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Natasha QuadlinView profile

Associate Professor

• Social Inequality
• Gender Studies
• Education Policy
• +3 more

Natasha Quadlin is an Associate Professor in the Department of Sociology at the University of California, Los Angeles (UCLA), and a faculty fellow at the California Center for Population Research. Her research focuses on social inequality in the U.S., particularly gender inequality, higher education access and economics, and debt's role in education. She holds advanced degrees from Indiana University (Ph.D., M.S. in Applied Statistics, M.A.) and a B.S. from Northwestern University. Key roles include associate editor for Science Advances and associate PI for Time-Sharing Experiments for the Social Sciences (TESS). She has been a Russell Sage Foundation Visiting Scholar (2022-23) and a William T. Grant Scholar (2024-29). Her work bridges sociology, statistics, and policy, emphasizing experimental methods to study social beliefs and behaviors. Research interests include: Gender disparities in education and labor markets Racial and economic stratification in education Public perceptions of social policies Notable awards include the Doris Entwisle Early Career Award (2021), Devah Pager Award (2019), and Lupia-Mutz Outstanding Publication Award (2021). Her book Who Should Pay? examines U.S. public views on college funding responsibilities. Grants and collaborations highlight her work with major foundations and institutions, including NSF support and partnerships with scholars at Harvard, Indiana University, and others. Ongoing projects address pandemic-era educational disparities, gendered wage gaps, and intersectional discrimination.

• Inverse Problems
• Applied Harmonic Analysis
• Machine Learning
• +3 more

Rima Alaifari is an Assistant Professor for Applied Mathematics at ETH Zürich, specializing in inverse problems, applied harmonic analysis, and scientific machine learning. She is an associated member of the ETH AI Center and will assume a full professorship at RWTH Aachen University in 2025. Her work focuses on stability analysis, regularization, and operator learning, with applications in phase retrieval and robustness of neural networks. Alaifari holds a Ph.D. in Mathematics from Vrije Universiteit Brussel (2014), where she studied under Prof. Ingrid Daubechies and Prof. Michel Defrise. She completed her M.Sc. in Applied and Industrial Mathematics at Johannes Kepler University, Linz (2010). Her academic career includes postdoctoral fellowships at ETH Zurich (2014–2016) and a Marie Curie-funded position (2016). Her research interests span inverse problems, phase retrieval, stability in machine learning, and operator learning. Notable projects include SNF-funded work on phase retrieval (2019) and collaborations on adversarial robustness in medical imaging (e.g., CT reconstruction). She has mentored PhD students Tandri Gauksson and Matthias Wellershoff, and postdocs Francesca Bartolucci (now at TU Delft) and Jesse Railo (Finnish Inverse Prize winner). Teaching includes courses on inverse problems, time-frequency analysis, and robustness of deep neural networks. She actively participates in international conferences, delivering plenary talks at venues like the International Conference on Computational Harmonic Analysis (2022) and ICERM (2023). Her work bridges mathematical foundations and practical applications, emphasizing stability and robustness in computational methods.

Huazheng WangView profile

Assistant Professor

• Reinforcement Learning
• Information Retrieval
• Machine Learning
• +4 more

Huazheng Wang is an Assistant Professor in the School of Electrical Engineering and Computer Science at Oregon State University. His research focuses on reinforcement learning, information retrieval, and trustworthy AI. He received his Ph.D. from the University of Virginia (2021) and B.E. from the University of Science and Technology of China (2015). He holds awards including the 2025 EECS Fabulous Teacher Recognition and SIGIR 2019 Best Paper Award. His work addresses challenges in robust reinforcement learning, adversarial attacks on bandit systems, and applications in scientific discovery. Education: Ph.D., Computer Science, University of Virginia (2021) B.E., Computer Science and Technology, University of Science and Technology of China (2015) Research interests emphasize developing efficient algorithms for reinforcement learning, multi-armed bandits, and their applications in recommendation systems, protein optimization, and security. Notable contributions include provably efficient risk-aware reinforcement learning frameworks and adversarial attack analysis on bandit systems. Recent work includes NSF-funded research on neural bandits (IIS-2403401) and publications in top venues like ICML, NeurIPS, and AAAI. His lab explores embodied LLM agents for team cooperation and federated collaborative online monitoring frameworks.

