جست‌وجوی استاد و پژوهشگر

Stephen Ramseyمشاهده پروفایل

دانشیار

• Computational Systems Biology
• Bioinformatics
• Precision Medicine
• +۶ مورد دیگر

Stephen Ramsey, an Associate Professor at Oregon State University, holds dual appointments in the School of Electrical Engineering and Computer Science (College of Engineering) and the Department of Biomedical Sciences (Carlson College of Veterinary Medicine). With a PhD in Physics from the University of Maryland, his postdoctoral training in computational genomics at the University of Washington, and professional experience at the Institute for Systems Biology and Center for Infectious Disease Research, Ramsey bridges computational methods with biomedical applications. Education : Ph.D., Physics, University of Maryland; M.S., Physics, University of Maryland; Sc.B., Mathematical Physics, Brown University Ramsey specializes in computational systems biology , focusing on bioinformatics , biomedical knowledge graphs , and precision medicine . His research integrates machine learning , gene regulatory network modeling , and multi-omics data analysis to address challenges in rare disease diagnostics , drug monitoring , and inflammatory disease mechanisms . Current work includes AI-driven biomedical translation and electrochemical biosensor development for non-invasive diagnostics . Recent publications highlight knowledge graph applications in translational biomedicine , causal network inference in clinical-environmental data integration , and cross-species cancer transcriptomics . His team develops tools like RTX-KG2 and PloverDB to standardize biomedical data sharing and semantic reasoning . Scientific Awards : 2019 Zoetis Award (Carlson College of Veterinary Medicine) 2016 NSF CAREER Award 2016 PhRMA New Investigator Award 2010 NIH K25 Mentored Quantitative Research Award Ramsey advises in computational biology courses (CS 446/546) and contributes to biomedical AI through projects like mediKanren for rare disease diagnostics . His NSF-funded research explores gene expression noise and regulatory network dynamics , while NIH and PhRMA grants support his translational medicine initiatives. He leads the Ramsey Laboratory , which develops graph-based reasoning tools for biomedical data translation and multi-omics integration . The lab's work spans comparative oncology models, electrochemical biosensors , and knowledge graph infrastructure for clinical decision support .

Marc Ratkovicمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Social Science Methodology
• +۳ مورد دیگر

Professor Marc Ratkovic leads the Chair of Social Data Science at the University of Mannheim's Faculty of Social Sciences. His work focuses on integrating machine learning and large language models (LLMs) into social science methodology, particularly for statistical and causal inference. Recent initiatives include organizing an AI hackathon with Springer Nature to explore AI's role in scientific writing and presenting at the 2025 University:Future Festival on GPT's impact on academic clarity. He collaborates with Thomas Gschwend and oversees a team advancing quantitative social science through server systems and master's curricula. Research: Prioritizes computational methods in political science and interdisciplinary applications Events: Organized international hackathons and participated in global academic festivals His team includes research assistants like Jacob Chen, who focuses on LLMs for scientific writing quality. No formal awards listed, but active in shaping AI-driven research practices. Secretariat contact: sekretariat.ratkovic@uni-mannheim.de

• Computational Neuroscience
• Machine Learning
• Neural Dynamics
• +۵ مورد دیگر

Dr. Emili Balaguer-Ballester is an Associate Professor in Computational Neuroscience at Bournemouth University, UK. He co-champions the Interdisciplinary Neuroscience Research Centre and serves as Senior Fellow of the Higher Education Academy. His research spans computational neuroscience, machine learning, and virtual reality applications, with collaborations across Europe (University of Barcelona, Heidelberg University, IDIBAPS, Polyra) and North America (Indiana Purdue University, University of British Columbia). PhD in Physics (Cum Laude) from University of Valencia (2001) MSc in Neuroscience and Biology of Behaviour from University of Seville (2004) Research focuses on cortical network dynamics, mesoscopic auditory cortex modeling, top-down modulation in cognition, and affective computing in VR. His work combines nonlinear time series analysis with neurodynamic modeling to study spontaneous and task-related brain activity states. Recent publications demonstrate expertise in causal inference, neuroevolutionary optimization, and affective state detection using EMG/PPG sensors in VR environments. Collaborators include neuroscience labs (Sanchez-Vives, Durstewitz) and tech companies (Sony London, Emteq). PhD thesis award - Culture Institute 'Juan Gil-Albert', Spain (2001) Senior Fellow Higher Education Academy (2020) Secured major grants including Royal Society funding for cortical network modeling (2022), Human Brain Project ERC support for neuromorphic hardware (2022), and Santander Bank research travel grants (2016). Supervised 14 PhD students through projects involving data streams, VR immersion, and cortical dynamics. Labs include Balaguer Lab (GitHub), Interdisciplinary Neuroscience Research Centre, and collaborations with Human Brain Project. Teaching encompasses doctoral-level neuroscience, MSc data analytics, and undergraduate systems design courses.

