Professor Javen Shi is the Founding Director of the Causal AI Group at the University of Adelaide and a director at the Australian Institute for Machine Learning (AIML). His research spans causation, artificial intelligence, metaphysics, and mind theories, with global recognition as Google Scholar ranks him 4th in causation and 7th in probabilistic graphical models. Active contributor to AI ecosystem as a panellist for Responsible AI Think Tank (2022-2024) and AI Industry Forum (2024 onward) Research applications across material discovery, agriculture, mining, sports, manufacturing, bushfire prediction, health, and education Developed NOBURN bushfire prediction app (2023) with over 50 media coverages Research Focus : Causal AI methods that identify root causes, discover latent variables, and build immunity from spurious correlations. His work enables generalization across domains, models intervention consequences, answers counterfactual questions, and determines optimal intervention sequences for outcome optimization. Scientific Recognition : Open Catalyst Challenge 2023 (AI for Science) - 1st place AUS/NZ Bushfire Data Quest 2020 - Winner Gawler Challenge 2020 - Finalist (judged as 'most innovative modeling') Explorer Challenge 2019 - 2nd place SAIC Volkswagen Logistics Innovation Day 2019 - 1st place
Jan Lemeire is an active researcher at Vrije Universiteit Brussel (VUB), affiliated with the Department of Electronics and Informatics within the Faculty of Engineering. Based in Brussels, Belgium at Pleinlaan 2, he maintains an active research profile with an h-index of 10 and 581 citations according to Scopus data. His research interests span multiple domains including GPU computing, machine learning, embedded systems, and their applications in diverse fields from biomedical engineering to forensic science. His work demonstrates strong interdisciplinary connections, bridging computer science with practical applications in health technology, crime analysis, and industrial systems. Analysis of his recent publications reveals a consistent focus on computational efficiency, with particular emphasis on GPU acceleration, embedded AI deployment, and machine learning applications. His work shows evolution from hardware-focused research toward more applied domains including healthcare technology and crime pattern analysis. Dr. Lemeire actively participates in multiple research projects including NSIS2: PRISMA network (2024-2029), IOF3016: GEAR (2021-2025), and Tech4Health (2024-2025), demonstrating sustained research funding and collaborative work across disciplines. His academic activities include supervision of graduate students, as evidenced by his role as advisor for the 2017 Master's thesis on GPU-accelerated holography, and regular participation in major conferences including the Conference on Uncertainty in Artificial Intelligence and the International Symposium on Embedded Multicore Systems. Dr. Lemeire maintains an active research laboratory focused on computational methods, with particular strengths in parallel processing techniques and their application to real-world problems across multiple domains including healthcare, industrial systems, and forensic science.
Professor Qingyuan Zhao is a University Assistant Professor in Statistics at the Department of Pure Mathematics and Mathematical Statistics, University of Cambridge. He previously held a postdoctoral fellowship at the Wharton School, University of Pennsylvania, and is currently affiliated with the Statistical Laboratory at Cambridge. Born in Wuhan, China, Zhao earned his BSc in Mathematics from the University of Science and Technology of China and a PhD in Statistics from Stanford University. Research Focus: His work centers on causal inference, particularly using Mendelian randomization and graphical models to analyze complex relationships in biomedical and social sciences. He develops statistical methodologies for observational studies, adaptive experiments, and high-dimensional data analysis. Publications: Zhao's recent research explores causal mediation analysis, off-policy evaluation, confounder selection, and sensitivity analysis in Mendelian randomization. His methodological contributions include matrix algebra for graphical models and iterative graph expansion techniques. Academic Roles: He serves as a Fellow and Director of Studies in Mathematics at his college, contributing to education and academic governance in mathematics and statistics.
