Frank Röttger is an Assistant Professor at Eindhoven University of Technology, specializing in Mathematical Statistics. His primary research focuses on graphical models, multivariate extremes, and statistical inference in high-dimensional settings. Research Interests : Extreme value theory, probabilistic graphical models, causal inference in extremes, and data-driven risk modeling. Awards : NWO Prize (Scientific) - 2024 Organized Activities : Eurandom Workshop on Graph Laplacians, Multivariate Extremes, and Algebraic Statistics (2024) Causality in Extremes Workshop (2024) Courses Taught : Dependence Modeling Foundations of Statistics Mathematical Statistics Contact : Email: f.rottger@tue.nl
Emek Demir serves as an Associate Professor in the Department of Molecular and Medical Genetics at Oregon Health & Science University's School of Medicine, where he directs the Computational Biology program at the Brenden-Colson Center for Pancreatic Care. His academic journey includes a Ph.D. in Computer Engineering from Bilkent University (2005) under Ugur Dogrusoz and postdoctoral training with Chris Sander at Memorial Sloan Kettering Cancer Center's Computational Biology Center. Dr. Demir's research centers on Pathway Informatics, integrating detailed biological pathway information with omic data to solve cancer biology problems. His work spans pathway curation, visualization, NLP, data standardization, machine learning, and mechanistic simulation. He pioneered the BioPAX pathway data standard and developed Pathway Commons—the largest process-level pathway database with over 2 million interactions and 400,000 detailed human reactions. His publication record demonstrates consistent innovation in computational oncology, with recent work focusing on transcription factor activity prediction, spatial tumor mapping, and causal network analysis. Key contributions include algorithms for detecting altered cancer sub-networks, identifying transcription factor modulators, and inferring active networks from proteomic data. His research bridges computational methods with clinical applications in leukemia, prostate cancer, and glioblastoma. Recipient of leadership roles in major NIH-funded initiatives Principal developer of Pathway Commons and BioPAX standards Extensive collaborations with Memorial Sloan Kettering and OHSU clinical departments Dr. Demir directs a computational biology program focused on translating pathway knowledge into clinical insights for pancreatic cancer, with ongoing projects in spatial omics, multi-dimensional tumor atlases, and antiviral nanomaterial applications.
Sebastian Angel is an Associate Professor and Chair of Undergraduate Curriculum in the Department of Computer and Information Science at the University of Pennsylvania . He leads research in systems, security, privacy, and networking, with a focus on privacy-preserving systems, accountability in online services, consistency in distributed systems, and next-generation operating systems. Research Areas: Systems, Security, Privacy, Networking, Distributed Systems, Operating Systems Labs & Teams: Distributed Systems Laboratory, Security and Privacy Laboratory, Warren Center for Network and Data Sciences Education: Ph.D. in Computer Science from the University of Texas at Austin (2018), ACM SIGOPS Dennis M. Ritchie Dissertation Award, Bert Kay Best Dissertation Award Recent Publications (2025–2022) span serverless computing, zero-knowledge proofs, distributed transactions, privacy-preserving ad tech, and verifiable execution. His 2025 work includes serverless workflow optimization and structural logic verification, while 2024 focuses on caching frameworks and stateful serverless. Earlier 2023–2022 projects include secure federated learning, confidential cloud services, and private information retrieval. Scientific Awards: NSF CAREER Award (2021) JPMorgan Faculty Award (2021) ACM SIGOPS Dennis M. Ritchie Dissertation Award (2018) Bert Kay Best Dissertation Award (2018) ACM SIGMOD Research Highlights Award (2024) VLDB 2024 Best Paper Award Nominee Advising: Current PhD Students: Elizabeth Margolin, Jess Woods, Yuxuan Zhang Current Masters Students: Felix Adena, Sydnie Shea Cohen Alumni: Eleftherios Ioannidis (2025), Yiping Ma (2025), Haoran Zhang (2024), Ke Zhong (2024), Selin Butun (2025), Martin Sander (2025), Seungmin Han (2024), Andrew Beams (2022), Yifeng Mao (2021), Varad Deshpande (2020) Current Courses: CIS 4510/5510: Computer and Network Security (Fall 2025). He also organizes conferences like S&P, OSDI, SOSP, PETS, and EuroSys.
