Find Academic Supervisors

Dr. Kaya de Barbaro is an Associate Professor in the Department of Psychology at the University of Texas at Austin (College of Liberal Arts). She holds a Ph.D. from the University of California San Diego. Her research focuses on bridging computer science and developmental/clinical psychology, particularly maternal mental health and infant social-emotional development. She directs the Daily Activity Lab, which uses mobile/wearable sensors and machine learning to analyze real-world interactions, aiming to develop just-in-time interventions for new mothers. Key research areas include maternal-infant dynamics, physiological synchronization, and the impact of environmental chaos on development. She has pioneered methods for analyzing high-density data, such as Granger causality and machine learning algorithms to detect behaviors like crying and holding. Recent work emphasizes leveraging ecological momentary assessment surveys and 24-hour LENA audio recordings to understand proximal mechanisms of development. Dr. de Barbaro teaches Psychology 333D (Introduction to Developmental Psychology) and has developed curricula for both in-person and online formats. She is actively involved in training students through the Eureka program at UT Austin. Her lab collaborates on tools like chatbots for postpartum mental health and has published extensively on sensor-based methodologies in developmental science.

• Developmental Psychology
• Clinical Psychology
• Social Interactions
• +5 more

Alexander Schwing is an Associate Professor in the Department of Electrical and Computer Engineering and Computer Science at the University of Illinois at Urbana-Champaign, affiliated with the Coordinated Science Laboratory. His research focuses on machine learning and computer vision with applications in 3D scene understanding, generative modeling, and multi-agent systems. Education: Diploma in Electrical Engineering and Information Technology, Technical University of Munich (TUM) PhD in Computer Science, ETH Zurich Postdoctoral Fellow, University of Toronto Research Interests: Structured prediction in deep learning Generative adversarial networks and stability Multi-modal vision-language models 3D scene reconstruction from single images Embodied agent collaboration Semantic segmentation with temporal coherence Recent Publications: Highlight trends in neural rendering, video object segmentation, and reinforcement learning with applications to 3D modeling and multi-agent systems. Notable innovations include SAIL-VOS dataset for amodal segmentation and NeRFDeformer for single-view scene transformation. Scientific Awards: NSF CAREER Award, 3M and Amazon research awards, multiple student recognition awards, ETH Zurich PhD medal, and best paper at Intelligent Tutoring Systems 2014. Teaching: Offers graduate courses in Pattern Recognition (ECE 544) and Machine Learning (CS 446/ECE 449). Previously taught at University of Toronto and ETH Zurich. Labs & Collaborations: Leads research at Coordinated Science Laboratory (UIUC) with collaborations across University of Toronto, ETH Zurich, and industry partners like Samsung SAIT and Amazon.

• Machine Learning
• Computer Vision
• Structured Prediction
• +7 more

Blase Ur is an Associate Professor of Computer Science at the University of Chicago, leading the UChicago SUPERgroup (Security, Usability, & Privacy Education & Research group). His research focuses on data-driven methods to improve online security, privacy, and usability of complex systems. He holds a PhD from Carnegie Mellon University and an AB from Harvard University. Education: PhD in Societal Computing, Carnegie Mellon University (2016) AB in Computer Science, Harvard University Research Interests: Blase's work spans computer security, privacy, HCI, and ethical AI. He develops tools like password meters, privacy transparency systems, and IoT security solutions. His SUPERgroup emphasizes interdisciplinary collaboration, with projects addressing data-driven decision-making and usability challenges in AI and IoT. Articles Trends: Recent work includes studies on ad transparency systems, user perceptions of privacy in AI, and barriers to passwordless authentication. Research often combines empirical user studies with technical implementations to bridge gaps between security mechanisms and user needs. Awards: NSF CAREER Award (2021) Quantrell Award for Teaching (2021) SIGCHI Outstanding Dissertation Award (2018) Advising & Grants: Advisor to PhD students like Kevin Bryson. Secured grants from NSF, Mozilla, and Meta. Active in outreach to broaden CS participation, including K-12 programs. Labs & Teams: Co-director of the SUPERgroup and affiliated with the Systems Group and CERES Center for Unstoppable Computing at UChicago.

