Amir Houmansadr is an Associate Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst. His research focuses on network and AI security, privacy-enhancing technologies, and censorship circumvention. He holds a PhD from the University of Illinois at Urbana-Champaign (2012) and a postdoctoral fellowship at the University of Texas at Austin (2012-2014). Key research areas include secure communication systems, adversarial ML attacks, federated learning defenses, and analyzing censorship mechanisms like the Great Firewall of China. He leads the SPIN research group, which develops tools like CensorLab and MassBrowser. Notable contributions include exposing GFW vulnerabilities and advancing privacy-preserving AI models. Awards include the DARPA Director’s Award (2024), ACM CCS Distinguished Paper (2023), and NSF CAREER Award (2016). His work has been featured in media outlets like The Guardian, MIT Technology Review, and MassLive. He advises over 20 students and has served on program committees for top security conferences (IEEE S&P, ACM CCS, USENIX Security). Current courses include CMPSCI 660: Advanced Information Assurance.
Daniel Varro is a Professor affiliated with McGill University (Faculty of Engineering, School of Computer Science), with strong ties to Budapest University of Technology and Economics and Linköping University. He is a leading researcher in model-driven engineering, cyber-physical systems, and software engineering, actively contributing to top-tier conferences such as MODELS, ICSE, and ASE. His research focuses on model-based systems engineering (MBSE) , automated model generation , model transformations , and constraint-based consistency checking . Recently, his work has expanded into integrating large language models (LLMs) and machine learning into modeling workflows, including model querying, domain modeling, and bug detection. The recent publications reveal a strong trend toward AI-augmented modeling, logic-based solvers (e.g., Refinery), and safety assurance of autonomous systems (e.g., COLREGs compliance). His work bridges formal methods with practical software engineering challenges in industrial and safety-critical domains. Scientific Awards: No specific awards mentioned in the text. Advising and Grants: While no explicit list of students or grants is provided, his mentorship in the Doctoral Symposium and repeated leadership roles suggest active supervision and likely grant funding. He has led projects on automated model generation, model quality, and AI integration in modeling. Labs and Teams: Daniel Varro is associated with research groups focused on model-driven engineering and software evolution, likely leading or co-leading teams working on the VIATRA and Refinery frameworks for model transformation and solving.
Timothy Ashe is an Assistant Professor of Spanish & Linguistics at the University of Alabama at Birmingham (UAB). He holds a PhD in Spanish Linguistics from Arizona State University and has extensive experience in language education, corporate language analysis, and cross-cultural communication. His work focuses on second language acquisition, technology-enhanced language learning, and intercultural competence. Education: BA: University of Illinois at Urbana-Champaign (Spanish and Political Science) MA: DePaul University (Spanish and World Language Teaching) MA: DePaul University (English as a Second Language & Bilingual/Bicultural Education) PhD: Arizona State University (Spanish Linguistics) Research Interests: Timothy's research explores pragmatics, language for specific purposes (e.g., business, law, medicine), and the use of technology (e.g., mobile apps, social media) in language learning. He emphasizes culturally responsive frameworks to enhance proficiency and has collaborated internationally with governments and institutions. Teaching & Engagement: He teaches courses in Spanish linguistics, business Spanish, and study-abroad programs, having led student groups to Spain and Mexico for over a decade. Fluent in English, Spanish, Portuguese, and Italian, he also serves as a certified interpreter/translator in Spanish. Professional Background: Before academia, Ashe worked in corporate language analysis and as a high school language instructor. His interdisciplinary approach bridges practical language use with academic research, particularly in immersive and digital learning environments.
