Sophie Dove is an Honorary Associate Professor at the School of Biological Sciences , University of Queensland, and an Affiliate Associate Professor at the Global Change Institute . Her research focuses on coral reef photobiology, climate change impacts, and symbiotic relationships in marine ecosystems. PhD in Biological Sciences, University of Sydney MA in Philosophy, University of Southern California MA (Hons) in Mathematics and Philosophy, University of Edinburgh Her work examines coral-dinoflagellate symbiosis , particularly how host and symbiont interactions maintain reef productivity under climate stress. Key questions include the role of symbiont parasitism, coral heterotrophy, and reef accretion-erosion balance under ocean acidification and warming. She collaborates with institutions like the Great Barrier Reef Marine Park Authority and CSIRO Publishing. Recent publications span coral bleaching mechanisms , ocean acidification effects , and biochemical adaptations in symbiotic organisms. Her studies integrate molecular biology, biogeochemistry, and ecophysiology to predict reef futures under climate stress. She has contributed to scientific consensus reports on the Great Barrier Reef and developed methodologies for analyzing coral and sponge physiology. Her interdisciplinary approach bridges marine ecology, protein biochemistry, and climate science.
Salem Lahlou is an Assistant Professor in the Machine Learning Department at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), having joined in September 2024. He previously served as a Senior Researcher at the Technology Innovation Institute (TII) in 2024. His academic background includes a PhD from Mila and Université de Montréal (UdeM) under Yoshua Bengio (2023), with prior studies in applied mathematics at École Polytechnique and statistical learning at École Normale Supérieure Paris-Saclay. His research focuses on developing more capable and reliable AI systems through three interconnected pillars: Novel Method Development : Core contributions to Generative Flow Networks (GFlowNets), uncertainty estimation techniques (DEUP), and curriculum learning frameworks Large Language Model Advancement : Enhancing reasoning capabilities and alignment through preference optimization and trace-based learning Community Tooling : Creation of torchgfn library for GFlowNets and benchmarks including BabyAI, FinChain, and LLM-BabyBench Core research areas span Machine Learning, GFlowNets, Uncertainty Estimation, LLM Reasoning, Reinforcement Learning, and AI for Science. Recent publications (2023-2025) demonstrate strong emphasis on GFlowNet theory/improvements (8+ papers), LLM reasoning evaluation (FinChain, LLM-BabyBench), uncertainty quantification, and societal AI impacts. Key application domains include mathematical reasoning, financial systems, privacy preservation, and cognitive science. He currently advises graduate students including Junyi (privacy risks in SNNs) and Abhijith (LLM reasoning). His group collaborates with MBZUAI faculty (Nils Lukas, Alham Fikri, Mingming Gong, Martin Takac) and industry partners on projects involving Conversational AI, Personalization, and Affective AI.
Iain Murray is Professor of Machine Learning and Inference at the School of Informatics, University of Edinburgh. His research focuses on developing flexible probabilistic models applicable across diverse domains including cosmology, neuroscience, perception, speech, sports, and text. Program Chair for ICLR (2018) Publications Chair for ICML (2017, 2018) Area Chair for AISTATS, ICLR, ICML, NeurIPS, and UAI Amazon Scholar (2018-2024), first appointed in Europe Murray's research interests center on probabilistic reasoning using machine learning, with specific expertise in density estimation and Markov chain Monte Carlo methods. His work spans theoretical foundations and practical applications, with significant contributions to neural autoregressive distribution estimation (NADE), real-valued NADE (RNADE), and pseudo-marginal slice sampling techniques. His research has enabled advances in flexible probabilistic modeling across multiple domains. His publications show consistent focus on advancing probabilistic modeling techniques, with recent work emphasizing neural autoregressive models, density estimation methods, and efficient sampling algorithms. The research trajectory demonstrates progression from foundational work on NADE to increasingly sophisticated deep learning approaches for density estimation and inference. Notable Paper Award for NADE work Amazon Scholar (2018-2024) Murray has supervised numerous PhD students who have gone on to prominent positions at Google DeepMind, NYU, stability.ai, and other leading institutions. His teaching responsibilities include the Machine Learning and Pattern Recognition course and project supervision. His research group focuses on developing tractable probabilistic models with applications across multiple scientific domains.
