Affiliations & Roles Professor of Computer and Information Science at University of Pennsylvania Faculty in Graduate Groups: Bioengineering (School of Engineering) Genomics & Computational Biology (School of Medicine) Operations, Information & Decisions (Wharton School) Psychology (School of Arts & Sciences) Research Affiliations: Annenberg Public Policy Center (Distinguished Fellow) Center for Cognitive Neuroscience Institute for Translational Medicine Research Interests Focuses on explainable AI, natural language processing (NLP), and machine learning applications in psychology and medicine. Key areas include: Language analysis for well-being and mental health Spectral methods for NLP (e.g., Eigenwords) Forecasting and decision-making models Bioinformatics and genomics Teaching Teaches advanced courses in Machine Learning, Deep Learning, and AI ethics, including: CIS 5200: Machine Learning CIS 5220: Deep Learning CIS 6200: Advanced Topics in Deep Learning Key Collaborations Works with interdisciplinary teams on projects like the Good Judgment Project (forecasting) and WWBP (Well-Being and Language). Collaborators include Martin Seligman (positive psychology), Dean Foster (statistics), and Michael Collins (NLP).
Brooks Casas, Ph.D., is a Professor at the Fralin Biomedical Research Institute at VTC, with joint appointments in the Department of Psychology (College of Science), Department of Biomedical Engineering and Mechanics (College of Engineering), and the Department of Psychiatry and Behavioral Medicine (School of Medicine) at Virginia Tech. He is also a College of Science Faculty Fellow, recognized for his contributions to decision neuroscience and computational psychiatry. Ph.D. in Psychology, Harvard University Postdoctoral Fellowship, Baylor College of Medicine Former Assistant Professor of Neuroscience and Psychiatry, Baylor College of Medicine Brooks Casas investigates the neural computations underlying social decision-making, focusing on how valuation, learning, and social preferences shape human choices. His research integrates decision neuroscience, behavioral economics, and social psychology to understand both normative and pathological decision processes. Key areas include trust, risk preferences, social influence, and impaired decision-making in psychiatric disorders such as substance abuse and borderline personality disorder. His lab employs fMRI, computational modeling, and longitudinal studies to explore these phenomena. His recent publications span topics such as machine learning applications in diagnosing borderline personality disorder, neural predictors of adolescent risk behaviors, and the role of cognitive control in substance use. His work often involves large-scale longitudinal studies, such as the decade-long investigation into early life adversity and brain development with Jungmeen Kim-Spoon. He has not received any explicitly mentioned scientific awards in the provided text. Casas leads the Casas Lab within the Center for Human Neuroscience Research and collaborates extensively with students and researchers across disciplines. His work is supported by grants from the National Institutes of Health and the Institute for Society, Culture, and Environment. He advises multiple graduate students and early-career researchers, contributing significantly to training in computational psychiatry and decision neuroscience. His lab, the Casas Lab, is part of the Fralin Biomedical Research Institute and focuses on human neuroscience research, particularly using neuroimaging and behavioral experiments to study social and economic decision-making.
Emily Falk is a Professor of Communication, Psychology, Marketing, and Operations, Information, and Decisions at the University of Pennsylvania, where she serves as Vice Dean of the Annenberg School for Communication, Director of the Communication Neuroscience Lab, and Director of the Climate Communication Division of the Annenberg Public Policy Center. Her interdisciplinary work bridges communication science, psychology, and neuroscience to understand behavior change and message effectiveness. Dr. Falk received her B.A. in Neuroscience from Brown University and her Ph.D. in Psychology from the University of California, Los Angeles. Her educational background reflects the interdisciplinary approach that characterizes her research program. Dr. Falk's research focuses on the science of behavior change, examining what makes messages persuasive, why and how ideas spread, and what makes people effective communicators. Her work employs tools from psychology, neuroscience, and communication to investigate neural predictors of message effectiveness, social influence, and the spread of ideas through networks. Key research areas include health communication (particularly tobacco use), climate communication, political communication, and the neuroscience of choice and decision-making. Her groundbreaking work has demonstrated how fMRI brain imaging in small groups can predict large-scale public health campaign success. Dr. Falk's research has been recognized with numerous prestigious awards, including early career awards from the International Communication Association and the Society for Personality and Social Psychology Attitudes Division, a Fulbright grant, Social and Affective Neuroscience Society award, DARPA Young Faculty Award, and the NIH Director's New Innovator Award. She was also named a Rising Star by the Association for Psychological Science. As an advisor, Dr. Falk has mentored numerous graduate students who have gone on to successful careers in academia, government, non-profit, and business sectors. Her lab, the Communication Neuroscience Lab, is funded by major organizations including DARPA, NIH, Google, and the Mind & Life Institute. The lab operates with a mission to increase health and happiness for people and the planet through communication science. The Communication Neuroscience Lab is an interdisciplinary research group that uses tools from biological, social, and network sciences to motivate choices that benefit individuals, communities, and the planet. Current major research projects include BB-PRIME (Brain-based Prediction of Message Effectiveness), BB-PRIME Phase II focusing on climate change interventions, and the GeoScan Smoking Study examining tobacco marketing effects.
