جست‌وجوی استاد و پژوهشگر

Charlotte Kuperwasserمشاهده پروفایل

استاد

• Breast Cancer Research
• Mammary Gland Biology
• Cancer Prevention
• +۴ مورد دیگر

Dr. Charlotte Kuperwasser is a distinguished Professor in the Department of Developmental, Molecular, and Chemical Biology at Tufts University School of Medicine . She directs the Tufts Convergence Laboratory and focuses on molecular mechanisms governing breast tissue development, cancer prevention, and organoid technologies. Her work integrates stem cell biology, epigenetics, and environmental influences such as endocrine disruptors. Education: Bachelor of Science (1997), University of Massachusetts Amherst PhD (2000), University of Massachusetts Amherst Jane Coffin Childs Postdoctoral Fellow (MIT/Whitehead Institute) Research Interests: Her lab develops 3D breast organoid models to study cancer initiation, microenvironmental interactions, and BRCA1-related mechanisms. Recent efforts include analyzing HPV-driven cancers via circulating tumor DNA and studying fibroblast signaling (e.g., DDR1) in tumor progression. Grants & Awards: Howard Hughes Fellowship, Merck Fellowship COG/Aventis Young Investigator Award Natalie V. Zucker Award NIH grants on V-ATPases and obesity-cancer links Professional Contributions: She chairs Tufts’ Sackler Convergence Laboratory and serves on AACR committees. Teaching includes courses on Molecular Cell Biology of Development and Cancer Genetics . Labs/Teams: Leads the Kuperwasser Lab, focusing on translational cancer research with cross-disciplinary collaborations in organoid engineering and clinical diagnostics.

Ivana Brekaloمشاهده پروفایل

پژوهشگر

• Chemistry
• Applied Chemistry
• Mechanochemistry
• +۴ مورد دیگر

Ivana Brekalo is a Researcher at the Ruđer Bošković Institute in Zagreb, Croatia, affiliated with the Division of Physical Chemistry and the Laboratory for Applied and Sustainable Chemistry. She holds a Ph.D. in Chemistry from Georgetown University (2019), with a thesis on "Solid State Synthesis and Study of Porous Materials," and completed her Master's (2012) and Bachelor's (2010) degrees in Chemistry at the University of Zagreb. Her work bridges mechanochemistry and materials science, focusing on scalable synthesis methods for functional materials. Doctor of Philosophy, Chemistry, Georgetown University (2013–2019) Master of Science, Chemistry, University of Zagreb (2010–2012) Bachelor of Science, Chemistry, University of Zagreb (2007–2010) Her research emphasizes mechanochemical synthesis, particularly for porous materials like metal-organic frameworks (MOFs) and coordination polymers. She explores solvent-free methods, polymorphism control, and the role of gas-phase catalysts in solid-state reactions. Recent publications highlight thermally controlled milling for agrochemical cocrystals, conductivity in alkali metal coordination polymers, and real-time monitoring of mechanochemical processes. Key publications include Nature Reviews Chemistry perspectives on advanced mechanochemical synthesis and Inorganic Chemistry studies on low-dimensional magnetism in MOF-74 materials. Her work appears in journals like ACS Sustainable Chem. Eng. and Chemical Science , with a focus on green and scalable methods. Scientific Awards Scholarship of the Polish National Agency for Academic Exchange – Ulam Programme (2020) Bepina Sabalić Kunin Fellowship (2013-2015, 2017-2018) Ludo Frevel Crystallography Scholarship, IUCr (2017) CCDC award for best presentation (2022) Brekalo contributes to outreach as the 2019 ACS Volunteer of the Year and has received recognition for her work on solvent-free polymorphism and mechanochemical templation of ZIFs.

