جست‌وجوی استاد و پژوهشگر

• Numerical Modeling
• Geotechnical Engineering
• Renewable Energy Systems
• +۳ مورد دیگر

Dr. Liang Cui is an Associate Professor at the University of Surrey , affiliated with the School of Sustainability, Civil and Environmental Engineering and Institute for Sustainability . With a PhD from University College Dublin (2006) and BE (1st honor) from Tsinghua University (2002) , his career spans geotechnical research and education since joining Surrey in 2009. Key roles: Undergraduate Programme Leader (2020-2022, 2023-on), MSc Programme Leader for Advanced Geotechnical/Civil/Structural Engineering (2022-2023) Professional memberships: Chartered Engineer (CEng), Member of Institution of Civil Engineers (MICE), Fellow of Higher Education Academy (FHEA) His primary research focuses on numerical modeling (DEM/FEM) for geotechnical applications including offshore wind foundations , geothermal energy systems , methane hydrate exploitation , and extra-terrestrial soil mechanics . Secondary interests involve material characterization of polymeric foams , porous media , and biological tissues . Recent 15 publications (2023-2025) demonstrate expertise in soil-structure interaction for renewable energy infrastructure, thermal feedback in groundwater heat pumps, and hypothesis-driven DEM simulations for lunar/martian environments. Collaborative projects span institutions including Tsinghua University , University of Bristol , and Indian Institute of Technology Bhubaneswar . Scientific Awards: Sustainability Fellow (University of Surrey, 2023) Chartered Engineer (CEng) and MICE FHEA for educational contributions Dr. Cui supervises 7 postgraduate researchers and contributes to teaching modules in soil mechanics and energy geotechnics. His work addresses challenges in hybrid marine energy systems , needleless drug delivery , and seismic resilience of critical infrastructure.

Jaap Zevenbergenمشاهده پروفایل

استاد

• Land Administration
• Geo-Information Management
• Property Rights
• +۵ مورد دیگر

Jaap Zevenbergen is a Full Professor at the University of Twente's Faculty of ITC, specializing in Land Administration and Geo-Information Management. He holds a PhD from Delft University of Technology (2002) and degrees in Geodetic Engineering (Delft) and Law (Leiden University). His research focuses on international land governance, digital transformation of property institutions, and pro-poor land tools, with projects in Africa, Southeast Asia, and Eastern Europe. He has contributed to UN Habitat initiatives and World Bank programs, emphasizing sustainable development goals (SDGs 1, 11, 13). Key roles: Theme leader at TU Delft's OTB Institute (2003–2010), Portfolio Manager for MSc Land Administration at ITC. Teaching: MSc programs in Land Administration, GIMA, and International Land Management. Research interests include: Land Administration Domain Model (LADM) implementation, legal-technical integration in geo-ICT systems, and post-disaster/post-conflict land governance. He has authored/co-authored over 340 publications and edited books like Real Property Transactions . Notable achievement: Co-winner of the 2018 FIG-Survey Review Prize for land policy analysis. Current projects explore 3D cadastral systems, UAV applications in land registration, and ethical geospatial practices. He advises on land reforms in Greece and Egypt and collaborates with institutions like UN Habitat and the World Bank.