Alejandro Kuratomi is an Assistant Professor in Data Science at the Department of Computer and Systems Sciences (DSV), Faculty of Social Sciences, Stockholm University. His academic journey includes a Ph.D. in Machine Learning (2024), M.Sc. in Engineering Design: Mechatronics (2019), and dual B.Sc. degrees in Industrial and Mechanical Engineering (2014). Ph.D., Machine Learning – DSV, Stockholm University M.Sc., Mechatronics – KTH Royal Institute of Technology B.Sc., Industrial Engineering – Universidad de Los Andes B.Sc., Mechanical Engineering – Universidad de Los Andes Kuratomi’s research focuses on Machine Learning Interpretability , Algorithmic Fairness , and Multivariate Time Series Classification , with applications in GNSS error estimation and healthcare decision-making. He develops interpretable models like CRITS and ORANGE to address technical and ethical challenges in AI. His recent work explores Transformer/LLM interpretability , mechanistic explanations , and integer-justified counterfactuals . While no awards or students are mentioned, his publications highlight interdisciplinary efforts combining computer science, ethics, and engineering.
Debabrota Basu is a tenured faculty member (Inria Starting Faculty Position - ISFP) at the Scool team (previously called SequeL) of Inria Centre at University of Lille in France. He teaches postgraduate-level courses on privacy, responsible machine learning, and research methods in AI at École normale supérieure-PSL University, Université de Lille, and Centrale Lille. He is also a member of the ELLIS Society (European Laboratory for Learning and Intelligent Systems) and the Paris unit of ELLIS. Dr. Basu earned his PhD in Computer Science from the Department of Computer Science, School of Computing, National University of Singapore, advised by Stéphane Bressan and Pierre Senellart. Prior to that, he obtained a B.E. degree with Honours in Electronics and Telecommunication Engineering from Jadavpur University. Before joining Inria, he was a postdoctoral researcher at Chalmers University of Technology's Data Science and AI Division. Dr. Basu's research focuses on constructing algorithms for developing efficient, robust, private, and ethical learning machines that solve real-world problems. His methodological approach blends statistics, machine learning, and optimization. His application interests span sustainable agro-ecology, medical and pharmaceutical applications, energy-efficient autonomous systems, and algorithmic audits. Recent collaborations include the Inria-Indian Statistical Institute associate team SeRAI for developing Sequential Testing and Learning Algorithms for Verifiably Robust and Responsible AI, and the Inria-INRAE collaboration on Resilient Agricultural Decision Making under Environmental Risks. His publication record demonstrates expertise in bandit algorithms, reinforcement learning, and privacy-preserving machine learning. Recent work shows a strong theoretical foundation with practical applications, particularly in pure exploration bandits, differential privacy mechanisms, constrained optimization, and fairness verification. His research bridges the gap between theoretical guarantees and real-world deployment challenges across multiple domains. Dr. Basu has received notable recognition for his contributions: Best Student Paper Award at ACM EAAMO 2022 for 'On Meritocracy in Optimal Set Selection' Young researcher (JCJC) grant from the French National Research Agency (ANR) in 2022 in 'Artificial Intelligence and Data Science' He leads the project 'RL under Real-life Constraints: Regrets and Algorithms' and supervises PhD students and postdoctoral researchers. His research is supported by multiple projects including REPUBLIC ('Vers l'IA responsable avec l'apprentissage par renforcement sous contraintes') and 'Foundations of robustness and reliability in artificial intelligence.' Dr. Basu actively collaborates with institutions worldwide, including establishing the RELIANT associate team with Kyoto University for investigating structured multi-armed bandit problems.
Anna Simoni is a Senior Researcher at CNRS/CREST and Professor of Econometrics and Statistics at ENSAE and École Polytechnique. She is a CNRS Research Fellow and Fellow of Hi! Paris and Institut Louis Bachelier. Her research spans econometrics, machine learning, and AI, focusing on high-dimensional models and Bayesian inference. Education: PhD in Economics, Toulouse School of Economics (2009) Habilitation à Diriger de Recherche (HDR), Toulouse School of Economics (2017) Research Interests: Her work integrates econometrics with machine learning to develop statistical methods for big data, including Google search data for macroeconomic forecasting and causal inference with minimal assumptions. Grants and Awards: She received the CNRS Bronze Medal in 2019 and leads the ANR-funded project "Moment Conditions Models and Bayesian Inference for Policy Evaluation" (2021-2026).
