جست‌وجوی استاد و پژوهشگر

• Formal Methods
• Software Engineering
• Software Verification and Validation
• +۷ مورد دیگر

Carlo Alberto Furia is an Associate Professor and Vice Dean at the Faculty of Informatics, Università della Svizzera italiana (USI). He is affiliated with the Software Institute, where he leads the ATOM research group. His academic journey includes prior roles as an Associate Professor at Chalmers University of Technology and a Senior Researcher at ETH Zurich’s Chair of Software Engineering. PhD in Computer Science, Politecnico di Milano Master of Science in Computer Science, University of Illinois at Chicago Laurea in Computer Science and Engineering, Politecnico di Milano His research centers on formal methods for software engineering, aiming to enhance software correctness, reliability, and quality through rigorous techniques. Key areas include automated program verification, contract-based development, loop invariant inference, and empirical evaluation using Bayesian data analysis. He emphasizes practical applicability and automation in formal methods. His recent publications reflect a strong focus on program analysis at the bytecode level, multilingual software analysis, automated repair of Android security issues, and empirical methodologies. These works span topics such as JVM substitutability, exception behavior in Java bytecode, and information flow security, demonstrating a consistent thread in improving software robustness through formal and automated techniques. He is actively involved in the software engineering research community as an Associate Editor of the Empirical Software Engineering (EMSE) journal and as a Program Committee member for major conferences including FASE, FM, ASE, ICSE, and CauSE. Carlo Furia has advised multiple research projects and supervised student theses. He has led and contributed to funded research initiatives, particularly in program analysis and verification. His group has developed tools such as AutoProof and other software artifacts available through the ATOM software page. He regularly teaches courses such as Software Analysis, Programming Fundamentals, and Software Design & Modeling. He leads the ATOM research group, which focuses on advancing automated techniques for software testing, analysis, and verification. The group develops practical tools and conducts empirical studies to validate research outcomes.

Brandon M. Stewartمشاهده پروفایل

دانشیار

• Quantitative Methods
• Statistical Methods
• Text Analysis
• +۵ مورد دیگر

Brandon M. Stewart is an Associate Professor of Sociology at Princeton University with extensive interdisciplinary affiliations. He serves as Director of the Statistics Core at the Office of Population Research and maintains formal connections with the Politics Department, Princeton Institute for Computational Science and Engineering, Center for Information Technology Policy, and Center for the Digital Humanities. Stewart holds editorial leadership as Co-Editor-in-Chief of Political Analysis and Associate Editor at Sociological Methods & Research . His educational background includes: Ph.D. in Government from Harvard University (2015) Master's degree in Statistics from Harvard University (2014) Stewart's research pioneers innovative quantitative methods for social science applications, specializing in automated text analysis and modeling complex heterogeneity in regression. His methodological frameworks enable researchers to uncover hidden structures in large datasets that were previously too costly or impossible to analyze. While his recent work has focused on using newspaper archives to study propaganda mechanisms in contemporary China, his tools are deliberately designed for broad applicability across diverse domains including education, human trafficking, forced migration, international relations, constitutional law, and psychology. His publication record demonstrates consistent innovation at the intersection of statistics, machine learning, and social inquiry. Stewart's work shows a clear trajectory from foundational methodological development to practical implementation across numerous substantive areas, with recurring themes of enhancing causal inference with textual data, developing robust topic modeling techniques, and creating accessible computational tools for social scientists. Stewart's scholarly excellence has been recognized through multiple prestigious awards: 2024 Leo Goodman (Early Career) Award from the Methodology Section of the American Sociological Association 2023 Emerging Scholar Award from the Political Methodology Society Edward R Chase Dissertation Prize Gosnell Prize for Excellence in Political Methodology Political Analysis Editor's Choice Award Recognition for Excellence in Mentoring Graduate Students As a mentor, Stewart has guided several successful graduate students to faculty positions at institutions including UCLA and Georgetown. His collaborative approach is evident in numerous multi-author projects spanning disciplines from political science to computational linguistics. His leadership extends to the Sociology Statistics Reading Group, which he founded to foster interdisciplinary methodological exchange, and his summer methods camp that trains social scientists in advanced quantitative techniques.

