Hao Su is an Associate Professor in the Department of Computer Science and Engineering at University of California, San Diego . He serves as Chairman & CTO of Hillbot Inc , and leads the SU Lab which focuses on building autonomous systems that learn actively in physical environments. His affiliations include the Institute for Learning-enabled Optimization at Scale , Artificial Intelligence Group , Contextual Robotics Institute , Halicioğlu Data Science Institute , and Center for Visual Computing . As a researcher in Computer Vision, Robotics, and Neural Geometry , he has made significant contributions to 3D foundation models, reward-free world models, diffusion policy frameworks, and GPU-accelerated simulation environments. His 2024-2025 publications include advancements in hand-eye calibration, dynamic mesh reconstruction, and multi-stage robotic manipulation. His scientific awards include: Frontiers of Science Award (2025) TPAMI Young Research Award (2025) NSF CAREER Award (2023) ACM SIGGRAPH Best Doctorate Thesis Honorable Mention (2019) He has served as Program Chair for CVPR 2025 and Area Chair for ICLR 2022 and NeurIPS 2023 , while previously serving as Publication Chair for 3DV 2016 and Program Committee for SIGGRAPH Asia Workshops .
Huan Zhang serves as an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign (UIUC), with affiliate appointments in the Department of Computer Science and the Coordinated Science Laboratory. His research focuses on building trustworthy AI systems with formal verification techniques to provide provable guarantees for safety-critical applications, particularly in machine learning and neural networks. Dr. Zhang received his Ph.D. in Computer Science from UCLA in 2020, advised by Professor Cho-Jui Hsieh. His academic journey includes an M.S. in Computer Engineering from UC Davis (2014) and a Bachelor of Engineering from Zhejiang University (2012). Prior to joining UIUC, he completed a postdoctoral fellowship at Carnegie Mellon University (2021-2023) with Professor Zico Kolter. Huan Zhang's research program centers on formal verification of machine learning systems, with particular emphasis on neural network verification, AI safety, robustness, and reliability. He pioneered the linear bound propagation-based verification framework that enables formal verification for networks with millions of neurons. His work spans five major research categories: formal verification of machine learning, training trustworthy ML models, machine learning safety and adversarial attacks, reinforcement learning safety, and optimization for scalable machine learning. His CROWN framework (NeurIPS 2018) established a foundational approach for neural network verification through efficient linear bound propagation. His recent publications demonstrate a strategic expansion from foundational verification techniques toward increasingly complex systems including large language models, vision-language models, and robotic control systems. The research trajectory shows a clear progression from theoretical frameworks to practical implementations with real-world applications, particularly in safety-critical domains. His work increasingly bridges formal methods with practical AI deployment requirements. Winner of International Verification of Neural Networks Competition (VNN-COMP) as team leader (2021-2024) Schmidt Futures AI2050 Early Career Fellowship ($300,000 research grant) Adversarial Machine Learning (AdvML) Rising Star Award (2021) IBM PhD Fellowship (2018) Dr. Zhang leads the development of α,β-CROWN, a neural network verifier that has won VNN-COMP 2021-2023, and auto_LiRPA, a PyTorch-based library for perturbation analysis on general computational graphs. He has mentored numerous graduate students from CMU, UCLA, UIUC, and Columbia University. His research is supported by significant funding including the Schmidt Futures fellowship and industry collaborations. He teaches courses including ECE 120, ECE 484, ECE 584, and ECE 598 HZ on topics ranging from computing fundamentals to safe autonomy and machine learning. Dr. Zhang maintains active research collaborations across multiple institutions and is affiliated with UIUC's Coordinated Science Laboratory. His work has significant implications for safety-critical AI applications in autonomous systems, healthcare, and other mission-critical domains where reliability guarantees are essential. He regularly gives guest lectures at institutions including Yale, Stony Brook, and the University of Nebraska Lincoln on formal verification techniques.
Heng Huang is the Brendan Iribe Endowed Professor in the Department of Computer Science and the Department of Electrical and Computer Engineering at the University of Maryland, College Park . He earned his Ph.D. in Computer Science from Dartmouth College and holds prior degrees from Shanghai Jiao Tong University. His research focuses on advancing the foundations and applications of artificial intelligence, particularly in machine learning, data mining, natural language processing, computer vision, and biomedical informatics . His work integrates large-scale optimization, fairness, and robustness in deep learning systems. Heng Huang’s recent publications demonstrate a strong trend in large language models, federated learning, model watermarking, continual learning, and medical image analysis . His work appears consistently in top venues like NeurIPS, ICML, CVPR, ICLR, and MICCAI, reflecting a broad impact across theoretical and applied AI. He actively mentors students and postdocs, seeking highly motivated researchers in machine learning and related domains. His work has significant implications for healthcare, privacy, and trustworthy AI.
