Janne Lindqvist is an Associate Professor at the Department of Computer Science, Aalto University. His research bridges security engineering, human-computer interaction (HCI), and privacy, with a focus on making security systems usable and user-centric. University: Aalto University Department: Department of Computer Science Rank: Associate Professor Email: janne.lindqvist@aalto.fi Lindqvist’s work spans security engineering , privacy systems , and user research , emphasizing practical authentication methods, password management, and human behavior in security contexts. He explores how users interact with systems like TPM APIs, gesture passwords, and mobile authentication mechanisms. Recent publications highlight trends in authentication systems , ubiquitous computing security , and mobile user behavior . Key themes include biometric authentication, gesture-based security, and balancing usability with cryptographic robustness. CHI'25 Honorable Mention Award CHI'24 Best Paper Award His research integrates empirical studies with technical implementations, such as analyzing password forgetting patterns and developing acoustic sensing for vehicle detection (e.g., Auto++, BO-Ear). Collaborative efforts span machine learning, psychology, and embedded systems.
Suchi Saria is the John C. Malone Associate Professor at Johns Hopkins University , with appointments in the Whiting School of Engineering (Computer Science), the Bloomberg School of Public Health (Health Policy & Management), and the Whiting School (Applied Math & Statistics). She directs the Machine Learning and Healthcare Lab and co-founded the Bayesian Health startup. Education: PhD in Computer Science from Stanford University (advisor: Daphne Koller), NSF Computing Innovation Fellowship at Harvard (2011), prior research at UMass (Barto, Madhavan), and industry experience at Aster Data Systems (acquired by Teradata). Research Focus: Saria develops statistical machine learning tools to extract insights from heterogeneous clinical data (structured/unstructured EHRs, sensor streams). Her work enables counterfactual reasoning for personalized treatment plans, dataset shift mitigation in healthcare AI, and weak supervision frameworks for mobile health apps. Key applications include sepsis prediction , Parkinson’s symptom tracking , and critical care optimization . Article Trends: Recent publications emphasize AI safety (2024-2025), addressing racial bias , transparency frameworks , and dynamic monitoring for clinical deployments. Her work spans conformal prediction , causal modeling , and policy guidelines for health AI. Scientific Awards: Sloan Research Fellowship (2018) DARPA Young Faculty Award (2016) MIT Technology Review TR35 Innovator (2017) Popular Science Brilliant 10 (2016) IEEE Intelligent Systems AI’s 10 to Watch (2015) NSF Computing Innovation Fellowship (2011) Rambus Fellowship (2004-2010) Best Paper Awards in ML, Informatics, and Medicine venues Advising & Grants: Saria mentors PhD students/postdocs in machine learning and health informatics , including funded projects like the NSF Smart and Connected Health Grant (2014) and Google Research Award (2014). Her lab’s TREWScore system (Science Translational Medicine 2015) is deployed in hospitals, while her Bayesian Health startup commercializes AI solutions for provider experience.