• Multisensory Perception
• Predictive Processing
• Speech Perception
• +۳ مورد دیگر

Prof. Helen Blank is a Professor leading the Multisensory Perception Group and the Prediction in Communication Lab at the Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf. Her work focuses on understanding how sensory information is integrated and predicted in contexts like speech perception and face recognition. She holds a Marie Curie Fellowship for her research on prior information's role in human communication. Fluent in German, English, and French, she contributes to experimental medicine and systems neuroscience. Her research spans predictive coding, neuroimaging, and clinical applications in Parkinson’s and developmental disorders. Education: Not explicitly stated in text, inferred as advanced degrees in neuroscience or related fields. Her research interests emphasize multisensory integration, predictive processing in speech and vision, and the neural bases of perception. Recent articles explore topics such as pupil responses to auditory surprise, face expectation hierarchies, and audio-visual speech processing. Awards include the Marie Curie Fellowship supporting her predictive communication work. She leads interdisciplinary teams within the Center for Experimental Medicine, advancing knowledge on perceptual mechanisms and their clinical implications.

Beata Gorczycaمشاهده پروفایل

استاد

• Environmental Engineering
• Water and Wastewater Treatment
• Solid/Liquid Separation
• +۵ مورد دیگر

Dr. Beata Gorczyca is a Professor in the Department of Civil Engineering at the University of Manitoba, affiliated with the Price Faculty of Engineering. Her research focuses on potable water treatment, with expertise in solid/liquid separation, chlorine disinfection by-products control, and fractal analysis of materials. She leads a research group collaborating with Canadian water utilities like Portage la Prairie and Pembina Valley Water Co-op. Education: PhD in Chemical Engineering (2000, University of Toronto), M.Sc. in Civil Engineering (1992, University of Toronto), B.Sc. in Geological Engineering (1986, AGH University, Poland). Active roles: Member of the Particle Specialist Group at the International Water Association, keynote speaker at conferences. Research interests include water purification processes, membrane filtration, and bioremediation. Her work addresses challenges in high-DOC and high-hardness water treatment, with contributions to nanofiltration fouling mechanisms and microbial remediation solutions. She has supervised numerous graduate students and is involved in advancing water treatment technologies through interdisciplinary collaborations.

Sean O'Rourkeمشاهده پروفایل

دانشیار

• Probability
• Random Matrix Theory
• Random Polynomials

Sean O'Rourke is an Associate Professor in the Department of Mathematics at the University of Colorado Boulder. His research focuses on probability, random matrix theory, and random polynomials. He has organized multiple workshops and minisymposia, including events at the Canadian Discrete and Algorithmic Mathematics Conference (CanaDAM) and ICERM, demonstrating his active role in academic community engagement. His research spans spectral properties of random matrices (e.g., singular values, elliptic matrices, Laplacian matrices), probabilistic behavior of polynomial roots under operations like differentiation and summation, and applications of free probability theory. Recent work includes universal behavior in eigenvalue gaps, non-Hermitian matrix controllability, and asymptotic refinements of classical theorems for random polynomials. Publications highlight collaborations with researchers such as Andrew Campbell, Kyle Luh, David Renfrew, and Van Vu. His work appears in leading journals like Annals of Probability , Electronic Journal of Probability , and Transactions of the American Mathematical Society , covering topics from Gaussian fluctuations to low-rank perturbations and noncommutative harmonic analysis.

Jürgen Bernardمشاهده پروفایل

استادیار

• Visual Analytics
• Information Visualization
• Interactive Machine Learning
• +۱۲ مورد دیگر