Peter Haasمشاهده پروفایل

استاد

• Data Management
• Machine Learning
• Simulation
• +۶ مورد دیگر

Peter Haas is a Professor at the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst, with an adjunct role in Industrial Engineering. Previously, he spent 30 years as a Principal Research Staff Member at IBM Research and held a consulting professorship in Management Science and Engineering at Stanford University. His research focuses on applying probability and statistics to data management, simulation of complex systems, and machine learning scalability. Education : PhD, Operations Research, Stanford University, 1986 MS, Statistics, Stanford University, 1984 MS, Environmental Engineering, Stanford University, 1979 SB, Engineering and Applied Physics, Harvard University, 1978 Research Interests : Haas’s work spans stochastic systems, probabilistic databases (e.g., MCDB and SimSQL), sampling techniques, and simulation optimization. He pioneered methods for managing uncertain data and scalable machine learning, including compressed linear algebra for declarative systems. His recent focus includes in-database decision support and hybrid simulation metamodeling with neural networks. Key Contributions : He developed the Online Aggregation framework (SIGMOD 1997), which earned a Test-of-Time Award in 2007. His work on matrix factorization and distributed stochastic gradient descent (DSGD) revolutionized large-scale machine learning. He also advanced techniques for estimating distinct-values and correlation discovery in databases. Awards : A six-time recipient of IBM’s Pat Goldberg Memorial Award, he is an ACM and INFORMS Fellow. His honors include the VLDB Best Paper Award (2016), EDBT Best Paper (2018), and recognition in Communications of the ACM. Advising & Grants : He advises four current PhD students and has graduated Matteo Brucato. His IBM career included over 30 patents, including foundational work for DB2’s sampling capabilities and IBM Watson analytics. He leads the DREAM Lab, focusing on data systems for exploration and analytics. Labs/Teams : Directs the Data systems Research for Exploration, Analytics, and Modeling (DREAM) Lab, advancing projects like Splash (health system simulation) and SuDocu (document summarization by example).

Sanford R. Studentمشاهده پروفایل

استادیار

• Educational Measurement
• Psychometrics
• Quantitative Methods
• +۳ مورد دیگر

Dr. Sanford R. Student is an Assistant Professor in the School of Education at the University of Delaware and a Resident Faculty member of the Data Science Institute. His research focuses on connecting psychometric methodologies with practical educational implications, particularly in academic growth measurement, large-scale science assessments, and instrument design. He holds a Ph.D. in Research and Evaluation Methodology from the University of Colorado Boulder (2023) and dual B.A.s in Philosophy and Computer Science from Brown University (2013). Dr. Student’s professional experience includes roles as a Research Associate at Lyons Assessment Consulting (2021–2023), Doctoral Researcher at the Center for Assessment Design, Research and Evaluation (2018–2023), and Lead Researcher for the American Bar Association’s Bar Exam study (2019–2020). Prior to academia, he worked as a Software Engineer at edX (2016–2018). His awards include selection for the 2020 AIR/NCES NAEP Data Training Workshop. Current research trends in his publications emphasize Bayesian methods, vertical scaling, and crosscutting concepts in science education. He actively contributes to educational policy discussions through work with state agencies and assessment developers. Dr. Student advises graduate students and collaborates on grants related to growth measurement and educational data systems. He is affiliated with the National Council on Measurement in Education and maintains a lab focusing on applied psychometric challenges in K-12 systems.

Giuseppe Colangeloمشاهده پروفایل

دانشیار

• Innovation Policy
• Intellectual Property Law
• Competition Policy
• +۳ مورد دیگر

Giuseppe Colangelo is an Associate Professor of Law and Economics at the University of Basilicata and holds adjunct roles at LUISS Guido Carli (Rome, Italy) and the Transatlantic Technology Law Forum (TTLF). He specializes in innovation policy, competition law, and economic analysis of legal frameworks. His research bridges EU and U.S. antitrust approaches, focusing on digital markets, data governance, and transnational litigation. Education includes a Law degree from LUISS Guido Carli, an LL.M. in Competition Law from Erasmus University Rotterdam, and a Ph.D. in Law and Economics from LUISS. He has been a TTLF Affiliate since 2017. Key research interests include AI-driven collusion, antitrust implications of data accumulation, and interoperability policies. Over 15 publications analyze topics like anti-suit injunctions in SEP litigation, EU-US regulatory divergence in digital platforms, and privacy-antitrust intersections. His work emphasizes transatlantic comparative law and policy recommendations for addressing market power in tech sectors.