Arvind Narayanan is a Professor of Computer Science at Princeton University and Director of the Center for Information Technology Policy (CITP). His research focuses on the societal impact of digital technologies, particularly artificial intelligence, with emphasis on policy implications, fairness, and privacy. He leads interdisciplinary efforts connecting technical research with real-world policy challenges. Dr. Narayanan earned his Ph.D. from the University of Texas, Austin in 2009. His academic journey has established him as a leading voice in the critical examination of AI systems and their societal consequences. Narayanan's research spans multiple domains where technology intersects with society. His work on AI includes critical analysis of AI capabilities versus marketing claims (AI Snake Oil), fairness in machine learning systems, and the reproducibility crisis in ML-based science. In privacy research, he led the Princeton Web Transparency and Accountability Project which uncovered how companies track users online, developing the OpenWPM tool used in over 100 studies. His early work demonstrated fundamental limits of de-identification techniques and how machine learning reflects cultural stereotypes. His recent publications reveal a consistent focus on demystifying AI capabilities while identifying genuine opportunities and risks. Narayanan's work bridges technical computer science with policy relevance, emphasizing the importance of evidence-based approaches to AI governance. His research increasingly addresses the limitations of prediction systems, the challenges of evaluating AI systems, and the need for transparency in foundation models. Presidential Early Career Award for Scientists and Engineers (PECASE) Privacy Enhancing Technologies Award (twice recipient) Privacy Papers for Policy Makers Award (three-time recipient) TIME's inaugural list of 100 most influential people in AI 2025 Graduate Mentoring Award Narayanan is recognized as an exceptional mentor, receiving Princeton's Graduate Mentoring Award in 2025. His policy engagement extends to congressional testimony, advisory roles, and frequent media commentary. He has secured significant research funding supporting his work on web transparency, AI policy, and cryptocurrency analysis. His research group has produced influential tools like OpenWPM for web privacy studies and contributed to foundational textbooks on cryptocurrencies and fairness in machine learning. At Princeton, Narayanan leads the Web Transparency and Accountability Project, a major research initiative that has conducted large-scale measurements of online tracking across millions of websites. He also co-founded and directs the CITP's AI Policy Initiative, which brings together researchers from multiple disciplines to address pressing AI governance questions. His work frequently involves collaboration with social scientists, legal scholars, and policymakers to develop practical solutions to technology governance challenges.
Matthew Kay is an Associate Professor in the Department of Communication Studies at Northwestern University's School of Communication, with a secondary appointment in Computer Science. He serves as Co-Director of Graduate Studies for the PhD in Technology and Social Behavior program. His research focuses on human-computer interaction and information visualization, specializing in uncertainty communication, usable statistics, and personal informatics. He employs mixed-method approaches including behavioral analysis, interactive system development, and visualization technique evaluation to address real-world data interpretation challenges. Analysis of his recent publications reveals dominant themes in visualization literacy development, uncertainty representation for decision-making, and health informatics applications. His work consistently bridges theoretical frameworks with practical implementations, particularly in educational assessment tools and election forecast visualizations. Professor Kay co-directs the Midwest Uncertainty Collective (MU collective), a research group advancing uncertainty communication methodologies. Previously faculty at the University of Michigan School of Information, he maintains active contributions to visualization tool development including the ggdist R package for uncertainty visualization.
Anujit Chakraborty is an Associate Professor in the Department of Economics at the University of California, Davis . His research bridges Economic Theory , Behavioral Economics , and Experimental Economics , focusing on decision-making under risk and time constraints, procrastination, and present-biased behavior. Education : Ph.D. in Economics (University of British Columbia, 2017), M.S. in Quantitative Economics (Indian Statistical Institute, 2011), B.E. in Electronics Engineering (Jadavpur University, 2009) His work explores procrastination , present bias , prosocial behavior , and interpersonal uncertainty , often using experimental methods to analyze anomalies in economic preferences. Recent publications investigate democracy measurement , news source diversity , and peer-grading systems . Dr. Chakraborty teaches Intermediate Economics (ECN 100B) and Behavioral Economics at both undergraduate and Ph.D. levels.
Luca Peretti is an Associate Professor in Electric Machines and Drives at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Department of Electrical Engineering, Division of Electric Power and Energy Systems. He works as a researcher in the EMD (Electric Machines and Drives) group and serves as Partner Director for KTH's strategic partnership with ABB. Education: M.Sc. in Electronic Engineering (2005) from University of Udine, Ph.D. from University of Padova (2008) Professional Experience: Postdoc at University of Padova (2009-2010), Principal Scientist at ABB Corporate Research (2010-2018), Associate Professor at KTH (2018-present) His research focuses on: Automatic parameter estimation in electric machines Multiphase drive systems Sensorless control algorithms Loss segregation in drive systems Condition monitoring of industrial and transportation applications Recent publications demonstrate expertise in variable phase-pole machines, harmonic plane decomposition, predictive control algorithms, and advanced modeling of permanent magnet motors. Key application areas include transportation electrification, wind energy systems, and industrial drive technologies. Scientific roles include: Associate Editor, IET Electric Power Applications Journal (2019-present) Theme Co-Leader, Swedish Electromobility Center (2020-present) Member, IEEE (2021-present) and IET (2006-present) He leads the strategic partnership with ABB and contributes to doctoral program committees at University of Padova.
