جست‌وجوی استاد و پژوهشگر

Jonas Schützenederمشاهده پروفایل

استاد

• Digital Journalism
• Artificial Intelligence in Media
• Social Media and Journalism
• +۷ مورد دیگر

Prof. Dr. Jonas Schützeneder is a Professor of Digital Journalism at the University of the Bundeswehr Munich, where he serves as Deputy Chair of the Examination Board. He holds a doctorate in Sports Journalism and Sports Communication and completed his habilitation in Innovation Communication. His academic responsibilities include teaching digital journalism in the Management and Media program (BA/MA), with a focus on data journalism, social media strategies, and online formats. Schützeneder's research explores transformative trends in journalism, with core interests in: Artificial intelligence applications in newsrooms Social media's impact on journalism practices Innovation communication and media evolution Podcast formats and audio storytelling strategies Ethical dimensions of digital journalism His publications demonstrate a consistent focus on journalism's digital transformation, particularly examining how emerging technologies like AI and social platforms are reshaping content production, distribution, and ethical frameworks across European media landscapes. His scientific recognitions include: University Teaching Award from Catholic University of Eichstätt-Ingolstadt (awarded twice) Multiple nominations for Teaching Award at Magdeburg-Stendal University of Applied Sciences Schützeneder leads the podcast project 'Held:innen von Magdeburg' where students research and produce content about contemporary and historical figures. Since 2018, he has served on the editorial board of Communicatio Socialis, an academic journal on media ethics, where he manages peer review processes. Since 2024, he co-heads the DGPuK Journalism Section, a 400-member organization within the German Society for Journalism and Communication Studies.

• Political Science
• Comparative Politics
• Latin American Politics
• +۵ مورد دیگر

Hélène Combes is a CNRS Research Professor affiliated with the Center for International Studies (CERI) at Sciences Po Paris. She directs research on political parties and social movements with a specific focus on Latin America, particularly Mexico. Her work bridges comparative politics, urban studies, and political sociology, examining how spatial organization, class dynamics, and gender intersect in political mobilization. Combes specializes in the relationship between political parties and social movements, with extensive fieldwork in Mexico spanning over 15 years. Her research examines political clientelism, urban protest movements, and the spatial dimensions of political mobilization. She has conducted in-depth studies on the emergence of the Morena party in Mexico and the transformation of social movements into political power, culminating in her 2024 book De la rue à la présidence. Foyers contestataires à Mexico . Her work reveals how working-class communities manage political occupations and how family dimensions shape protest movements in Latin America. Combes has served as director of the journal Critique internationale (2017-2023), is a member of the board of the French Political Science Association (AFSP) since 2017, and serves on the scientific committee of Casa de Velázquez since 2018. Her international recognition is evident through her invited professorships at Columbia University, University of Salamanca, Pablo de Olavide University, University of Mar de Plata, University of Chile, and various Mexican institutions. Currently teaching in the Master's research program in political science, university college, and School of International Affairs at Sciences Po Paris, Combes has previously taught at Beaux-Arts de Paris and University of Dauphine. Her work on Mexican politics has gained particular relevance with Claudia Sheinbaum (whom she followed for 15 years) becoming Mexico's presidential candidate. Combes holds a doctorate in engineering with specialization in climate change and ecology, bringing a transnational perspective to her political analysis.

• Mathematics Education
• Geometry
• Pedagogy
• +۲ مورد دیگر

Michael Weiss is a Lecturer in the Department of Mathematics at the University of Michigan. He teaches foundational courses such as Linear Algebra (MATH 217) and Explorations in Euclidean Geometry (MATH 431). His office is located in East Hall (Room 1868) at 530 Church Street, Ann Arbor, MI. Current Courses: MATH 217-004 (Linear Algebra), MATH 217-011 (Linear Algebra), MATH 431-001 (Explorations in Euclidean Geometry) Weiss’s research focuses on mathematics education, particularly on pedagogical strategies for teaching geometry and mathematical reasoning. His work explores the use of visual representations, interactive technologies, and narrative tools (e.g., comics) to enhance learning and teacher development. He emphasizes the role of theory-building in secondary geometry instruction and investigates how students translate abstract mathematical concepts into concrete reasoning. His publications highlight interdisciplinary approaches to education, integrating technology (e.g., animated stories, voice assistants) with traditional teaching methods. Keywords across his work include Mathematics Education , Geometry , Interactive Media , Visual Learning , and Teacher Training . Contact: mweiss@umich.edu

