social computing Professors and Supervisors | ApplySharif

جست‌وجوی استاد و پژوهشگر

استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۷۳۶-۷۵۰ از ۳۷٬۷۷۸ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

Santosh Basapurمشاهده پروفایل

استادیار

Illinois Institute of TechnologyUnited States

Santosh Basapur is an Assistant Professor in the Department of Family and Preventive Medicine at Rush Medical Center and Director of Design at Rush University. He also serves as an Adjunct Faculty Lecturer and planning coordinator for human factors and systems design at the Institute of Design (ID) at Illinois Institute of Technology. His expertise bridges human-centered design, healthcare systems, and user experience research, with a focus on applying methods from HCI, social sciences, and anthropology to complex healthcare challenges. Education: PhD in Design from the Institute of Design (ID), MS in Industrial and Systems Engineering (Human Factors) from SUNY Buffalo, and BS in Mechanical Engineering from Karnatak University. Research Interests: Santosh focuses on innovative systems design in healthcare, including UX research methodologies, smart technologies integration, and cross-disciplinary healthcare innovation. His work emphasizes human factors engineering and culturally sensitive design approaches to improve healthcare delivery and patient outcomes. Industry Roles: Director of Project Management at Rush University Medical Center, Founder/Principal of UX Yantra Inc., and former Chief Experience Architect at Vizlore. He has over 19 years of industry experience in UX design, including roles at Motorola Research Labs and Mobility (Google), where he led projects in Smart Media, Connected Home, and Wellness Experiences. Award Recognition: Notable contributions include patents in media-related systems (2016, 2017) and invited speaking engagements at global conferences like Human-Centered Design (Leuven, 2016) and Service Design Week (Chicago, 2019). His work has been published in venues such as the International Conference on Intelligent Human Systems Integration and BCS Human Computer Interaction Conference. Grants & Collaborations: Collaborated on an NIH-funded project to improve sickle cell care via design interventions. His cross-disciplinary approach integrates clinical, technical, and design expertise to address systemic healthcare challenges. Labs/Teams: Associated with the Center for Collaborative Healthcare Design at ID, focused on equitable healthcare solutions through design innovation.

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Margret Keuperمشاهده پروفایل

استاد

University of MannheimGermany

Prof. Margret Keuper is a Professor of Machine Learning at the University of Mannheim's School of Business Informatics and Mathematics, leading the Data and Web Science Group. She is also affiliated with the Max-Planck-Institute for Informatics and ELLIS (fellow since 2024). Her research focuses on robust deep learning, neural architecture search, and computer vision tasks like motion segmentation and adversarial defense. She holds a PhD from the University of Freiburg and previously held positions at the University of Siegen and the University of Mannheim. Her work spans projects funded by DFG and BMBF, including Climate Visions for social media analysis and TrackOpt for motion tracking. She teaches courses on computer vision, generative models, and reinforcement learning. She actively serves on program committees for top conferences like CVPR, ECCV, and NeurIPS, and is an associate editor for IEEE TPAMI and JAIR. Education: PhD in Computer Science from University of Freiburg (advisor: Thomas Brox) Research Projects: Learning to Sense (DFG), Climate Visions (BMBF), TrackOpt (BMBF) Key Roles: Head of Mannheim Master in Data Science Examination Board, Member of MSc Business Informatics Board Her research emphasizes robustness in AI systems, with contributions to adversarial attacks, domain generalization, and efficient solvers for large-scale problems. She advises over 15 PhD students across academic and industry partnerships.

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Adriana I. Kovashkaمشاهده پروفایل

دانشیار

University of PittsburghUnited States

Adriana I. Kovashka is an Associate Professor in the Department of Computer Science at the University of Pittsburgh's School of Computing and Information. She serves as Chair of the Department of Computer Science. Her research focuses on computer vision, machine learning, and their intersections with human-machine communication and visual rhetoric analysis. Kovashka earned her BA in Computer Science and Media Studies from Pomona College (2008) and her PhD in Computer Science from the University of Texas at Austin (2014). She joined Pitt in 2015. Her work emphasizes improving image retrieval systems through semantic attributes, human-in-the-loop feedback, and crowd-sourced data. Notable projects include analyzing advertisements' persuasive strategies, developing object detection models resilient to domain shifts, and exploring multimodal learning with linguistic and visual inputs. She has secured significant grants, including NSF awards for geographic diversity in object detection (2023), CAREER funding for weak supervision methods (2021), and multiple Google Faculty Research Awards. Kovashka advises PhD students on topics ranging from multimodal intent modeling to domain generalization. She has organized workshops on advertising understanding and subjective attributes in vision conferences. Her lab's datasets, such as the 64,832-image ad repository and video ad collections, are widely used in vision research. Recent efforts include quantifying perceptual diversity in multilingual systems and mitigating bias in CNNs through shape regularization. Awards and recognitions include the NSF CAREER Award, Pitt's CRDF grants, and leadership roles in CVPR and WACV conferences. Her research bridges technical innovation with societal impact, addressing challenges in visual communication, ethical AI, and educational robotics.