Ya-Jane Wangمشاهده پروفایل

استاد

• Carbohydrate Chemistry
• Starch Chemistry
• Rice Quality
• +۳ مورد دیگر

Jane Wang is a Professor in the Department of Food Science at the University of Arkansas , where she has served since 1999, progressing from Assistant to Full Professor. She also holds the title of Director of the Experiment Station in the Department of Food Science. Her research focuses on starch structure-functionality relationships , rice quality , and biomaterial utilization , with over 120 refereed publications and 5 patents. Education: B.S. in Agricultural Chemistry (1986) from National Taiwan University , M.S. in Food Science (1989) from the University of Minnesota , and Ph.D. in Food Science (1992) from Iowa State University . Postdoctoral research in starch chemistry at Iowa State University (1993-1994). Research Interests: Jane Wang's work explores starch chemistry, rice processing optimization, and value-added applications of agricultural byproducts. She investigates how starch modifications affect food and pharmaceutical properties, with a particular focus on parboiling, germination, and enzymatic treatments. Her research also examines the impact of environmental factors on rice starch development and quality. Scientific Awards: Outstanding Departmental Research Award (2008) Outstanding Volunteer, IFT Carbohydrate Division (2007) Outstanding Mentor, University of Arkansas (2005) Grants & Professional Service: She has secured over $3M in research funding, including USDA-NIFA grants and industry contracts with more than 50 food companies. Jane has served on numerous academic committees (Patent, Promotion & Tenure, Curriculum) and held leadership roles in professional organizations like IFT and AACC. She has also acted as associate editor for Cereal Chemistry and Carbohydrate Polymers , and reviewed for multiple journals and agencies. Labs & Teams: Dr. Wang leads the Carbohydrate Research Program at the University of Arkansas, focusing on starch structure-functionality, rice fortification, and biomaterial development. Her lab collaborates with industry partners and academic institutions to advance food science applications.

Stephen Thompsonمشاهده پروفایل

مدرس ارشد

• Strength and Conditioning
• Resistance Training
• Velocity-Based Training (VBT)
• +۴ مورد دیگر

Stephen Thompson is a Senior Lecturer and Course Leader for the MSc in Strength and Conditioning (S&C) Coaching at Sheffield Hallam University. He holds accreditation as a Strength and Conditioning Coach (ASCC) with the UKSCA and is completing a PhD focusing on Velocity-Based Training (VBT), specifically load-velocity profiling for maximal intensity prediction. His research interests include resistance training, technology in training, and youth development, with publications in journals like Journal of Strength and Conditioning Research and Sports Medicine . He teaches across undergraduate and postgraduate programs in the Department of Sport and Physical Activity, leading courses such as the MSc in Strength and Conditioning Coaching and Applied Sport and Exercise Science. His academic roles include Employability Lead, fostering partnerships with local clubs and organizations for student placements. Thompson’s work spans academia and applied practice, with consultancy roles for elite teams like GB Diving and a Championship football academy. His research emphasizes VBT methodologies, load prescription, and the validation of sports technologies. Key contributions include studies on barbell velocity measurement, 1RM prediction models, and athlete performance profiling. He has secured grants from national governing bodies and collaborated internationally on projects. His expertise bridges theoretical research and practical applications in elite sport conditioning and athlete development.

Jean Fanمشاهده پروفایل

استادیار

• Single-cell omics
• Spatial omics
• Computational biology
• +۳ مورد دیگر

Jean Fan, PhD is an Assistant Professor of Biomedical Engineering at Johns Hopkins University, affiliated with the Center for Computational Biology and Institute for Computational Medicine. Her research focuses on developing machine learning methods to analyze spatially resolved and single-cell omics data. Her team, the JEFworks Lab, creates open-source tools for analyzing high-dimensional biological data to understand cellular identity, tissue organization, and disease progression. Dr. Fan's work bridges computational biology with clinical applications, particularly in leukemia and pediatric brain cancers. Education: BS in Biomedical Engineering & Applied Math from Johns Hopkins (2013); PhD in Bioinformatics from Harvard Medical School (2018); Postdoc in Chemical Biology/Physics at Harvard (2018–2020) under Xiaowei Zhuang, focusing on spatial transcriptomics. Research emphasizes spatial genomics and computational tool development, with key contributions to spatial alignment algorithms (STalign), normalization techniques, and cell-type deconvolution methods. Her lab's work has advanced understanding of kidney ischemic injury, glioblastoma spatial dynamics, and CLL pathogenesis. Awards include Forbes 30 Under 30, NSF CAREER Award, and 2025 PECASE. She founded CuSTEMized, a nonprofit providing STEM storybooks for girls. Recent collaborative projects include HuBMAP 3D reference atlas construction and Discovery Award-funded interdisciplinary initiatives. Labs/Teams: JEFworks Lab (primary); active collaborations with Dana-Farber Cancer Institute and Harvard Medical School. Current efforts focus on spatially resolved multi-omic integration and clinical translation of computational tools.