Jürgen Bernard is an Assistant Professor of Computer Science at the University of Zurich , leading the Interactive Visual Data Analysis (IVDA) Group . He is associated with the Digital Society Initiative (DSI) and holds a PhD in Computer Science from Technische Universität Darmstadt (2015) with a focus on time-oriented data analysis. His academic journey includes postdoctoral research at TU Darmstadt and the University of British Columbia. Education : Diploma in Computer Science (2009, TU Darmstadt) PhD in Computer Science (2015, TU Darmstadt) Research Interests : Dr. Bernard specializes in interactive visual data analysis , explainable machine learning , and human-centered AI . His work explores time series analysis , multivariate data exploration , and user-driven preference elicitation . He develops visual analytics systems for domains like healthcare , digital humanities , and industrial applications , with a particular focus on responsible AI and transparency in algorithmic systems . Research Trends : His publications emphasize interactive machine learning workflows , visual analytics for healthcare , and time-stamped event sequence analysis . Recent work includes LLM validation frameworks (Human-Data-Model Interaction Canvas) and personalized ranking systems funded by the Swiss National Science Foundation. He integrates temporal data with multivariate analysis across applications from medical manufacturing to chronic disease management . Scientific Recognition : EuroGraphics Young Researcher Award (2022) EuroVis Young Researcher Award (2021) Best Paper Awards at IEEE VIS (2021), EuroVA (2021, 2025) Dirk Bartz Prize (2017), Hugo-Geiger Preis (2016) Teaching & Grants : He teaches Interactive Visual Data Analysis (6 ECTS), Digital Health Seminars , and People-Oriented Computing . Currently leads a SNF Grant on Personalized Visual Analytics for multi-criteria decision support (2024-2028) with ETH Zurich's Prof. M. El-Assady.

Teresa Wangمشاهده پروفایل

مدرس ارشد

• Entity/User Modeling
• Relational/Structural Machine Learning
• Spatial/Temporal Machine Learning
• +۵ مورد دیگر

Dr. Teresa Wang is a Senior Lecturer in Data Science at Monash University's Faculty of Information Technology, specializing in entity/user modeling, relational/structural machine learning, and graph/network analysis. She holds a Ph.D. from the University of Queensland and degrees from Nanjing University. Currently, she directs the Master of Data Science Program and teaches courses like FIT5201 Machine Learning. Her research focuses on social, e-commerce, and health data modeling, with notable projects including the Knowledge Enriched Approach for Effective Personalization (2025–2027) and collaborations on AI in Mental Health and Site Safety. Dr. Wang has co-authored over 59 publications, emphasizing areas like ontology matching and multimodal data analysis. She actively supervises PhD students and contributes to initiatives like the CSIRO Next Generation Graduates Program for clean energy and sustainability. Education: Ph.D. in Computer Science (2017), University of Queensland Master of Computer Science (2013), Nanjing University Bachelor of Software Engineering (2010), Nanjing University Research Interests: Entity modeling, spatio-temporal data analysis, graph mining, recommender systems, and health/medical records mining. She explores applications in social media, e-commerce, and healthcare sectors. Projects: "Knowledge Enriched Approach for Effective Personalization" (2025–2027) "AI for Clean Energy and Sustainability" (2023–2027) "CSIRO Next Generation Graduates Program: AI in Mental Health" (2023–2027) "Large-scale multimodal knowledge management" (2022–2025) Grants & Collaborations: Engaged with CSIRO, Crank Group, and Pola Practice Pty Ltd. Her work aligns with UN SDGs in education and sustainable energy systems. Labs/Teams: Part of the Monash Energy Institute and Monash Data Futures Institute, contributing to interdisciplinary AI and energy research.