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James T. Hamiltonمشاهده پروفایل

استاد

Stanford UniversityUnited States

James T. Hamilton is the Vice Provost for Undergraduate Education and Hearst Professor of Communication at Stanford University, where he also directs the Stanford Journalism Program. He previously taught at Duke University’s Sanford School of Public Policy and led the De Witt Wallace Center for Media and Democracy. His academic career spans over three decades, with a focus on media economics, investigative journalism, and environmental policy. Hamilton holds a B.A. (summa cum laude) and Ph.D. in Economics from Harvard University. His research explores how markets shape news content, the economics of investigative reporting, and the societal impact of information access. He co-founded the Stanford Computational Journalism Lab and is a Senior Fellow at the Stanford Institute for Economic Policy Research. His work emphasizes computational tools to enhance journalism’s accountability role, including automated fact-checking and data-driven story discovery. Key contributions include groundbreaking books like Democracy's Detectives (2016) and All the News That’s Fit to Sell (2004), which analyze media markets and transparency policies. Scientific Awards: David N Kershaw Award, Goldsmith Book Prize (twice), Frank Luther Mott Research Award (twice), Tankard Book Award Teaching Honors: Allyn Young Prize, Trinity College Distinguished Teaching Award, Susan Tifft Mentoring Award His current research addresses digital inequality, the psychological effects of online financial ads, and algorithmic transparency in journalism. He advises on media innovation through affiliations with the Brown Institute for Media Innovation and the JSK Fellowships Board.

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Michael C. Frankمشاهده پروفایل

استاد

Stanford UniversityUnited States

Michael C. Frank is the Benjamin Scott Crocker Professor of Human Biology at Stanford University and Director of the Symbolic Systems Program. He leads the Stanford Language and Cognition Lab and has pioneered large-scale collaborative projects including Wordbank (open vocabulary data), MetaLab (developmental meta-analyses), ManyBabies (replication network), childes-db (language transcripts), and Peekbank (eye-tracking repository). His research examines children's language learning and its interaction with social cognition, utilizing computational modeling, large datasets, and open science frameworks. Key interests include: Mechanisms of early language acquisition Pragmatic inference in social contexts Cross-cultural variability in cognitive development Data-driven approaches to developmental science Reproducibility and meta-scientific innovation Recent publications (2022-2025) demonstrate strong emphases on: 1) Novel methods for measuring language environments and cognitive abilities, 2) Computational models of learning and perception, 3) Cross-cultural investigations of social cognition, and 4) Infrastructure for open developmental science. The majority employ multimodal data, meta-analytic techniques, and large-scale collaborations. He teaches courses including Experimental Methods, Developmental Psychology, and interdisciplinary seminars on language, cognition, and computation. His lab maintains active research teams across multiple continents through initiatives like ManyBabies and LEVANTE.

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Mark W. Newmanمشاهده پروفایل

دانشیار

University of Michigan-Ann ArborUnited States

Mark W. Newman is an Associate Professor in the School of Information at the University of Michigan, with a joint appointment in the EECS Department. His research focuses on human-computer interaction, ubiquitous computing, and health informatics, emphasizing the design of technologies that integrate seamlessly into everyday life. He teaches courses on user experience research, programming, and application development, contributing to curriculum design efforts such as the User Experience Design track and programming curricula for undergraduate and graduate programs. His work spans smart home technologies, mobile health interventions, and collaborative health management systems. Notably, he collaborates across disciplines to address challenges in healthcare technology, including patient-generated data integration and sustainable energy solutions. Newman actively mentors students and leads design projects that prioritize user-centered approaches, such as the UX Research & Design Specialization on Coursera. His recent scholarship explores adaptive mHealth interventions, context-aware systems, and participatory design methods to enhance health equity.