Daniel Grier is an Assistant Professor jointly appointed in the Computer Science and Engineering and Mathematics departments at the University of California, San Diego (UCSD). His research focuses on quantum complexity theory , particularly exploring near-term quantum computing paradigms and proving quantum advantage over classical systems. He holds a Ph.D. from MIT and was previously a postdoctoral fellow at the University of Waterloo’s Institute for Quantum Computing. Education: Ph.D. in Computer Science, MIT B.S. in Computer Science and Mathematics, University of South Carolina Research Interests: Grier’s work bridges theoretical computer science and quantum computing, emphasizing algorithm design, complexity class separations, and foundational questions about quantum supremacy. He studies how low-depth quantum circuits, boson sampling, and other near-term technologies can achieve computational tasks classically deemed intractable. Recent Article Trends: His publications explore efficient quantum state learning (e.g., classical shadows), hardness results for quantum sampling problems (e.g., bipartite Gaussian boson sampling), and circuit lower bounds (e.g., depth-2 QAC circuits). These contributions highlight his focus on rigorously defining quantum computational advantages. Awards: None explicitly listed in the text. Advising & Grants: Advises at least one student, Jackson Morris. His research is supported by grants exploring quantum complexity and algorithm design. Teaches advanced courses on quantum complexity theory, computability, discrete mathematics, and quantum computing fundamentals. Labs/Teams: Maintains an active lab focused on quantum complexity theory, collaborating with colleagues on topics like interactive protocols and shallow quantum circuits.
Dr. Khaldoun H. Shami is a Lecturer in Documentary Film at the School of Media, Language and Communication Studies, within the School of Art, Media and American Studies at the University of East Anglia (UEA), Norwich, UK. He is actively involved in research, teaching, and academic event organization, and is currently accepting PhD students. Education: PhD in Film, Television and Media, University of East Anglia MA in Documentary Practice, Brunel University London Master of Communication in Screen Studies, Universiti Sains Malaysia Diploma in Leadership for Media, United Nations University, Tokyo His research focuses on Middle Eastern documentaries and TV news, with a strong emphasis on secularism, minority representation, censorship, and industrial challenges in media production. He specializes in ethnographic documentary, cinéma vérité, militant cinema, and alternative radio, contributing to broader discourse in media studies and film theory. His work explores how media shapes identity, resistance, and public discourse in politically sensitive contexts. Dr. Shami is the director of the documentary Secular | Aa'La'Ma'Ni (2024), which presents filmmaker perspectives from Lebanon, Tunisia, Jordan, and Palestine on religion, sectarianism, and censorship. His research contributes to the UN Sustainable Development Goals, particularly in the area of quality education and inclusive media representation. Scientific Awards: Vice-Governor of Jakarta Award in Relief (2008) USIM Film Award - Documentary, Kuala Lumpur (2009) University of Jordan Excellence Award in Community Service (2005) Dr. Shami has been actively involved in academic and public engagement, serving as a peer reviewer for the Journal of Political Ideologies and as a Community Radio Consultant for Japan Platform JPF. He has organized and participated in numerous events, including the International Symposium on Media and the Middle East, focusing on resistance, activism, and visual stereotypes. He also contributes to public film screenings and academic forums, such as the Al Jazeera Forum for Journalism Faculties in the Arab World. He is a member of the Research and Symposium Committee for Media and the Middle East at UEA, collaborating with scholars internationally. His work bridges academic research with practical filmmaking and community outreach, fostering interdisciplinary dialogue on media, religion, and society.
Tanja Käser is a Tenure Track Assistant Professor at EPFL's School of Computer and Communication Sciences (IC), leading the Machine Learning for Education Laboratory (ML4ED). Her interdisciplinary research bridges machine learning, data mining, and educational technology, focusing on personalized learning systems and human behavior modeling. PhD in Computer Science (ETH Zurich, 2015) - honored with Fritz Kutter Award Former Senior Data Scientist at Swiss Data Science Center (ETH Zurich) Postdoctoral Researcher at Stanford University's Graduate School of Education Research Focus Explainable AI for education Adaptive learning environments Behavioral pattern recognition Generative AI applications in pedagogy User modeling and personalization Learning analytics in unstructured settings Recent Publication Trends Her 2024-2023 work demonstrates: Interpretable clustering of learners Transformer-based language learning prediction GAN applications for creative education Teacher-AI collaboration frameworks Explainability validation methods Modular network architectures Scientific Recognition Fritz Kutter Award for best Swiss computer science thesis (2015) Advising & Collaborations Currently supervises multiple PhD students including: Cock Jade Maï L Glandorf Dominik Güres Fatma-Betül Neshaei Seyed Parsa Radmehr Bahar Shibu Abhinand Shved Ekaterina Research Infrastructure Operates from EPFL's ML4ED laboratory with hybrid on-site and digital educational systems research capabilities.