Falk MÜLLER-RIEMENSCHNEIDERمشاهده پروفایل

دانشیار

• Public Health
• Epidemiology
• Cardiovascular Diseases
• +۴ مورد دیگر

Falk MÜLLER-RIEMENSCHNEIDER is an Associate Professor and Vice Dean (Research) at NUS Saw Swee Hock School of Public Health and holds a joint affiliation with NUS Yong Loo Lin School of Medicine. He leads the Physical Activity and Nutrition Determinants in Asia (PANDA) research programme. With a medical degree from the University of Cologne (2003) and advanced training in Public Health at the London School of Hygiene & Tropical Medicine (2005), he has built a career focused on cardiovascular disease prevention, physical activity promotion, and obesity epidemiology. His academic qualifications include a Doctoral degree from Charité University Medical Centre Berlin (2005) and Habilitation (2011). Professional roles include Senior Lecturer/Research Fellow positions at Charité (2005–2012), and administrative leadership roles such as Teaching Coordinator and Project Division Coordinator for Cardiovascular Research (2008–2012). Research interests center on lifestyle determinants of chronic diseases, with emphasis on physical activity interventions, dietary patterns, and early childhood development. Notable work includes the Singapore Preconception Study and the GUSTO cohort study, examining long-term health trajectories. Media engagements highlight his work on screen time effects in children and urban green space benefits. His recent publications (2021–2025) address mHealth intervention design, park-based physical activity, and 24-hour movement guidelines. Key themes include digital health adoption barriers, longitudinal cohort studies, and culturally tailored public health strategies.

Bradley Buchsbaumمشاهده پروفایل

دانشیار

• Cognitive Neuroscience
• Working Memory
• Functional Neuroimaging
• +۵ مورد دیگر

Bradley Buchsbaum is an Associate Professor and Senior Scientist at the Rotman Research Institute, Baycrest , Toronto, Canada. His research focuses on cognitive neuroscience, particularly on working memory, episodic memory, and functional neuroimaging using fMRI. PhD in Cognitive Science (2003) from University of California, Irvine BSc in Bio-Psychology (1997) from University of California, Santa Barbara His lab investigates how memories are stored, represented, and reactivated in the brain, combining functional neuroimaging, eye-tracking, and computational modeling. Key areas include multivariate statistics, machine learning applications in neuroimaging, and quantifying memory fidelity through behavioral and neural data. Recent publications emphasize neuroimaging methodology (e.g., MRI data consistency checks), memory pattern completion mechanisms, and feature-specific neural reactivation during episodic recall. Research trends highlight interdisciplinary approaches merging cognitive neuroscience with advanced statistical techniques. Lab team includes post-doctoral fellows (Stephen Rhodes), graduate students (Carolyn Guay, Corey Loo, Michael Bone, Nick Hoang, Ryan Barker), and research assistants. The lab actively develops open-source tools like rMVPA and neuroim2 for fMRI analysis.

Valentyn Panchenkoمشاهده پروفایل

استاد

• Financial Econometrics
• Time Series Analysis
• Dependence Measures and Copulae
• +۱۱ مورد دیگر

Professor Valentyn Panchenko is a leading academic in Economics at the UNSW Business School, specializing in advanced econometric methodologies and financial modeling. Holding a PhD from the University of Amsterdam and an MPhil from the Tinbergen Institute, his research bridges theoretical econometrics with real-world financial applications, emphasizing big data analysis, network structures, and dependence modeling in economic systems. His expertise spans financial econometrics, time series analysis, non-parametric statistics, and agent-based economic simulations. He focuses on Granger causality, model evaluation, structural economic modeling, and bounded rationality with heterogeneous agents. His work has secured significant grants including ARC Discovery Projects and DECRA fellowships, enabling cutting-edge research on market dynamics and economic interactions. Professor Panchenko's publications appear in top-tier journals like the Journal of Econometric Theory, AEJ: Micro, Journal of Economic Dynamics & Control, and Journal of Banking & Finance. His methodological contributions include novel approaches to copula-based forecasting, nonlinear causality testing, and evolutionary learning models in strategic economic environments. While specific student advising details aren't provided, his research leadership demonstrates sustained impact across econometric theory, financial markets, and experimental economics.