Farhat ChowdhuryView profile

Assistant Professor

• Education Policy
• Health Economics
• Gender Studies
• +3 more

Farhat Chowdhury is an Assistant Professor of Teaching in the Department of Economics at Wayne State University's College of Liberal Arts and Sciences. Their research focuses on education and health policy, particularly the impact of menstrual product access in schools on educational outcomes, alongside broader international economic and policy issues related to innovation, gender, and development. Education: Ph.D. in Economics, Bryan School of Business and Economics, University of North Carolina at Greensboro (2024) Teaching responsibilities include Principles of Microeconomics and Macroeconomics courses across multiple terms (Fall 2024 to Winter 2025). Recent publications explore gender dynamics in innovation ecosystems, AI research funding spatial patterns, and pandemic-era government policies' sectoral impacts. No scientific awards are explicitly listed in the provided information. Current roles include advising in economics education and contributing to policy-relevant research initiatives in public health and innovation sectors.

Amalia FokaView profile

Assistant Professor

• Artificial Intelligence Applications in Arts
• Computer Science for Creative Practices
• Digital Interaction
• +4 more

Amalia Foka is an Assistant Professor in Computer Science Applications for the Arts at the Department of Fine Arts & Art Sciences , School of Fine Arts , University of Ioannina , Greece. She has held academic positions at the University of Patras (2005-2013), University of Ioannina (2005-2008), and Computer Technology Institute & Press "Diophantus" (2014-2015). Her research bridges Artificial Intelligence with Digital Art , focusing on Generative AI , Social Media Mining , and Human-Computer Interaction within artistic contexts. Education: BEng in Computer Systems Engineering (1998) from the University of Manchester Institute of Science & Technology (UMIST) , UK MSc in Advanced Control (1999) from UMIST PhD in Robotics (2005) from the Department of Computer Science , University of Crete Her artistic research includes projects like Bushwalking (StyleGAN2 landscape generation), Breaking the Silence (NLP analysis of taboo topics), and The Invisible Structures of the Artworld (social media-driven network visualization). She leads the Multimedia Lab at the University of Ioannina, focusing on AI in Creative Processes and Digital Interaction methodologies.

Rozenn DahyotView profile

Professor

• Computer Vision
• Machine Learning
• Digital Signal Processing
• +4 more

Rozenn Dahyot is a Professor of Computer Science at Maynooth University within the Faculty of Science & Engineering. She previously held roles as Assistant and Associate Professor in Statistics at Trinity College Dublin (2008-2021) and Lecturer in Computer Science (2005-2008). Her research interests bridge Digital Signal Processing, Computer Vision, Machine Learning, and Statistical Analysis. She organized the European Signal Processing Conference (EUSIPCO2021) in Dublin and served as President of the Irish Pattern Recognition and Classification Society (IPRCS) from 2014-2020. Her work spans topics like semantic scene understanding, CNN compression, and medical image segmentation. Key contributions include advancements in graph-based image analysis, reinforcement learning optimization, and AI-driven systems for disaster management. Dahyot is a member of IEEE, ACM, and EURASIP, contributing to both academic and industrial collaborations.

Sonia PetroneView profile

Professor

• Bayesian Statistics
• Predictive Modeling
• Bayesian Nonparametrics
• +1 more

Dr. Sonia Petrone is a Full Professor of Statistics at Bocconi University's Department of Decision Sciences. She earned her PhD in Statistics from Bocconi University and has held academic positions at the University of Pavia and University of Insubria before joining Bocconi. Her extensive international experience includes research visits across North America, Latin America, Europe, India, and Russia. Her research specializes in Bayesian statistics, with contributions to foundational theory, predictive modeling, Bayesian nonparametrics, and stochastic processes. She currently directs the Bocconi Summer School in Advanced Statistics and Probability and previously led the PhD program in Statistics (2011-2018). Her research portfolio demonstrates consistent focus on Bayesian nonparametric methods, predictive modeling, and applications to complex data structures. Recent work explores urn processes, time series analysis, and network modeling using innovative Bayesian approaches. Awards & Honors: IMS Medallion Lecture Award (2018) ISBA Foundational Lecture Award (2016) Fellow of International Society for Bayesian Analysis Fellow of Institute of Mathematical Statistics Fellow of European Laboratory for Intelligent Systems Fellow of Bocconi Institute of Data Science She has held editorial leadership positions as Editor of Statistical Science (2020-2022) and Bayesian Analysis (2010-2014), and served as President of the International Society for Bayesian Analysis (2014).