Umut Simsekli is a Researcher at INRIA - SIERRA team and École Normale Supérieure de Paris, Computer Science Department. He earned his PhD (2015) and MSc (2010) from Boğaziçi University, and a BSc from Sabancı University (2008). His career includes roles at Télécom Paris (2016-2020) and visiting positions at Oxford and Boğaziçi University. Research Focus: Mathematical Machine Learning, emphasizing Deep Learning theory, Heavy-Tailed Optimization, Bayesian Inference, and Stochastic Dynamics. Grants: Principal Investigator for ERC Starting Grant DYNASTY (€1.5M, 2022-2027) and co-PI for ANR/TUBITAK grant FBIMATRIX (2016-2022). Recent Article Trends: Explores PAC-Bayesian generalization, heavy-tailed SGD dynamics, and topological stability in optimization algorithms. Scientific Awards: ERC Starting Grant (2021) ICASSP Best Student Paper Award (2020) Multiple IEEE/SPS Student Travel Grants (2012-2020) Victor L. Wooten Bass/Nature Camp Scholarship (2005) Teaching & Advising: Teaches Deep Learning at École Polytechnique and previously led courses on Probabilistic Graphical Models at Télécom Paris. Supervises PhD students Dario Shariatian, Benjamin Dupuis, and others.
Tomi Janhunen is a Professor in Computing Sciences at Tampere University, specializing in knowledge representation, automated reasoning, and logic programming. He previously served as Adjunct Professor at Aalto University (2019–2024) and maintains a Doctor of Science (Tech.) degree. His research spans answer set programming, satisfiability checking, optimization, and distributed computation. PhD, Aalto University (Doctor of Science (Tech.)) Adjunct Professor of Computer Science (Aalto University, 2019–2024) His research focuses on Answer Set Programming (modularity, verification, optimization), Satisfiability Modulo Theories , Nonmonotonic Logics , and Computational Complexity . He integrates logic programming into real-world applications like preventive maintenance scheduling and AI security systems. Recent work includes translating logic programs into integer programming, developing probabilistic reasoning systems (Plingo), and creating interpretable classifiers for tabular data. His publications emphasize stable model semantics , optimization techniques , and constraint networks . He supervises M.Sc., Lic.Sc., and Ph.D. theses and has completed pedagogical studies. Janhunen actively reviews for journals like Artificial Intelligence Journal and ACM Transactions on Computational Logic .
Gautam Dasarathy is an Associate Professor in the School of Electrical, Computer, and Energy Engineering at Arizona State University, where he also holds a courtesy appointment in the School of Computing and Augmented Intelligence. Additionally, he serves as an Amazon Scholar, working on machine learning and optimization problems relevant to Amazon Last Mile. His academic journey spans prestigious institutions including Rice University, Carnegie Mellon University, and the University of Wisconsin-Madison. Dasarathy's educational background reflects a strong foundation in electrical engineering and machine learning: Ph.D. in Electrical Engineering, University of Wisconsin-Madison (2014) M.S. in Electrical Engineering, University of Wisconsin-Madison (2010) B.Tech. in Electronics and Communication Engineering, VIT University, India (2008) Dasarathy's research lies at the intersection of machine learning, statistics, information processing, and networked systems. He specializes in developing data- and compute-efficient learning algorithms for resource-constrained environments, with a particular focus on interactive learning where algorithms decide what data to collect next. His work frequently leverages structural constraints such as graphs, manifolds, or physical laws to inform both inference and data acquisition. His expertise spans multiple domains including Machine Learning, Network Science, Phylogenetics, Signal Processing and Communications, Statistics, and Systems and Control Theory. Recent applications of his research include power grid monitoring, neuroscience, meta-science, circuit design, and epidemiological forecasting. Dasarathy's recent publications demonstrate a consistent focus on graph-based learning, active learning methodologies, and resource-constrained machine learning. His work spans theoretical foundations in statistical learning and practical applications across diverse domains. A notable trend is the integration of domain-specific constraints (particularly graph structures) into learning algorithms to improve efficiency and accuracy. His research increasingly addresses challenges in federated learning, Bayesian optimization, and meta-science applications. Dasarathy has received numerous prestigious awards recognizing both his research and teaching excellence: 2024 Top 5% Teaching Award from ASU's Fulton Schools of Engineering 2022 IEEE Transaction on Haptics Best Application Paper Award Distinguished Alumni Award (Academics) from VIT University NSF CAREER Award for research on graph structure learning AISTATS 2021 Oral Paper (top ~3% of submissions) Multiple papers accepted to top-tier conferences including NeurIPS, ICASSP, and ECCV Dasarathy actively mentors graduate students, with Parth Thaker recently completing his thesis on bandits, interactive learning, multi-agent systems, and nonconvex optimization. His research program is supported by significant funding from multiple federal agencies. He serves as PI or co-PI on grants from NSF (including CAREER, RAPID, and PIPP programs), DARPA (Geometries of Learning program), ONR (Active Meta Learning), and NIH (Graphical Model Selection from Partial Measurements). His collaborative projects span disciplines from power grid monitoring to epidemiological forecasting, demonstrating the broad applicability of his methodological contributions. Dasarathy leads a research group focused on machine learning and networked systems at ASU. His team works on both theoretical foundations and practical applications of learning algorithms. He is part of several interdisciplinary initiatives at ASU, including collaborations with the Learning and Teaching Hub on AI in education. As an Amazon Scholar, he bridges academic research with industry applications, particularly in last-mile delivery optimization.
