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Delphine Périé-CurnierView profile

Professor

• Biomedical Technology
• Biomechanics
• Solid Mechanics
• +6 more

Delphine Périé-Curnier is a Full Professor in the Department of Mechanical Engineering at Polytechnique Montréal and Director of Graduate Studies. Her research focuses on developing quantitative MRI techniques for non-invasive characterization of living tissue mechanical properties, particularly in cardiotoxicity detection and musculoskeletal mechanobiology . She leads the Bioperformance Analysis and Innovation Laboratory (LIAB) and contributes to the Institute of Biomedical Engineering. Education: Ph.D. from Paul Sabatier University, Toulouse, France Her work bridges medical imaging , biomechanical modeling , and finite element analysis to predict disease progression through pathomechanism understanding. Key projects include exercise-induced cardiac changes in childhood cancer survivors and spinal biomechanics in scoliosis. Recent publications (2023-2024) emphasize cardiovascular MRI for childhood cancer survivorship and hemodynamic modeling in left ventricle analysis. She supervises 26 graduate students, with completed theses spanning topics like doxorubicin cardiotoxicity , knee replacement stability , and spatial cardiac MRI protocols . Teaching includes graduate courses in biomedical design , advanced biomechanics , and modeling techniques .

Deborah TalmiView profile

Associate Professor

• Emotional Cognition
• Memory Mechanisms
• Neurobiology of Emotion
• +7 more

Dr. Deborah Talmi is a University Associate Professor in the Department of Psychology and a Fellow of Lucy Cavendish College at the University of Cambridge. Her research focuses on the cognitive, computational, and neurobiological mechanisms underlying emotional processes, particularly their intersection with memory, perception, and attention. She leads the Emotional Cognition Lab, which employs behavioral experiments, EEG, fMRI, and computational modeling to explore how emotions influence memory formation and retrieval. Roles: Associate Professor (Department of Psychology), Fellow (Lucy Cavendish College), Principal Investigator (Emotional Cognition Lab) Affiliations: School of Biological Sciences, Cambridge Neuroscience Her research investigates questions such as why emotional experiences feel a certain way, how emotion shapes memory, and the neural basis of similarity perception. A recent study demonstrated that emotionally charged stimuli evoke more similar neural representations than neutral ones, even when controlling for thematic content. This work was published in the Journal of Neuroscience and is part of broader efforts to model emotional memory using frameworks like the Bayesian Brain hypothesis. Dr. Talmi collaborates with institutions like the Swiss Centre for Affective Sciences and employs interdisciplinary methods, including animal models of emotional memory with Prof. Amy Milton. She actively mentors PhD students and welcomes volunteers through the SONA platform. Her lab’s findings have implications for understanding chronic pain, decision-making, and mental health. Her work has been featured in The Conversation and TEDx, emphasizing public engagement. She is also committed to advancing education, as seen in her reflections on remote teaching during the pandemic.

• Stochastic Analysis
• Mathematical Finance
• Option Pricing
• +5 more

Blanka Horvath is a Lecturer at King's College London and Honorary Lecturer at Imperial College London's Department of Mathematics (Faculty of Natural Sciences). Her research focuses on stochastic analysis and mathematical finance, particularly in numerical methods, machine learning applications, and volatility modeling (e.g., SABR and rough volatility models). She holds a PhD from ETH Zurich (2015), a Diplom in Mathematics from the University of Bonn, and an MSc in Economics from the University of Hong Kong. She has organized major conferences such as the SIAM MMF 2017 mini-symposium and co-organized the Rough Volatility Meeting at Imperial College. Her honors include the 2019 Risk Rising Star Award and the 2024-25 LMS Emmy Noether Fellowship. She collaborates with institutions like UBS, The Alan Turing Institute, and Quantennium LTD. Her teaching includes courses on numerical methods in finance and Python/R programming. She supervises PhD and MSc students in areas like rough volatility, machine learning, and quantitative finance. Recent work explores quantum GANs for option pricing and regime detection using Wasserstein distances.