• Computational Design
• Architectural Education
• Artificial Intelligence in Architecture
• +9 more

Assoc. Prof. Dr. Sema Alaçam Doğan has been affiliated with Istanbul Technical University since 2014, serving as an Associate Professor in the Department of Architecture . She has held administrative roles including Deputy Head of Department and Erasmus Coordinator. Education : PhD in Informatics in Architectural Design (2008-2014), MS in Informatics in Architectural Design (2005-2008), and BS in Architecture (1999-2005) from Istanbul Technical University. Her research explores Computational Design , Artificial Intelligence in Architecture , and Sustainable Material Innovation . She investigates digital tools for heritage preservation, daylight optimization in BIM, and cognitive development in architecture students. Recent publications analyze AI-assisted design literacy , machine learning for Sinan mosques , and environmental comfort in Harran houses . Her work integrates algorithmic frameworks with sustainable practices. Scientific awards include multiple ITU Publication and Performance Awards (2021-2024), FABFEST Prizes , and the 2024 Artemis Educator Award from NASA. Active projects like "Physical Computation in Architectural Drawing" and "Robotic Fabrication with Recycled Wind Turbine Blades" demonstrate her leadership in computational and sustainable research.

• Health services for older people
• Collaborative models of care
• Frailty
• +4 more

Janani Thillainadesan is an NHMRC Senior Research Fellow at the Sydney Medical School/Concord Clinical School and the Centre for Education and Research on Ageing, affiliated with the Faculty of Medicine and Health at the University of Sydney. She is also a member of the Charles Perkins Centre. Her research focuses on optimizing healthcare delivery for older adults, particularly in geriatric surgery, perioperative care, and dementia interventions. Key research areas include frailty assessment, deprescribing strategies, collaborative care models, and leveraging technology (e.g., mobile health) in geriatric medicine. She has contributed to multidisciplinary studies involving clinical datasets, quality indicators, and mixed-methods analyses of patient experiences and care expectations. Awards: 2023 RACP Vincent Fairfax Research Fellowship 2021 ANZSGM Career Investigator Prize 2021 UK Age Anaesthesia Association Best Oral Presentation Prize 2019 Evidence Based Perioperative Medicine Asia Congress Runner Up Prize Her work bridges clinical practice, education, and policy through grants like the 2024 electronic frailty index project. Media engagement includes a 2019 Studio 10 segment highlighting her contributions to aging-related healthcare innovations.

Barış ÇOBANView profile

Professor

• Alternative Media
• Social Movements
• Citizen Journalism
• +5 more

Prof. Dr. Barış ÇOBAN is a prominent academic and administrator at Doğuş Üniversitesi, serving as Dean of the Faculty of Arts and Sciences and Chair of the Department of Communication Sciences. He holds a PhD from İstanbul Üniversitesi (2004) and has held academic positions at multiple institutions since 1997, including roles as Assistant Professor at universities like Yeni Yüzyıl Üniversitesi and Okan Üniversitesi before becoming a full professor at Doğuş Üniversitesi in 2016. His academic journey also includes administrative roles like Dean (2018–present) and Department Chair at various institutions. His research focuses on alternative media, social movements, citizen journalism, and surveillance studies. Notable publications include works on digital journalism, Gezi Resistance activism, and counter-surveillance in Turkey. He has contributed to edited volumes such as Panoptikon 2.0 and Assault on Journalism , and his work frequently addresses media ethics, globalization impacts on communication, and the role of social media in societal change. Courses taught include 'Research in Mass Media,' 'Alternative Media,' and 'Media Ethics' in both Turkish and English. He has supervised over 10 master’s and doctoral theses on topics ranging from citizen journalism to media ethics in Turkey. His interdisciplinary approach bridges media theory with activism, emphasizing critical discourse analysis and media democratization efforts. Academic service includes roles on university boards, editorial work for journals like Communications , and contributions to international conferences on media freedom and journalist safety. His work highlights the intersection of technology, power dynamics, and media’s role in shaping societal narratives.