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Edwin Olsonمشاهده پروفایل

دانشیار

University of Michigan-Ann ArborUnited States

Edwin Olson is an Associate Professor of Computer Science and Engineering at the University of Michigan, where he directs the APRIL Robotics Lab. He also serves as CEO of May Mobility Inc., a company focused on developing driverless shuttles. His research spans autonomy, perception, robotics, and learning, with notable contributions to technologies like AprilTags and the LCM middleware. Olson has led groundbreaking projects, including the 2010 MAGIC competition-winning robot team and the DARPA Urban Challenge. He has been recognized with awards such as Popular Science's 'Brilliant Ten' (2012), the DARPA Young Faculty Award (2013), and the College of Engineering Education Excellence Award (2015). His work emphasizes real-world applications of autonomous systems, including risk assessment, multi-policy decision making, and sensor fusion. Recent articles focus on autonomous agent behavior prediction, remote assistance systems, and infrastructure calibration. Olson's academic contributions are complemented by industry roles, including his tenure at Toyota Research Institute as Co-Director for Autonomous Driving Development. Education: PhD in Computer Science from MIT (2008) Key Projects: MAGIC 2010, DARPA Urban Challenge, Toyota Research Institute Labs: APRIL Robotics Lab Awards: DARPA Young Faculty Award, Brilliant Ten, Education Excellence Award

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Michael Schaubمشاهده پروفایل

استادیار

RWTH Aachen UniversityGermany

Michael Schaub is a tenure-track Assistant Professor in the Department of Computer Science at RWTH Aachen University, specializing in Computational Network Science. His research focuses on analyzing complex systems through network and graph models, integrating dynamical systems, control theory, and machine learning. He leads the Computational Network Science group, advancing methodologies for higher-order network models like simplicial complexes and hypergraphs. Schaub holds a PhD from Imperial College London and has held postdoctoral positions at MIT and Oxford. He is an ERC Starting Grant recipient (2022) and a Marie Curie Fellow, recognized for contributions to network dynamics and topological data analysis. Education: PhD in Mathematics, Imperial College London (2011-2015) MSc in Biomedical Engineering, Imperial College London (2010) BSc in Electrical Engineering, ETH Zurich (2007-2010) Research Interests: Schaub’s work spans interdisciplinary applications of network science, including biological systems, social networks, and technical infrastructures. Key areas include: Higher-order network models (hypergraphs, simplicial complexes) Graph signal processing and dynamics on networks Community detection and dynamical systems analysis Topological data analysis and machine learning Grants & Awards: ERC Starting Grant (2022): HIGH-HOPeS project Marie Skłodowska-Curie Fellowship (2017-2019) Junior Fellow, German Informatics Society (GI) Member of Junges Kolleg (North Rhine-Westphalia Academy) Labs & Teams: Leads the Computational Network Science Lab at RWTH Aachen, collaborating internationally on projects like the ELLIS Society and the European Laboratory for Learning and Intelligent Systems (ELLIS). Active in organizing workshops (e.g., Toponets, SIAM MDS).

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Ming Caoمشاهده پروفایل

استاد

University of GroningenNetherlands

Ming Cao is a Full Professor at the University of Groningen (Netherlands), holding positions in the Department of Discrete Technology and Production Automation, the Engineering and Technology Institute Groningen, and serving as Chair of the Jantina Tammes School of Digital Society, Technology and AI. His academic roles include Director of the Jantina Tammes School and membership in prestigious organizations such as the International Federation of Automatic Control (IFAC) and the European Commission’s DG CNECT. Cao’s research focuses on multi-agent systems, autonomous robotics, complex networks, and cooperative control, with applications in robotics, epidemic modeling, and biomimetic sensors. Education: PostDoc in Mechanical Engineering from Princeton University (2008), PhD in Electrical Engineering from Yale University (2007). Research Interests: Multi-agent systems, distributed decision-making, cooperative control, robotic teams, seal whisker-inspired flow sensing, and privacy-preserving control systems. Recent Trends in Articles: Recent work emphasizes co-evolutionary dynamics in social-technical systems, privacy in control systems, and biomimetic robotics. Key topics include feedback mechanisms in cooperation, hypergraph-based epidemic models, and seal whisker mechanics for underwater sensing. Awards: European Control Award (2016), Manfred Thoma Medal (2017), ERC Grant (2012). Grants: Vidi Grant from NWO (2015) for agent coordination research. Labs/Teams: Jan C. Willems Center for Systems and Control, Research Center for Data Science and Systems Complexity (DSSC). Active in editorial roles for journals like Artificial Life and Robotics and the SIAM Journal on Control and Optimization .