Marc Sebban is a Professor in Computer Science at the Hubert Curien Laboratory (LabHC) and Deputy Director of this research unit. He leads the Inria project-team MALICE, focusing on machine learning, domain adaptation, and metric learning. Research Interests Metric learning with theoretical guarantees Domain adaptation via optimal transport Physics-informed neural networks Imbalanced data classification Tree-structured data similarity learning Recent Publications His 2025 work introduces provably accurate adaptive sampling for collocation points in PINNs and theoretically grounded quadrature methods using residual Hessians. 2024 publications explore physics-informed ML for laser-matter interaction, predictive modeling of body shape changes, and approximation error analysis in tanh neural networks. Earlier works address graph diffusion Wasserstein distances, metric learning for imbalanced data, and boosting algorithms with confidence oracles.
Vu-Linh Nguyen is a Junior Professor (Assistant Professor) in Trustworthy Artificial Intelligence at the University of Technology of Compiègne (UTC), France, within the Heudiasyc research unit (UMR CNRS 7253) since December 2022. His educational background includes: Bachelor in Mathematics from VNU University of Science (VNU-HUS), Vietnam (2013) Master in Knowledge Science from Japan Advanced Institute of Science and Technology (JAIST), Japan (2015) PhD in Machine Learning from University of Technology of Compiègne (UTC), France (2018) His research centers on advancing Trustworthy AI through rigorous machine learning methodologies. Specializing in handling imprecision and uncertainty in models, he develops novel approaches in classification systems, active learning frameworks, and ensemble techniques. His current work integrates probabilistic reasoning with multimodal data analysis to enhance AI reliability and interpretability. Notable recognitions include: National Mathematics Olympiad Third Prize for Undergraduate Students, Vietnam (2010) IJAR Young Researcher Award Honorable Mention at ISIPTA conference, Belgium (2019) Prior to his faculty position, Nguyen completed significant postdoctoral research: at Paderborn University, Germany (2018-2020) focusing on multi-label classification and ensemble methods, followed by work on probabilistic graphical models and multi-dimensional classification at Eindhoven University of Technology, Netherlands (2020-2022). He is actively contributing to the Heudiasyc research unit's mission in human-machine systems and decision support.
Prof. Bastian Leibe serves as a University Professor at RWTH Aachen University, leading the Computer Vision Group within the Chair of Computer Sciences 8 (Computer Graphics, Computer Vision, and Multimedia). His research focuses on developing computer vision applications for mobile devices, robotic systems, and autonomous vehicles, with strong institutional ties to the Cluster of Excellence "UMIC - Ultra High-Speed Mobile Information and Communication". His core research spans visual object recognition, tracking, self-localization, and 3D reconstruction, with increasing emphasis on integrated solutions for real-world deployment. Recent work demonstrates deep specialization in autonomous driving perception systems, human-robot interaction interfaces, and foundational computer vision methodologies that bridge theoretical advances with practical engineering constraints. Analysis of recent publications reveals dominant trends in LiDAR-based anomaly detection for autonomous systems, efficient 3D scene understanding frameworks, and novel applications of foundation models in robotics. The group consistently contributes to top-tier conferences with innovations in diffusion models, vision transformers, and interactive segmentation techniques that push the boundaries of real-time mobile vision. The Computer Vision Group maintains active educational engagement through specialized lectures and seminars in computer vision and machine learning, while operating from the UMIC Research Centre facility in Aachen with direct industry and academic collaborations in mobile information systems.