Cesare Franchini is a full Professor at the University of Vienna's Faculty of Physics, leading the Computational Materials Physics research group. His work focuses on theoretical understanding and computational modeling of quantum materials using first principles methods, particularly VASP. He maintains an active research program with numerous postdocs, PhD students, and collaborations across multiple institutions including the University of Bologna. Professor Franchini's research centers on quantum materials with many interacting degrees of freedom (lattice, spin, and electron orbital) that enable novel electronic and magnetic phases. His specific interests include metal-insulator transitions, polaron physics (electron-phonon interactions), non-collinear spin orderings, topological Dirac/Weyl phases, multiferroism, and superconductivity. He has increasingly incorporated machine learning data-driven tools and diagrammatic Monte Carlo techniques into his computational approaches. Analysis of his recent publications (2024-2025) reveals a strong focus on polaron physics across multiple material systems, with significant work on hematite, titanium dioxide, and quantum paraelectrics like KTaO3. His research increasingly integrates machine learning with traditional first-principles methods, particularly for studying hydrogen diffusion, surface science phenomena, and electronic structure calculations. There's also substantial work on single-atom catalysis and the application of advanced computational techniques to understand fundamental charge transport mechanisms in energy materials. Professor Franchini actively supervises numerous PhD students and postdocs, including Andrea Angeletti, Viktor Birschitzky, Lorenzo Celiberti, and several others working on diverse aspects of computational materials physics. He leads or participates in major research projects including TACO (Taming Complexity in Materials Modeling), DCAFM (Doctoral College Advanced Functional Materials), and the recently launched Spin-orbit entangled anharmonic polarons project. His group maintains strong collaborations with experimentalists at Charles University, Technical University of Vienna, and other international institutions.
William Sulis is an Associate Clinical Professor in the Department of Psychiatry and an Associate Member of the Department of Psychology at McMaster University, where he also directs the Collective Intelligence Lab (CILab). With a unique interdisciplinary background spanning mathematics, physics, and psychiatry, Dr. Sulis bridges the gap between theoretical science and clinical practice. His educational journey is exceptionally diverse: B.Sc. (Hon) in Mathematics with minor in Theoretical Physics, Carleton University (1976) M.D., University of Western Ontario (1980) M.A. in Mathematics, University of Western Ontario (1984) Ph.D. in Mathematics, University of Western Ontario (1989) FRCPC in Psychiatry (1984) Ph.D. in Theoretical Physics, University of Waterloo (2014) CRCPC in Geriatric Psychiatry (2015) Dr. Sulis's research explores the intersection of complex systems theory with psychological and psychiatric phenomena. His work on Collective Intelligence investigates how group dynamics emerge from individual interactions, while his research on Temperament and Psychobiology examines the continuum between normal personality variations and mental illness. He has made significant contributions to understanding Synchronization in Complex Systems and developed the concept of Transient Induced Global Response Synchronization (TIGoRS) , which has implications for neural coding and information processing. His theoretical work extends to Quantum Foundations and Process Algebra Theory , where he proposes novel approaches to quantum mechanics. Analysis of his recent publications reveals a consistent thread connecting complex systems theory with psychological and psychiatric applications. His work increasingly focuses on bridging the gap between temperament theory and clinical psychiatry, using mathematical and computational approaches to understand mental illness. Simultaneously, he continues to develop theoretical frameworks in quantum physics through process algebra models, demonstrating remarkable interdisciplinary range. Dr. Sulis has received several prestigious awards including The Governor General's Medal for having the highest overall grade point average in his graduating class, the Henry Marshall Tory Scholarship, and multiple Harry Stevenson Southam Scholarships. Throughout his career, Dr. Sulis has mentored numerous students across disciplines, supervising research projects spanning collective intelligence, semantic space modeling, network dynamics, and temperament studies. His Collective Intelligence Lab has served as a hub for interdisciplinary research connecting computer science, psychology, and psychiatry. Dr. Sulis has also been actively involved in professional organizations, serving as President of The Society for Chaos Theory in Psychology and the Life Sciences (1996-1998) and holding editorial positions for several journals including "Dynamical Psychology" and "Nonlinear Dynamics in Psychology and the Life Sciences." As Director of the Collective Intelligence Lab at McMaster University, Dr. Sulis fosters research exploring how complex adaptive systems can model cognitive and social phenomena. The lab serves as an intellectual nexus where mathematics, computer science, psychology, and psychiatry converge to address fundamental questions about intelligence, both individual and collective.
Hongfu Sun is a Senior Lecturer at the School of Engineering, University of Newcastle. His research focuses on innovating MRI mechanisms for clinical and research applications, particularly in Quantitative Susceptibility Mapping (QSM). He is internationally recognized as a pioneer in QSM and integrates MR physics, signal processing, and AI for medical imaging advancements. Sun holds a Ph.D. in Biomedical Engineering from the University of Alberta, Canada. Professional Experience: Senior Lecturer at University of Newcastle (current) ARC DECRA Research Fellow at University of Queensland (2021–2023) Postdoctoral Researcher at University of Calgary (2015–2019) Research Interests: Focuses on MRI innovation, including QSM, deep learning for medical imaging, and AI-driven reconstruction techniques. His work addresses challenges like sub-millimeter resolution and artifact reduction in MRI. Recent projects involve generative AI models for MRI analysis and accelerated quantitative imaging methods. Grants and Funding: AU$1.69M in grants, including a 2021 ARC DECRA for microscopic MRI techniques 2024 NHMRC grant for Parkinson’s disease MRI diagnostics Teaching: Course coordinator for Medical Imaging and Signal Processing at University of Newcastle Focus on biomedical imaging, computational methods, and signal analysis Labs/Teams: Leads research in MRI innovation, collaborating on QSM, deep learning applications, and translational imaging techniques. Active in interdisciplinary projects combining physics, AI, and clinical medicine.