• Computer Security
• Privacy
• Human-Computer Interaction (HCI)
• +4 more

Robert Ghrist is the Andrea Mitchell University Professor at the University of Pennsylvania with dual appointments in the Department of Mathematics and the Department of Electrical and Systems Engineering. He serves as Associate Dean for Undergraduate Education for Penn Engineering. His educational background includes a B.S. in Mechanical Engineering from the University of Toledo (1991), and M.S. and Ph.D. degrees in Applied Mathematics from Cornell University (1994, 1995). Ghrist's research bridges pure and applied mathematics, focusing on applied algebraic topology , dynamical systems , and geometric methods in data science. His work extends to network theory, topological data analysis, and computational geometry, with applications spanning robotics, neuroscience, and social dynamics. Key innovations include developing sheaf-theoretic approaches for networked systems and persistence homology techniques for high-dimensional data. Analysis of his recent publications reveals a strong emphasis on lattice-theoretic frameworks , topological robotics , and network dynamics , with emerging applications in neural data interpretation and geometric computing. His research consistently integrates category theory with real-world engineering challenges. Significant scientific recognition includes: Presidential Early Career Award (PECASE, 2004) Scientific American 'Top 50' Research Leader (2007) Mathematical Association of America's Chauvenet Prize (2013) University of Pennsylvania Lindback Award for Distinguished Teaching (2015) DoD National Security Science and Engineering Faculty Fellowship (NSSEFF, 2015) Ghrist leads multiple federally funded research initiatives supported by AFOSR, DARPA, NSF, and ONR. He directs the development of educational tools including the Calculus BLUE/GREEN Project video series and custom GPTs for mathematical pedagogy. His open online courses have reached over 100,000 learners globally.

• Applied Topology
• Dynamical Systems
• Algebraic Topology
• +5 more

Oscar Westlund serves as Professor at the Department of Journalism and Media Studies within the Faculty of Social Sciences at Oslo Metropolitan University (OsloMet), where he co-leads the OsloMet Digital Journalism Research Group. He maintains a secondary appointment at the University of Gothenburg and acts as Editor-in-Chief of Digital Journalism . His research spans digital journalism, fact-checking, platform dynamics, media management, news consumption, and mobile media. Westlund's scholarly focus centers on journalism safety in digital environments, sociotechnical dimensions of misinformation combat, and platform-induced challenges for news organizations. His work examines professional fragilities of journalists, epistemological foundations of data journalism, and legitimacy paradoxes in public service media-platform relationships. Current projects address disinformation countermeasures through the SCAM initiative (Source Criticism And Mediated Disinformation). Analysis of his 2022-2024 publications reveals dominant themes: journalism safety frameworks (33%), platform-journalism tensions (27%), and fact-checking as sociotechnical practice (23%). Key methodological approaches include cross-national comparative studies, epistemological analysis, and practice theory applied to digital news production. Westlund actively contributes to academic discourse through editorial leadership at Digital Journalism and special issue guest editing. His research group coordinates interdisciplinary collaboration on digital journalism studies, while the SCAM project develops practical methodologies for disinformation detection in democratic contexts. Recent work demonstrates increasing emphasis on mobile news ecosystems and journalist safety cultures. He co-leads the OsloMet Digital Journalism Research Group, which investigates platform impacts on news production, mobile journalism evolution, and epistemological shifts in data-driven reporting. The group maintains strong international partnerships, particularly with Swedish institutions through his University of Gothenburg affiliation.

• Digital Journalism
• Fact-checking
• Media Platforms
• +7 more

Andrew Stephen is the L’Oréal Professor of Marketing and Deputy Dean for Faculty and Research at Saïd Business School, University of Oxford. He leads the Marketing faculty and directs the Oxford Future of Marketing Initiative, focusing on emerging technologies like AI and data-driven strategies in marketing, media, and advertising. His research explores digital marketing, consumer behavior, and the impact of new technologies, earning him recognition as a top global marketing scholar. His academic contributions span leading journals such as the Journal of Consumer Research and Harvard Business Review. He serves as editor of the Journal of Consumer Research, co-editor of the International Journal of Research in Marketing, and actively engages with industry through councils like the Mobile Marketing Association’s Brand Safety Council. Teaching responsibilities include marketing curriculum oversight and courses across MBA, Executive MBA, and DPhil programs, focusing on digital transformation, marketing analytics, and the future of marketing. His work bridges academic rigor with industry relevance, addressing challenges in tech-enabled marketing and global brand strategies. Awards: Top marketing academic in the UK (AMA), featured in Poets & Quants’ top 40 under 40, multiple research excellence awards. Labs/Initiatives: Oxford Future of Marketing Initiative, fostering collaborations with leading companies to tackle industry challenges. Grants/Advising: Extensive industry partnerships and academic grants not explicitly detailed in the text.