Pablo Durango-Cohenمشاهده پروفایل

دانشیار

• Transportation Infrastructure Management
• Optimization
• Econometrics
• +۵ مورد دیگر

Pablo Durango-Cohen is an Associate Professor of Civil and Environmental Engineering at Northwestern University, located in Evanston, IL. He holds a Ph.D. in Industrial Engineering and Operations Research from UC Berkeley, following an M.S. from the same program and a B.S. in Industrial and Systems Engineering from the University of Southern California. His research focuses on developing and analyzing optimization and econometric models for transportation infrastructure systems, integrating environmental design, life-cycle assessment, and policy analysis to address decarbonization challenges in freight systems. He also explores dynamic segmentation models for nonprofit fundraising strategies. Education: Ph.D. Industrial Engineering and Operations Research, University of California, Berkeley (2006) M.S. Industrial Engineering and Operations Research, University of California, Berkeley B.S. Industrial and Systems Engineering, University of Southern California Research Interests: Prof. Durango-Cohen’s work bridges transportation engineering, environmental science, and operations research. He emphasizes infrastructure management through data-driven frameworks, including statistical process control for condition monitoring and predictive maintenance. His recent projects address decarbonization of freight rail systems, electric vehicle impacts on road infrastructure, and optimal auction designs for road concessions. He also applies mathematical models to analyze donor behavior and fundraising efficiency in universities, aiming to improve nonprofit resource allocation strategies. Awards: NSF Faculty Early CAREER Development Award (2006) Young Author Prize, 2007 World Congress on Transport Research Matthew G. Karlaftis Best Paper Awards (2020–2025) Advising & Grants: He advises current PhD candidates including Jing Yu, Adrian Hernandez, and Callahan Skiles, while mentoring former students across sustainability, infrastructure, and fundraising analytics. His research is supported by agencies like the National Science Foundation, Department of Energy (through ARPA-E), and Department of Transportation. He co-leads the LOCOMOTIVES project with ANL researchers, focusing on decarbonizing rail networks, and founded the Virtual Inter-university Symposium on Infrastructure Management (VISIM) to foster academic collaboration. Labs & Teams: As Principal Investigator (PI) on major initiatives like LOCOMOTIVES and VISIM, he collaborates with multidisciplinary teams at Northwestern and Argonne National Laboratory. His group develops tools such as the Locomotives interactive dashboard and a computational framework for input-output lifecycle assessments, accessible via repositories like CivEnv304 .

• Machine Learning
• Health Informatics
• Public Health
• +۳ مورد دیگر

Ke Wu is a Professor in the Department of Computer Science and Engineering at the University of Michigan. Their research focuses on the intersection of machine learning, biostatistics, and healthcare technology, with an emphasis on mobile health interventions, causal inference, and Bayesian methods. They lead a small, hands-on research group mentoring PhD students and postdocs. Key interests include developing predictive models for health outcomes, improving treatment effect estimation, and leveraging mobile technology for caregiver support. Their work has addressed critical challenges in clinical decision-making, public health surveillance, and healthcare innovation. Research projects span synthetic data generation for electronic health records, mHealth app development for care partners of traumatic brain injury patients, and algorithmic fairness in reinforcement learning. Ke Wu emphasizes interdisciplinary collaboration and has contributed to global health studies, including analyses of pneumonia etiology in low-resource settings and the PERCH study. Their group's methodologies often integrate wearable sensor data and machine learning to address real-world health challenges. Advising priorities include fostering student independence while maintaining close mentorship, with expectations for consistent research productivity and professional development. Students are encouraged to pursue teaching roles (e.g., GSI positions) and internships aligned with career goals. Funding support for conference participation is available through institutional and external grants. Ke Wu's contributions extend to statistical methodology, including Bayesian latent class models and dynamic risk prediction frameworks. They actively engage in translational research, bridging computational methods with clinical and public health applications, and prioritize open-source software development to advance reproducible research practices.