Pengtao Xie is an Associate Professor (with tenure as of June 2025) in the Department of Electrical and Computer Engineering at the University of California San Diego. He also serves as Associate Adjunct Professor in the Division of Biomedical Informatics, Department of Medicine, and holds affiliate appointments with the Halıcıoğlu Data Science Institute, School of Biological Sciences, Shu Chien-Gene Lay Department of Bioengineering, Skaggs School of Pharmacy and Pharmaceutical Sciences, and multiple research institutes including the AI Group, Center for Machine-Intelligence, Computing and Security, Institute of Engineering in Medicine, and Institute for Genomic Medicine. Education: PhD in Machine Learning, School of Computer Science, Carnegie Mellon University Research Interests: His research focuses on machine learning inspired by human learning skills, such as self-explanation, small-group learning, and learning by teaching. He applies these techniques to large language models, foundation models, healthcare, and biomedicine. His work spans generative AI, medical imaging, protein modeling, and drug discovery. Recent Research Trends: His 2024–2025 publications emphasize generative AI for ultra-low-data medical image segmentation, multimodal large language models for biomedical applications, protein function prediction, and novel training strategies like task-adaptive pretraining and bi-level optimization for model adaptation. Scientific Awards: NIH MIRA Award (2025) NSF CAREER Award (2024) Best Graduate Teacher Award – UCSD ECE (2023) ICLR Notable-Top-5% Paper (2023) Global Top-100 Chinese Young Scholars in AI (2022) UCSD Faculty Career Development Award (2022) Tencent Faculty Award (2021) Outstanding Reviewer – ICLR (2021) AMIA Doctoral Dissertation Award Finalist (2020) Amazon AWS Research Award (2020) Tencent AI-Lab Faculty Award (2020) Innovator Award – Pittsburgh Business Times (2018) Siebel Scholarship (2014) Advising and Grants: He currently advises PhD students, postdocs, and master’s students. He has received major grants including the NIH MIRA and NSF CAREER awards, and actively mentors Schmidt AI in Science postdocs and graduate students. Teaching and Labs: He teaches ECE285 Deep Generative Models and ECE175B Probabilistic Reasoning and Graphical Models . His lab focuses on foundational and translational AI research with applications in biomedicine and healthcare.
Virginia Smith is the Leonardo Associate Professor of Machine Learning at Carnegie Mellon University's School of Computer Science, with a courtesy appointment in the Electrical and Computer Engineering Department. She leads research addressing critical challenges in machine learning systems, particularly focusing on safety and efficiency. Her research interests span federated and collaborative learning, efficient training methods, data privacy, and AI safety. Recent work has explored topics such as model unlearning, LLM security, and resource-efficient distributed learning systems. She has made significant contributions to understanding how to make machine learning systems more robust, private, and efficient while maintaining performance. Professor Smith's publication record demonstrates a clear progression toward addressing practical challenges in deploying machine learning systems at scale. Her recent work shows strong emphasis on large language model safety, privacy-preserving techniques, and efficient distributed learning approaches. The research spans theoretical foundations to practical implementations, with numerous papers appearing in top-tier venues including NeurIPS, ICML, ICLR, and MLSys. AFOSR Young Investigator Award Sloan Research Fellowship 2023 Samsung AI Researcher of the Year Best Paper Award at ICML 2025 Exploration in AI Workshop Outstanding Paper Award at MLSys 2023 As an educator, Professor Smith mentors numerous PhD students and postdocs while teaching advanced machine learning courses at CMU. She serves as Program Chair for ICML 2025 and co-organizes a semester program on Federated and Collaborative Learning at the Simons Institute. Her research group maintains strong collaborations with industry partners including Amazon, where she has received research awards.