Madelon Hulsebos is a Researcher at CWI in Amsterdam, where she leads the Table Representation Learning (TRL) Lab and contributes to the Database Architectures group. She is also a faculty member of the European Laboratory for Learning and Intelligent Systems (ELLIS) Amsterdam unit. Her career bridges academia and industry, including a postdoctoral fellowship at UC Berkeley and prior industry experience in automating data analysis pipelines with ML. Education : PhD in Computer Science (University of Amsterdam, 2023), with research at Sigma Computing and MIT; Postdoctoral Fellow (UC Berkeley, 2024). Her research focuses on establishing tabular data as a key AI modality through Table Representation Learning , generative models for relational data, and robust systems for data analysis. Key interests include: Relational Table Embeddings LLMs for QA/text2SQL and data wrangling Retrieval over Data Lakes and Databases Agentic Systems for Data Science Democratizing insights from structured data Recent work highlights trends in benchmarking table retrieval (TARGET), semantic column detection (AdaTyper, Sherlock), and large-scale tabular data curation (GitTables, SchemaPile). These projects address challenges in metadata utilization, data lake search, and end-to-end systems for structured data. She has secured significant funding, including the NWO AiNed Fellowship Grant ($1M) for her 5-year DataLibra project. Madelon organizes workshops at NeurIPS , SIGMOD , and ACL , and reviews for top venues like VLDB and NeurIPS. Scientific Awards : NWO AiNed Fellowship Grant ($1M) She actively mentors students and collaborates on European AI initiatives, including monthly TRL seminars and workshops. Her lab's tools (GitTables, TARGET) are widely adopted for training foundation models on tabular data.
Özgür Atalay (ORCID: 0000-0003-1050-0685) is an Associate Professor at the Department of Textile Engineering , Istanbul Technical University . His research focuses on Textile-based Engineering with emphasis on Capacitive , Actuator , and Soft Robotics Engineering . Current projects include TexSoRVA (Textile-Based Soft Robotics for VR Applications), SMARTWASTE (Agricultural/Natural Waste Textiles), and TEXWEAROTS (Self-powered Wearable Soft Robotics). Collaborations span EU-funded initiatives, Turkish Scientific and Technological Research Council (TUBITAK), and industrial partnerships. His work integrates Internet of Things (IoT) with Electronic Textiles (E-textiles) , focusing on applications in Telerehabilitation , Wearable Robotics , and Smart Textiles . Key technologies include Capacitive Sensors , Conductive Yarns , and Fog Computing Frameworks . Recent publications highlight advancements in auxetic sensor design , cloud-controlled textile gloves , and self-powered wearable systems . His research output (58 publications) demonstrates a focus on practical implementations in Therapeutic Applications and Human-Machine Interaction . Özgür leads 6 active research projects, including: TexSoRVA : 04/2025-09/2026, EU-funded SMARTWASTE : 01/2023-12/2026, TTO-funded TEXWEAROTS : 11/2022-10/2027, EU-funded With over 1623 citations (h-index 15), his work contributes to Textile Electronics , Soft Robotics , and Wearable Technology fields.
Dr. Joey Paquet is a Tenured Associate Professor and Department Chair in the Department of Computer Science and Software Engineering at Concordia University in Montreal, Canada. He holds a PhD and specializes in research areas including Design and Implementation of Programming Languages, Context-Driven Computing, and Demand-Driven Computing. His work focuses on advancing programming paradigms and their applications in cybersecurity, distributed systems, and autonomic computing. Dr. Paquet is actively involved in thesis supervision across Computer Science and Software Engineering programs at both master's and doctoral levels. Research interests are centered around programming language design, particularly in demand-driven and context-aware systems. His contributions include frameworks like GIPSY and OpenISS, enabling scalable data processing and forensic computing. Recent work explores autonomic intent-driven networking, real-time gesture recognition, and IoT forensics. These efforts highlight his expertise in both theoretical constructs and practical implementations. His advising role supports students in MCompSc, MASc, and PhD programs. While specific grants are not detailed, his projects often involve interdisciplinary collaboration. Dr. Paquet is affiliated with platforms like LinkedIn and ResearchGate, reflecting an active academic presence. Key technical contributions include pioneering work on forensic computing backends, intent expression languages, and multimodal interaction systems. His research addresses challenges in cybersecurity, distributed systems, and service composition, with a focus on resilience and scalability.