Benjamin Eysenbach is an Assistant Professor in the Department of Computer Science at Princeton University's School of Engineering and Applied Science since 2023. His research focuses on developing principled reinforcement learning (RL) algorithms that improve simplicity, scalability, and robustness in state-of-the-art systems, particularly through probabilistic inference techniques. Ph.D., Machine Learning, Carnegie Mellon University (2023) B.S., Mathematics, Massachusetts Institute of Technology Research interests center on reinforcement learning with emphasis on long-horizon reasoning, exploration strategies, and robustness. He explores intersections with probabilistic inference and self-supervised learning to enhance RL capabilities. Recent publications highlight trends in contrastive learning for goal-conditioned RL, temporal distance modeling , and hierarchical control . Key themes include reward-free learning, scalable architectures, and uncertainty quantification in decision-making systems. 2025: Junior Faculty Award for Excellence in Research and Teaching, Princeton School of Engineering and Applied Science Eysenbach's work bridges theoretical foundations with practical implementations in AI training frameworks, emphasizing performance optimization and safety mechanisms.
Roel C.G.M. Loonen is an Associate Professor at the Unit Building Physics and Services within the Department of the Built Environment at Eindhoven University of Technology (TU/e), Netherlands. He holds joint appointments with EAISI High Tech Systems and EIRES Research groups, focusing on building performance simulation and energy systems. His work bridges academic research with practical applications through collaborations with SMEs in the building industry. Loonen received his BSc and MSc (cum laude) in Building Services from Eindhoven University of Technology, followed by a PhD in 2018 with a dissertation on 'Approaches for computational performance optimization of innovative adaptive facade concepts.' His educational background has positioned him as a leading expert in building performance simulation and sustainable building technologies. His research interests center on developing and applying modeling and simulation strategies to support decision-making for designing buildings that combine high indoor quality with minimal environmental impact. Key areas include adaptive facades, building-integrated renewable energy systems, and energy-efficient building envelopes. He specializes in creating and validating new building performance simulation models to advance innovative building technologies. His recent publications demonstrate a strong focus on practical applications of building performance simulation, with emphasis on residential energy efficiency, photovoltaic systems, and occupant-centered approaches to building design. The work shows increasing integration of machine learning techniques with traditional building simulation methods, particularly for sensitivity analysis and optimization of building performance. REHVA Young Scientist Award (2021) Best PhD supervisor award from Department of the Built Environment, TU/e (2018) First prize - REHVA International student competition (2011) Smart daylight control for optimal building performance (NWO Take-off award, 2018) Best paper award (2021) Loonen actively supervises PhD and Master's students, evidenced by his Best PhD Supervisor Award in 2018. He manages multiple research projects including Sustainable Summer Comfort (2024-2027), Modeling Innovative Use Scenarios for Future Domestic Comfort (2023-2026), and Just Prepare (2022-2026), with funding from sources including the Dutch Research Council (NWO). His professional service includes being a board member of the Dutch-Flemish IBPSA affiliate and co-chair of IBPSA World's website committee, plus reviewing for 35 academic journals. He leads research within the Building Performance group, focusing on creating practical tools and methodologies that bridge the gap between theoretical building performance models and real-world implementation in the construction industry. His work particularly emphasizes the integration of occupant behavior and practices into building performance models, recognizing that human factors are critical to achieving sustainable building performance in practice.
Professor Tony Shardlow is affiliated with the Department of Mathematical Sciences at the University of Bath , UK. His research spans Stochastic Differential Equations , Bayesian Inverse Problems , Statistical Shape Modelling , and Numerical Analysis , with applications in data science, medical imaging, and computational physics. Labs/Teams : IMI (Institute for Mathematical Innovation), Prob-L@b (Probability Laboratory at Bath), SAMBa (EPSRC Centre for Doctoral Training in Statistical Applied Mathematics). Recent Research Trends : Focus on geometric shape analysis using flow fields, stochastic PDEs for particle dynamics, and Bayesian inference techniques in industrial and medical contexts. Collaborative work bridges computational mathematics with applications in hip dysplasia assessment and pesticide delivery systems. Advising : Supervised Fengpei Wang's PhD thesis on dimension reduction and Sinkhorn algorithms. Collaborates with researchers like N. D. F. Campbell and C. Poon. Teaching : Offers MA30170 - Numerical Solution of Elliptic PDEs.