Elaine Treharne serves as the Roberta Bowman Denning Professor of Humanities at Stanford University, holding primary appointment in the Department of English with courtesy appointments in German Studies and Comparative Literature. She concurrently acts as Senior Associate Vice Provost for Undergraduate Education and Director of Curriculum, while directing Stanford Text Technologies—a major initiative exploring textual transmission across historical periods. Her leadership extends to co-directing SILICON and spearheading NEH-funded projects that redefine digital approaches to manuscript studies. Her academic foundation includes a B.A. in English Language and Literature (First Class Honors) from the University of Manchester (1986), a Master of Archive Administration from the University of Liverpool (1987), and a Ph.D. in English from the University of Manchester (1992). This archival training underpins her dual expertise in traditional manuscript scholarship and digital innovation. Treharne's research pioneers intersections between medieval materiality and contemporary technology, investigating the haptic experience of medieval books, AI applications for manuscript analysis, and the long history of text technologies. She challenges conventional periodization through projects like 'Disrupting Categories, 1050-1250' while developing computational frameworks for fragmentology and textual distortion. Her work consistently bridges paleography with digital methodology to examine how writing systems shape cultural memory. Recent publications reveal a decisive shift from foundational medieval scholarship toward integrative digital-humanities frameworks, with increasing emphasis on phenomenological approaches to both physical and digital texts. This trajectory culminates in current projects applying machine learning to manuscript transmission patterns and developing ethical guidelines for digital archival tools. Her scientific recognition includes: Fellow of the Society of Antiquaries Fellow of the Royal Historical Society Honorary Lifetime Fellow of the English Association (former Chair and President) Fellow of the Learned Society of Wales American Philosophical Society Franklin Fellow Princeton Procter Fellow Fellow of the Stanford Clayman Institute for Gender Studies Treharne actively supervises graduate students in early literature, Book History, and Digital Humanities while securing major grants including NEH funding for Stanford Global Currents, AHRC support for the Production and Use of English Manuscripts project, and Stanford Impact Labs fellowship for archival tool development. She maintains commitment to ethical scholarly environments through her leadership in VPUE initiatives and digital pedagogy. She directs the Stanford Text Technologies initiative hosting the annual Collegium series, co-directs SILICON for internet longevity research, and leads specialized projects including 'Digital Ker' for Anglo-Saxon manuscript cataloging and 'Medieval Networks of Memory' analyzing mortuary rolls. These interconnected efforts form a comprehensive ecosystem for advancing textual scholarship across temporal and technological boundaries.
Mingyu Ding is a tenure-track Assistant Professor at the Department of Computer Science, University of North Carolina at Chapel Hill. His research bridges robotics, embodied AI, and computer vision, focusing on building agents that interact effectively with physical environments. PhD in Robotics, University of Hong Kong (2022), advised by Ping Luo Postdoctoral Fellow, BAIR@UC Berkeley (with Masayoshi Tomizuka) Visiting Scholar, CSAIL@MIT (with Joshua Tenenbaum) B.S. in Computer Science, Renmin University of China (under Zhiwu Lu) His work emphasizes robot learning through physical simulation, multimodal foundation models, and self-supervised methods. Key contributions include Embodied Concept Learner (ECL) and Sparse Diffusion Policy frameworks. Recent publications highlight trends in 3D vision, diffusion-based planning, and language-driven robotic behavior synthesis. Awards include ICRA Best Paper (2024), ME Rising Star (2023), and CVPR Doctoral Consortium (2023). Session Chair for ICRA 2025 Associate Editor for IROS 2025 Guest Editor for Robotics Special Issue: Embodied Intelligence