Peter K. Kitanidis is a Professor in the Department of Civil and Environmental Engineering and the Institute for Computational and Mathematical Engineering at Stanford University . His research focuses on groundwater flow , hydrologic forecasting , and stochastic inverse modeling , with applications to pollutant remediation and CO₂ storage monitoring . Education : Diploma, National Technical University of Athens (1974) M.S., MIT (1976) Ph.D., MIT (1978) Research Interests : Groundwater modeling and contaminant transport Hydraulic tomography and aquifer characterization Stochastic methods for uncertainty quantification Bioremediation and enhanced in-situ pollutant decay Dilution and mixing processes in heterogeneous media Real-time river flow forecasting Scientific Awards : L.G. Straub Award (1979) W.L. Huber Research Prize (1994) ISI Highly Cited Researcher (2001) AGU Hydrologic Sciences Award (2011) ASCE Pioneers in Groundwater Lecturer (2011) Advising and Grants : Advised 20+ PhD and MS students (1978–2018) Principal investigator on NSF, EPA, and DOE-funded projects Developed software for groundwater data analysis and CO₂ monitoring Contributed to bioremediation protocols and hydraulic tomography algorithms Labs and Teams : Kitanidis Laboratory for groundwater crisis solutions Collaborated with Oak Ridge National Laboratory and Stanford Hydrogeology Group Mentored postdocs (2000–2017) in reactive transport and inverse modeling
Aleksander Madsen is a Researcher at the University of Oslo (UiO), affiliated with the Department of Sociology and Human Geography under the Faculty of Social Sciences. His primary research interests include social stratification, immigrant assimilation in labor markets, welfare state policies, and statistical methods. He holds a PhD from Oslo Metropolitan University and an MA from UiO. Education: PhD in Sociology, Centre for the Study of Professions, Oslo Metropolitan University (2014–2018) MA in Sociology, University of Oslo (2013) His research explores labor market integration of immigrants, gender disparities in male-dominated workplaces, and the impact of education-occupation mismatch on health outcomes. He has conducted longitudinal studies on long-term sickness absence and labor market trajectories using sequence analysis. Dr. Madsen has held postdoctoral and research positions at UiO and NIFU, and was a visiting scholar at Stanford University's SCANCOR (2024) funded by the Fulbright Foundation. His work spans interdisciplinary collaborations, including projects on workplace diversity, ethnic stratification, and policy evaluations. Awards: U.S. - Norway Fulbright Foundation Grant (2023–2024) His research group affiliations include the Social Inequalities and Population Dynamics team and the ORGMIGRANT project, investigating work organizations and immigrant assimilation. Recent publications address residential networks among immigrants, workplace ethnic diversity, and women’s attrition from male-dominated sectors.
Suguman Bansal is an Assistant Professor in the School of Computer Science at Georgia Institute of Technology. Her research focuses on formal methods and their applications to artificial intelligence, programming languages, and machine learning. Previously, she held an NSF/CRA Computing Innovation Postdoctoral Fellowship at the University of Pennsylvania (2020-2022) and completed her PhD at Rice University (2016-2020). Education: PhD in Computer Science, Rice University (2016-2020) MS in Computer Science, Rice University (2014-2016) BSc (Honors) in Mathematics and Computer Science, Chennai Mathematical Institute (2011-2014) Research Interests: Formal methods, reinforcement learning, reactive synthesis, quantitative verification, and trustworthy AI systems. Her work bridges logic-based formal methods with modern AI challenges, emphasizing safety, reliability, and generalization in AI systems. Key Contributions: Pioneering work on specification-guided reinforcement learning, compositional synthesis algorithms, and formal verification of AI systems. Notable tools include Lisa (LTLf synthesis tool) and DiRL (compositional reinforcement learning framework). Awards: 2020 NSF CI Fellowship, 2021 MIT EECS Rising Star, 2023 ATVA Best Paper Award, and Keynote Speaker at SAS 2022. Advising & Grants: Advises PhD and Master’s students in reinforcement learning and formal methods. Lead PI on a collaboration grant with IIT Bombay (2024) and recipient of a ~$250K NSF/CRA postdoctoral grant. Labs/Teams: Leads the BansalLab at Georgia Tech, focusing on trustworthy AI through formal methods and algorithmic innovation.