Aleksandr (Sasha) Aravkin is Associate Professor in the Department of Applied Mathematics and Adjunct Associate Professor of Health Metrics Sciences, Mathematics, and Statistics at the University of Washington. He serves as Director of Mathematical Sciences at the Institute for Health Metrics and Evaluation (IHME), where he leads the Mathematical Sciences and Computational Algorithms team and contributes to strategic direction for applying mathematical sciences to analytic challenges. Dr. Aravkin earned his PhD in Mathematics (Optimization) and MS in Statistics from the University of Washington in 2010, following a BSc in Mathematics and Computer Science in 2004. His educational background forms the foundation for his interdisciplinary research approach. His research expertise spans large scale optimization, machine learning, data science, convex and variational analysis, algorithm design, robust statistics, inverse problems, and uncertainty quantification . These methodologies are applied across diverse domains including health metrics, computational medicine, tracking and navigation, seismic imaging, computational finance, and neuroscience. Dr. Aravkin has pioneered approaches for fusing physics-based and data-driven models, enabling innovative solutions to complex problems. Dr. Aravkin's publication record demonstrates a strong focus on health metrics and Global Burden of Disease studies, with recent work emphasizing meta-analytic approaches for risk-outcome relationships. His research spans epidemiological modeling, health effects of various exposures, and forecasting disease burden across populations. The publications reveal a consistent pattern of high-impact work in top journals including The Lancet and Nature Medicine, often addressing critical public health questions through rigorous statistical and mathematical frameworks. At IHME, Dr. Aravkin has led significant projects including the Burden of Proof Studies (2019-2024) which analyzed 153 risk-outcome pairs, and COVID-19 Modeling (2020-2023) where his team developed forecasting models and excess mortality estimation methods. He has also been instrumental in developing the Evidence Score model, requiring novel algorithmic approaches. His work bridges theoretical mathematical sciences with practical applications to improve global health policy and practice.
Dr. Tatsuya Mori is a Professor at the Department of Computer Science and Communication Engineering , Faculty of Science and Engineering, Waseda University . He also holds visiting researcher positions at RIKEN Center for Advanced Intelligence Project (since 2018) and National Institute of Information and Communications Technology (since 2019). Education: Ph.D. in Information Science (2005), Waseda University Research Interests: Spanning information security and privacy across emerging technologies like autonomous driving , AI , 3D sensing , VR , biometric measurement , and Web3 . His work focuses on offensive security and interdisciplinary research , including physical-layer attacks on sensors and behavioral studies on phishing detection. Scientific Awards: Recipient of multiple prestigious awards, including the Distinguished Paper Award Runners-Up at IEEE EuroS&P 2024 , IPSJ Outstanding Paper Award 2024 , and CSS2024 Concept Research Prize . His research has been recognized in top conferences like USENIX Security , NDSS , and ACM CCS . Professional Leadership: Active in academic governance as Chief Investigator for NISC Working Groups and Committee Member for JST Research Areas . He serves on program committees for NDSS , IMC , and ACM CCS .