Rattikorn Hewett is a Professor of Computer Science at Texas Tech University's College of Engineering, where she joined the Department of Computer Science in 2004. Previously, she served as a faculty member at Florida Atlantic University, Washington State University Vancouver, and was a research scientist at the Institute for Human and Machine Cognition. She completed her postdoctoral fellowship at Stanford University (1987-1990) after earning her Ph.D. in Computer Science from Iowa State University in 1986. Her research spans multiple areas of artificial intelligence with significant contributions to intelligent data understanding, model-based reasoning, agent control mechanisms, and blackboard systems. She has developed the SORCER system for classification rule induction from databases and has applied her expertise to diverse domains including software risk assessment, genomic data analysis, and hydrological forecasting. Her work on blackboard systems introduced efficient activation and agenda maintenance mechanisms that significantly improved performance in BB1-like systems. Hewett's publications demonstrate consistent contributions across several domains, with recent work focusing on software risk assessment, bioinformatics applications of machine learning, and intelligent data understanding. Her research shows a clear progression from foundational work in knowledge representation and blackboard architectures to practical applications in healthcare, environmental science, and software engineering. She has successfully secured funding from prestigious sources including NSF, DARPA, ONR, NASA, EPRI, and industry partners like Boeing and IBM. Scientific Awards and Recognition: National Science Foundation Research Initiation Award (1993-1997) Exceptional Professor award from Florida Atlantic University Hewett has established a research program on Capability Engineering at TTU to apply and enhance AI techniques for emerging problems in software automation and security. She has served as a referee for numerous prestigious journals and currently serves on the editorial boards of Advances in Artificial Intelligence, Journal of Computational Intelligence in Bioinformatics, and other scholarly publications. Her teaching portfolio includes graduate courses in Artificial Intelligence, Knowledge Discovery and Data Mining, and undergraduate courses in Algorithms and Formal Language Theory. Her research laboratory has focused on developing practical applications of AI techniques to real-world problems, with notable projects including automated assessment of software risks, classification of genetic phenotypes of Osteogenesis Imperfecta, prediction of Lake Okeechobee inflows, and intelligent domain-specific software synthesis control. These projects reflect her commitment to applying theoretical AI concepts to solve concrete problems in healthcare, environmental management, and software engineering.
Corentin Dumery is a Doctoral Assistant at École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the Computer Vision Laboratory (CVLAB) and Machine Learning and Optimization Laboratory (MLO) within the School of Computer and Communication Sciences . His research bridges Computer Vision and Computer Graphics , focusing on 3D scene reconstruction, garment modeling, and neural rendering techniques. Previously, he interned at Meta Redmond , worked at CEA Paris-Saclay on polycube mapping, and was a visiting researcher at ETH Zurich under Prof. Olga Sorkine-Hornung . Corentin holds dual MSc degrees in Computer Science from National University of Singapore (NUS) and Télécom Paris . His work emphasizes 3D content creation for AR/VR Diffusion models for garment reconstruction Neural radiance field optimization Polycube mapping for hexahedral meshing His recent publications (2022–2025) span top venues like SIGGRAPH , ICCV , and CVPR , addressing challenges in 3D Gaussian splatting, view-consistent NeRF training, and single-view garment recovery. He also contributes to academic service as an Outstanding Reviewer at CVPR25 and co-organizes workshops like OpenSUN3D . At EPFL, he serves as Head Teaching Assistant for courses CS433 Machine Learning (2023–2024) and CS442 Computer Vision (2023–2024). Additionally, he is the VP/Treasurer of EPIC , EPFL's computer science PhD association.