David R. Koes is an Associate Professor in the Department of Computational and Systems Biology at the University of Pittsburgh, affiliated with the School of Medicine. He holds roles such as Associate Director of the Joint CMU-Pitt Computational Biology PhD Program (CPCB) and is involved in multiple graduate programs including Intelligent Systems and Computational Biomedicine. His research focuses on developing computational algorithms and systems for drug discovery, emphasizing open-source software and machine learning applications in biomedical data. Koes teaches courses like MSCBIO2025 (Bioinformatics Programming in Python) and MSCBIO2065 (Scalable Machine Learning for Big Data Biology). He has secured NIH funding (R35GM140753) and collaborated on projects with institutions like NVIDIA and Google Cloud. His lab develops tools such as GNINA, Pharmit, and 3Dmol.js, and actively contributes to open drug discovery initiatives. Education: PhD in Computer Science from Carnegie Mellon University (CMU). Research Interests: Leveraging computation and AI for drug design, molecular docking, pharmacophore modeling, and open science. Specific areas include developing scalable machine learning pipelines, virtual screening systems, and tools for 3D molecular analysis. Grants and Funding: Current NIH R35 grant and prior support from NSF, Relay Therapeutics, and others. His work emphasizes translating computational methods into practical drug discovery solutions. Lab and Teams: Directs a lab focused on computational drug discovery, collaborating with multiple academic and industry partners. Supervises graduate students and postdocs in projects spanning AI-driven drug design, molecular modeling, and software development.
Na Du is an Assistant Professor in the Department of Informatics and Networked Systems at the University of Pittsburgh's School of Computing and Information. She holds a PhD in Industrial & Operations Engineering from the University of Michigan (2021) and a Graduate Certificate in Data Science. Her research focuses on human factors in smart cities, human-centered computing, and user experience design. She is affiliated with the Intelligent Systems Program, Pitt Cyber, and the Center for Governance and Markets. Education: PhD in Industrial & Operations Engineering (University of Michigan, 2021); Undergraduate in Psychology (Zhejiang University). Research emphasizes explainable AI, human-AI teaming, and smart technologies. Recent grants include funding from Honda Research Institute and Pitt Cyber Accelerator for projects on emotions in Human-AI interaction and Metaverse privacy awareness. Her work has been recognized with awards like the HFES Best Paper Award and the IOE Outstanding Student Award. Advising includes PhD students and researchers in human factors and UX design. The HAT Lab under her leadership explores interdisciplinary challenges in human-computer interaction and smart systems.
Dr. Seyyed Hamed Hosseini Nasab is a Lecturer at the Department of Health Sciences and Technology at ETH Zürich, affiliated with the Institute for Biomechanics and the Laboratory for Movement Biomechanics. His research focuses on biomechanical analysis of musculoskeletal systems, particularly knee mechanics, implant design, and ligament behavior in total knee arthroplasty. He integrates experimental, computational, and clinical approaches to improve surgical techniques and prosthetic design. Key research interests include knee joint loading, ligament elongation patterns, and the influence of implant conformity on post-surgical outcomes. He has contributed to standardized methods for measuring tibiofemoral implant loads and kinematics, earning the European Society of Biomechanics SM Perren Award in 2022. His publications emphasize computational modeling, in vivo testing, and finite element analysis to address challenges in orthopedic engineering. Recent work explores artificial neural networks for real-time knee contact force estimation and the biomechanical implications of surgical procedures like posterior cruciate ligament substitution.