Tyler McCormick is a Professor in both the Department of Statistics and Department of Sociology at the University of Washington. He also serves as a Senior Data Science Fellow at the eScience Institute and maintains affiliations with the Center for Statistics and the Social Sciences, the Center for Studies in Demography and Ecology, and the Responsible AI Systems & Experiences (RAISE) initiative. Dr. McCormick earned his Ph.D. in Statistics from Columbia University in 2011. His academic journey has established him as a leading researcher at the intersection of statistical methodology and social science applications. McCormick's research program focuses on developing innovative statistical approaches to address complex societal challenges: Bayesian methods for modeling high-dimensional dependence structures in social networks Estimating vital demographic rates from sparse data sources Developing interpretable predictive models with proper uncertainty quantification Creating methodological frameworks for verbal autopsy analysis in global health His publication record reveals a consistent trajectory of methodological innovation with practical impact. Recent work demonstrates increasing sophistication in handling network interference, integrating machine learning with statistical theory, and addressing data scarcity challenges in global health contexts. His research bridges theoretical advances with applications that inform public health policy and social science understanding. McCormick has received significant recognition for his scholarly contributions: NIH Director's New Innovator Award (2019) Election as Fellow of the American Statistical Association (2023) As an educator, McCormick teaches advanced graduate courses including Hierarchical Modeling for the Social Sciences and Quantitative Techniques in Sociology. His research has been supported by competitive grants from NICHD (2015-2020) focused on vital rate estimation in developing countries and NSF (2016-2018) funding for compact Bayesian models of social networks. His work has influenced policy discussions through media coverage in the Wall Street Journal and Washington Post. McCormick leads the OpenVA initiative, providing open-source tools for verbal autopsy analysis, and has developed multiple R packages implementing his methodological contributions to network analysis and causal inference. His research continues to address critical challenges at the intersection of statistical theory, computational methods, and societal impact.
Daniel Gutknecht, Ph.D., is a Professor in the Department of Economic Policy & Quantitative Methods (EQ) at the Faculty of Economics, Goethe University Frankfurt am Main. His academic work spans econometrics, applied microeconomics, and quantitative methods, with a focus on causal inference and time series analysis. Econometrics Methodology Quantile Regression Panel Data Analysis Nonlinear Models Recent research contributions include advanced econometric techniques such as staggered adoption DiD designs, sparsity tests for high-dimensional regressions, and intercept estimation in nonlinear selection models. His publications address critical challenges in quantile forecast optimality, nowcasting monotonicity, and heaped duration data modeling, reflecting interdisciplinary applications in public health and macroeconomic policy. Current teaching includes Advanced Econometrics 1 and Fundamentals of Econometrics for the Winter semester 2025/26. Contact details: Office RuW 3.211, Theodor-W.-Adorno-Platz 4, Frankfurt am Main; email: gutknecht@wiwi.uni-frankfurt.de .
Professor Danijela Gnjidic is affiliated with the Sydney Pharmacy School at the University of Sydney , serving as Interim Deputy Associate Dean of Research in the Faculty of Medicine and Health . She leads the Clinical Practice Guideline for Deprescribing Opioid Analgesics and is a member of the Charles Perkins Centre . Education : PhD (University of Sydney, 2010), MPH (concurrent with PhD), BSc (Hons) International Collaborations : Ghent University (Belgium), Karolinska Institute (Sweden), University of Kentucky and Yale University (USA) Research Focus : Her work spans Geriatric Pharmacology , Deprescribing , Pharmacoepidemiology , and Quality Use of Medicines , with specific emphasis on: Dementia and Medication Management Frailty Assessment in Older Adults Opioid Safety and Chronic Pain Drug Burden Index and Polypharmacy Clinical Guidelines for Deprescribing Healthcare Implementation Strategies Recent Publications analyze trends in opioid deprescribing , frailty measurement , adverse drug reactions , and technology-driven medication management , reflecting interdisciplinary approaches combining geriatrics , epidemiology , and health informatics . Awards include the Thompson Fellowship (2023) , NHMRC Dementia Leadership Fellowship (2017-21) , and multiple accolades for mentorship and leadership in Geriatric Pharmacology . Teaching & Supervision : Coordinates Honours Unit of Study in Pharmacotherapeutics , Public Health , and Evidence-Based Medicine . Supervises projects on dementia care and medication optimization . Associations : Past President of ASCEPT , Deputy Chair of HDR Examinations Subcommittee , and editorial roles for British Journal of Clinical Pharmacology and PLOS One .