Diego GarlaschelliView profile

Professor

• Network Theory
• Statistical Physics
• Econophysics
• +7 more

Diego Garlaschelli is Professor of Theoretical Physics at the IMT School for Advanced Studies in Lucca, Italy, and at the Lorentz Institute for Theoretical Physics, University of Leiden, the Netherlands. He leads the NETWORKS research unit at IMT and the Econophysics and Network Theory group at Leiden. He is also an external faculty member at the Complexity Science Hub in Vienna and an associate member of the Enrico Fermi Research Center in Rome. His affiliations reflect a strong international and interdisciplinary research profile in network science and statistical physics. He holds a master's degree in theoretical physics from the University of Rome III (2001) and a PhD in Physics from the University of Siena (2005). His postdoctoral experience includes positions at the Australian National University, the University of Siena, the University of Oxford, and the Sant’Anna School of Advanced Studies in Pisa. Garlaschelli’s research spans network theory, statistical physics, econophysics, financial complexity, ecological networks, and social dynamics. He applies maximum entropy models, information theory, and random graph frameworks to understand complex real-world systems. His teaching includes courses in Network Theory, Econophysics, and Complex Systems at both PhD and MSc levels. The 15 most recent publications highlight a consistent focus on network reconstruction, ensemble inequivalence, renormalization, and applications to financial and socio-economic systems. Key themes include statistical inference in networks, resilience, and multi-scale modeling, with publications in top journals such as Nature Reviews Physics , Physics Reports , Science , and Physical Review Letters . His scientific awards include the Best Paper Award at the 6th International Workshop on Self-Organizing Systems (2012) and the Jan Kijne Prize (2013) as supervisor. He has secured multiple grants from NWO, the European Union, and the Royal Society, and has supervised over 40 students at PhD, master’s, and bachelor’s levels. He also mentors postdocs and visiting scientists. Garlaschelli leads and organizes major international workshops and schools in network science and complex systems. He serves on scientific committees and is an active referee for journals like Nature and Physical Review Letters , as well as funding agencies including the ERC and NWO.

Ninette PilegaardView profile

Professor

• Transportation Policy
• Road Charging Systems
• Bicycle Infrastructure
• +6 more

Ninette Pilegaard is a Professor and serves as Deputy Head of Division and Head of Section for Transport Policy at the Department of Technology, Management and Economics, Technical University of Denmark (DTU). Her academic career spans multiple research domains with significant contributions to transportation policy and economics. Her research interests focus on transportation policy analysis with particular expertise in: Road charging systems and pricing mechanisms Bicycle infrastructure and safety analysis Commuting behavior and accessibility impacts Car ownership and usage patterns Relationship between transportation accessibility and labor market outcomes Dr. Pilegaard's scholarly work demonstrates a strong empirical approach combining transportation engineering with economic analysis. Her recent publications reveal a consistent focus on evidence-based policy evaluation, particularly in the Danish context. She frequently employs quasi-natural experimental methods to assess transportation policy impacts, with particular attention to road pricing, cycling infrastructure, and the economic implications of transportation systems. Her research has been supported by major funding bodies including Innovation Fund Denmark and Forskningsrådsfinansiering, and has resulted in publications in high-impact transportation journals such as Transportation Research Parts A and D, and Journal of Safety Research. As an academic supervisor, Dr. Pilegaard has served as Main Supervisor for PhD projects, including the 'Productivity and agglomeration' project. She has been actively involved in numerous research collaborations both within DTU and with external partners. Her laboratory work focuses on transportation data analysis, particularly utilizing Danish transportation datasets to examine policy impacts. She has developed expertise in analyzing hospital data for traffic safety research and has contributed to methodologies for assessing infrastructure effects on traffic accidents.