Michael Baldea is an Associate Professor in the Department of Chemical Engineering at the University of Texas at Austin . He holds a Ph.D. in Chemical Engineering from the University of Minnesota (2006), with prior degrees from 'Babeş-Bolyai' University in Romania (M.Sc. 2001, Diploma 2000). His research group develops theoretical and computational methods for Process and Energy Systems Engineering , focusing on integrated decision-making, performance optimization, and process intensification with industrial validation. Education: Ph.D., Chemical Engineering, University of Minnesota (2006) M.Sc., Interface Process Engineering, 'Babeş-Bolyai' University (2001) Diploma, Chemical Engineering, 'Babeş-Bolyai' University (2000) Research Thrusts: Integrated decision-making in chemical/energy supply chains Process performance monitoring and optimization Process integration and intensification Key applications include grid-responsive chemical plants, intensified distillation/column designs, and renewable energy integration for building systems. Scientific Awards: Frank A. Liddell, Jr. Fellowship NSF CAREER Award (2015-2020) Moncrief Grand Challenges Faculty Award (2014) AIChE Outstanding Young Researcher Award (2017) Implementation : His group has translated research into commercial tools through partnerships with industrial test beds and is working to integrate methods into commercial simulators. They explore predictive approaches for building energy management and strategic capital investment analysis in next-generation energy systems.
Apostolos Fasianos is a Lecturer in Economics at Brunel University London, specializing in macroeconomic implications of household financial behavior. Prior roles include economist positions at the Hellenic Ministry of Finance (2017-2020) and Central Bank of Ireland (2016-2017) , with collaborative research spanning the Bank of England and Reserve Bank of New Zealand . PhD in Economics, University of Limerick MSc in Economic Development, University of Glasgow MPhil in Economics, University of Athens Research focuses on household finance , housing economics , monetary policy , and economic inequalities . Recent work explores AI-enabled technological shocks on UK labor markets via Bayesian VAR modeling and textual patent analysis. Publications span topics like wealth inequality , housing market asymmetries , and financialization trends . Selected publications highlight interdisciplinary approaches, merging macroeconomic theory with empirical analysis of crises (e.g., Covid-19 ), housing markets, and historical financial trends. Key methodologies include textual analysis , VAR modeling , and spatial econometrics . Active in policy analysis, Fasianos represented Greece in international forums such as the EPC - Ageing Working Group and OECD Working Party 1 . Current projects include a 2023-2024 BRIEF AWARDS grant on AI’s macroeconomic impacts.
Raquel Fernández is Full Professor of Computational Linguistics and Dialogue Systems at the University of Amsterdam, where she leads the Dialogue Modelling Group at the Institute for Logic, Language & Computation (ILLC). As Vice-Director for Research at ILLC and a Fellow of the ELLIS Society, she bridges computational linguistics, cognitive science, and artificial intelligence through her research on language use in multimodal and conversational contexts. PhD in Computational Linguistics from King's College London Prior research positions at University of Potsdam and Stanford University's CSLI Her work explores how cognitive constraints, social interaction, and perception shape language use, with a focus on: Visually-grounded language processing Multimodal dialogue modeling Model uncertainty and calibration Language grounding in multimodal data Language learning and semantic change Dialogue reference resolution Recent publications analyze multimodal reasoning limitations, cross-lingual knowledge consistency, and uncertainty modeling in dialogue systems. She has received multiple accolades including an ERC Consolidator Grant , NWO VENI/VIDI/Aspasia fellowships , and EMNLP/GenBench awards . Outstanding Paper Award (EMNLP 2023) Best Data Award (GenBench Workshop 2023) ELLIS Society Fellow ERC Consolidator Grant #819455 recipient NWO VENI/VIDI/Aspasia awardee As a leader in academic service, she serves on the SIGDAT Executive Committee and chairs multiple conference committees. Her lab develops models for multimodal dialogue, visual storytelling, and grounded language understanding.