• Digital Marketing
• Social Media Strategy
• Consumer Behavior
• +5 more

Raman Arora is an Associate Professor in the Department of Computer Science at Johns Hopkins University, with affiliations to the Mathematical Institute for Data Science (MINDS), the Center for Language and Speech Processing (CLSP), and the Institute for Data-Intensive Engineering and Science (IDIES). His research spans theoretical and practical aspects of machine learning, focusing on robustness, privacy, representation learning, and optimization. Research Interests: Machine Learning Theory Representation Learning (e.g., Deep CCA, Multi-view Learning) Privacy-Preserving Machine Learning (Differential Privacy) Robustness in Deep Learning Online and Reinforcement Learning Stochastic Optimization Algorithms His recent publications, primarily in top-tier venues like NeurIPS, ICML, and ICLR, demonstrate a strong focus on the theoretical foundations of adversarial robustness, multi-task learning, offline reinforcement learning, and differentially private optimization. His work often bridges theory and practice, with applications in speech, language, and data-intensive systems. Scientific Awards and Honors: NSF CAREER Award (2020) ICML Test-of-Time Award Finalist (2023) for Deep CCA Member, Institute for Advanced Study (2019–2020) Visiting Scientist, Simons Institute (2019, 2020, 2022) Advising and Grants: Raman Arora has advised numerous PhD and master’s students, many of whom are now researchers at leading tech companies like Google, Meta, and Microsoft. His research is supported by significant grants from the NSF (including CAREER, BIGDATA, TRIPODS, and CRCNS awards), DARPA, and other agencies, focusing on foundational aspects of machine learning such as inductive biases, privacy, robustness, and computational neuroscience. Laboratory and Research Group: He leads a dynamic research group at Johns Hopkins, comprising current PhD students and postdoctoral researchers working on the intersection of theory and applications in machine learning. The group is actively involved in projects related to adversarial robustness, meta-learning, offline reinforcement learning, and private optimization.

• Machine Learning
• Statistical Signal Processing
• Stochastic Approximation Algorithms
• +16 more

Panos Ipeirotis is a Professor at the Leonard N. Stern School of Business at New York University, affiliated with the Department of Technology, Operations, and Statistics. He also serves as the George A. Kellner Faculty Fellow and is associated with the Center for Data Science and Computer Science departments at NYU. PhD in Computer Science (Columbia University, 2004) MSc in Computer Science (Columbia University, 2001) BSc in Computer Engineering & Informatics (University of Patras, 1999) His research spans crowdsourcing, machine learning, human-AI collaboration, online labor markets, and social media analytics. He pioneered human-machine loop systems that combine human and machine intelligence to achieve superior outcomes. His work has applications in data quality assurance, visual media search (e.g., Google Project Glass), and economic valuation of user-generated content. Recent publications focus on algorithmic fairness in hiring systems, occupational segregation analysis, and theoretical advancements in crowdsourcing consensus mechanisms. Earlier work includes foundational studies on data quality in crowdsourcing platforms, economic impacts of product reviews, and query optimization for text-centric tasks. 2015 Lagrange Prize in Complex Systems NSF CAREER Award SIGKDD Test of Time Award (2020) Multiple Best Paper awards (WWW 2011, KDD 2008, SIGMOD 2006) He has received significant grants, including a $1.5 million Google Research Grant (2013) for integrating crowdsourcing with machine learning algorithms. His work bridges computer science, economics, and social psychology, with implications for policy-making and business strategy.