Mattia Andreolettiمشاهده پروفایل

مدرس

• Philosophy of Medicine
• Bioethics
• Clinical Ethics
• +۵ مورد دیگر

Dr. Mattia Andreoletti is a Lecturer at the Department of Health Sciences and Technology at ETH Zurich, working within the Professorship for Bioethics. His research spans philosophy of medicine and bioethics, with a focus on the ethical dimensions of AI in healthcare, dementia policy, drug regulation, and clinical reasoning. PhD from the European School of Molecular Medicine (SEMM), affiliated with the European Institute of Oncology (IEO, Milan) ORCID: 0000-0003-1880-0770 His work intersects with clinical ethics, particularly examining replicability in scientific research, evidential pluralism in drug regulation, and the ethical landscape of digital biomarkers. He contributes to courses such as Ethics in Drug Development and Ethics Workshop: The Impact of Digital Life on Society . The 15 most recent articles (2025-2023) highlight his engagement with AI ethics, dementia prevention, regulatory science, and clinical reasoning. Topics include ethical frameworks for digital biomarkers, evidential pluralism in drug approval, and philosophical foundations of rehabilitation sciences.

• Cancer Genetics
• Prostate Cancer
• Genomic Evolution
• +۵ مورد دیگر

Colin Cooper is a Professor of Cancer Genetics at the Norwich Medical School, University of East Anglia. He is also a member of the Metabolic Health and Cancer Studies research groups. His work focuses on genomic evolution, tumor microbiome dynamics, and biomarker development for prostate cancer and musculoskeletal health. Cooper’s recent research explores the interplay between cancer genetics and microbial communities, emphasizing their role in prognosis and treatment outcomes. He investigates clonal evolution in tumors, mutational signatures, and non-invasive diagnostic tools like urinary extracellular vesicles. His collaborations span genomics, microbiology, and clinical applications. 2025: Causes of evolutionary divergence in prostate cancer (Genomics, Precision Medicine) 2024: Applications of urinary extracellular vesicles... (Biomarker Development, Liquid Biopsy) 2023: Caution regarding pan-cancer microbial structure (Methodological Considerations, Microbiome Analysis) In 2022, Cooper received the European Urology Oncology SoMe Award for his contributions. His work is frequently cited and has been featured in media outlets globally, highlighting his impact on cancer and aging research.

Akihiko Nishimuraمشاهده پروفایل

استادیار

• Bayesian Statistics
• Statistical Computing
• Public Health Data Science
• +۹ مورد دیگر

Akihiko Nishimura is an Assistant Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. He holds a PhD from Duke University (2017) and MS and BS degrees from Stanford University (2011 and 2010). His research focuses on Bayesian methods, statistical computing, and public health data science, with applications in precision medicine and observational health data analytics. PhD, Duke University, 2017 MS, Stanford University, 2011 BS, Stanford University, 2010 Nishimura's research centers on developing advanced statistical and computational methodologies for real-world health data. His work emphasizes Bayesian inference, large-scale computing, and software development for reproducible research. He is particularly interested in using observational health data to improve clinical decision-making and advance precision medicine. He co-leads the Bayesian Learning and Spatio-Temporal modeling group (BLAST Group) and the inHealth/OHDSI Lab , collaborating with clinicians and data scientists across institutions. His recent publications reflect a strong trend in methodological innovation in Monte Carlo methods (e.g., Hamiltonian and Zigzag samplers), scalable Bayesian inference, and applications in pharmacovigilance, diabetes management, and infectious disease modeling. The articles span disciplines including biostatistics, computational statistics, public health, and bioinformatics, demonstrating a consistent focus on high-impact, computationally intensive problems in health data science. Nishimura actively contributes to the scientific community through methodological development and open science. He develops statistical software and shares teaching materials on GitHub, emphasizing reproducibility and performant computing. His involvement in the OHDSI community enables large-scale, multi-institutional studies that would not be feasible with single-source data. His work has been recognized through publications in top-tier journals such as the Journal of the American Statistical Association , Biometrika , and JAMA Ophthalmology , and has been picked up by numerous news outlets and social media platforms, indicating broad scientific and public impact. Nishimura teaches courses on performant statistical computing and advanced Monte Carlo methods, training the next generation of data scientists in efficient algorithm and software design. He mentors students and collaborators in statistical methodology and software development, fostering a culture of rigorous, reproducible, and impactful research.