Elaine J. Francis is a Professor of English and Linguistics at Purdue University, where she also serves as the Associate Head of the Department of English. She holds affiliate appointments in the Department of Linguistics and the Department of Speech, Language, and Hearing Sciences. At Purdue, she directs the Experimental Linguistics Lab and teaches linguistics courses at both graduate and undergraduate levels. Francis completed her B.A. in Linguistics at the College of William and Mary in 1993, followed by her M.A. (1995) and Ph.D. (1999) in Linguistics at the University of Chicago under the direction of Salikoko Mufwene. Her dissertation examined variation among members of the same lexical category in English using Sadock's Autolexical Grammar framework. Prior to joining Purdue in 2003, she served as an Assistant Professor at the University of Hong Kong from 1999 to 2002, where she collaborated with Stephen Matthews on research concerning syntactic categories and relative clauses in Cantonese. Her research focuses on syntax and its interfaces with semantics, discourse information structure, and language processing in production and comprehension. Francis employs experimental methods to investigate syntactic, semantic, discourse-pragmatic, and cognitive factors underlying the grammar and usage of complex sentence structures. Her specific interests include word order alternations, filler-gap dependencies, resumptive pronouns, relative clauses, grammatical categories, and syntactic alternations. She has published extensively in top linguistics journals including Language and Cognition, Glossa Psycholinguistics, Linguistics, Lingua, Cognitive Linguistics, and the Journal of Psycholinguistic Research. Analyzing her recent publications reveals a consistent focus on experimental approaches to syntactic phenomena. Her work demonstrates a strong interest in gradient acceptability, syntactic priming, cross-linguistic comparison (particularly involving English and Cantonese), and the relationship between grammatical theory and language processing. Her 2022 book Gradient Acceptability and Linguistic Theory represents a major contribution that synthesizes experimental findings with theoretical linguistics frameworks. Francis plays an active role in the broader linguistics community. She regularly teaches short courses at the Linguistic Society of America Linguistic Institutes, serves on the LSA Ethics Committee, and is on the editorial board of Glossa Psycholinguistics. She has also edited several books and special journal issues, including Mismatch: Form-Function Incongruity and the Architecture of Grammar (2003) and Polymorphous Linguistics: Jim McCawley's Legacy (2005). As an educator and administrator, Francis has supervised numerous graduate students and currently serves as Associate Head of the Department of English at Purdue. Her Experimental Linguistics Lab provides research opportunities for students interested in the intersection of theoretical syntax and experimental methodology. While she recently announced she will not be accepting new graduate students for the 2025-2026 cycle, her established mentorship record demonstrates her commitment to training the next generation of linguists. The Experimental Linguistics Lab, which she directs, serves as a hub for research combining theoretical linguistics with experimental methods. Her collaborative work extends across disciplines, including collaborations with researchers in speech-language pathology, cognitive science, and computational linguistics, reflecting the interdisciplinary nature of modern linguistic research.
Tamara Broderick is an Associate Professor in the Department of Electrical Engineering and Computer Science at MIT, specializing in machine learning and statistics. Her research focuses on developing methods for uncertainty quantification in data analysis, Bayesian nonparametrics, and scalable inference algorithms. She leads a research group advising PhD students and postdocs in statistical machine learning. Her work spans Bayesian modeling, variational inference, spatial statistics, and applications in epidemiology and environmental science. Recent projects involve uncertainty-aware forecasting, robustness analysis of statistical methods, and efficient algorithms for high-dimensional inference. Broderick teaches Bayesian Modeling and Inference and contributes to MIT's statistics and data science initiatives.