Andrea Maurinoمشاهده پروفایل

استاد

• Data Science
• Knowledge Graphs
• Machine Learning
• +۵ مورد دیگر

Andrea Maurino is a Full Professor at the University of Milano-Bicocca and leads the Insid&s LAB. His research focuses on data quality, knowledge graphs, machine learning, and their applications in healthcare, finance, urban planning, and organizational analysis. He explores cutting-edge techniques like Large Language Models (LLMs) for decision support systems and semantic annotation of tabular data. Key research interests include improving data quality frameworks for large RDF datasets, developing enterprise knowledge graphs for organizational insights, and applying AI to social media analysis and hate speech detection. His work bridges theoretical advancements with real-world applications such as smart city mobility prediction and nutritional strategies for healthy aging. Notable contributions include scalable tools like ABSTAT-HD for knowledge graph profiling and the 3d-clost mobility prediction model. Maurino’s interdisciplinary approach integrates data science with fields like psychology (ICD-11 decision support) and environmental science (ESG activity detection in financial texts). His lab collaborates on projects like Food NET, combining nutrition science with social network analysis. While no formal awards are listed here, his prolific publication record reflects sustained innovation in data-driven methodologies.

Raul A. Urrutiaمشاهده پروفایل

استاد

• Genomics
• Epigenomics
• Precision Medicine
• +۴ مورد دیگر

Raul A. Urrutia, MD is a Professor in the Department of Surgery & Biochemistry and the Director of the Linda T. and John A. Mellowes Center for Genomic Sciences and Precision Medicine at the Medical College of Wisconsin. He holds the Warren P. Knowles Endowed Chair of Genomics and Precision Medicine and directs the Pancreas Cancer Program. His research focuses on genomics, epigenomics, and precision medicine, particularly in pancreatic diseases such as cancer and diabetes. He has discovered key tumor suppressor genes and epigenetic pathways operational in pancreatic cancer and other diseases. His lab employs a multidisciplinary team of biochemists, geneticists, and bioinformaticians to advance precision medicine. Education: MD, University of Cordoba Postdoctoral Fellowship, National Institute on Deafness and Other Communication Disorders, NIH Research Interests: Dr. Urrutia’s work integrates genomic and epigenomic approaches to uncover mechanisms underlying pancreatic cancer, diabetes, and other diseases. His lab has made seminal contributions to understanding KLF proteins, histone-modifying enzymes (HDACs, HATs, HMTs), and histone-protein subcodes. Recent studies focus on epigenetic regulators in cancer progression and therapy resistance, mitochondrial genomic variants in transplantation outcomes, and precision medicine simulation models. Publications Trends: Recent articles emphasize targeting epigenetic regulators (e.g., CBX5, EZH2), mitochondrial genomics in hematopoietic transplantation, and systems biology approaches combining germline/somatic mutations for tumor analysis. The work spans disciplines from molecular biology to clinical translation. Awards: Warren P. Knowles Endowed Chair Member, American Society of Clinical Investigation Advising & Grants: Dr. Urrutia has mentored over 50 investigators. His lab is funded by NCI grants, the CIBMTR Data Resource (U24), and philanthropic support like the Theodore W. Batterman Family Foundation. Research units include the Precision Medicine Simulation Unit and collaborations with global institutions. Labs & Teams: Leads the Urrutia Research Laboratory and the Mellowes Center, collaborating with experts in epigenetics, computational biology, and clinical genomics. Key lab members include Angela J. Mathison, PhD (Technology Director) and Gareth Pollin, PhD (Bioinformatics).