Elad Hazanمشاهده پروفایل

استاد

• Machine Learning
• Mathematical Optimization
• Control Theory
• +۴ مورد دیگر

Elad Hazan is a Professor of Computer Science at Princeton University and co-founder/director of Google AI Princeton. His research focuses on algorithmic foundations of machine learning and optimization, with significant contributions to online learning, nonstochastic control, and adaptive gradient methods. Princeton University (Faculty) Google AI Princeton (Co-founder & Director) His work bridges mathematical optimization, control theory, and computational complexity. Key contributions include the AdaGrad algorithm, sublinear-time optimization methods, and spectral filtering techniques for sequence modeling. Recent research emphasizes efficient neural architectures and provable guarantees in online control. Scientific awards include the Bell Labs Prize, IBM Goldberg Best Paper Award (twice), Google Research Award (twice), European Research Council grant, Marie Curie fellowship, and ACM Fellowship. He has served as program chair for COLT 2015 and on the Association for Computational Learning steering committee. His publications highlight trends in online convex optimization, spectral methods for dynamical systems, and adaptive gradient algorithms. Collaborations span Princeton, Google Brain Research, and interdisciplinary projects in robotics and AI safety.

• Politics
• Political Sociology
• Computational Social Sciences
• +۶ مورد دیگر

Etienne Ollion is a Professor of Sociology at École Polytechnique and a Research Director at the Centre national de la recherche scientifique (CNRS). He maintains dual appointments that position him at the intersection of traditional political sociology and emerging computational methods. His academic work spans both French and international institutions, with teaching invitations at ENS-Paris, Berkeley, University of Chicago, Sciences Po Paris, and other leading universities worldwide. His research program focuses on two interconnected domains: the sociology of politics and power, and computational social sciences. Ollion's work examines political professionalization, parliamentary dynamics, and the transformation of political fields, particularly through his ethnographic study of the 2017 French National Assembly documented in his book The Candidates: Amateurs and Professionals in Politics (Oxford University Press, 2024). Simultaneously, he pioneers methodological innovations applying machine learning and natural language processing to social science research. Ollion's recent publications reveal a trajectory increasingly focused on the intersection of AI and social science methodology, with numerous 2024-2025 publications addressing LLM applications, text annotation, and the ethical considerations of proprietary AI systems in research. His work demonstrates how computational methods can enhance traditional social science approaches while maintaining critical awareness of technological limitations. As an academic leader, Ollion directs the Computational Social Sciences initiative at the IPP and has developed educational resources including online courses and the aweSOM software for data analysis. He regularly organizes summer schools (SICSS-Paris) and workshops to disseminate computational methods across the social sciences. Ollion serves on the editorial board of Actes de la recherche en sciences sociales and maintains an active public presence through media appearances, including a notable interview on France Inter about AI and Social Sciences in September 2024. His upcoming book talk at The Seminary Co-op in Chicago on March 7, 2025 further demonstrates his active engagement with the international academic community.