Wei Sun is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Central Florida, where he also serves as the Director of the Siemens Digital Grid Lab. His research focuses on power system restoration, self-healing smart grids, cyber-physical security, and renewable energy integration. Dr. Sun received his Ph.D. from Iowa State University in 2011, and his M.S. and B.S. from Tianjin University in 2007 and 2004, respectively. Prior to joining UCF, he was an Assistant Professor at South Dakota State University (2013-2015), a power system engineer at Alstom Grid (2011-2012), a visiting scholar at the University of Hong Kong (2011), and an intern at California Independent System Operator (2010). His research interests include: Power System Restoration and Self-healing Smart Grid Resilient and Secure Critical Infrastructure Cyber-Physical Systems Renewable Energy and Microgrid Distributed Energy Resources Integration Dr. Sun's recent publications demonstrate strong focus on cyber-physical security in power systems, distributed energy resource integration, and resilient grid operations. His work shows increasing emphasis on AI and machine learning applications for grid security and resilience, particularly in the context of high renewable penetration. His notable scientific awards include: Microsoft Software Engineering Innovation Foundation Award (2014) Best Paper Award, 2019 IEEE PES ISGT Asia Mentor of the Year, UCF Graduate Student Association (2019) Dr. Sun has successfully secured multiple research grants totaling millions of dollars from agencies including the US Department of Energy, National Science Foundation, Florida Center for Cybersecurity, and Microsoft. He currently serves as PI or Co-PI on several major projects including "Secure and Resilient Operations Using Open-Source Distributed Systems Platform (OpenDSP)" funded by the Department of Energy. He leads the Siemens Digital Grid Laboratory at UCF, which is equipped with utility-grade software and hardware including Spectrum Power Microgrid Management System, Power System Simulator for Engineering, and Siemens Distribution Feeder Automation. The lab provides capabilities for both software modeling and hardware-in-the-loop testing of power systems.
François-Xavier Briol is an Associate Professor in the Department of Statistical Science at University College London (UCL). He co-leads the Fundamentals of Statistical Machine Learning research group and holds roles including theme lead for Computational Statistics and Machine Learning (CSML), co-director of the UCL CDT in Data-Intensive Science, and ELLIS scholar. His research focuses on merging scientific models with data through robust statistical and machine learning methods, emphasizing computational efficiency and model misspecification robustness. Education: BSc/MMORSE from the University of Warwick, PhD from the joint Warwick-Oxford CDT in Statistics. Postdoctoral roles at Imperial College London (Mathematics) and University of Cambridge (Engineering), followed by a Group Leader position at The Alan Turing Institute (2020–2023). Research interests include probabilistic numerical methods, Bayesian inference for intractable models, Stein’s method, and kernel-based approaches. His work has been recognized via awards like the AISTATS Best Paper Award and Blackwell-Rosenbluth Award, with funding from EPSRC and Amazon. Advising: Supervised PhD students in areas like Bayesian quadrature, robust inference, and Gaussian processes. Current students focus on topics such as Bayesian filtering and sensitivity analysis. Grants include EPSRC funding and an Amazon Research Award. Labs/Teams: Active in UCL’s ELLIS unit, CSML, and Turing Institute collaborations. Editorships include SIAM/ASA Journal on Uncertainty Quantification and Bayesian Analysis.
Eli Ben-Michael is an Assistant Professor jointly appointed in the Heinz College of Information Systems and Public Policy and the Department of Statistics & Data Science at Carnegie Mellon University. He is affiliated with the CMU-NIST AI Measurement Science & Engineering Cooperative Research Center (AIMSEC), contributing to cutting-edge research at the intersection of statistics, policy analysis, and artificial intelligence. His educational background includes a PhD in Statistics from U.C. Berkeley and undergraduate studies at Columbia University where he earned a dual degree in Computer Science and Statistics. Prior to his current position, he completed a postdoctoral fellowship at Harvard University's Institute for Quantitative Social Science and Department of Statistics. Ben-Michael's research focuses on developing innovative statistical and computational methods for causal inference and policy evaluation, with particular emphasis on integrating machine learning techniques to address complex problems in public policy and social science. His work bridges theoretical statistics with practical applications in healthcare, criminal justice, education, and social policy. Current research directions include safe policy learning, sensitivity analysis for clustered data, and methodological innovations for the synthetic control method. His publication record shows a strong trajectory in top-tier journals including Journal of the American Statistical Association, Journal of the Royal Statistical Society, and Proceedings of ICML. Recent work demonstrates increasing focus on policy-relevant applications including abortion legislation impacts, pre-trial risk assessment, and healthcare disparities, while maintaining methodological rigor in causal inference frameworks. Ben-Michael has developed open-source software tools including augsynth and multical R packages, which implement his methodological contributions for synthetic controls and multilevel calibration weighting. These packages have been adopted by researchers in multiple disciplines for causal inference applications.