Baishakhi Ray is an Associate Professor of Computer Science at Columbia University, working at the intersection of AI, Software Engineering, and Security. She received her Ph.D. from the University of Texas, Austin, and has established herself as a leading researcher in applying artificial intelligence to software engineering challenges. Her educational background includes a Ph.D. from the University of Texas, Austin, which provided the foundation for her research career at the forefront of AI and software engineering. Dr. Ray's research focuses on leveraging artificial intelligence to solve fundamental challenges in software engineering and security. Her work spans multiple areas including code generation with large language models, vulnerability detection, software testing, and program analysis. She has pioneered approaches that combine deep learning with traditional software engineering techniques to create more robust, secure, and efficient software development processes. Her research has practical implications for improving code quality, enhancing software security, and accelerating development cycles through AI assistance. Her recent work demonstrates a strong emphasis on semantic-aware code generation, execution reasoning, and addressing hallucinations in code language models. She has also made significant contributions to evaluating the functionality and security of AI-generated code, identifying critical challenges in the practical adoption of AI for software development. Dr. Ray has received numerous prestigious awards recognizing her contributions to the field: IEEE TCSE Rising Star NSF CAREER award IBM faculty award VMware Faculty award Distinguished Paper awards at FSE'17, ASE'22, and ISSTA'23 ICSME Most Influential Paper award Publications featured in CACM Research Highlights As an Amazon Visiting Academic and active participant in major software engineering conferences, Dr. Ray has established herself as a thought leader in AI for software engineering. Her research has been widely covered in trade media, indicating its relevance and impact on industry practices. She has mentored numerous students through their research and has been instrumental in shaping the next generation of researchers in this interdisciplinary field. Her work demonstrates a consistent focus on bridging theoretical advances with practical applications, ensuring that her research has tangible benefits for the software development community. The trajectory of her publications shows an evolving research agenda that has successfully adapted to the rapidly changing landscape of AI and its applications to software engineering.
Fredrik Johansson is an Associate Professor in the Department of Data Science and AI at Chalmers University of Technology. His research focuses on developing machine learning methods for healthcare applications, causal inference, and handling imperfect data. He leads multiple funded projects including WASP AI/MLX and research on causal machine learning for healthcare applications. Johansson's core research interests include: Machine learning for clinical decision support and healthcare analytics Causal inference methods for observational data Handling missing values and data quality issues Interpretable and robust ML models Domain adaptation and transfer learning Reinforcement learning for treatment policies His recent publications demonstrate strong focus on clinical ML applications (dermatology, rheumatology, Alzheimer's) and methodological work on causal inference. Frequent themes include handling missing data, model interpretability, and healthcare policy optimization. Collaborative work spans multiple medical domains using registry data, proteomics, and medical imaging. He leads significant research projects including: Kausalitet och sidoinformation för effektiv maskininlärning (VR-funded) Maskininlärning för kausal inferens från observationsdata (Wallenberg) Förutsättningar för inlärning av överförbara koncept (Wallenberg) Fattigdomsfällor i Afrika (Formas-funded)
Sungsoo Ahn is an Assistant Professor at the Graduate School of AI, KAIST, where he leads the Structured and Probabilistic Machine Learning (SPML) Lab. His research focuses on developing machine learning algorithms for molecular science, particularly in drug discovery, material design, and generative modeling. He directs a team of 13 researchers (including 2 post-docs and 11 students) and maintains collaborations with institutions like Mila and industry partners. His core research integrates probabilistic machine learning , generative models , and AI for science , with applications spanning molecular dynamics simulation, language model reasoning, combinatorial optimization, and graph neural networks. Key methodologies include flow matching, diffusion models, GFlowNets, and equivariant neural networks applied to chemical and biological domains. Recent publications (2023–2025) demonstrate strong emphases on: (1) Molecular generation/optimization for drug design, (2) Enhancing reliability and reasoning in large language models, (3) Graph-based machine learning for scientific discovery, and (4) Efficient training paradigms for generative samplers. These appear predominantly in NeurIPS, ICML, ICLR, and ACL. He advises multiple PhD/master's students and post-doctoral researchers in the SPML Lab. Current research directions include torsion-aware molecular generation, causal AI safety, neural operators for quantum chemistry, and multi-agent systems for molecular optimization.