• Political Psychology
• Public Opinion
• Political Behavior
• +۵ مورد دیگر

John G. Bullock is a Professor in the Department of Political Science at Northwestern University. His research focuses on political psychology, public opinion, and methodological issues in political science. He teaches courses including Introduction to American Politics (undergraduate lecture), Political Behavior (graduate seminar), Political Psychology (undergraduate lecture), Public Opinion and Representation in the United States (undergraduate seminar), and Quantitative Causal Inference (graduate seminar). Dr. Bullock's research interests span several interconnected domains: Political Psychology : Examining how partisanship affects factual beliefs and political decision-making Public Opinion : Studying political knowledge measurement, the influence of elite cues, and attitudes toward redistribution Methodology : Developing rigorous approaches to mediation analysis, survey design, and causal inference Political Behavior : Investigating how voters process information and respond to party cues His publication record demonstrates a consistent focus on methodological rigor, particularly in survey design and causal inference. Bullock has made significant contributions to understanding partisan bias in factual beliefs (showing such differences may be more illusory than real), the measurement of political knowledge (demonstrating how response options affect knowledge estimates), and the challenges of mediation analysis (arguing conventional approaches are fundamentally flawed). His recent work has expanded to examine leadership competence in government, particularly through his analysis of the U.S. response to the COVID-19 pandemic. Dr. Bullock maintains an active scholarly presence through his personal website and GitHub profile, where he shares academic tools including R packages and LaTeX classes. His work appears consistently in top political science journals including the American Political Science Review, Journal of Politics, and British Journal of Political Science.

Kaat Alaertsمشاهده پروفایل

استاد

• Neurorehabilitation
• Oxytocin Research
• Autism Spectrum Disorder
• +۶ مورد دیگر

Professor Kaat Alaerts is a leading researcher at KU Leuven's Faculty of Movement and Rehabilitation Sciences, where she serves as Head of the Neurorehabilitation Research Group and Professor in the Department of Rehabilitation Sciences. Her work bridges neuroscience, psychology, and rehabilitation science to develop innovative interventions for stress regulation and social-cognitive functioning, with a particular focus on autism spectrum disorder and related conditions. Her primary research interests span neurorehabilitation , oxytocin research , autism spectrum disorder , stress regulation , mindfulness and meditation , neurostimulation , and social cognition . Dr. Alaerts employs a multidisciplinary approach that combines neuroscientific, physiological, and behavioral methods to study both clinical effectiveness and underlying brain mechanisms of neuromodulatory interventions. The research output demonstrates a strong focus on exploring the therapeutic potential of oxytocin, particularly for autism spectrum disorder. Her work examines both the standalone effects of oxytocin and its synergistic effects when combined with mindfulness training or other interventions. A growing body of research also investigates the gut-brain axis in autism and the role of microbiome composition in social and stress-related difficulties. Dr. Alaerts has received notable recognition including the KAGB Clinical Medicine Award in September 2024 for her work on oxytocin administration in children with autism. Her research group has also been honored with multiple "Belgium's got talent" prizes from the Belgian College of Neuropsychopharmacology and Biological Psychiatry. As a dedicated mentor, Dr. Alaerts supervises numerous PhD students and postdoctoral researchers, including Margaux Evenepoel, Jellina Prinsen, Elise Tuerlinckx, and others who have made significant contributions to the field. Her research is supported by multiple substantial grants, including several ongoing projects running through 2028-2029 that investigate oxytocin's role in stress regulation for breast cancer survivors, autism, and other conditions. The Neuromodulation Laboratory, which Dr. Alaerts leads, focuses on three core domains: oxytocin neuropsychopharmacology, contemplative science and combinatory approaches, and neuroregulation techniques. The lab operates within a multidisciplinary network that includes the LBI - KU Leuven Brain Institute and maintains strong collaborative relationships across various research institutions.

John Hastingsمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Natural Language Processing
• +۵ مورد دیگر

Dr. John Hastings is a Professor in the Department of Computer Science at Dakota State University, part of the Beacom College of Computer & Cyber Sciences. With nearly 35 years of experience, he specializes in teaching and curriculum development in computer science, combining academic rigor with industry insights from his roles as an AI/ML engineer, team leader, and business owner. Education: Ph.D., Computer Science, University of Wyoming M.S., Computer Science, University of Wyoming B.S., Computer Science, University of Wyoming Research interests include machine learning, AI applications in natural language processing (LLMs), generative AI, computer vision, ecological/environmental AI, AI in games, and gamification in education. Recent publications focus on cybersecurity challenges, AI ethics, and insider threat detection. His work has been recognized with awards such as the AAAI’s Innovative Applications of Artificial Intelligence (IAAI) Award and an International IPM Award for Excellence related to the CARMA AI tool. Teaching emphasizes active learning and practical skills, including courses on programming, data structures, AI, and cybersecurity. He advocates for gamification in education to enhance student engagement and success.

Arpan Mehtaمشاهده پروفایل

پژوهشگر

• Neurodegenerative Diseases
• Amyotrophic Lateral Sclerosis (ALS)
• Molecular Neuroscience
• +۳ مورد دیگر

Dr. Arpan Mehta is an Independent Investigator at the MRC Protein Phosphorylation and Ubiquitylation Unit, specializing in neurodegenerative diseases with a focus on Amyotrophic Lateral Sclerosis (ALS) and frontotemporal dementia. He was awarded the 51st Sir John Halliday Croom Medal and Lectureship by the Royal College of Physicians of Edinburgh for his groundbreaking research on ALS mechanisms. His work explores the role of kinases and genetic mutations (e.g., C9ORF72) in neurodegeneration, alongside mitochondrial dysfunction and clinical trial methodologies. Research interests include molecular mechanisms underlying ALS, neuroimmunology, and improving clinical trial designs for neurodegenerative diseases. Dr. Mehta advocates for rigorous research standards, addressing reproducibility challenges, and optimizing therapeutic approaches. His recent studies investigate mitochondrial bioenergetic deficits in ALS motor neurons and the impact of cerebrospinal fluid (CSF) toxicity. Awards: Sir John Halliday Croom Medal and Lectureship (2025) His publications span clinical neurology, neurodegenerative disease mechanisms, and ethical aspects of medical practice. Dr. Mehta emphasizes interdisciplinary collaboration to advance understanding of ALS pathogenesis and translational therapies.

Anna Dreberمشاهده پروفایل

استاد

• Reproducibility Science
• Behavioral Economics
• Experimental Economics
• +۲ مورد دیگر

Anna Dreber is the Johan Björkman Professor of Economics at Stockholm School of Economics and Editor at the Journal of Political Economy Microeconomics. Her research focuses on meta-science, investigating the credibility of scientific results through replication studies and prediction markets. She has led large-scale replication projects in experimental economics, psychology, and social sciences. Additional research examines gender differences in economic preferences and hormonal influences on decision-making. She teaches Gender Economics and develops meta-science methodologies through Lab2 research hub and Institute for Replication.