• MRI Technology
• Biomedical Imaging
• Physical Chemistry
• +3 more

Dr. William M Holmes is a Senior Research Fellow and Senior MRI Physicist at the University of Glasgow's School of Psychology & Neuroscience, affiliated with the Glasgow Experimental MRI Centre. He holds a PhD in Physical Chemistry from the University of Nottingham and specializes in advancing MRI techniques for biomedical and physical sciences. His work bridges physics, neuroscience, and clinical applications, with a focus on cerebral blood flow imaging, glymphatic system dynamics, and disease modeling in rodents. Key roles: MRI method development, neuroimaging biomarkers, porous media analysis Leadership: Glasgow Experimental MRI Centre Research interests emphasize novel MRI applications in stroke, neurological disorders, and material science. Over 80 peer-reviewed publications demonstrate contributions to perfusion imaging, biofilm dynamics, and translational MRI techniques. Recent projects include: Quantitative arterial spin labeling methods Glymphatic system's role in multiple sclerosis Non-invasive rodent disease modeling

• Mathematical Biology
• Biophysics
• Stochastic Processes
• +3 more

Professor Kenneth A Lindsay is an Honorary Senior Research Fellow at the University of Glasgow's School of Mathematics & Statistics. His work bridges mathematical biology, biophysics, and financial econometrics, with a focus on stochastic processes and computational modeling. Key contributions include advancements in compartmental modeling of neurons, Fokker-Planck equation solutions for stochastic differential equations, and applications in energy market forecasting. He has authored/co-authored over 25 peer-reviewed articles and edited the influential 2005 book Modeling in the Neurosciences . Collaborations with researchers like David Brillinger, Adrian Hurn, and Jonathan Rosenberg highlight interdisciplinary expertise. His research addresses topics ranging from dendritic branching patterns to econometric optimization techniques. Publications span journals such as Biological Cybernetics , Journal of Financial Econometrics , and European Journal of Heart Failure . Notable work includes modeling electricity price spikes and analyzing magnetic field effects on nerve excitability. Lindsay's edited volumes and book chapters further underscore his role in advancing neuroscience and quantitative finance methodologies. His academic contributions reflect a commitment to mathematical rigor applied to real-world systems, with applications in biomedical engineering, energy economics, and computational neuroscience.

Andrew MaidenView profile

Professor

• Computational Optics
• Ptychography
• Coherent Diffractive Imaging
• +5 more

Professor Andrew Maiden is a faculty member at the University of Sheffield's School of Electrical and Electronic Engineering, specializing in Computational Imaging. He leads the Semiconductor Materials and Devices Research Group. His research focuses on advancing optical systems through computational methods like ptychography, which enhances microscopy and imaging precision. With a PhD from Durham University (2005) and an MEng from the University of Birmingham (2001), Maiden's career includes pioneering work with Professor John Rodenburg on ptychography and a brief industry stint commercializing microscopy technologies. He teaches Digital Signal Processing (DSP) to third-year students and advises researchers such as Cao S (PhD graduate). Research interests include Coherent Diffractive Imaging (CDI), electron microscopy phase imaging, and inverse problem solutions. His work bridges computational algorithms with practical applications in optics and materials science. Maiden has contributed to over 50 peer-reviewed publications and holds patents on ptychography-related imaging techniques. His lab focuses on developing high-resolution imaging tools without traditional lenses, emphasizing low-dose radiation and high-throughput bio-imaging. Teaching and mentorship play key roles in his academic contributions, shaping future engineers in signal processing and computational methods. Collaborations span academia and industry, reflecting his dual focus on innovation and real-world application.