Claude DELPHAمشاهده پروفایل

استاد

• Signal Processing
• Fault Diagnosis and Prognosis
• Electrical Engineering Systems
• +۴ مورد دیگر

Claude DELPHA is a Full Professor at Université Paris Saclay, affiliated with CentraleSupélec’s Laboratoire des Signaux et Systèmes (L2S). He holds an IEEE Senior Member status and has been with L2S since 2001. His expertise spans signal processing, fault diagnosis, electrical engineering systems, and machine learning. He leads the Modelling and Estimation team (GME) at L2S and oversees engineering admissions at Polytech Paris Saclay. Education: PhD in Instrumentation & Measurements and Signal Processing from Université de Metz, with a focus on intelligent sensor systems. Graduate degree in Electrical and Signal Processing Engineering. Research Interests: Multidimensional/statistical signal processing, fault diagnosis/prognosis (modeling, detection, estimation), electrical systems (drives, converters, PV), data hiding (watermarking), and pattern recognition (machine/deep learning). Active in energy systems, industry 4.0, and health/biology applications. Professional Roles: Director of GME research team, Polytech admissions lead, member of Polytech’s executive and academic boards, and IUT department council member. Engaged in labs like SYCOMORE and ILOCOS. Publications: Over 200 works since 2015, focusing on fault diagnosis in electrical systems, photovoltaic modules, bearings, and tidal turbines. Key methods include Kullback-Leibler divergence, Jensen-Shannon divergence, Mahalanobis distance, and PCA-based approaches. Awards: Not explicitly listed in provided texts.

Philip Pughمشاهده پروفایل

مدرس ارشد

• Antarctic biogeography
• Cladistics
• Multivariate analysis
• +۴ مورد دیگر

Philip Pugh is a Senior Lecturer in the Faculty of Science and Engineering at Anglia Ruskin University, specializing in Life Sciences. He has extensive experience in Antarctic biogeography, cladistics, and multivariate analysis, with a focus on biodiversity and environmental biology. His teaching includes the BSc (Hons) Marine Biology with Biodiversity and Conservation course. BSc Zoology, University of Wales Swansea, 1980 PhD Marine Biology, University of Wales Swansea, 1985 PGCE Biology with Integrated Science, University of Cambridge, 1988 Philip's research spans Antarctic biogeography, cladistics, and multivariate analysis. He investigates the origins of Antarctic fauna, human impacts on ecosystems, and develops computational methods for biogeography. His work integrates ecological modeling, taxonomy, and conservation biology. Recent research trends include Antarctic ecology, biodiversity analysis using multivariate techniques, and the ecological consequences of climate change and tourism. His publications address topics like tardigrade population dynamics, seabird entanglement, and land snail evolution in sub-Antarctic regions. Fellow, Linnean Society of London Fellow, Zoological Society of London Fellow, Royal Entomological Society of London Philip supervises graduate students in Antarctic ecology, taxonomy, and microbial environmental interactions. He is affiliated with the Applied Ecology Research Group and collaborates on projects involving seabirds, fungal pathogens, and conservation strategies.

Gaël Kermarrecمشاهده پروفایل

پژوهشگر

• Boundary Layer Meteorology
• Atmospheric Turbulence
• GNSS Applications
• +۳ مورد دیگر

Dr. Gaël Kermarrec is a researcher at the Boundary Layer Meteorology Group , part of the Institute of Meteorology and Climatology within the Faculty of Mathematics and Physics at Leibniz University Hannover . His work focuses on atmospheric turbulence, GNSS applications, and remote sensing for environmental monitoring. Boundary layer meteorology Turbulence theory GNSS signal processing Terrestrial laser scanning Climate change impacts Geodetic time series analysis His research integrates advanced mathematical models like LR B-splines and Matérn covariance with large eddy simulations to study: Atmospheric turbulence effects on optical/GNSS signals Hydrospheric mass loading Deformation analysis of terrain/port infrastructure Climatic sea-level changes Machine learning for remote sensing The 15 most recent articles (2025-2023) demonstrate his focus on: GNSS-based turbulence detection AI-enhanced climate mapping Advanced surface approximation techniques Multi-sensor data fusion Stochastic modeling of geodetic observations Environmental impacts on optical measurements He has developed tools like the Klimascanner QGIS plugin for urban climate resilience and contributes to: Understanding atmospheric scale lengths Improving TLS/GNSS deformation monitoring Analyzing hydrospheric changes Wavefront modeling Ionospheric corrections