Soheil Salehi is a tenure-track Assistant Professor in the Department of Electrical and Computer Engineering at the University of Arizona, with a joint appointment in Systems and Industrial Engineering. He is the Director of the Privacy-preserving, Intelligent, and Secure Computing (PRISM) Lab, established in August 2022. Prior to this, he was an NSF-Sponsored Computing Innovation Fellow and Postdoctoral Research Fellow at the University of California, Davis. Ph.D., Electrical and Computer Engineering, University of Central Florida, 2020 M.S., Electrical and Computer Engineering, University of Central Florida, 2016 B.S., Isfahan University of Technology, Iran, 2014 Dr. Salehi's research focuses on the intersection of hardware, AI, and security. His work spans hardware and AI-enabled security in IoT , Generative AI for hardware design and security , neuromorphic and biologically-inspired AI hardware , emerging spin-based devices , reconfigurable architectures , low-power VLSI circuits , and digital twins and mixed reality for semiconductor workforce development . He also explores the application of Generative AI in personalized education . His recent publications, spanning 2023–2025, reveal a strong trend toward integrating AI and machine learning into hardware security and design. Key themes include automated secure IC design flows , AI-driven hardware obfuscation , firmware and side-channel attack analysis , security in neuromorphic and spiking neural networks , and educational frameworks using digital twins and generative models . His work appears in top venues like DAC, ICCAD, USENIX Security, IEEE TCAS-I, and ISCAS. Outstanding Reviewer Award, IEEE/ACM Design Automation Conference (DAC), 2023 Best Presentation of the Symposium Award, UC Davis Postdoctoral Research Symposium, 2021 UCF Excellence by a Graduate Teaching Assistant (University-Level), 2016 Nominated for 30-under-30 Award, UCF, 2020 Nominated for Postdoctoral Research Excellence Award, UC Davis, 2022 Dr. Salehi has secured significant research funding as PI and Co-PI, including a $300K NSF SaTC EAGER grant on Generative AI-based Personalized Cybersecurity Tutor, a $174,000 University of Arizona PIF Award, and multiple RII grants totaling over $198K. He has also received industry funding from CHEST. He actively mentors students and leads the PRISM Lab, which focuses on privacy-preserving and intelligent secure computing. His service includes roles as Technical Program Committee (TPC) Member and Session Chair at premier conferences such as DAC, ICCAD, CCS, NDSS, and GLSVLSI. The PRISM Lab, under his direction, conducts cutting-edge research in secure and intelligent hardware systems, with applications in IoT, edge computing, and workforce development. The lab emphasizes interdisciplinary collaboration and innovation in both research and education.
Professor Thomas Blumensath is a Professor of Signal and Image Processing at the University of Southampton and a Fellow at the Alan Turing Institute. He is the Academic Lead in Image Processing and Reconstruction at the University's μ-VIS X-ray Imaging Centre and Director of Research at the Institute of Sound and Vibration Research (ISVR). His research focuses on advanced algorithms for solving inverse problems in tomographic imaging, combining machine learning, optimization, and statistical methods. Key areas include X-ray tomography strategies, GPU-accelerated reconstruction, and multimodal imaging applications. Education: B.Sc. (Hons) Music Technology and Audio System Design, University of Derby (2002) PhD in Electronic Engineering (Bayesian Signal Processing), University of London (2006) Research Interests: Professor Blumensath's work spans theoretical and applied signal/image processing, with emphasis on tomographic imaging techniques. His current projects address efficient reconstruction methods, spectral X-ray CT, and applications in manufacturing and plant science. He collaborates with advanced imaging facilities like Diamond Light Source and ISIS neutron imaging beamline. Key Contributions: His research bridges computational methods (e.g., compressed sensing) with practical imaging challenges, including limited-angle tomography and stereo imaging strategies. He leads the National Research Facility for Lab X-ray CT and has developed the TIGRE reconstruction toolbox. Grants & Projects: Active funding includes EPSRC projects on tomographic sensitivity monitoring and CT-based manufacturing inspections. Completed projects cover constrained reconstruction, AM process verification, and industrial CT metrology. Awards: Alan Turing Institute Fellowship Teaching & Leadership: He teaches modules on machine learning, biomedical image processing, and robotics. Leads the BEng Control Engineering program at the Joint Education Institute with Harbin Engineering University. Labs/Teams: Active in the Signal Processing, Audio and Hearing research group (SPAH) and the Institute for Life Sciences. Oversees the μ-VIS X-ray Imaging Centre's research initiatives.