• Crowdsourcing
• Machine Learning
• Human-AI Collaboration
• +7 more

Celestine Mendler-Dünner is a Principal Investigator at the ELLIS Institute in Tübingen, co-affiliated with the Max Planck Institute for Intelligent Systems and the Tübingen AI Center. She leads the Algorithms and Society research group, focusing on machine learning in social contexts and the role of prediction in digital economies. Her work bridges theoretical machine learning with practical societal impact, developing tools for safe, reliable, and equitable AI ecosystems. Her educational background includes a PhD from ETH Zurich in collaboration with IBM Research, followed by an SNSF postdoctoral fellowship at UC Berkeley hosted by Moritz Hardt. She was previously a group leader at the Max Planck Institute for Intelligent Systems before joining the ELLIS Institute. Mendler-Dünner's research spans several interconnected themes including performative prediction (where predictions change the behavior they aim to predict), algorithmic collective action (how participants can steer AI systems toward common goals), and the role of LLMs in social science research. Her work combines theoretical foundations with practical implementations, addressing challenges in interactive machine learning, optimization in dynamic environments, and context-specific evaluation of AI systems. She particularly examines how algorithmic predictions mediate services and platforms at societal scale, exploring concepts of economic power in digital markets. Her publication record shows a clear evolution from system-aware machine learning algorithms (including foundational work on IBM Snap ML) toward increasingly sociotechnical questions at the intersection of machine learning, economics, and policy. Recent work focuses on measuring performative power in digital economies, evaluating LLMs as risk scores, and developing frameworks for algorithmic collective action in recommender systems and labor markets. Among her notable recognitions are the ETH Medal for her dissertation, the IBM Research Division Award, the Fritz Kutter Award, and the IBM Eminence and Excellence Award. She is an ELLIS Scholar, a fellow of the Elisabeth-Schiemann-Kolleg, and affiliated with several prestigious research programs including the International Max Planck Research School for Intelligent Systems and the Max Planck ETH Center for Learning Systems. ETH Medal (dissertation award) IBM Research Division Award Fritz Kutter Award IBM Eminence and Excellence Award SNSF Early Postdoc Mobility Fellowship Mendler-Dünner actively mentors the next generation of researchers, advising PhD student Patrik Wolf and supervising research interns including Joachim Baumann, Haiqing Zhu, and Anna Badalyan, as well as Master's student Dorothee Sigg. She serves as core faculty for the International Max Planck Research School and associated faculty for the Max Planck ETH Center for Learning Systems. Her group has secured significant research funding through fellowships and institutional support, enabling work on projects like Powermeter (measuring search engine influence) and Snap ML (resource-efficient machine learning library with over 1 million PyPI downloads). She leads the Algorithms and Society research group, which examines machine learning as part of broader sociotechnical ecosystems. The group explores human-population interactions with algorithmic systems and incorporates these insights into learning system fundamentals. Current projects include investigating economic incentives in digital platforms, developing tools for systematic LLM evaluation in social science contexts, and creating frameworks for collective action in algorithmic systems. Mendler-Dünner also co-organizes the Algorithmic Collective Action workshop at NeurIPS 2025, demonstrating her leadership in emerging research directions at the AI-society interface.

• Machine Learning in Social Context
• Algorithmic Collective Action
• Performative Prediction
• +6 more

Clifford Stein is a Professor of Industrial Engineering and Operations Research (IEOR) and Computer Science at Columbia University, and Associate Director for Research at the Data Science Institute. He holds a Ph.D. (1992), M.S. (1989), and B.S.E. (1987) from MIT and Princeton University, respectively. His research focuses on algorithms, combinatorial optimization, operations research, scheduling, and computational biology. A co-author of the best-selling textbook Introduction to Algorithms , Stein has published widely in top venues and holds prestigious awards like ACM Fellow and NSF Career Award. His work includes foundational contributions to minimum cut algorithms, scheduling theory, and network optimization, supported by NSF and Sloan Foundation grants. Stein has advised over 40 graduate and undergraduate students, many now in academia and industry.

• Algorithms
• Combinatorial Optimization
• Operations Research
• +4 more

Ram Vasudevan is an Associate Professor and Associate Chair of Graduate Studies in the Department of Robotics at the University of Michigan. His research focuses on developing tools for safe and robust deployment of robotic systems, emphasizing optimization, nonlinear control, and real-world applications. Key areas include legged robot locomotion, shared control systems, and safety-critical autonomous systems. Research Interests: Optimization and control of nonlinear systems, locomotion of legged robots, shared control active safety systems, and automation of diagnostic/rehabilitative tasks. His ROAHM Lab prioritizes mathematical guarantees for robotic performance, with applications in medical robotics, autonomous vehicles, and soft robotics. Recent work emphasizes trajectory optimization, sensor fusion, and safety-aware control strategies. He has contributed to benchmarks for autonomous vehicle perception and novel methods in thermal image restoration using neural radiance fields. Awards: None explicitly listed in provided text. Labs/Teams: Directs the ROAHM Lab, collaborating on projects like robotic tail mechanics, real-time motion planning, and sensor data analysis. Active in academic conferences including RSS and ICRA.

• Optimization and control of nonlinear systems
• Legged robot locomotion
• Autonomous systems safety
• +4 more

Osman Yağan is a Research Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), with affiliate faculty status in the School of Computer Science. He is also a core member of CyLab Security and Privacy Institute. Prior to joining CMU in 2013, he was a Postdoctoral Research Fellow at CyLab. He holds a Ph.D. in Electrical and Computer Engineering from the University of Maryland (2011) and a B.S. from Middle East Technical University (2007). His research focuses on modeling, analysis, and optimization of computing systems, leveraging applied probability, network science, data science, and machine learning. Key areas include multi-armed bandits, resilient machine learning, contagion processes in networks, and cybersecurity. Research Interests include Machine Learning, Data Science, Network Science, Cybersecurity, and Robustness in Cyber-Physical Systems. He has advised numerous students, including current PhD candidates Yurun Tian, Orkun İrsoy, and Ishank Juneja, as well as notable alumni such as Mansi Sood (now at MIT) and Jun Zhao (Assistant Professor at Nanyang Technological University). Key Awards include the CIT Dean's Early Career Fellowship, IBM Academic Award, and Best Paper Awards at ICC 2021, IPSN 2022, and ASONAM 2023. His work spans theoretical contributions (e.g., contagion models in multi-layer networks) and applied research (e.g., mitigating cascading failures in power systems). He leads or co-leads grants from ONR, NSF, and ARO, focusing on resilient machine learning, network robustness, and pandemic modeling.