Björn Ross is a Lecturer in Computational Social Science at the University of Edinburgh's School of Informatics, where he is affiliated with the Institute for Language, Cognition and Computation. He serves as Director of the SMASH research group and is part of the management team for the Centre for Doctoral Training in Natural Language Processing (CDT in NLP). His educational background includes: PhD (2019) from the University of Duisburg-Essen MSc in Computer Science (2016) from the University of Münster Exchange year (2014-2015) at the University of Strasbourg BSc in Information Systems (2013) from the University of Münster Ross's research focuses on computational social science, particularly using natural language processing, social network analysis, and agent-based modeling to study social media phenomena. His work examines misinformation, hate speech, bot activity, and the ethical implications of computational methods. He also investigates how social media can be leveraged for social good, especially in crisis communication contexts. A significant portion of his recent research addresses bias and fairness issues in AI systems, particularly regarding marginalized communities and LGBTQ+ identities. His work spans both technical development of computational methods and critical examination of their societal impacts. Analysis of his recent publications reveals a strong trend toward examining bias in AI systems, particularly regarding marginalized communities. His work spans multiple methodologies including computational analysis of social media data, development of NLP techniques, and critical examination of AI ethics. He frequently collaborates across disciplines, working with researchers in computer science, social sciences, and healthcare domains. His professional recognition includes: Best Paper Award at the Hawaii International Conference on System Sciences (HICSS) in 2018 Associate Editor for Business & Information Systems Engineering Active membership in professional organizations including AIS, ACL, and ACM Ross currently supervises multiple PhD students including Agostina Calabrese, Eddie Ungless, Sandrine Chausson, Wendy Zheng, Seraphina Goldfarb-Tarrant, and Mahmoud Ibrahim. At the University of Edinburgh, he teaches courses such as 'Text Technologies for Data Science' and 'Evidence, Argument and Persuasion in a Digital Age,' reflecting his expertise at the intersection of computational methods and social implications.
Jonathan P. Caulkins is the H. Guyford Stever University Professor of Operations Research and Public Policy at Carnegie Mellon University's Heinz College and a member of the National Academy of Engineering. He specializes in systems analysis of problems pertaining to drugs, crime, terror, violence and prevention. His educational background includes: Bachelor of Science and Master of Science in systems science from Washington University S.M. in electrical engineering and computer science from MIT Doctorate in operations research from MIT Dr. Caulkins' research focuses on drug policy analysis, particularly cannabis legalization, optimal control, reputation management, human trafficking, and black markets. He has taught quantitative decision-making on four continents to students from 50 countries across all academic levels. His recent work examines how cannabis use has evolved from primarily recreational/weekend use to being part of daily routines for approximately 40% of current users. His notable awards include: David Kershaw Award from the Association of Public Policy Analysis and Management Robert Wood Johnson Health Investigator Award INFORMS President's Award Dr. Caulkins serves as past Co-director of RAND's Drug Policy Research Center and founding Director of RAND's Pittsburgh office, continuing to collaborate with RAND on drug policy projects. He teaches courses including Optimization, Decision and Risk Modeling, and Decision Analysis at CMU.
Paul Grubbs is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan. His research focuses on applied cryptography, security, and systems, particularly at the intersection of cryptographic protocols and real-world deployments. Education: PhD in Computer Science (Cornell University), BS in Mathematics and Computer Science (Indiana University) His work explores vulnerabilities in cryptographic systems, the design of secure key-value stores, and the societal implications of information security. Recent publications highlight advancements in zero-knowledge proofs, post-quantum cryptography, and encrypted database analysis. Key trends in his publications include: 1) Zero-Knowledge Proofs and Post-Quantum Security; 2) Privacy in Encrypted Systems; 3) Cryptographic Protocol Vulnerabilities; 4) Interdisciplinary approaches at the intersection of technology and societal impact. Scientific awards: IEEE Symposium on Security and Privacy 2023 Distinguished Paper Award USENIX Security 2020 Distinguished Paper Award Cornell Computer Science Dissertation Award Students include current advisees like Jiwon Kim, Anna Pui Yung Woo, and Chad Sharp (co-advised with Chris Peikert), plus former students Yang Du (MSc 2024), Quang Dao (MMath 2022), and Pengxiang Wang (BSE 2023). Grants include DARPA SIEVE (2021), Meta Privacy-Enhancing Technologies (2022), and NSF CAREER (2023). He has served on program committees for CRYPTO, IEEE S&P, and other leading conferences.