Ruoyu (Fish) Wang is an Associate Professor at the School of Computing and Augmented Intelligence, Arizona State University (Tempe campus). He also holds affiliations as Associate Director of Impact at the Global Security Initiative, Center for Cybersecurity & Trusted Foundations, and with the Biodesign Center for Biocomputing, Security and Society. His educational background includes: Ph.D. in Computer Science, University of California, Santa Barbara Professor Wang's research focuses on system security, with an emphasis on automated binary program analysis and reverse engineering of software. He is the co-founder and core developer of the angr binary analysis platform, which won third place in the DARPA Cyber Grand Challenge (2018). His work spans vulnerability discovery, fuzzing, and security tool development for binary program analysis. His current research interests include: Binary program analysis and reverse engineering Automated vulnerability discovery and mitigation Fuzzing techniques and robust testing Phishing and fraud detection in e-commerce Security of firmware and embedded systems Application of machine learning to security problems His recent publications (2024-2025) demonstrate cutting-edge research in fraud detection for e-commerce using LLMs, advanced fuzzing methodologies, and binary decompilation techniques. Key trends include bridging theoretical program analysis with practical security tools, as evidenced by extensions to the angr platform, and addressing emerging threats in financial ecosystems and client-side security. Dr. Wang has received notable recognition: Third place in DARPA Cyber Grand Challenge (2018) with team Shellphish As an active educator, he supervises graduate research (CSE 599/799) and teaches core cybersecurity courses including Software Security (CSE 545) and Information Assurance (CSE 365). His teaching spans multiple semesters through 2025, covering practicums, internships, and special topics in computing security. Dr. Wang co-founded the angr binary analysis platform and contributes to Arizona State University's security research ecosystem through leadership roles in the Center for Cybersecurity & Trusted Foundations and Biodesign Center for Biocomputing, Security and Society.
Tan Chuan Hoo is an Associate Professor (tenured) and Deputy Head of the Department of Information Systems and Analytics at the National University of Singapore's School of Computing. With a distinguished academic career spanning multiple continents, he brings expertise in digital transformation, healthcare informatics, and enterprise systems to his teaching and research. His educational background includes: B.Sc. (1st Class Honours, National University of Singapore) M.Sc. (Accelerated, National University of Singapore) Ph.D. (National University of Singapore) Professor Tan's research focuses on digital transformation, particularly designing, deploying, and evaluating technological innovations. His work centers on two critical areas: digital commerce (provision of digital services such as online shopping aids) and digital organization (ensuring operational efficiency and performance). His research has significant implications for healthcare institutions, corporations, and crisis preparedness and response organizations. He conducts comprehensive analyses using various scientific methodologies including field experiments and mixed methods to understand how digital technologies reshape business operations and enhance societal well-being. His publication record shows a consistent focus on information systems, with recent articles (2020-2025) emphasizing healthcare informatics, digital transformation, and open innovation. His work demonstrates a clear trajectory toward understanding how technology intersects with organizational effectiveness and societal implications, with increasing attention to healthcare applications and digital crisis management. Professor Tan has received numerous prestigious awards for his contributions to the field: Faculty Teaching Excellence Award, NUS (2024, 2017) Information Management Research Award, China Information Economics Society (2023) Reviewer Hall of Fame, Journal of AIS (2020) Outstanding Associate Editor Award, MIS Quarterly (2016) Best Reviewer Award, Journal of AIS (2016) INFORMS ISS Design Science Award (2013) Honorable Mention, Journal of AIS (2015) He has successfully advised PhD students and collaborated with public and private entities on research projects. His editorial service as Associate Editor for Information Systems Research and MIS Quarterly, along with board memberships at other leading journals, demonstrates his significant influence in the field. Professor Tan has secured research grants supporting projects on digital crisis preparedness, disaster response technology, and healthcare digitalization. His research group focuses on understanding how technology can be designed and implemented to support organizations in crisis situations, enhance healthcare services, and improve digital commerce. Current projects include examining digital crisis management, technology for disaster response, and the digital transformation of healthcare services.