• Machine Learning
• Data Science
• Network Science
• +7 more

Dr. Haneen Farah is an Associate Professor in the Department of Transport & Planning at Delft University of Technology and co-director of the Traffic and Transportation Safety Lab. She also serves as head of the Traffic Systems Engineering section. Her research focuses on road infrastructure design, road user behavior, and traffic safety, integrating transportation engineering, human factors, and econometrics. Prior to TU Delft, she was a postdoc at KTH Royal Institute of Technology and earned her M.Sc. and Ph.D. in Transportation Engineering from the Technion-Israel Institute of Technology. Her work includes national/international projects like SAMEN (mixed automated/human traffic implications), AfroSAFE (road safety in Africa), and XCARCITY (sustainable city mobility). She teaches undergraduate and graduate courses on road design and traffic safety, including online programs for low/middle-income countries. Farah supervises multiple PhD and Master students in her research areas, contributing to over 50 peer-reviewed publications. Key research themes include infrastructure design for automated vehicles, driver behavior modeling, cyclist safety, and policy implementation of the Safe System approach. Her interdisciplinary approach bridges engineering and psychology to enhance traffic safety and efficiency through advanced analytics and simulation models.

• road infrastructure design
• road user behavior
• traffic safety
• +7 more

Yang P. Liu is an Assistant Professor at Carnegie Mellon University 's Department of Computer Science . He received his PhD from Stanford University under the supervision of Aaron Sidford and previously studied at MIT . Fields of Interest : Graph Algorithms, Optimization, High-Dimensional Geometry, Additive Combinatorics, Theoretical Computer Science. His research focuses on algorithmic design and analysis for graph problems, optimization, and combinatorics, with applications in machine learning and complexity theory. Recent work includes advancements in parallel repetition games , combinatorial lines , and dynamic graph algorithms . In 2024, his research spanned FOCS , STOC , and RANDOM conferences, addressing problems in k-CSPs , min-cost flow , and hypergraph sparsification . Earlier contributions (2023) included deterministic flow algorithms and spectral hypergraph techniques. Scientific Awards : NDSEG Fellowship (2018-2021), Google PhD Fellowship (2022-2023), FOCS Best Paper (2022), STOC Best Student Paper (2022), FOCS Best Student Paper (2021). He teaches CS 15-759 , a graduate course on convex optimization theory and applications, covering gradient descent, interior point methods, and algorithmic sparsification techniques.

• Graph Algorithms
• Optimization
• High-Dimensional Geometry
• +3 more

Jeff Huang is an Associate Professor and Associate Chair of Computer Science at Brown University. His research focuses on Human-Computer Interaction (HCI), with emphases on personalized systems using behavioral data, health informatics, and user interfaces. Huang holds a PhD from the University of Washington and degrees from the University of Illinois at Urbana-Champaign. His work is funded by NSF, NIH, and ARO, and he has received awards including the NSF CAREER Award and ARO Young Investigator Award. Education: PhD in Information Science, University of Washington (2013) MS in Computer Science, University of Washington (2013) BS and MS in Computer Science, University of Illinois at Urbana-Champaign (2007) Research Interests: His work spans health informatics (e.g., sleep tracking systems like Self-E and SleepCoacher), user interface design, and leveraging behavioral data for personalized systems. Notable projects include WebGazer (webcam eye tracking), filtered.ink (creative illustration tools), and Sochiatrist (mental health analytics). Teaching: He teaches courses on user interfaces, HCI, and research methods, including CSCI 0130/1300 (User Interfaces), CSCI 2300 (HCI Seminar), and CSCI 2000 (Research Methods). Awards: NSF CAREER Award Facebook Fellowship ARO Young Investigator Award Grants & Advising: Huang leads a research group advising over 50 students, many of whom have pursued academic and industry roles. His grants support projects in HCI, health tech, and collaborative systems.

• Human-Computer Interaction
• Data Science
• Health Informatics (eHealth/mHealth)
• +3 more