Christoph Völterمشاهده پروفایل

پژوهشگر

• Comparative Cognition
• Animal Behavior
• Dog Cognition
• +۴ مورد دیگر

Christoph Völter is a Researcher and Research Group Leader at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany. He holds concurrent roles as Senior Postdoctoral Researcher at the Messerli Research Institute (Vienna) and Honorary Lecturer at the University of St Andrews (UK). His work focuses on comparative cognition, particularly in primates, dogs, and parrots. Key areas include active learning, explanation-seeking behavior, and the application of machine learning techniques such as 3D pose estimation and eye tracking. PhD in Biology from Max Planck Institute/Bielefeld University (2014) Postdoctoral experience at Max Planck Institute (2014–2016) Research emphasizes understanding how animals infer causal relationships and form abstract knowledge. Collaborations include the ManyPrimates and ManyDogs initiatives, advancing large-scale comparative studies. His studies explore canine perspective-taking, primate tool use, and the cognitive foundations of social behavior. Publications span journals like Proceedings of the Royal Society B and Animal Cognition .

• Software Security
• Software Testing
• Program Analysis
• +۲ مورد دیگر

Dr. Seongmin Lee is a researcher at the Max Planck Institute for Security and Privacy, specializing in software security and program analysis. Their work bridges theoretical and practical aspects of software testing, with a particular focus on automated testing techniques, dependency modeling, and genetic improvement. Research interests include: Software Security Software Testing and Fuzzing Program Analysis and Slicing Machine Learning Applications in Software Engineering Genetic Algorithms for Code Optimization Statistical and Causal Analysis of Code Behavior Recent publications (2016–2025) demonstrate a trajectory from foundational work on GPU parameter optimization to cutting-edge research on LLM-driven regression testing. Key trends include: Statistical modeling of software behavior Machine learning for bug classification and optimization Approximate analysis techniques for scalability Advancements in greybox fuzzing and coverage prediction Application of causal inference to mutation testing

Christos Diouمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence
• Healthcare Applications
• +۳ مورد دیگر

Christos Diou is an Associate Professor of Artificial Intelligence and Machine Learning at the Department of Informatics and Telematics, Harokopio University of Athens, Greece. His academic career spans over 15 years of participation in national and international research projects, with a focus on machine learning algorithms, domain generalization, causal inference, and bias mitigation. He earned a BSc and Ph.D. in Electrical and Computer Engineering from Aristotle University of Thessaloniki. His research emphasizes the application of machine learning to healthcare, addressing challenges such as visual bias mitigation, causal effect estimation from observational data, and fairness-aware representation learning. Notable projects include REBECCA and RELEVIUM , both EU-funded, and MELIORA , targeting lifestyle interventions for breast cancer risk reduction. He has published extensively in top-tier venues like IEEE TPAMI, CVPR, and ICCV. Christos is a leading voice in AI ethics and healthcare innovation, with over 150 publications and best paper awards at IEEE Big Data Service 2023 and AIAI 2022. His work includes developing platforms like Effector for feature effects and Beam for behavior studies. He collaborates with institutions such as Karolinska Institutet and CERTH/ITI, and his students include PhD candidates Ioannis Sarridis and Aristotelis Ballas.