Stephen Meisenbacher is a Research Associate at the Technical University of Munich (TUM) , affiliated with the School of Computation, Information and Technology and the Department of Computer Science, I19 . He has been part of the Software Engineering for Business Information Systems (SEBIS) chair since March 2022. His research focuses on Privacy-Preserving Natural Language Processing (NLP) , Differential Privacy , and Privacy-Enhancing Technologies (PETs) , with a particular interest in their integration into software development and business applications. His work also explores Hybrid, Expert-Driven Classification Systems and Usable Privacy solutions. Stephen’s recent publications address trends in AI Privacy Risks , Text Rewriting with DP , Legal AI Use Cases , and Data Protection Compliance . He has contributed to GDPR-related research and PETs adoption in small enterprises. His teaching includes Natural Language Processing seminars and Software Engineering lecture courses for Master’s and Bachelor’s students at TUM. He also organizes Entrepreneurship for Small Software-Oriented Enterprises seminars. Stephen holds a Master’s in Informatics from TUM (DAAD Graduate Scholarship) and a Bachelor’s in Computer Science from the University of Notre Dame, with additional studies in German Language and Literature. Contact: stephen.meisenbacher@tum.de | LinkedIn
Stefania Dumbrava is an Associate Professor of Computer Science at ENSIIE (École Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise) and a permanent member of the ACMES team in the SAMOVAR laboratory at Télécom SudParis, Institut Polytechnique de Paris. She is also actively involved in the Property Graph Schema Working Group and the European Research Network on Formal Proofs (EuroProofNet). Education PhD in Computer Science, Université Paris-Sud (2016) MSc in Computer Science, Jacobs University Bremen (2012) BSc in Mathematics, Jacobs University Bremen (2010) Research Interests Dumbrava's research lies at the intersection of formal methods and data management . She designs and verifies algorithms and systems for graph databases , with emphasis on property graphs , schema discovery , query optimization , and distributed graph processing . Recently, her work focuses on certifying large-scale distributed graph systems under the ANR JCJC VERDI project (2025–2029). Awards & Honors SIGMOD Best Paper Award 2023 – “PG-Schema: Schemas for Property Graphs” SIGMOD Research Highlight Award 2023 – “Threshold Queries” VLDB 2022 Best Regular Research Paper Runner-Up – “Threshold Queries in Theory and in the Wild” SIGMOD 2025 Distinguished Reviewer Award ICDE 2025 Best Program Committee Member Award EASST Best Software Science Paper Award, ICGT 2025 Students & Grants Dumbrava has supervised numerous research interns and is actively recruiting PhD students for her ANR VERDI project on verified foundations of large-scale distributed graph systems. She has also served on six PhD thesis committees as examiner since 2021. Labs & Teams She leads the ACMES research group within the SAMOVAR laboratory (Télécom SudParis, Institut Polytechnique de Paris), where her team develops formally verified graph-database engines and tools such as GRASP, VerDILog, and DatalogCert.
Claire Donnat is an Assistant Professor in the Department of Statistics at the University of Chicago, specializing in statistical and machine learning methods for graph-structured and high-dimensional data. Her work bridges theoretical innovation with applications in biomedical research, environmental science, and public health. Education: B.S. and M.S. in Applied Mathematics from Ecole Polytechnique; Ph.D. in Statistics from Stanford University (2020). Her research focuses on three methodological directions: (1) statistical foundations for graph neural networks (GNNs), (2) structured estimation with graph constraints, and (3) multimodal data integration with uncertainty quantification. Key applications include thermotolerance in photosynthetic microbes, family network analysis for child welfare, and spatial transcriptomics. The 15 most recent publications highlight her work in GNNs, CCA, tensor modeling, and epidemic analysis, with keywords spanning statistics, machine learning, and network science. Her methodological contributions address challenges in sparsity, graph topology, and heterogeneous data fusion. Scientific Awards: Facebook Research Award (2021), C3.AI COVID Grand Challenge winner (2020), Lumiata hackathon winner (2020), Stanford Centennial Award (2019), and others. Claire's research group actively recruits postdocs and students for projects involving graph-based modeling, data integration, and biomedical applications. She also provides research consulting in statistical methodology and graph modeling for life sciences.