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Jean Goodwinمشاهده پروفایل

استاد

North Carolina State UniversityUnited States

Dr. Jean Goodwin is the SAS Institute Distinguished Professor of Rhetoric & Technical Communication at NC State University's Department of Communication, part of the College of Humanities and Social Sciences. She specializes in science communication ethics, civil argumentation, and the rhetoric of controversial scientific topics such as climate change and GMOs. Her work bridges theory and practice, including NSF-funded initiatives like Teaching Responsible Communication of Science, which develops case studies for STEM graduate students. She also leads the Leadership in Public Science cluster through NC State's Chancellor’s Faculty Excellence Program. Education: B.A. in Mathematics (University of Chicago, 1979), J.D. (University of Chicago, 1984), and Ph.D. in Communication/Rhetoric (University of Wisconsin-Madison, 1996). Her career includes over 25 years of teaching rhetoric and mentoring students across communication subfields. Research focuses on how scientists communicate effectively with non-experts, leveraging discourse analysis and conceptual frameworks like speech act theory. Key themes include trust-building in contentious contexts, ethical advocacy roles for scientists, and historical rhetorical strategies from Cicero to modern policy debates. Her work often intersects with civic engagement and interdisciplinary collaboration, such as organizing conferences for science communication scholars and advising organizations like the AAAS. Major publications explore ethical dimensions of science communication, including climate change advocacy, disinformation detection, and pandemic messaging. Awards include the EB Knight Journal Award (2007) and her distinguished professorship. Current initiatives emphasize translating scholarly insights into practical tools for scientists and fostering dialogue between scientific and public communities through funded research partnerships.

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Dino Pedreschiمشاهده پروفایل

استاد

National Research Council of Italy (CNR)Italy

Dino Pedreschi is a Full Professor of Computer Science at the University of Pisa, affiliated with the Department of Computer Science (DI-UNIPI). He co-leads the Pisa KDD Lab, a joint research initiative between the University of Pisa and the Italian National Research Council’s Institute of Information Science and Technology, one of the earliest labs focused on data mining and knowledge discovery. His research spans Big Data Analytics , Social Network Analysis , Human Mobility Analysis , Privacy-by-Design , Explainable AI (XAI) , and ethical data mining . He is a pioneer in privacy-preserving data mining and has contributed significantly to understanding societal impacts of AI and big data. Recent publications highlight trends in explainable AI , fairness-aware data mining , urban mobility modeling , and socio-economic nowcasting , reflecting a strong interdisciplinary focus combining computer science, social science, and policy. Notable scientific awards include: Google Research Award on Privacy (2009) University of Pisa Ordine del Cherubino (2017) Pedreschi has played leadership roles in major conferences such as ECML/PKDD (Co-Chair 2004), ICDM (Vice-Chair 2005), and ICDE (Vice-Chair 2014). He founded the Business Informatics MSc program at the University of Pisa to train interdisciplinary data scientists. He has been a visiting scientist at the University of Texas at Austin, CWI Amsterdam, UCLA, and the Barabási Lab at Northeastern University. He actively contributes to European research initiatives including SoBigData++, FAIR, and TAILOR, and has advised on AI policy, including testimony before the Italian Parliament on AI and labor markets. He is a key member of the Pisa KDD Lab, a leading research group in data science and AI ethics, fostering collaboration between academia and public institutions.

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Caren Walkerمشاهده پروفایل

استادیار

University of California, San DiegoUnited States

Caren Walker is an Assistant Professor in the Department of Psychology at the University of California, San Diego (UCSD), leading the Early Learning and Cognition (ELC) Lab. Her research focuses on how children learn abstract causal principles, particularly through activities like analogy, explanation, and engagement with imaginary scenarios. She explores developmental trajectories in scientific reasoning and the role of cultural context in shaping cognitive processes. Dr. Walker’s work combines interdisciplinary approaches from psychology, philosophy, and computational modeling. She investigates children’s understanding of uncertainty, causal inference, and the decline of relational reasoning with age. Her lab conducts experiments in diverse settings, including partnerships with museums, to study learning mechanisms in real-world contexts. Notable achievements include the 2024 APA Boyd McCandless Award and the 2021 NSF CAREER Award. The ELC Lab actively engages undergraduate researchers and collaborates on projects addressing cognitive diversity across cultures. Recent lab milestones include studies on US-China differences in causal reasoning and the impact of stereotypes on causal judgments.