Zakir Durumeric is an Assistant Professor of Computer Science at Stanford University, leading the Stanford Empirical Security Research Group. His research focuses on Internet security, trust, and safety, emphasizing large-scale network measurement and open-source tool development. He founded Censys, a platform providing global Internet device data, and maintains tools like ZMap, ZGrab, and Retina. Research interests include cybercrime prevention, censorship analysis, disinformation tracking, and platform governance for online harassment. Notable contributions include studies on the Mirai botnet, TLS certificate ecosystems, and vulnerabilities like Heartbleed and Logjam. Awards include the IRTF Applied Networking Research Prize (2015) and a Test of Time Award (2022). Teaches courses: CS155 (Computer & Network Security), CS356 (Systems & Network Security), and CS249i (Modern Internet). Advises over 20 students, including Catherine Han, Kimberly Ruth, and Liz Izhikevich. Develops open-source software such as ZMap Toolkit and ASdb, and maintains datasets like CrUX Top Million Websites. Recent work explores toxic online behavior, misinformation ecosystems, and regional censorship mechanisms in China. His lab’s tools are widely adopted in academia and industry for security research and policy guidance.
Dr. Nicholas Nelson is an Associate Professor in the Department of Physics at California State University, Chico. His research spans interdisciplinary areas including astrophysics, dynamical chaos, and medical education curriculum development. He specializes in stellar evolution models, solar convection dynamics, and magnetic field generation in stars. His work bridges physics and healthcare, addressing structural competency in medical training and social determinants of health through innovative curricula. Research interests include: solar magnetic loop formation, chaotic dynamics in celestial bodies, and integrating social determinants of health into residency programs. His publications reflect a dual focus on computational astrophysics and healthcare equity. Notable contributions include studies on knuckleball aerodynamics, early career challenges in astrophysics, and curriculum design for addressing health disparities. Though no awards are explicitly listed, his work demonstrates impactful cross-disciplinary engagement. No advising relationships or grant information was provided in the source material. His office is located in PHSC 121B on campus.
Malvina Nissim is a leading researcher in computational linguistics and NLP at the University of Groningen's Department of Artificial Intelligence, with a focus on multilingual modeling, bias mitigation, and human evaluation frameworks. Key Contributions : Developed CALAMITA (Italian LLM benchmark), IT5 models for Italian language processing, and ReproHum framework for NLP evaluation reproducibility Research Pillars : Multilingual reasoning consistency, perspective-based text analysis, and figurative language modeling Her work spans activation steering techniques, cross-lingual transfer learning, and the creation of specialized language resources like the EurekaRebus dataset and MAGPIE idiom corpus. She pioneered methods for gender bias measurement in BERT and developed the SocioFillmore tool for perspective visualization. Recent publications explore model uncertainty as MCQ difficulty proxy, Italian headline generation benchmarks, and multilingual multi-figurative language detection. She actively participates in teaching initiatives like the "NLP with Bracelets" workshop for Italian high school students. Scientific Awards : ACL Best Paper Award (2025) EMNLP Outstanding Reviewer (2023) EVALITA Leadership Recognition (2024) She advises PhD students in model bias analysis and has contributed to the development of the Dutch Abusive Language Corpus (DALC) and the ReproNLP reproducibility framework. Her collaborations span institutions in Italy, Netherlands, and international NLP communities.
Julia Len is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill, where she conducts research in applied cryptography and computer security. She co-leads the Cryptography Group with Saba Eskandarian and is currently recruiting Ph.D. students for Fall 2026. She earned her Ph.D. in Computer Science from Cornell University in 2024 under the supervision of Tom Ristenpart, following a B.S. in Computer Science from UC San Diego in 2018, where she worked with Mihir Bellare. Before joining UNC, she was a METEOR postdoctoral fellow at MIT. Her research focuses on improving the security and privacy of deployed cryptographic protocols, particularly in end-to-end encrypted messaging. Key areas include interoperability, abuse prevention, key transparency, and authenticated encryption. She applies principled approaches ranging from identifying flaws in existing protocols to designing new cryptographic schemes and definitions. Her recent publications span top venues such as USENIX Security, CCS, Eurocrypt, and Crypto, demonstrating a strong and consistent research output in applied cryptography. Trends in her work show increasing focus on real-world protocol design, security analysis of widely used schemes, and privacy-preserving moderation mechanisms for secure communication platforms. METEOR Postdoctoral Fellow at MIT Julia Len has advised and collaborated with numerous researchers; current advisees are being recruited for Fall 2026. She has received research support through collaborations with industry partners including Zoom, Microsoft Research, and Meta. Her work on Partitioning Oracle Attacks has led to updates in Shadowsocks, age, OPAQUE, and HPKE, demonstrating significant real-world impact. She co-leads the Cryptography Group at UNC Chapel Hill, fostering research and education in cryptography. She also serves on the program committees of IEEE S&P 2026, USENIX Security 2025, and CATS 2023, contributing to the broader academic community.