Geoffrey Lindsay Hermanمشاهده پروفایل

استاد آموزشی

• Computer Science Education
• Engineering Education
• Learning Theories
• +۴ مورد دیگر

Dr. Geoffrey L. Herman serves as the Severns Teaching Professor in the School of Computing and Data Science at the University of Illinois at Urbana-Champaign. He earned his Ph.D. in Electrical and Computer Engineering from UIUC as a Mavis Future Faculty Fellow and completed postdoctoral research at Purdue University's School of Engineering Education. His research focuses on understanding how students learn engineering and computing concepts and developing systemic approaches to improve teaching methods in higher education. With over $7 million in research funding and more than 120 peer-reviewed publications, his work spans educational technology, cognitive aspects of learning, and faculty development initiatives. His recent publications demonstrate a consistent focus on evidence-based instructional practices, assessment methodologies, and collaborative learning approaches, with particular emphasis on computer science education, proof writing tools like Proof Blocks, and mastery learning techniques. These works collectively advance the understanding of effective educational strategies in STEM fields. IEEE Education Society Mac Van Valkenburg Early Career Teaching Award Scott H. Fisher Computer Science Teaching Award Best paper award in the first 50 years of the ACM Special Interest Group, Computer Science Education As a mentor, Dr. Herman guides graduate students interested in engineering and computing education research, focusing on designing better instruction. He also works with undergraduates on improving educational experiences through platforms like PrairieLearn. His leadership extends to founding the Grainger College of Engineering's Strategic Instructional Innovations Program, which has secured millions in external funding. He serves on the Computer Research Association Education committee and as associate editor for the Journal of Engineering Education. Dr. Herman leads national workshops on professional development for computer science teaching faculty and has created peer mentoring networks through the Teaching Professionals Program, significantly impacting faculty development in engineering education.

• Semiconductor Materials
• Laser Crystallization
• Phase Transformations
• +۴ مورد دیگر

Professor James Im serves as Professor of Materials Science in the Departments of Earth and Environmental Engineering and Applied Physics and Applied Mathematics at Columbia University, with an office at 1106 S.W. Mudd (Mail Code 4701). His academic career spans over three decades at Columbia, where he progressed from Assistant Professor (1991-1994) to Associate Professor (1995-2002), and ultimately to full Professor (2002-present), including a tenure as Chair of the Materials Science and Engineering Program (2002-2014). His educational background includes a B.S. with Distinction in Materials Science from Cornell University (1984) and a Ph.D. in Electronic Materials from MIT (1989), followed by postdoctoral research at Caltech (1989-1991). Cornell University: B.S. Materials Science (1984) MIT: Ph.D. Electronic Materials (1989) Caltech: Postdoctoral Scholar (1989-1991) Im's research centers on ultra-rapid phase transitions in beam-irradiated thin films, specifically focusing on laser crystallization of silicon films , energy-beam-induced melting and solidification , and nucleation in discontinuous phase transitions . His work employs experimental, computational, and theoretical approaches to develop innovative semiconductor materials for advanced displays, solar cells, and integrated circuits. Notably, his invention of Sequential Lateral Solidification (SLS) technology has been licensed to major display manufacturers (Samsung, LG, Sharp) and implemented in products by Apple, Blackberry, and Nokia. Current research focuses on advancing the Spot-Beam Crystallization (SBC) platform using fiber lasers for next-generation microelectronics. His publication record spans environmental aerosol studies (2019-2024), oilfield operations technology (2002-2014), and foundational atmospheric research (1980s), reflecting interdisciplinary expertise bridging materials science, environmental engineering, and petroleum technology. The most recent works emphasize low-cost sensor development and aerosol monitoring. Professional recognition includes membership in prestigious societies: Bohmisch Physical Society Sigma Xi Alpha Sigma Mu Materials Research Society American Physical Society Im's research group maintains strong industry connections through technology licensing and collaborative projects, particularly in display manufacturing. His leadership as former department chair demonstrates administrative commitment alongside scientific innovation. The laboratory leverages state-of-the-art laser systems and beam delivery optics for materials development, with recent focus shifting toward environmental monitoring applications while maintaining core semiconductor research.