Franco ZAMBONELLIمشاهده پروفایل

استاد

• Pervasive Computing
• IoT (Internet of Things)
• Multiagent Systems
• +۷ مورد دیگر

Franco ZAMBONELLI is a Full Professor in the Department of Engineering Sciences and Methods at the University of Modena and Reggio Emilia. He holds positions in both the Reggio Emilia and Modena campuses, offering courses such as Software Engineering and Distributed Artificial Intelligence. His research focuses on IoT, pervasive computing, multiagent systems, and self-organization in distributed systems, with applications in smart cities, healthcare, and mobility. He leads projects like FLUIDWARE (PRIN 2017) and CONNECARE (H2020), exploring adaptive IoT systems and integrated healthcare solutions. ZAMBONELLI is an IEEE Fellow, ACM Distinguished Scientist, and member of the Academia Europaea. His work bridges theory and practice, emphasizing software engineering methodologies for IoT and agent-based systems. Education: Not explicitly detailed in provided texts. Research Grants: FLUIDWARE (2019-2022), CONNECARE (2016-2019). His research interests include causal discovery in pervasive environments, reinforcement learning for cybersecurity, and digital twin technologies. He contributes to editorial boards of journals like ACM Transactions on Autonomous and Adaptive Systems and IEEE Technology and Society Magazine. His teaching spans software engineering, distributed AI, and IoT-oriented methodologies. The Agents and Pervasive Computing Lab (agentgroup.unimore.it) is a focal point for his experimental work. Professional memberships include IEEE, ACM, and the Italian Association for Artificial Intelligence. Recent achievements include successful final reviews for CONNECARE and advancements in fluidware programming paradigms.

Sheng Liمشاهده پروفایل

استادیار مدعو

• Machine Learning
• Artificial Intelligence
• Computer Vision
• +۴ مورد دیگر

Sheng Li is an Adjunct Assistant Professor in the School of Computing at the University of Georgia (UGA). He holds roles such as Graduate Program Faculty and Courtesy Faculty in the Institute of Bioinformatics. Previously, he served as an Assistant Professor at UGA from August 2018 to July 2022. His research focuses on Machine Learning, Computer Vision, Data Mining, Natural Language Processing, Causal Inference, and User Modeling. Li earned his Ph.D. in Computer Engineering from Northeastern University in 2017, following degrees from Nanjing Institute of Post and Telecommunications in China. His work bridges theoretical advancements and practical applications, including AI solutions for healthcare (e.g., medication adherence monitoring), computer vision for wildlife tracking (fish re-identification), and causal inference methodologies. He has received notable awards like the Fred C. Davison Early Career Scholar Award (2022) and the Aharon Katzir Young Investigator Award (2020). His research is funded by grants from agencies such as the US Department of Defense and NIH, supporting projects like Knowledge-Guided Scene Graph Generation and Reasoning for Visual Understanding. Li’s publications span top venues in AI and computer science, addressing challenges in domain adaptation, trustworthy AI, and multimodal learning. He has collaborated on interdisciplinary projects, including bioinformatics and agricultural NLP. His contributions emphasize both technical innovation and societal impact.

Ben Duanمشاهده پروفایل

عضو هیئت علمی

• Reinforcement Learning
• Graph Neural Networks
• Intelligent Transportation Systems (ITS)
• +۴ مورد دیگر

Ben Duan is an Adjunct Lecturer at the Department of Data Science & AI, Faculty of Information Technology, Monash University. He holds a PhD in Big Data and Electronics from the University of Technology Sydney. Previously, he was a Research Fellow at the Hong Kong University of Science and Technology. His research focuses on reinforcement learning, graph neural networks, intelligent transportation systems (ITS), and fintech. He teaches courses such as FIT5221: Intelligent Image and Video Analysis, FIT5215: Deep Learning, and FIT5217: Statistical Data Modeling at Monash’s Suzhou campus. Dr. Duan’s work aligns with UN Sustainable Development Goals related to sustainable cities and communities (SDG 11) and industry innovation (SDG 9). His recent research explores AI-driven solutions for traffic congestion, stock trend prediction using graph neural networks, and spiking neural networks for neural activation control. He has contributed to over 28 publications and a patent with the Monash Suzhou Research Institute. He supervises PhD students interested in reinforcement learning, graph neural networks, or ITS, offering full scholarships at Monash Suzhou. Key collaborations span transportation systems, fintech, and data-driven industrial processes.