Claudia Wagnerمشاهده پروفایل

استاد

• Computational Social Science
• Algorithmic Fairness
• Digital Behavioral Data
• +۷ مورد دیگر

Claudia Wagner is a full professor for Applied Computational Social Sciences at RWTH Aachen University and the Scientific Director of the Computational Social Science department at GESIS—Leibniz Institute for the Social Sciences. She is also an External Faculty member at the Complexity Science Hub Vienna. Her work bridges computer science and the social sciences to study algorithmic systems and their societal impacts. Her research focuses on socio-technical phenomena such as inequality, sexism, and perception bias in algorithmically infused societies. She investigates methodological challenges in using digital behavioral data to study human behavior, attitudes, and group dynamics. Her interests span computational social science, algorithmic fairness, network science, and AI ethics. The analysis of her recent publications reveals a strong emphasis on bias, fairness, and methodological rigor in digital data analysis. Her work spans AI psychometrics, gender inequality in online platforms, and validation frameworks for digital traces. She frequently publishes in top-tier venues such as Nature , Science , and AAAI conferences. DOC-fFORTE fellowship from the Austrian Academy of Sciences Four best paper awards at international conferences (ICWSM, CSCW, WWW, AAAI) Associate Editor, EPJ Data Science Steering Committee Member, International AAAI Conference on Web and Social Media Board Member, International Society for Computational Social Science Claudia Wagner has led and co-led substantial research projects funded by national and international agencies. She mentors a diverse group of PhD students working on topics like algorithmic bias, data quality, and dehumanization. She has organized training events such as the CSS Methods Summer School and delivered keynotes globally on inequality and computational social science. She leads the Computational Social Science department at GESIS and collaborates with interdisciplinary teams at RWTH Aachen and the Complexity Science Hub. Her group develops tools for measuring algorithmic impacts and visualizing disparities in socio-technical systems, such as the 'Planets of Disparity' dashboard.

Joel Greenhouseمشاهده پروفایل

استاد

• Statistical Methodology
• Biostatistics
• Meta-Analysis
• +۴ مورد دیگر

Joel Greenhouse is a Professor of Statistics at Carnegie Mellon University (CMU), affiliated with the Department of Statistics & Data Science. He has been on the faculty since 1983 and held leadership roles, including serving as Associate Dean of the College of Humanities and Social Sciences from 1997 to 2002. He also holds an adjunct appointment as Professor of Epidemiology and Psychiatry at the University of Pittsburgh. His expertise spans statistical methodology, clinical trial design, and meta-analysis, with a focus on integrating data from multiple sources to address complex healthcare and public health challenges. Greenhouse earned his Ph.D. in Biostatistics from the University of Michigan and completed a postdoctoral fellowship at CMU. His research emphasizes developing statistical tools for observational studies, clinical trials, and meta-analytic frameworks, particularly in neurology, mental health, and public policy contexts. Notable contributions include analyzing the impact of media on youth suicide rates, improving aphasia classification through automated speech analysis, and evaluating highway safety through driver health data. Education: Ph.D. in Biostatistics, University of Michigan Affiliations: Adjunct Professor at University of Pittsburgh, Member of National Academy of Sciences’ committees Professional Service: Data and safety monitoring boards for NIH/VA studies, co-chair of Federal Motor Carrier Safety Administration review panels His awards include CMU’s Doherty Award for Education, Ryan Teaching Award, and E. Dunlop Smith Award for teaching excellence. His work bridges theoretical statistics with real-world applications, particularly in interdisciplinary collaborations across medicine, psychology, and public policy. Greenhouse’s recent articles highlight trends in leveraging large datasets for clinical insights (e.g., aphasiaBank), re-evaluating environmental and behavioral health associations, and advancing causal inference methods. His interdisciplinary approach ensures statistical rigor addresses societal challenges, from suicide prevention to highway safety.