Ronny Schererمشاهده پروفایل

استاد

• Meta-analysis
• Educational Measurement
• Digital Divides
• +۶ مورد دیگر

Ronny Scherer is Center Director and Professor at CEMO (Center for Educational Measurement) and Deputy Director at CREATE (Center for Research on Equality in Education) at the University of Oslo's Faculty of Educational Sciences. His work bridges educational measurement, assessment, and evaluation with a focus on research syntheses and complex sampling surveys. Dr. Scherer's research spans two interconnected domains: substantive areas including digital divides, equity and equality in education, and measurement of complex cognitive skills (such as complex problem solving, adaptability, computational thinking, and executive functioning); and methodological areas focusing on advanced meta-analytic techniques, multilevel structural equation modeling, and spatial analysis of complex survey data. His work frequently utilizes international large-scale assessment data from PISA, ICILS, TIMSS, PIRLS, PIAAC, and TALIS. His publication record demonstrates a clear trajectory toward increasingly sophisticated meta-analytic approaches, with recent work focusing on second-order meta-analyses, AI-assisted screening methods, and advanced techniques for handling complex survey data. His research consistently addresses critical educational challenges related to equity, digital literacy, and measurement of 21st century skills. Dr. Scherer has secured significant research funding for projects including ARISE (Academic resilience in mathematics and science among vulnerable students), DiDiRes (Digital inequalities in education), and ADAPT21 (Educational assessments of the 21st century: Measuring and understanding students' adaptability in complex problem solving situations). Co-director of CREATE (Centre for Research on Equality in Education) since 2023 Professor of Educational Assessment and Measurement at CEMO since 2019 Extensive experience with international large-scale assessments including ICILS, TALIS, and PIAAC As an educator, Dr. Scherer teaches advanced courses in measurement models, multilevel models, meta-analysis, and equity in education. He actively supervises graduate students interested in his research areas and has developed numerous workshops on structural equation modeling and meta-analytic methods for international audiences.

Eldan Cohenمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Optimization
• +۶ مورد دیگر

Eldan Cohen serves as an Assistant Professor of Industrial Engineering within the Department of Mechanical & Industrial Engineering at the University of Toronto's Faculty of Applied Science and Engineering. His academic journey includes a PhD from the same department followed by a postdoctoral fellowship in Computer Science at the University of Toronto and the Vector Institute for Artificial Intelligence. His educational background is detailed as follows: PhD in Mechanical & Industrial Engineering, University of Toronto Postdoctoral Fellowship in Computer Science, University of Toronto and Vector Institute for Artificial Intelligence Dr. Cohen's research centers on machine learning, deep learning, heuristic search, and optimization with strong emphasis on interpretable and human-compatible AI systems. His work bridges theoretical advancements with practical applications in healthcare (e.g., patient-physician interaction analysis, surgical safety diagnostics), automated planning, natural language processing, and software engineering. Recent projects develop interpretable clustering methods for medical data and optimization techniques for constrained sequence generation. Analysis of his 2023-2025 publications reveals a concentrated focus on healthcare AI applications, particularly using large language models for clinical text analysis and diagnostic support systems. Significant work also addresses interpretable machine learning for medical imaging, diverse plan selection in optimization, and constrained sequence generation in domains like vehicle routing. No major scientific awards or fellowships are documented in the available information. As an academic advisor, Dr. Cohen mentors graduate students in mechanical and industrial engineering, guiding research in optimization and machine learning. His OptiMaL research group fosters collaboration between computer science and industrial engineering to solve real-world decision-making challenges through human-centered AI approaches. The Optimization and Machine Learning (OptiMaL) research group, led by Dr. Cohen, serves as the primary hub for developing scalable, interpretable AI solutions for complex healthcare, planning, and engineering problems, with active projects in medical diagnostics and automated planning systems.