Dr. Hai Phan is an Associate Professor in Data Science at New Jersey Institute of Technology's Ying Wu College of Computing. He holds a Ph.D. in Computer Science and Engineering from CNRS, University Montpellier 2 (2013), an M.S. from Konkuk University (2010), and a B.S. from HCM City University of Technology (2008). His research explores privacy-preserving machine learning and computational health analytics: Federated learning systems and optimization Privacy-enhancing technologies (differential privacy) Health informatics and social media analysis Cybersecurity defenses and adversarial learning Fair and ethical AI systems Dr. Phan's publications demonstrate strong emphasis on federated learning architectures with privacy guarantees, defenses against emerging security threats, and analysis of health-related behaviors through social media. Recent work focuses on IoT applications, large language model security, and mobile federated learning ecosystems. His research integrates techniques from distributed systems, cryptography, and machine learning. No scientific awards are mentioned in available sources. Information regarding student advising, research grants, or laboratory affiliations is not provided in available documentation.
Professor Pinar Akman is a Professor of Law at the University of Leeds, specializing in competition law, particularly in digital markets. She holds positions as a judge at the UK's Competition Appeal Tribunal and a member of the Financial Conduct Authority's Innovation Advisory Group. Her roles include former directorships of the Jean Monnet Centre of Excellence on Digital Governance (2019-2023) and the Centre for Business Law and Practice (2017-2020). She was promoted to full Professor within five years of her PhD. Education: Holds LLB and LLM from the University of Ankara Law School (ranked first overall undergraduate), and a PhD from UEA Law School/ESRC Centre for Competition Policy, funded by ORSAS and UEA Scholarships. Her doctoral work focused on abuse of dominance under EU competition law. Research interests include competition law's application to digital markets, market power, online platforms, and the interplay of competition law with consumer and contract law. She emphasizes legal and economic foundations of competition policy, including historical origins and modern challenges in regulating tech giants. Awards: Philip Leverhulme Prize (£100k), Women@Competition 5-Star Award, and recognition as a top global competition scholar by Global Competition Review. Her research on digital markets and antitrust policy has been funded by UKRI, ESRC, and international bodies. Advising & Grants: Led a £4M ESRC grant for the Centre for Competition Policy (2009), and a UKRI-funded empirical study on digital platforms. Provides policy advice to the IMF, World Economic Forum, and international organizations like UNCTAD and OECD. Engages in global policymaking through roles with the Canadian Chamber of Commerce, ASEAN, and Bhutan. Labs & Teams: Senior Fellow at George Washington University's Competition & Innovation Lab, Affiliated Scholar at Berkeley's Dynamic Competition Initiative, and member of Stanford Law School's Computational Antitrust Advisory Board. Editorial roles include World Competition: Law and Economics Review and Oxford Competition Law.
David Lydon-Staley is an Associate Professor at the Annenberg School for Communication, University of Pennsylvania, where he serves as Principal Investigator of the Addiction, Health, & Adolescence (AHA!) Lab. His research integrates neuroscience with communication science to examine substance use, media effects, and curiosity using fMRI, ecological momentary assessment, and network analysis. Education includes a Ph.D. in Human Development & Family Studies from The Pennsylvania State University, an M.S. from Penn State, an M.F.A. in Creative Writing from Drexel University (2025), and a B.A. in Psychology and English Literature from Trinity College Dublin. Research focuses on three interconnected areas: curiosity in media environments and health communication, media engagement in emotion dynamics, and substance use through dynamic network perspectives. Work emphasizes intensive longitudinal measurement of brain-behavior interactions during daily life. Recent publications (2023-2025) predominantly explore tobacco behavior, neural mechanisms of addiction, curiosity modulation, and social media's emotional impacts. Articles demonstrate consistent themes: fMRI analysis of inhibitory control, real-time geospatial tracking of smoking triggers, curiosity-based health messaging, and emotion regulation networks. Research has been supported by the National Institute on Drug Abuse, Jacobs Foundation, International Society for Behavioral Development, Center for Curiosity, and Brain & Behavior Research Foundation. Leads the Addiction, Health, & Adolescence (AHA!) Lab investigating substance use through network science approaches. Collaborates with the Complex Systems Lab at Penn's Department of Bioengineering.