• Neuroscience
• Machine Learning
• Neuroimaging
• +۴ مورد دیگر

Dustin Scheinost is an Associate Professor at Yale School of Medicine, affiliated with the Department of Radiology & Biomedical Imaging, Yale Child Study Center, Department of Statistics, and Yale Biomedical Imaging Institute. His research focuses on connectomics , machine learning , and neuroinformatics through the Multi-modal Imaging, Neuroinformatics, & Data Science (MINDS) Lab. Radiology & Biomedical Imaging (Primary) Child Study Center (Secondary) Statistics (Secondary) Wu Tsai Institute Yale Stress Center Research Interests include developing novel statistical and machine learning methods for functional connectivity in big neuroscience data, leading the BioImage Suite Web (BISWeb) platform, and advancing early life neuroimaging through the Fetal, Infant, Toddler Neuroimaging Group (FIT’NG). His work is supported by grants from NIMH, NIAA, NIDA, and NHLBI. Selected Scientific Contributions span functional connectivity in laterality preferences, anti-racist AI governance in psychiatry, self-citation trends in neuroscience, and predictive modeling of mood disorders. He collaborates extensively with Todd Constable and others on multimodal neuroimaging studies.

Miklós Korenمشاهده پروفایل

استاد

• International Trade
• Economic Development
• Productivity Analysis
• +۵ مورد دیگر

Miklós Koren is a Professor of Economics at Central European University and Senior Research Fellow at the HUN-REN Centre for Economic and Regional Studies. His work bridges international trade , economic development , and managerial economics , focusing on trade policy, productivity spillovers, and the role of managers in development. Ph.D., Harvard University (2005) M.A., Central European University (2000) M.Sc., Budapest University of Economics (1999) His research explores trade facilitation , managerial impact on firm performance , and technological diversification . Recent work includes studies on expatriate managers, pandemic-related business disruptions, and the legacy of communist-era management practices. Key trends in his publications (2020–2024) emphasize managerial mobility and firm productivity (2024) machine learning vs. gravity models (2024) trade volatility and development (2023) data transparency standards (2022) Scientific awards include ERC Starting Grant (2012) Nicholas Káldor Prize (2014) Young Economist Award (2002, 2004) As Data Editor for Review of Economic Studies and Associate Editor for Journal of International Economics , he shapes methodological rigor in empirical research. His 2013 paper on technological diversification remains foundational for understanding volatility in developing economies.

• Computational Sociology
• Gender Studies
• Social Movements
• +۴ مورد دیگر

Laura K. Nelson is an Associate Professor of Sociology at the University of British Columbia , where she also directs the Centre for Computational Social Science . Her work bridges computational methods with sociological inquiry, focusing on gender inequality, social movements, and organizational dynamics. She previously held faculty roles at Northeastern University and affiliated with institutions like the NULab for Texts, Maps, and Networks and the Network Science Institute . Education: PhD in Sociology (2014), University of California, Berkeley MA in Sociology (2009), University of California, Berkeley BA in Sociology (2006), University of Wisconsin-Madison (Phi Beta Kappa) Research Interests span computational sociology, social movement strategy, intersectionality, and STEM equity. She pioneered frameworks like computational grounded theory and radical objectivity , integrating machine learning with qualitative paradigms. Recent publications analyze gender dynamics in emergency medicine, feminist movement histories, and the NSF ADVANCE program’s impact on equity. Her 2024 Social Science Quarterly paper quantifies ADVANCE’s interdisciplinary reach. Awards include the 2020 Best Meta-Reviewer at SocInfo20 and Outstanding Faculty of the Year at Northeastern University. She serves on editorial boards for American Journal of Sociology , Poetics , and Acta Sociologica . She co-PIs a National Science Foundation grant studying gender-equity dissemination in higher education networks and supervises graduate student Jinyang Yu . Her lab, Centre for Computational Social Science , drives open-source methodological innovation.