• Requirements Engineering
• Goal Modeling
• Conceptual Modeling
• +7 more

Sotirios Liaskos is an Associate Professor in the School of Information Technology at the Faculty of Science, York University. He is a leading researcher in requirements engineering and conceptual modeling, with a focus on goal models, empirical evaluation, and model-driven engineering. Research Interests: Requirements Engineering Goal and Conceptual Modeling Empirical Software Engineering Model-Driven Design and Automation Uncertainty and Decision-Theoretic Reasoning in Models Applications in Blockchain and Reinforcement Learning His recent work emphasizes the empirical validation of modeling constructs, the integration of decision theory into goal models, and the automated generation of secure workflows and AI training environments. He has led and co-authored numerous experimental studies on model comprehensibility and semantic quality. Publication Trends: His publications consistently appear in top venues like ER, RE, and iStar. The last five years show a strong trend toward empirical evaluation of modeling languages, decision-theoretic goal models, and applications of goal modeling in emerging domains such as blockchain and reinforcement learning simulation design. Scientific Contributions: Developed frameworks for empirical evaluation of modeling language ontologies (Peira framework). Advanced decision-theoretic approaches to goal modeling under uncertainty. Pioneered model-driven methods for generating blockchain simulators and reinforcement learning environments. Conducted foundational empirical studies on the comprehensibility of contribution links and visualization alternatives in goal models. Advising and Collaboration: He has advised several researchers including Ibrahim Jaouhar, Wisal Tambosi, Mehrnaz Zhian, and Saba Zarbaf. He maintains a highly collaborative research profile with frequent co-authorship with John Mylopoulos, Shakil M. Khan, and other international researchers. He has served as a co-editor for multiple iStar workshop proceedings, indicating leadership in the goal-oriented requirements engineering community. Labs and Teams: While no specific lab name is mentioned, his work is closely associated with research groups focused on requirements engineering and conceptual modeling, likely within York University’s software engineering research cluster. His collaborations span institutions in Canada, Europe, and beyond.

• Catalysis
• Sustainable Energy
• Renewable Fuels
• +7 more

Mark Martinez-Klimov is a researcher in the Department of Chemical Engineering at Åbo Akademi University, Faculty of Science and Engineering. His work focuses on catalysis for sustainable energy and renewable fuel production, with an emphasis on heterogeneous catalysis, biomass conversion, and CO2 utilization. He is actively involved in experimental and kinetic studies of catalytic processes. Research Interests: His primary research areas include hydrodeoxygenation, dry methane reforming, combustion synthesis, and catalytic upgrading of bio-oil and biomass derivatives. He investigates catalyst design, deactivation mechanisms, and process optimization using advanced characterization techniques such as X-ray diffraction, scanning electron microscopy, and thermogravimetric analysis. His work supports the development of cleaner energy technologies and circular chemical processes. The analysis of his recent publications (2021–2025) reveals a consistent focus on sustainable catalytic processes, particularly in renewable jet fuel production, hydrogenation of sugars, and CO2 valorization. His research spans both fundamental catalyst development and applied reaction engineering, often in continuous flow systems such as trickle bed reactors. The work integrates material science with chemical engineering principles to address challenges in energy transition. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: While specific students or grants are not listed, his collaborative publication pattern with senior researchers like Dmitry Murzin and Pavel Mäki-Arvela suggests involvement in major research projects, likely funded by national or EU-level grants. He appears to contribute to team-based research in catalysis and sustainable technologies, potentially mentoring junior researchers and PhD students within the group. Labs and Teams: Mark is part of a prominent catalysis research group at Åbo Akademi University, specializing in sustainable chemical processes. The team leverages advanced synthesis methods (e.g., solution combustion, impregnation) and characterization tools to develop novel catalysts for energy and environmental applications. Their work is highly collaborative, involving both national and international partners in the field of green chemistry and renewable fuels.

Ingo ScholtesView profile

Professor

• Network Science
• Graph Neural Networks
• Computational Social Science
• +4 more

Prof. Dr. Ingo Scholtes is Chair of Machine Learning for Complex Networks at Julius-Maximilians-Universität Würzburg's Center for Artificial Intelligence and Data Science (CAIDAS). His research spans network science, graph machine learning, and computational social science, with applications in software engineering, ecology, biology, and physics. He received a Juniorfellowship from the German Informatics Society (2014) and an SNSF Professorship (CHF 1.5Mio, 2018). Current affiliations: JMU Würzburg (since 2021), University of Zurich (2018-2024), Bergische Universität Wuppertal (2019-2021) Research focus: Higher-order network modeling, temporal graph analysis, AI for collaborative systems, causality-aware machine learning His recent publications demonstrate strong trends in temporal network analysis , graph neural networks for time-series, and higher-order models across software engineering and social science domains. He co-chairs multiple international workshops on complex networks and serves as associate editor for EPJ Data Science and Advances in Complex Systems. Key scientific contributions: Foundational work on higher-order network models published in Nature Physics Methodological innovations in temporal network visualization (HOTVis) and path-based analysis (pathpy) As both educator and organizer, he leads the Computational Social Science Section at GI e.V., mentors across disciplines, and develops tools like git2net for collaboration analysis. His work bridges theoretical foundations with practical applications in network science.