Dr. Ian Wood is a Lecturer in Statistics at the School of Mathematics and Physics within the Faculty of Science at The University of Queensland. He received his PhD from the same institution in 2004. His teaching responsibilities include instructing 4th-year advanced statistics, 1st-year pharmacy students, and postgraduate data science courses. Dr. Wood's research spans multiple interdisciplinary domains including: Statistical methodologies for classification and mixture models Machine learning algorithms and stochastic optimization techniques Bioinformatics applications in genomics and medical research Evolutionary computation and optimization frameworks His work frequently bridges theoretical statistics with practical applications in biomedicine and computational biology. Analysis of Dr. Wood's recent publications reveals three primary research trends: Advanced algorithm development for statistical modeling and optimization Applications in biomedical contexts including cancer research, tissue engineering, and pathogen genomics Methodological innovations in machine learning and computational statistics His research consistently demonstrates strong interdisciplinary collaboration across statistics, computer science, and life sciences. Dr. Wood maintains an active supervision portfolio, serving as principal advisor for PhD candidates working on: Evolutionary algorithms for variable-length problems MRI brain segmentation using Markov random fields Estimation of distribution algorithms He also serves as associate advisor for multiple doctoral and master's projects spanning statistics, optimization, and bioinformatics. Dr. Wood is affiliated with the Centre for Organic Photonics and Electronics and maintains collaborations across multiple research teams focused on statistical applications in health sciences and engineering.
Alfred Hero III is the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan, where he is affiliated with the College of Engineering and the Electrical and Computer Engineering Department. His research spans multiple interdisciplinary domains, connecting theoretical foundations with practical applications in complex biological and physical systems. Professor Hero's research interests focus on the intersection of machine learning, information theory, and computational biology. His work particularly emphasizes developing advanced computational methods to understand complex biological systems, with significant contributions in gut microbiome analysis, protein-protein interaction prediction, and biomedical sensor applications. He has pioneered approaches using recurrent neural networks to model and design synthetic human gut microbiome dynamics, enabling prediction of microbial community behaviors and metabolic profiles. His research also extends to astrophysics applications, where he applies deep learning techniques to predict solar flares. Analysis of his recent publications reveals a strong trend toward developing theoretically grounded machine learning methods with applications across diverse domains. His work consistently bridges statistical theory with practical implementations, focusing on information-theoretic approaches to complex data analysis. Key themes include developing robust algorithms for high-dimensional data, creating secure distributed computing frameworks, and applying graph-based machine learning to biological networks. Professor Hero has led significant collaborative research efforts, particularly evident in his work on gut microbiome modeling which involved partnerships between biologists and engineers from the University of Michigan and the University of Wisconsin. His research has received substantial attention, with multiple publications picked up by numerous news outlets and cited extensively in academic literature. His laboratory work involves sophisticated computational approaches combined with experimental validation, as evidenced by references to the Venturelli Lab's robotic systems for creating microbial communities used to train and test algorithms. This integration of computational modeling with physical experimentation represents a hallmark of his interdisciplinary research approach.
H. M. Zafer is an Associate Professor at the University of Washington , specializing in Arabian and African Languages through pre-modern networks of knowledge spanning 6th-16th century Horn of Africa and Western Arabian societies. He also serves as a Senior Lecturer in the Program in African Studies. Education: PhD from Cornell University (2014) Zafer's research focuses on historical linguistics, medieval studies, and Islamic studies, particularly analyzing the Ecumenical Community in the context of Red Sea wars and Early Muslim communitarianism. His current projects include The Comrades of the Ship (Early Muslim historiography) and The Ethiopic Quran (16th-century Ge'ez commentary on the Quran). His scholarly contributions bridge interdisciplinary themes in comparative literature, religious studies, and computational methodologies through his earlier work in machine learning applications for bioinformatics and natural language processing. Scientific Awards: Mellon-Sawyer Fellow, University of Notre Dame Katz Fellow, University of Pennsylvania His publications from 1997-2009 explore hierarchical classification, Bayesian aggregation techniques, and neural networks applied to genomic prediction, linguistic analysis, and automated puzzle generation, demonstrating technical expertise in algorithm development and data modeling.
Tsvetomila Mihaylova is a Postdoctoral Researcher in the Department of Computer Science at Aalto University , specializing in machine learning and human-robot interaction. Her work bridges theoretical advancements in neural networks with practical applications in autonomous systems. Fields of Interest : Latent structure learning, autonomous driving, visual-language models, and natural language processing Email : tsvetomila.mihaylova@aalto.fi Research Focus : • Latent structure modeling using discrete and undirected neural networks • Autonomous driving safety through conflict simulation and trajectory prediction • Cross-modal integration in robotic vision-language systems Publication Trends : Recent work explores 2025 in structured neural architectures for autonomous vehicles and 2024 in human-robot interaction quality evaluation, with foundational contributions to natural language processing fact-checking systems since 2019 .