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Rachel Bestمشاهده پروفایل

دانشیار

University of Michigan-Ann ArborUnited States

Rachel Best is an Associate Professor and Director of Graduate Studies in the Department of Sociology at the University of Michigan. Her research focuses on the intersections of politics, culture, inequality, and health, with a particular emphasis on disease advocacy, disability discrimination, and the sociological implications of medical policy. She directs the Sociology graduate program and leads computational studies on disease stigma, advocacy strategies, and ADA litigation outcomes. Her work bridges medical sociology, legal studies, and public health, addressing how societal structures shape health outcomes and policy responses. Her research interests include analyzing how disease campaigns influence health policy, the political consequences of overestimating individual health control, and the role of stigma in disability rights litigation. Notable projects use computational methods to trace policy advocacy impacts and explore visibility dynamics in disability cases. Recent publications (2022–2025) highlight her focus on disease stigma’s societal effects, legal interpretations of invisible disabilities, and the evolving nature of advocacy in health policy. Her earlier works, such as Common Enemies: Disease Campaigns in America , critique single-disease advocacy frameworks and their unintended policy consequences. Rachel Best advises on graduate studies and collaborates with interdisciplinary teams. Her research has been supported by grants focusing on sociological methods in health policy analysis. She is affiliated with the University of Michigan’s Sociology Department and the LSA College, contributing to discussions on inequality and institutional sociology.

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Rodrigo Venturaمشاهده پروفایل

دانشیار

University of LisbonPortugal

Rodrigo Ventura is an Associate Professor at the Department of Electrical and Computer Engineering, Instituto Superior Técnico (IST), University of Lisbon. He is also a senior researcher at the Institute for Systems and Robotics (ISR-Lisbon), leading the Space and Aeronautics thematic line. His research focuses on the intersection of Robotics and Artificial Intelligence, emphasizing human-robot interaction, space robotics, and cognitive architectures. He coordinates the Minor in Space Sciences and Technologies at IST and the MBE on Space Systems for Tecnico+. As Adjoint Faculty at the International Space University (ISU), he contributes to global academic initiatives. His work includes experiments on the International Space Station and participation in analog space missions. Research interests span biologically inspired systems, machine learning, and teleoperation interfaces. Recent publications address reinforcement learning for UAVs, microgravity experiments, and pseudo-haptic feedback for robotic control. He teaches subjects like Artificial Intelligence and Decision Systems, Satellite Engineering, and Autonomous Systems.

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Steve Whittakerمشاهده پروفایل

استاد

University of California, Santa CruzUnited States

Steve Whittaker is Professor of Human-Computer Interaction at the University of California at Santa Cruz. He conducts interdisciplinary research at the intersection of social science and computer science, focusing on how technology affects human memory, communication, and personal information management. His current research explores human-centric AI systems, mental health technologies, and digital identity. His research interests center on designing interactive systems that support human needs in digital environments. He investigates how people manage digital information, remember personal experiences through lifelogging, and interact with conversational agents and social robots. His work emphasizes computational well-being, affective computing, and the social implications of technology use. He has made foundational contributions to the fields of personal information management (PIM), computer-mediated communication (CMC), and human-robot interaction. The recent publications reflect a strong trend toward mental health technology, human-AI interaction, and digital well-being. His work spans from theoretical models of emotion and memory to practical systems for mental health apps, chatbots, and immersive visualization. He frequently publishes in top-tier venues such as CHI, CSCW, and IUI, often in collaboration with researchers across disciplines. Lifetime Research Achievement Award from SIGCHI Fellow of the Association for Computational Machinery (ACM) Member of the CHI Academy Lasting Impact Award from ACM CSCW Best Paper Award at CSCW10 Best Paper Award at CHI07 Honourable Mention at ACM CHI 2020 Multiple best paper nominations at CHI, CSCW, and IUI MIT Siegel Prize Steve Whittaker has supervised numerous PhD and Master’s students, though specific names are not listed in the provided text. His research has been funded by major grants from NSF, NIH, and industry partners, enabling long-term studies on digital behavior and system development. He is Editor of the journal Human Computer Interaction and has authored over 200 peer-reviewed publications. His most recent book, The Science of Managing Our Digital Stuff (MIT Press), co-authored with Ofer Bergman, synthesizes decades of research on personal information management. He leads a vibrant research lab at UC Santa Cruz that focuses on human-centered computing, where students and collaborators work on projects involving AI, mental health, digital memory, and social interaction. The lab has produced influential work on lifelogging, email management, telepresence robots, and algorithmic transparency. The team employs mixed methods, combining qualitative studies with system design and evaluation.

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