• Artificial Intelligence
• Semantic Web
• Ontology Engineering
• +۲ مورد دیگر

Asunción Gómez Pérez is a Spanish computer scientist and Full Professor at the Technical University of Madrid (UPM) . She currently serves as Vice-Rector for Research, Innovation and Doctoral Studies at UPM and holds a seat at the Real Academia Española . She has authored over 300 publications and accumulated 20,000 citations. Education : PhD in Computer Science (UPM, 1993), MBA (Comillas Pontifical University) Leadership Roles : Director of the Department of Artificial Intelligence (2008–2016), Academic Director of AI Master’s/PhD programs (2009–2016), Executive Director of UPM’s Artificial Intelligence Lab (1995–1998) Her research focuses on Semantic Web and Ontology Engineering , with applications in knowledge representation, machine-machine communication, and multilingual data integration. She pioneered methods for ontology validation, metadata licensing, and AI-driven social inclusion. Key publication trends include: Ontology evaluation frameworks (e.g., OOPS!) Linked Data quality models and validation tools Multilingual and cross-lingual AI applications Interoperability solutions for smart cities and healthcare Machine Learning for social exclusion prediction Ontology-driven library and lexicography systems Scientific Awards Fellow of the European Academy of Sciences Ada Byron Prize She has led projects like the NeOn Methodology for ontology development and contributed to the European framework for linked data rights (LD Terms). Her work bridges theoretical research with practical implementations in AI and Semantic Technologies.

Jessie P. Buckleyمشاهده پروفایل

دانشیار

• Environmental Toxicants
• Exposure Assessment
• Exposure Mixtures
• +۳ مورد دیگر

Jessie P. Buckley, PhD, MPH is an Associate Professor in the Department of Epidemiology at the University of North Carolina Gillings School of Global Public Health. She serves as Director of Chemical Exposure Methodology for the NIH Environmental influences on Child Health Outcomes (ECHO) Program, focusing on data harmonization and pooled analyses of chemical exposures in children's health. Education: PhD in Epidemiology (UNC Chapel Hill, 2014) MPH in Environmental and Occupational Health (George Washington University, 2007) AB in Biology and English (Bowdoin College, 2002) Her research investigates environmental toxicants' health effects, particularly: Exposure assessment of environmental chemicals Methods for estimating effects of exposure mixtures Environmental influences on cardiometabolic and bone health Endocrine-disrupting chemicals Children's environmental health Recent publications analyze: PFAS exposure trends in adolescent bone health Chemical mixture effects using item response theory Perinatal exposure to melamine analogues Diet-exposure interactions in ultra-processed foods Scientific Awards: Teaching Innovation Award (UNC Gillings, 2025) NIEHS ONES Award (2019) Pediatric Loan Repayment Program Awards (2020, 2022, 2023) Dr. Buckley contributes to academic service as: Member of the PhD Admissions Committee (2024-present) Co-Chair of the North America Chapter, International Society of Environmental Epidemiology (2023-present)

Praveen K. Kopalleمشاهده پروفایل

استاد

• Marketing
• Machine Learning
• Artificial Intelligence
• +۱۰ مورد دیگر

Praveen K. Kopalle is the Signal Companies' Professor of Management, professor of marketing, and area chair at the Tuck School of Business, Dartmouth College. He served as associate dean for the MBA program (2015–18) and chairs the marketing area (2012–15, 2022–present). He teaches courses in retail pricing analytics, marketing concepts, and new product development. Education: PhD in Marketing (Columbia University, 1992), MBA (Indian Institute of Management, 1988), BE in Mechanical & Product Engineering (Osmania University, 1986) His research focuses on marketing , machine learning , artificial intelligence , pricing strategy , retail analytics , and new product innovation . Recent work addresses digital customer orientation, loyalty programs in the big data era, and predictive analytics in retailing. He has published in top journals like Journal of Consumer Research , Management Science , and Marketing Science . Praveen serves as departmental editor for Production and Operations Management and associate editor for multiple journals. He has held leadership roles in the INFORMS Society for Marketing Science and contributed to editorial boards of 10+ journals. Scientific Awards: 2018 Lifetime Achievement Award (American Marketing Association’s Retailing and Pricing Special Interest Group) 2015 Core Teaching Excellence Award (Tuck School) 2011 Distinguished Alumni Award (IIM Bangalore) Multiple John Little and Davidson Awards 2022 Distinguished Alumnus Award (Osmania University) His teaching spans MBA core courses ( Marketing , Statistics for Managers ) and electives ( Retail Pricing Strategy , Marketing New Products ). He also leads executive education programs and the Tuck Integrative Experiential Learning (TuckINTEL) initiative.