Tyler DerrView profile

Assistant Professor

• Data Mining
• Social Network Analysis
• Graph Representation Learning
• +5 more

Dr. Tyler Derr is an Assistant Professor in the Department of Computer Science at Vanderbilt University, with affiliate roles in the Data Science Institute and the Frist Center for Autism and Innovation. He holds a PhD from Michigan State University (2020) and focuses on data mining, machine learning, and graph neural networks with applications in drug discovery, ethical AI, and neurodiversity research. Education: PhD in Computer Science, Michigan State University (2020) M.S. in Computer Science, Pennsylvania State University B.S. in Computer Science and Mathematics, Pennsylvania State University His research emphasizes interdisciplinary social good, including drug discovery, education equity, and autism innovation. He directs the Network and Data Science (NDS) Lab, mentoring students who have won 50+ awards. Notable contributions include tools like FairNNV for fairness certification and datasets like WelQrate for drug discovery benchmarking. Awards include the NSF CAREER Award (2023), NVIDIA Academic Grant (2024), and Vanderbilt’s Career Catalyst Impact Award (2025). He co-founded the MLoG workshop series and serves on editorial boards for ACM TKDD and IEEE Transactions on Big Data . Grants & Leadership: NSF Grant for student travel to KDD2024 NVIDIA BioNeMo Platform grant for peptide design DOE Computational Science Fellowship advising His work bridges theory and practice, with tutorials on graph ML at AAAI/KDD and keynotes on ethical AI. The NDS Lab hosts interdisciplinary collaborations in computational biology, transportation, and neurodiversity advocacy.

Anirban MondalView profile

Associate Professor

• Bayesian Inference
• Markov Chain Monte Carlo Methods
• Spatial Statistics
• +5 more

Anirban Mondal is an Associate Professor and Director of Graduate Studies at Case Western Reserve University's Department of Mathematics, Applied Mathematics and Statistics, specializing in Bayesian Inference, Markov Chain Monte Carlo Methods, and Uncertainty Quantification. Holding a Ph.D. in Statistics from Texas A&M University, his research spans spatial statistics, inverse problems, and data mining applications across biomedical, materials science, and public health domains. Education: Ph.D. in Statistics, Texas A&M University His recent publications (2022-2024) demonstrate interdisciplinary applications including heart disease prediction via optimized machine learning, additive manufacturing defect analysis, and pandemic transmission modeling. While primarily focused on Bayesian frameworks and computational statistics, his work extends to geomechanics, remote sensing, and environmental risk assessment. Current research explores advanced sampling algorithms, functional data emulation, and multiscale hierarchical modeling for complex systems. Key trends include uncertainty quantification in machine learning systems (2024), Bayesian calibration methods (2023), and pandemic modeling (2022). His work balances methodological innovation with real-world applications in medical diagnostics, materials science, and climate science. Contact: anirban.mondal@case.edu

Samantha CurleView profile

Professor

• English Medium Instruction (EMI)
• Integrating Content and Language in Higher Education (ICLHE)
• Advanced Quantitative Research Methods (AQM)
• +4 more

Samantha Curle is a Reader in the Department of Education at the University of Bath, serving as Director of all MRes Programmes in the Humanities and Social Sciences Faculty. She also holds roles as Institutional Academic Lead for the South West Doctoral Training Partnership and Director of Studies for the MRes in Advanced Quantitative Methods. Her research focuses on English Medium Instruction (EMI) in global higher education, particularly its academic and linguistic outcomes, technology integration, and psychological dimensions. Career highlights include a PhD from the University of Oxford (2018) on stakeholders’ attitudes toward EMI in Japanese higher education. She has extensive editorial roles, including guest editorships at Frontiers in Educational Psychology and Data in Brief . Her expertise spans quantitative methods, applied linguistics, and bilingual education, contributing to UN Sustainable Development Goal 4 (Quality Education). Education: PhD (University of Oxford, 2018) MSc Research Methods (University of Oxford, 2014) Bachelor of Education (Open University of Hong Kong, 2010) Grants and Projects: Leading projects on EMI in Saudi Arabia, Brazil, and Hong Kong-UK collaboration Focus on educational inequalities in Spain using CART models Awards: Adjunct Professor (2024) Her research has yielded 88+ publications, emphasizing EMI’s role in STEM, teacher professional development, and sustainable education. She advises doctoral students on quantitative EMI studies and advocates for evidence-based policy in multilingual education.