Luke Sanfordمشاهده پروفایل

استادیار

• Environmental Policy
• Political Institutions
• Remote Sensing
• +۵ مورد دیگر

Luke Sanford is an Assistant Professor of Environmental Policy and Governance at Yale School of the Environment with a secondary appointment in Political Science. His research examines how political institutions shape environmental policymaking, particularly the relationship between public preferences and policy outcomes, alongside spatial and temporal distributional impacts of natural resource decisions. His educational background includes: Ph.D. in Political Science and International Affairs from University of California San Diego M.A. in Political Science from University of California San Diego, School of Global Policy and Strategy B.A. in Politics from Whitman College Sanford specializes in integrating digital data methodologies—including satellite imagery and text analysis—with environmental policy research. His work bridges political institutions, remote sensing, machine learning, economic development, and causal inference to measure policy preferences and outcomes. He develops innovative techniques for analyzing deforestation patterns, air pollution disparities, and carbon market effectiveness. Recent publications reveal a pronounced shift toward data-intensive environmental governance research, with increasing emphasis on satellite-driven spatial analysis and machine learning applications. His 2021-2025 work demonstrates methodological innovation in causal inference using remote sensing, particularly in deforestation monitoring, air pollution justice, and jurisdictional climate initiatives like REDD+. Sanford is affiliated with The Forest School and Yale Institute for Biospheric Studies, teaching courses including ENV 821a Environmental Policymaking: From Local to Global. He will be on leave during Spring 2026 semester.

Jae Yeon Kimمشاهده پروفایل

پژوهشگر ارشد

• Public Policy
• Political Science
• Urban Policy
• +۷ مورد دیگر

Jae Yeon Kim is a Research Fellow at Harvard Kennedy School and Better Government Lab Fellow at University of Michigan's Ford School of Public Policy. In January 2026, she will join UNC-Chapel Hill as Assistant Professor of Public Policy. Her PhD in Political Science is from UC Berkeley. Dr. Kim's research examines how governments build capacity for effective policy implementation, with focus areas including: Administrative burdens in safety net programs Civic infrastructure's role in democratic governance AI applications in public service delivery Racial group formation during the War on Poverty (book project: Unseen and Uncounted ) Her methodological approach combines computational techniques with traditional political science methods, including field experiments, archival research, and machine learning. Recent publications span top journals like Nature Human Behaviour , Journal of Policy Analysis and Management , and Nature: Scientific Data . Notable recognition includes: Paul Volcker Junior Scholar Research Grant (2025) Emerging Scholar Award in Civic Engagement (2024) APSA Best Dissertation Award in Urban Politics (2022) WPSA Best Paper Award in Asian Pacific American Politics (2020) Her research is supported by Carnegie Corporation, Gates Foundation, and Russell Sage Foundation. Dr. Kim co-founded Data for Good Roundtables and serves on committees for Summer Institute in Computational Social Science, GovAI Coalition, and APSA Task Force on AI. She maintains active government/nonprofit partnerships to ensure research directly informs policy practice.

Joseph Romanoمشاهده پروفایل

استاد

• Nonparametric Statistics
• Bootstrap Methods
• Multiple Testing
• +۷ مورد دیگر

Joseph Romano is a Professor of Statistics and Economics at Stanford University, where he has served since 1986. He holds dual appointments in both the Department of Statistics and the Department of Economics. His research focuses on theoretical statistics, nonparametric methods, bootstrap techniques, multiple testing, and their applications in econometrics, climate science, genetics, and clinical trials. He has received numerous awards, including the LGBTQ+ Scientist of the Year (2021) and the Presidential Young Investigator Award (1989–1994). Education: Ph.D. in Statistics, University of California, Berkeley (1986) M.S. in Statistics, University of California, Berkeley (1983) B.A. in Statistics, Princeton University (1982), Summa Cum Laude Research interests include developing robust statistical methodologies for high-dimensional data, controlling errors in multiple testing, and applying resampling techniques to complex datasets. He has authored over 100 papers, focusing on topics like permutation tests, bootstrap methods, and econometric modeling. Notable works include advancements in fixed sequence testing and randomized inference for modern data challenges. Awards and Recognition: Fellow, Institute of Mathematical Statistics (2000) Fellow, International Association of Applied Econometrics (2020) Canadian Journal of Statistics Award (1989) Romano has advised numerous students, including doctoral candidates in statistics and economics. He has held administrative roles at Stanford, such as Associate Chair and member of faculty committees. His grants include NSF-funded projects on resampling methods, multiple testing, and econometric theory. He is a vocal advocate for LGBTQ+ visibility in academia and has contributed to initiatives like 500 Queer Scientists. Beyond academia, he enjoys music, tennis, cooking, and architecture.