Shushanik Margaryanمشاهده پروفایل

پژوهشگر ارشد

• Health Economics
• Education Economics
• Labour Economics
• +۲ مورد دیگر

Dr. Shushanik Margaryan is a Research Fellow at the University of Potsdam's Department of Economics, affiliated with the Berlin School of Economics and IZA. She holds a Ph.D. from the University of Hamburg and specializes in health, education, and labour economics with a focus on applied microeconometrics. Her work addresses societal challenges such as climate policy co-benefits, educational interventions, and healthcare disparities. Education: Ph.D. in Economics, Universität Hamburg (2016-2021) M.Sc. in Economics, Universität Hamburg (2014-2016) B.A. in Labour Economics, Armenian State University of Economics (2009-2013) Research Interests: Her research spans climate policy impacts, educational equity, and labour market dynamics. Notable projects include evaluating low-emission zones' health benefits and the effects of randomized educational interventions on disadvantaged children. Key Contributions: Recent work examines spillover effects of war on adolescents and replicates studies on long-term vaccination impacts. She co-leads a DFG-funded project on labour market transformations. Awards: HCHE Young Researcher Award (2021) Joachim Herz Fellowship (2024-2026) Teaching & Service: Teaches graduate and undergraduate courses on labour/education economics. Organizes seminars like the Potsdam Research Seminar in Economics and serves on hiring committees.

Arjun (Raj) Manraiمشاهده پروفایل

استادیار

• Clinical Genomics
• Machine Learning in Healthcare
• Algorithmic Bias in Medicine
• +۳ مورد دیگر

Arjun (Raj) Manrai is an Assistant Professor at Harvard Medical School and a faculty member in the Computational Health Informatics Program (CHIP) at Boston Children’s Hospital. He earned an A.B. in Physics (Harvard, Highest Honors) and a Ph.D. in Bioinformatics and Integrative Genomics from Harvard-MIT. His research focuses on improving medical decision-making through computational approaches in clinical genomics, algorithmic bias mitigation, and healthcare AI ethics. Key projects include race-free kidney function equations, genetic variant classification, and semi-supervised learning for medical imaging. Education: B.A. in Physics, Harvard University (Highest Honors) Ph.D. in Bioinformatics and Integrative Genomics, Harvard-MIT Division of Health Sciences and Technology Research interests span AI-driven diagnostics, health equity, and reproducibility in biomedical studies. His work has been featured in New England Journal of Medicine , JAMA , and highlighted by the New York Times and NPR. Notable contributions include advancing race-free diagnostic algorithms, addressing biases in clinical genomics, and advocating for transparent AI systems in healthcare. The Manrai Lab collaborates widely to translate computational methods into clinical practice.

Resat Kasabaمشاهده پروفایل

استاد

• Middle Eastern History
• Political Science
• Sociology
• +۳ مورد دیگر

Reşat Kasaba is a Professor at the Henry M. Jackson School of International Studies, University of Washington, holding the Anne H.H. and Kenneth B. Pyle Chair in American Foreign Policy. He has taught for over 30 years, specializing in Middle Eastern history, U.S. foreign policy, and socio-political dynamics. His research focuses on migration, state-society relations, urbanization, and Turkey's modern political trajectory. Kasaba currently directs the Near and Middle Eastern Studies Ph.D. program and previously served as Jackson School director (2010-2020). Education: PhD in Sociology (SUNY Binghamton, 1986), MA Sociology (SUNY Binghamton, 1978), BSc Economics/Statistics (Middle East Technical University, 1976) Award: UW Distinguished Teaching Award Research examines transnational migration patterns, Ottoman legacy in modern Turkey, and socio-economic transformations. His recent work analyzes urban-rural divides in Turkey and U.S. foreign policy impacts. He actively engages in global academic networks as former APSIA president and MESA board member.