Sergej StoppelView profile

Researcher

• Visual Data Science
• Data Visualization
• Human-Computer Interaction
• +3 more

Sergej Stoppel is currently a researcher (PostDoc) in the visualization group at the University of Bergen. His academic background includes a Mathematics master's degree (2014) and a PhD in 2018, both from the University of Bergen. His research focuses on visual data science, with an emphasis on interactive visualization techniques, human-computer interaction, and physical visualization systems. He has authored/co-authored numerous papers on topics such as hexagonal map enhancements, spatio-temporal data interaction, and low-cost physical art generation. Education: M.Sc. Mathematics (2014), University of Bergen PhD in Visualization (2018), University of Bergen Research Interests: His work spans data visualization, scientific visualization, and interactive techniques. Notable contributions include Vol²velle (printable interactive volume visualization widgets), Firefly (virtual illumination drones), and LinesLab (low-cost art generation systems). He explores methods to bridge the gap between virtual and physical visual representations, emphasizing user-centric design. Publications: His recent work includes advancements in hexagonal map visualization (2022), spatio-temporal selection techniques (2020), and illumination automation (2019). These contributions highlight his focus on enhancing data interpretation through novel interaction paradigms. Labs/Teams: Active member of the VisGroup at the University of Bergen, collaborating on projects like MetaVis and VIDI. His work is supported by grants involving visualization and medical imaging applications.

Sarah BrattView profile

Assistant Professor

• Scholarly Communication
• Research Data Management
• Science of Science
• +6 more

Sarah Bratt is an Assistant Professor at the University of Arizona College of Information Science (InfoSci), focusing on Digital Curation & Preservation. Her research explores scholarly communication, research data management, and the science of science, aiming to advance long-term data sustainability and actionable science policy. BS in Philosophy from Ithaca College MS in Library and Information Science with a Data Science certificate from Syracuse University Her work addresses practices, metrics, and infrastructures for data-intensive science, including international collaborations and AI applications in libraries. Recent projects analyze GenBank metadata, dataset co-authorship, and software use in computational biology. She has presented at ASIS&T, 4S, ICSSI, and NetSci conferences. Masters’ prize in Library & Information Science at Syracuse University 2022 Better Scientific Software (BSSw) Fellow honorable mention Bratt collaborates with institutions like UIUC, University of Tennessee, and Metadata Lab @Syracuse University. She co-supervises students with Dr. Cristian Román-Palacios and leads the Metadata Analytics Lab at UA.

Katherine B. EnsorView profile

Professor

• Data Science
• Environmental Statistics
• Computational Finance
• +9 more

Katherine B. Ensor is the Noah G. Harding Professor of Statistics at Rice University's George R. Brown School of Engineering and Computing. She serves as Director of the Center for Computational Finance and Economic Systems (CoFES) and previously led the Kinder Institute Urban Data Platform (UDP). Her expertise spans statistical methods for complex problems in public health, environmental science, and finance. Ensor has a 35-year academic career at Rice, including 14 years as Department Chair. She pioneered wastewater surveillance for pathogens, expanding to monitor 29 communicable illnesses through Houston Wastewater Epidemiology. Education: Ph.D. in Statistics (Texas A&M University), M.S. and B.S.E. in Mathematics (Arkansas State University). Research focuses on high-dimensional time series, spatial processes, Bayesian methods, and computational finance. She leads interdisciplinary initiatives, such as the UDP providing urban data for Greater Houston studies. Research interests include data science applications in public health, environmental analytics, and quantitative finance. Her work bridges academia and community, addressing issues like flood impacts via the Texas Flood Registry and asthma surveillance through collaborations with Houston Health Department. Ensor chairs the National Academies committee on Statistics in Science and Engineering and holds leadership roles in AAAS and ASA. Awards include the ASA Founders Award (2024), AAAS Fellowship (2013), and Texas A&M's Distinguished Former Students honor. She advises on national boards, including NSF's IPAM and ABET's CSAB. Her grants and collaborations drive innovations in statistical methods for societal challenges. Labs/Teams: Co-directs Houston Wastewater Epidemiology and Rice Environmental Statistics & Health (RESH) group with Dr. Loren Hopkins. Active in CoFES, UDP, and the Kinder Institute's urban data initiatives.