• Persuasive Communication
• Social Media Influencers
• Digital Advertising
• +۴ مورد دیگر

Dr. Sophie C. Boerman is an Associate Professor of Persuasive Communication in the Strategic Communication Group at Wageningen University & Research. She holds a PhD from the University of Amsterdam (2014) and leads research on digital persuasion mechanisms in social media environments. Her work examines how communication from influencers, user interactions, and advertisements shape beliefs, attitudes, and behaviors, with applied focus on sustainable and health decision-making. Research interests center on: Algorithmic persuasion frameworks in digital advertising Privacy calculus and surveillance perceptions in behavioral targeting Effectiveness of sponsorship disclosures across media formats Influencer marketing dynamics and regulatory compliance Behavioral interventions for sustainability and health promotion Her publications demonstrate strong focus on sponsored content disclosure effectiveness, privacy protection behaviors, influencer marketing regulations, and algorithmic persuasion across health/sustainability contexts. Recent works show increased attention to AI ethics, vulnerable populations, and cross-platform disclosure standardization. Honors include: Top 5 finalist for Women in AI Researcher Award 2023 Best Reviewer Award from International Journal of Advertising (2022) Research awards from ICA, EAA, ASCoR, and SWOCC Leadership roles: Board member: European Advertising Academy, SWOCC, GvR, Wageningen Young Academy Chair: Environmental Communication TWG of NeFCA Member: ECoLab, Cosmetic Procedures expert group, Better Personalized Ads initiative Editorial positions include Associate Editor for International Journal of Advertising (current) and former Associate Editor for Journal of Interactive Advertising (2020-2022).

Nicholas J. Provartمشاهده پروفایل

استاد

• Bioinformatics / Computational Biology
• Genetics / Genomics
• Molecular Biology
• +۳ مورد دیگر

Nicholas J. Provart is a Professor and Chair of the Department of Cell & Systems Biology at the University of Toronto. He is a founding member of the Centre for the Analysis of Genome Evolution and Function (CAGEF) and a key contributor to the Bio-Analytic Resource (BAR), which provides essential bioinformatics tools for plant research. His work focuses on integrating genomic and transcriptomic data to uncover mechanisms of plant stress biology, development, and evolution. Provart earned his Ph.D. from Freie Universität Berlin (1996), and his M.Sc. and B.Sc. from the University of Toronto (1993 and 1990, respectively). Research Interests: Provart’s lab employs systems biology approaches to analyze plant responses to abiotic and biotic stresses, particularly using cluster analysis of gene expression data. Key areas include plant stress signaling, seed development, and the functional roles of transcription factors and cytochrome P450s. His team develops tools like the BAR platform to facilitate large-scale data integration and visualization. Recent Grant: $2.5M NSERC grant (2024) to advance plant health visualization tools. Award: Recognized with the Arabidopsis community award (2025) for decades of research and outreach. Provart also serves on the Multinational Arabidopsis Steering Committee and chairs the Bioinformatics and Computational Biology Program. His lab’s work spans diverse plant species, including Arabidopsis, maize, and wheat, with a focus on translational applications of genomic data.

Emmanuel Fonsecaمشاهده پروفایل

استادیار

• Radio Astronomy
• Astrophysics
• Neutron Stars
• +۳ مورد دیگر

Emmanuel Fonseca is an Assistant Professor in the Department of Physics and Astronomy at West Virginia University (WVU), joining in Fall 2021. Previously, he was a postdoctoral researcher at McGill University (2016–2021) and completed his Ph.D. in Astronomy at the University of British Columbia (2016). His research focuses on radio astronomy, particularly pulsars and fast radio bursts (FRBs), leveraging facilities like CHIME, the Green Bank Telescope, and NANOGrav. He specializes in using pulsars as laboratories for testing fundamental physics and detecting gravitational waves via pulsar timing arrays. Education: Ph.D. in Astronomy, University of British Columbia (2016) M.Sc. in Astronomy, University of British Columbia (2012) B.Sc. in Physics and Astronomy, Pennsylvania State University (2010) Research Interests: Emmanuel’s work spans three key areas: Compact Objects: Investigating neutron stars and extreme environments using pulsar binaries and relativistic dynamics. CHIME Pulsar/FRB Science: Developing instrumentation and analyzing data from the Canadian Hydrogen Intensity Mapping Experiment to study FRBs and pulsars. Gravitational Waves: Contributing to NANOGrav’s efforts to detect nanohertz gravitational waves via millisecond pulsar timing arrays. Collaborations: He is a core member of NANOGrav and instrumental in maintaining CHIME’s pulsar and FRB backend systems. His work bridges hardware/software development with observational astronomy. Labs/Teams: Involved with the CHIME/FRB Collaboration and the NANOGrav Collaboration, advancing both observational infrastructure and theoretical astrophysics.