Kimin Lee is an assistant professor at the Graduate School of AI at Korea Advanced Institute of Science and Technology (KAIST), where he focuses on developing safe and capable decision-making agents. His research spans multiple aspects of artificial intelligence with a strong emphasis on safety and reliability. Dr. Lee completed his educational journey at KAIST, earning a Ph.D. in Electrical Engineering with a focus on Machine/Deep Learning (2015-2020), advised by Professor Jinwoo Shin. He also holds a Master's degree in Electrical Engineering (Wireless Communication Networks, 2013-2015) and a Bachelor's degree in Electrical Engineering (2009-2013), both from KAIST. His primary research interests include: Physical AI - developing AI systems that can interact safely and effectively with the physical world Alignment - particularly reinforcement learning from human feedback (RLHF) and scalable oversight techniques Monitoring - safety evaluation frameworks and benchmarking for AI systems LLM Agents - enhancing the capabilities and safety of large language model-based agents Dr. Lee's recent publications reveal a strong trajectory toward addressing critical challenges in AI safety. His work consistently bridges theoretical advances with practical applications, particularly in the areas of reinforcement learning, computer vision, and natural language processing. A notable trend in his research is the development of methods to evaluate and enhance the safety of AI systems, especially large language models and diffusion models, while maintaining or improving their capabilities. As an active member of the academic community, Dr. Lee serves as an area chair for major conferences including NeurIPS, ICLR, and ICML, and regularly reviews for top-tier AI venues. He has also organized workshops focused on safe and trustworthy AI agents. Dr. Lee's research group at KAIST appears to focus on AI safety and decision-making, with research projects spanning from theoretical foundations to practical implementations of safe AI systems. His collaborative work with institutions like UC Berkeley and Google Research demonstrates the interdisciplinary nature of his research approach.
Prof. Mehdi Dastani is a Professor and chair of the Intelligent Systems group within the Department of Information and Computing Sciences at Utrecht University's Faculty of Science. He leads the Master's program in Artificial Intelligence and focuses on formal and computational models in AI, particularly multi-agent systems. His research integrates insights from philosophy, psychology, and law to develop autonomous agents that reason about social and cognitive concepts like norms, emotions, and responsibility. Dastani has held academic roles at Utrecht University since 2001, including postdoctoral research and faculty positions. Education: M.Sc. Computer Science (University of Amsterdam, 1991), M.Sc. Philosophy (University of Amsterdam, 1992), Ph.D. in Humanities (University of Amsterdam, 1998). His work spans theoretical and applied projects, including grants for initiatives like Golden Agents (simulating Golden Age creative industries) and traffic control systems using virtual organizations. He is actively involved in academic committees, editorial boards, and organizing international conferences like AAMAS and PRIMA. Research Interests: Multi-Agent Programming, Normative Systems, Autonomous Agents, Cognitive Robotics, and Human-Centered AI. His projects address challenges like norm enforcement, decision-making in complex systems, and ethical AI integration with societal needs. Advising & Grants: Supervised numerous PhD students (e.g., Birna van Riemsdijk, Bas Testerink) and secured grants for projects such as 'Controllable AI: Human-Centered Approach'. His work includes collaborations on urban governance, autonomous driving, and AI tools for literacy support in children. Labs & Teams: Leads the Intelligent Systems group, contributing to agent-based simulations, ethical AI frameworks, and interdisciplinary collaborations with social scientists and urban planners.
Pieter Simoens is an Assistant Professor at Ghent University and affiliated with the imec research institute. He works at the intersection of distributed artificial intelligence, edge computing, and collective intelligence, with a focus on AI applications for resource-constrained environments and robotic systems. His research explores innovative approaches to machine learning deployment in heterogeneous infrastructures, task planning for IoT-integrated robotics, and modeling collective decision-making processes. He has contributed to frameworks like DIANNE for distributed deep learning and developed methods for cognitive modeling in reinforcement learning scenarios. With over 100 publications, his recent work spans adaptive neural networks, privacy-preserving surveillance, UAV hyperspectral data analysis, and computational fairness in AI systems. He leads research initiatives within the Internet Technology and Data Science Lab (IDLab) and contributes to educational programs in software engineering and applied machine learning. Responsible for courses on software engineering, mobile development, system design, and applied machine learning Active in edge computing and neuromorphic algorithms research Develops AI solutions for robotics, surveillance, and industrial IoT applications
