systems and ethical frameworks for health data reuse. Current initiatives emphasize patient-centered digital tools and strengthening health system resilience through technology.
Ammar Hoori, PhD, is a Research Assistant Professor in the Department of Biomedical Engineering at Case Western Reserve University, affiliated with both the Case School of Engineering and School of Medicine. His research focuses on cardiac image analysis, leveraging advanced techniques like image registration, deep learning, and survival analysis. He leads NIH-funded projects involving CT calcium scoring, IVOCT, and chest CT imaging, collaborating with cardiologists from University Hospitals Cleveland and engineers from CWRU. His work emphasizes developing calcium-omics and fat-omics features to improve cardiovascular disease prediction. His research team employs cutting-edge methods such as deep learning segmentation for epicardial adipose tissue analysis, aiming to optimize patient care through AI-driven risk stratification. Hoori’s contributions include the development of the DeepFat algorithm for automated fat quantification and collaborations on stent under-expansion prediction using OCT imaging. He is also involved in the Biomedical Imaging Laboratory (BMIL), contributing to advancements in cryo-imaging and 3D visualization techniques. Key areas of innovation include AI-enabled risk prediction for heart failure, MACE (Major Adverse Cardiovascular Events), and coronary artery disease using opportunistic data from routine CT scans. His work bridges clinical and engineering expertise to advance non-invasive diagnostic tools and personalized medicine strategies.
Duncan Temple Lang is a Professor in the Department of Statistics and Associate Dean for Graduate Programs at the University of California, Davis. He previously served as Director of the Data Sciences Initiative at UC Davis and has been a faculty member since 2004. His work bridges statistics, computer science, and data science through the development of advanced computational tools. His research focuses on enhancing the R programming language and statistical computing ecosystems. Key areas include language compilation using LLVM, parallel and distributed computing, GPU acceleration (RCUDA), web-based data access (RCurl), and dynamic visualization. He emphasizes software integration across disciplines via the inter-system interface model, enabling seamless data and code exchange between environments. His recent publications center on data technologies in R and data science education, reflecting a strong commitment to both innovation and pedagogy. These works highlight trends in reproducibility, modern web-based data pipelines, and computational reasoning. Associate Dean for Graduate Programs, UC Davis Former Director, Data Sciences Initiative, UC Davis Active contributor to R packages and statistical computing infrastructure Lang has received no explicitly mentioned scientific awards in the provided texts. He advises graduate students and leads research projects involving compilation tools, language interoperability, and data science education, though specific advisees are not listed. He leads the Omegahat project and develops key R packages such as Rllvm, RCurl, and RCUDA, fostering a collaborative research environment focused on future-ready statistical computing.
Dr. Antoine Cully is the director of the Adaptive and Intelligent Robotics Lab at Imperial College London. He previously served as a Research Associate in the Personal Robotics Lab (2016–present) and earned his PhD in Robotics and Artificial Intelligence from UPMC (Paris), focusing on algorithms enabling robots to adapt to mechanical damage swiftly. His work has been internationally recognized, including a Nature cover publication and awards for his PhD thesis. His research interests span evolutionary robotics, stochastic optimization, and quality-diversity algorithms, with current projects involving the EU H2020 'PAL' initiative to develop adaptive robotics for user preferences. Education: M.Sc. in Intelligent Systems and Robotics (UPMC, 2012), Engineer degree in Robotics (Polytech-Paris UPMC, 2012), PhD in Robotics and AI (UPMC, 2015). Research emphasizes adaptive robotics, damage recovery, and autonomous learning. Notable contributions include the T-Resilience algorithm and MAP-Elites optimization framework. Awards include the 'Outstanding Paper 2015' and 'Best Thesis' accolades. His lab focuses on advancing AI-driven robotics for real-world applications.
Joshua Rabinowitz is a Professor of Chemistry and Director of the Ludwig Princeton Branch at Princeton University. His research focuses on quantitative analysis of cellular metabolism, leveraging advanced mass spectrometry and computational modeling. Key areas include metabolic regulation in microbes (E. coli, yeast), cancer cell metabolism, viral infection impacts, and biofuel production. His lab has pioneered methods to measure metabolites and fluxes, leading to discoveries like novel cancer metabolites and antiviral strategies via fatty acid inhibition. Notable honors include the NIH Pioneer Award and Agilent Thought Leader Award. Research emphasizes systems-level understanding, with projects combining biological experiments, metabolomics, and computation. Recent work explores metabolic heterogeneity in humans, immunotherapy enhancements through metabolic modulation, and metabolic vulnerabilities in cancers like rhabdomyosarcoma. Collaborations include engineering yeast for biofuel production and developing therapeutics targeting metabolic pathways. Lab Website: Rabinowitz Lab Affiliations: Lewis-Sigler Institute for Integrative Genomics, Princeton University Major contributions include identifying metabolic shifts in viral infections, modeling microbial nutrient responses, and advancing technologies for metabolite quantification. Current efforts aim to integrate metabolomic data with enzyme regulation dynamics to predict metabolic networks across organisms.
John Shaw is a Lecturer in Psychology at Edge Hill University since July 2023. Previously, he held roles at De Montfort University (2019-2023) and Aberdeen University (2018-2019). He is a Chartered Psychologist with the British Psychological Society, holding a PhD in Psychology from Lancaster University (2018). His teaching responsibilities include modules such as Developmental Psychology and Work Placement Supervision for undergraduate and postgraduate students. Education: PhD in Psychology, Lancaster University (2014–2018) MSc in Psychological Research Methods, Lancaster University (2013–2014) BSc in Organisation Studies & Psychology, Lancaster University (2010–2013) Shaw’s research focuses on sleep’s role in cognitive development, including how sleep impacts social media effects on preadolescents, numerical cognition, autism spectrum disorder (ASD) ownership perception, and neurodiversity advocacy in academia. He collaborates with FORRT (Forum for Research into the Education of Autistic and Neurodivergent Talents) to promote inclusive academic practices, securing a grant from the Society for the Improvement of Psychological Science to create a neurodiverse author database. His publications span cognitive psychology, developmental psychology, and open science advocacy. Notable work includes studies on sleep’s effect on memory consolidation, ASD ownership identification, and neurodiversity in academia. He emphasizes participatory research methodologies and open scholarship to enhance scientific rigor and inclusivity. Grants: Awarded a grant from the Society for the Improvement of Psychological Science (2023) to develop a database of neurodiverse authors. Career Trajectory: Shaw’s work bridges empirical research with educational practice, advocating for neurodiverse representation and reproducible science. He actively contributes to policy discussions on academic inclusivity and open access publishing.