• Service-Oriented Architectures
• Cloud Computing
• Data Management
• +۷ مورد دیگر

Professor Kyriazis Dimosthenis holds a faculty position at the Department of Digital Systems, University of Piraeus. He earned his diploma in Electrical & Computer Engineering from the National Technical University of Athens (2001) and a cross-disciplinary MSc in Techno-Economic Systems (2004). His academic rank is Professor specializing in service-oriented architectures with a focus on quality of service and workflow management. He has led European projects like BigDataStack, CrowdHEALTH, and CYBELE, addressing challenges in cloud computing, edge computing, and AI-driven solutions for healthcare, finance, and industrial sectors. His research emphasizes resilient service-oriented systems, AI explainability, and human-centric digital transformation. Notable contributions include frameworks for dynamic resource allocation in hybrid cloud/edge environments, AI applications for maritime safety, and data governance solutions for cross-sector integration. He coordinates initiatives such as the Future Internet Architecture Board and Cloud QoS&SLAs, driving advancements in federated data marketplaces and sustainable computing practices. His recent work explores Large Language Model (LLM) applications in financial decision-making, conversational AI for MLOps, and neurosymbolic systems for defect detection. He also investigates XAI methodologies, including VirtualXAI, which leverages GPT-generated personas for explainability assessment. His projects often bridge technical innovation with societal impact, such as the iHELP platform for holistic health records and SmartCHANGE for behavioral change strategies in youth health. Key themes in his publications include bias mitigation in machine learning, dynamic deployment prediction in hybrid cloud settings, and energy-efficient data spaces for mobility. He has contributed to standards like the H2020-funded IRMOS and 5GTANGO, emphasizing interoperability and fault-tolerant architectures. His work frequently intersects with EU policy frameworks, particularly in data governance and ethical AI implementation.

Frédéric BERTRANDمشاهده پروفایل

استاد

• Statistical Modelling
• Statistical Learning
• Artificial Intelligence
• +۵ مورد دیگر

Frédéric BERTRAND is a Full Professor at the Université de Technologie de Troyes (UTT), part of the Computer Laboratory and Digital Society (LIST3N). He previously held positions at the Université de Strasbourg and Institut de Recherche Mathématique Avancée (IRMA). His academic qualifications include a habilitation to direct research (HDR) from the Université de Strasbourg, a Ph.D. from Université Louis Pasteur, and membership in the École Normale Supérieure de Lyon. He specializes in statistical modeling, machine learning, and artificial intelligence, with applications in bioinformatics, biostatistics, and medical data analysis. Roles and Affiliations: Full Professor at UTT Director of the Mastère Spécialisé in Big Data Analytical and Decisional Aspects Responsible for the OSS ED361 SPI specialty Responsible for the PEA Impact program Member of the LIST3N laboratory Research Interests: His work focuses on statistical and machine learning methods applied to complex systems, including bioinformatics, medical data analysis, and process mining. Key areas include Bayesian networks, partial least squares regression (PLS), variable selection algorithms, and handling missing data in large-scale datasets. He has developed multiple R packages, such as selectBoost , bootPLS , and Patterns , which are widely used in statistical modeling and data analysis. Publications and Contributions: He has authored/co-authored over 38 scientific articles, 14 books, and 14 R packages. His research emphasizes methodological advancements in statistics and their practical applications in biology, healthcare, and industry. Notable contributions include developing algorithms for high-dimensional data analysis and collaborative projects on cancer genomics and proteomics. Teaching: He teaches advanced courses in statistics, mathematics, and data analysis at the graduate and postgraduate levels, including modules on uncertainty analysis and complex systems at UTT and the Université de Strasbourg.

• Data Governance
• Semantic Modeling
• Knowledge Graphs
• +۵ مورد دیگر

Zaiwen Feng is a researcher actively contributing to data governance, semantic modeling, and causal inference. His work focuses on knowledge graphs, graph-based methods, and service-oriented architectures through collaborations with institutions like the University of Queensland and universities in China. Research Focus : Graph Differential Dependencies, Entity Resolution, Causal Effect Estimation, and Ontology Alignment Methodologies : Machine Learning, Variational Autoencoders, Prompt Engineering, and Semantic Retrieval Application Areas : Biomedical Data, Property Graph Recommendation, and Process Model Repositories Key trends in his publications include automated semantic modeling , neural approaches for entity resolution , and causal inference with graph structures . He frequently collaborates with researchers like Keqing He, Wolfgang Mayer, and Selasi Kwashie across conferences such as HPCC, BIBM, and WISE.