• Life Course Perspective
• Sociology of the Family
• Transitions to Adulthood
• +۱۲ مورد دیگر

Magda Nico is an Assistant Professor in the Department of Social Research Methods at ISCTE - University Institute of Lisbon and an Integrated Researcher at CIES-Iscte - Center for Research and Studies in Sociology. She holds a PhD in Sociology from ISCTE-IUL and has a strong background in both quantitative and qualitative research methods. Her academic work is deeply rooted in the life course perspective, focusing on youth, family, social inequalities, and biographical research. PhD in Sociology, ISCTE-IUL (2011) Postgraduate in Data Analysis in Social Sciences, ISCTE-IUL (2008) Master’s in Family and Society, ISCTE-IUL (2006) Bachelor’s in Sociology, Faculty of Social Sciences and Humanities (2001) Her research interests include the sociology of the family, transitions to adulthood, longitudinal methods, and social mobility. She critically examines the interplay between structure and agency in shaping life trajectories, especially among young people. She has published extensively in high-impact journals and with leading academic publishers, contributing to debates on de-standardization, reflexivity, and biographical research. The analysis of her recent publications reveals a consistent focus on life course dynamics, biographical methods, and social inequalities. Her work combines empirical rigor with theoretical depth, often employing mixed-methods and longitudinal designs. She frequently explores how macro-level events like the economic crisis and the pandemic affect individual life paths. CEEC Assistant Researcher (2017, FCT) Co-editor, Journal of Youth Studies Local Coordinator, COORDINATE (Horizon 2020 project) Magda Nico actively mentors and collaborates with researchers, having co-organized the 'Biographies and Trajectories' seminar series since 2017. She has led and co-led multiple funded research projects, including 'Linked Lives' and 'Biographical Echoes.' She is also a member of the Society for Longitudinal and Life Course Studies and contributes to public sociology through opinion pieces in platforms like Público and Barómetro Social. She is affiliated with CIES-Iscte, where she conducts interdisciplinary sociological research, and is involved in national and international research networks focused on youth, family, and life course studies.

• Artificial Intelligence and Data Science
• Decision Making & Optimization
• Machine Learning & Intelligence
• +۸ مورد دیگر

Min Lee is a Full-time Assistant Professor of Computer Science at Singapore Management University's School of Computing and Information Systems (SCIS). Holding a PhD from Carnegie Mellon University (2021), Lee specializes in Artificial Intelligence with a focus on Human-AI Collaborative Systems and their applications in healthcare. Their research bridges technical innovation with human-centric design, emphasizing trustworthiness, explainability, and accessibility in AI systems. Research interests include decision-making optimization, human-machine collaboration, and AI-driven solutions for healthcare challenges such as stroke rehabilitation, elderly care monitoring, and clinical decision support. Lee has pioneered low-cost AI/robotic solutions for post-stroke rehabilitation exercises, integrating socially assistive robotics with real-time feedback mechanisms. Recent work highlights AI's role in enabling trustworthy clinical decision-making through explainable AI (XAI) techniques like counterfactual explanations and gradient-based methods. Their systems address ethical concerns in predictive healthcare and advocate for stakeholder-inclusive design processes. Awards and grants: No specific awards mentioned in the profile, though research has been supported through collaborative projects with clinical partners and iterative user evaluations involving therapists and patients. Advising focuses on multidisciplinary projects spanning AI, healthcare, and human-computer interaction. Current advisees include BHOSALE Rimmon Saloman, TRAN Truong Thuy, and YANG Xinlin. Lee collaborates closely with clinical stakeholders to translate technical advancements into practical healthcare solutions, emphasizing iterative design and real-world applicability. Labs/teams: Active in SCIS's AI and Data Science initiatives, leading projects on intelligent decision support systems, socially assistive robotics for rehabilitation, and human-centered AI ethics frameworks.