Simon SpencerView profile

Professor

• Bayesian Inference
• Epidemiology
• Statistical Modeling
• +3 more

Simon Spencer is a Professor of Statistics at the University of Warwick, affiliated with the Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER) and the Warwick Analytical Sciences Centre (WASC). His research focuses on Bayesian inference applied to epidemiology, stochastic epidemic models, and statistical methods for analytical science. He has held previous positions at the University of Nottingham and Massey University in New Zealand. His teaching includes advanced courses such as CH923: Statistics for Data Analysis , ST925: Graduate Topics in Statistics , and MA4M1/MA6M1: Epidemiology by Example . His research group currently includes PhD students Matthew Adeoye and Richard Haughey, and MSc students Olli Smith and Sangavi Pirabakaran. He collaborates extensively with global health institutions on projects addressing infectious disease modeling and public health policy. Spencer’s work bridges statistical methodology and real-world applications, with a focus on outbreak detection, model comparison, and the integration of geostatistical data with transmission models. His recent contributions include frameworks for lymphatic filariasis elimination projections and analyses of HIV transmission dynamics in Uganda. He actively contributes to interdisciplinary research in systems biology and analytical chemistry, leveraging advanced statistical techniques to address complex health challenges.

Arieh WarshelView profile

Professor

• Theoretical Chemistry
• Biophysics
• Enzyme Catalysis
• +7 more

Arieh Warshel is a Distinguished Professor of Chemistry and Biochemistry at the University of Southern California’s Dornsife College of Letters, Arts and Sciences. He holds the Dana and David Dornsife Chair in Chemistry and is a Nobel Laureate in Chemistry (2013) for pioneering multiscale simulation methods for biological molecules. His work integrates quantum mechanics and molecular mechanics (QM/MM) to model enzymatic reactions and biological processes. Warshel earned a B.S. from the Technion in Israel (1966), M.S. and Ph.D. from the Weizmann Institute (1967, 1969). He joined USC in 1976 and has since led groundbreaking research in theoretical chemistry, including studies of enzyme catalysis, molecular motors, ion channels, and drug design. His research focuses on computational modeling of biological systems, emphasizing electrostatic effects, proton/electron transfer, and protein dynamics. Key contributions include the EVB (Empirical Valence Bond) method and the QM/MM approach, which revolutionized simulations of enzymatic reactions. Current projects involve multiscale modeling of G-proteins, ion pumps, and molecular machines. Warshel has authored over 500 publications, including seminal works on enzyme catalysis, electrostatic interactions, and molecular dynamics. His honors include membership in the U.S. National Academy of Sciences, the Royal Society of Chemistry’s Honorary Fellowship, and the Biophysical Society’s Founders Award. He oversees a research group with expertise in computational chemistry, mentoring postdocs like Ashim Nandi (plastic-degrading enzymes) and graduate students (e.g., Aoxuan Zhang, Lingfeng Hu). His lab uses advanced computing resources, including 38 dedicated nodes on USC’s HPC cluster, to simulate biological systems at unprecedented scales.

Vlad PojogaView profile

Lecturer

• Romanian Literature
• Digital Humanities
• Literary Analysis
• +6 more

Vlad Pojoga is Deputy Dean of the Faculty of Letters and Arts at Lucian Blaga University of Sibiu, holding a Lecturer position in the Department of Romance Studies. His research focuses on Romanian literature, digital humanities, and literary analysis, with particular emphasis on quantitative methods and spatial narratives. He leads projects like the Digital Museum of the Romanian Novel, exploring 19th-20th century literary trends through digital curation and geocritical approaches. His academic background includes advanced studies in literary theory and digital methodologies. Research interests span translation studies, postmodernism, and the intersection of narrative forms with cultural geography. Recent work analyzes character networks in classical novels and metaphor systems in contemporary poetry. Pojoga frequently collaborates on international literary projects, contributing to FORTHEM Alliance initiatives and organizing events like the International Week at ULBS. His publications bridge traditional literary criticism with computational methods, offering innovative frameworks for understanding Romanian literary history.