Dr. Vincent Fortuin is a tenure-track Assistant Professor at the Technical University of Munich (TUM) and a research group leader at Helmholtz AI in Munich. He leads the Efficient Learning and Probabilistic Inference for Science (ELPIS) group and holds multiple prestigious fellowships including the Branco Weiss Fellowship. His academic affiliations include the TUM School of Computation, Information and Technology, the Konrad Zuse School of Excellence in Reliable AI, and the Munich Center for Machine Learning. Dr. Fortuin earned his BSc in Molecular Life Sciences from the University of Hamburg (2012-2015), followed by an MSc in Computational Biology and Bioinformatics from ETH Zürich (2015-2017), where he received the ETH Excellence Scholarship and the Willi Studer Prize. He completed his PhD in Machine Learning at ETH Zürich (2017-2021) under the supervision of Gunnar Rätsch and Andreas Krause, supported by a Swiss Data Science Center PhD Fellowship. Prior to joining TUM, he was a Research Fellow at St. John's College, University of Cambridge (2022-2023). His research focuses on the intersection of Bayesian statistics and deep learning, specifically developing methods for more robust, data-efficient AI systems with reliable uncertainty estimates. His work addresses critical limitations in standard deep learning approaches, particularly their tendency to be overconfident in predictions and require large datasets for training. He investigates better priors and more efficient inference techniques for Bayesian deep learning, deep generative modeling, meta-learning, and PAC-Bayesian theory, with applications in scientific and biomedical domains. Dr. Fortuin's recent publications demonstrate a consistent focus on improving uncertainty quantification in deep learning systems, with increasing emphasis on practical applications in scientific contexts. His work spans from theoretical foundations of Bayesian deep learning to practical implementations in protein design, materials science, and medical applications. A notable trend is his exploration of how to make Bayesian methods more scalable and applicable to modern large-scale AI systems while maintaining theoretical guarantees. Branco Weiss Fellowship (2023) St John's College Research Fellowship (2022) Swiss National Science Foundation Postdoc.Mobility Fellowship (2022) Swiss Data Science Center PhD Fellowship (2018) ETH Excellence Scholarship (2015) Willi Studer Award (2018) Dr. Fortuin actively supervises PhD and Master's students through his ELPIS research group at Helmholtz AI. He serves as a regular reviewer and area chair for major machine learning conferences and is an action editor for TMLR. He co-organizes the Symposium on Advances in Approximate Bayesian Inference (AABI) and the ICBINB initiative, demonstrating his commitment to advancing the field through community building. His research group receives funding from multiple sources including Helmholtz AI, the Branco Weiss Fellowship, and collaborations with international institutions. Dr. Fortuin leads the Efficient Learning and Probabilistic Inference for Science (ELPIS) group at Helmholtz AI, which focuses on fundamental machine learning research motivated by real-world scientific problems. The group collaborates extensively with researchers across Helmholtz centers and international institutions, particularly in biomedical applications where reliable uncertainty estimates are crucial.
Carlos Cinelli is an Assistant Professor in the Department of Statistics at the University of Washington, where he conducts research at the intersection of causal inference, statistical methodology, machine learning, and artificial intelligence. He is also a data science fellow at the eScience Institute and affiliate faculty of the Center for Statistics and the Social Sciences, demonstrating his interdisciplinary approach to causal methodology. Dr. Cinelli received his Ph.D. in Statistics from the University of California, Los Angeles, advised by Chad Hazlett and Judea Pearl, two prominent figures in causal inference. His research focuses on developing new causal and statistical methods for transparent and robust causal claims in empirical sciences, with particular attention to challenges faced by social and health scientists. His work spans theoretical developments in causal identification, sensitivity analysis frameworks, and practical software implementations that enable researchers to assess the robustness of their causal conclusions. Cinelli's research program addresses fundamental questions about how unobserved confounding affects causal estimates and develops tools to quantify how sensitive findings are to potential violations of causal assumptions. His work on omitted variable bias frameworks has been particularly influential across multiple disciplines. Through his publications, Cinelli has established himself as a leading researcher in causal inference methodology, with papers appearing in top journals across statistics, machine learning, epidemiology, and social sciences. His work demonstrates both theoretical rigor and practical relevance, often accompanied by open-source software implementations that make his methods accessible to applied researchers. Best paper award at SBE 2024 in Econometrics Royalty Research Fund (RRF) Award recipient NSF/MMS research support As an advisor, Cinelli has successfully guided PhD students like Nick Irons to dissertation completion. He actively seeks new students with strong interests in causal inference. His research is supported by multiple funding sources including the National Science Foundation and the University of Washington's Royalty Research Fund. Cinelli contributes to the academic community through editorial work for the Journal of Causal Inference and by developing widely used software packages like sensemakr for sensitivity analysis.