Kshitij Sabnis is a Lecturer in Aerospace Engineering at the School of Engineering and Materials Science, Queen Mary University of London. He serves as Admissions Lead and Outreach & Recruitment Lead for Aerospace Engineering, and Deputy Director of Industrial Engagement (Graduate Attributes). He is affiliated with the Centre for Intelligent Transport and conducts experimental research in high-speed aerodynamics. Education: PhD in Experimental Aerodynamics, University of Cambridge Master’s in Physics Dr Sabnis's research focuses on experimental aerodynamics across various speed regimes, particularly shock/boundary-layer interactions, vortex dynamics, and supersonic flows. His work involves wind tunnel experiments on simplified models to understand complex fluid mechanics in applications ranging from racecar wings to supersonic aircraft intakes. He employs advanced diagnostics and develops novel experimental setups to enhance physical insight into flow phenomena. His recent publications (2019–2025) reflect a strong emphasis on high-speed flow behavior, including shock-induced separation, vortex interactions, and nacelle aerodynamics. Key themes include flow control, wind tunnel design, and validation of turbulence models. His work bridges fundamental fluid dynamics with practical aerospace engineering challenges. Scientific Awards: FHEA (Fellow of the Higher Education Academy) Dr Sabnis actively supervises PhD students and leads externally funded research projects. He has secured grants from EPSRC and the Royal Society, supporting work on schlieren imaging enhancement and small-scale wind turbines for rural energy. He teaches advanced aerodynamics modules and contributes to curriculum and industrial engagement. He leads a research group focused on experimental high-speed aerodynamics and is involved in developing new diagnostic techniques and test rigs. His team investigates vortex interactions and aerodynamic performance under extreme flow conditions.
Maciej Zięba is an academic researcher affiliated with the Faculty of Information and Communication Technology at Wrocław University of Science and Technology, specifically within the Department of Artificial Intelligence . His work spans machine learning, deep learning, and computer vision, with a focus on hyperspectral imaging, autonomous systems, and 3D modeling. Recent research includes uncertainty-aware sensor deployment for autonomous vehicles, low-light image enhancement algorithms, and probabilistic regression frameworks for tabular data. He has co-authored publications on flow-based models, hypernetworks, and neural radiance fields (NeRF) applied to 3D face rendering. Contact: maciej.zieba@pwr.edu.pl
Professor Barak Weiss is a distinguished faculty member in the School of Mathematical Sciences at Tel Aviv University's Faculty of Exact Sciences. His research focuses on the intersection of dynamical systems, number theory, and geometry, particularly in the areas of homogeneous dynamics, ergodic theory, and Diophantine approximation. Professor Weiss has made significant contributions to the understanding of translation surfaces, lattice orbits, and the dynamics of flows on homogeneous spaces. His work often bridges pure mathematics with applications in number theory and geometry, revealing profound connections between seemingly disparate fields. His research on horocycle dynamics, measure rigidity for fractal carpets, and the classification of cut-and-project sets has advanced our understanding of geometric structures and their dynamical properties. His recent publications (2023-2025) demonstrate a strong focus on equidistribution phenomena, statistical properties of dynamical systems, and the application of homogeneous dynamics to problems in geometric number theory. A notable trend in his work is the interplay between geometric structures and their arithmetic properties, particularly in the context of Diophantine approximation. Professor Weiss actively organizes the "Homogeneous Dynamics and Applications" seminar at Tel Aviv University, which has been running continuously since at least 2014 with detailed schedules available through 2025. This seminar serves as a hub for cutting-edge research discussions, featuring both local and international speakers working on dynamical systems and related areas. He teaches advanced courses in analysis and supervises graduate students, with recent teaching assignments including Real Analysis for summer semester 2025. His office is located in Schreiber building, room 329, and his regular office hours are Tuesdays from 15:00-16:00.
Benjamin Eysenbach leads the Princeton Reinforcement Learning Lab, where he designs algorithms that enable artificial intelligence systems to learn intelligent behaviors through trial-and-error, specializing in self-supervised methods that eliminate the need for human labels. He joined Princeton after completing his PhD in machine learning at Carnegie Mellon University under Ruslan Salakhutdinov and Sergey Levine, supported by the NSF Graduate Research Fellowship and Hertz Fellowship. His research bridges fundamental machine learning principles with practical applications in robotics and decision-making systems. Eysenbach's research focuses on developing self-supervised reinforcement learning algorithms that enable autonomous skill acquisition without external rewards. His investigations span contrastive learning methods, temporal abstraction techniques, and scalable architectures for goal-conditioned behaviors. These innovations aim to create more efficient and generalizable learning systems that can discover useful behaviors from unlabeled experience. Eysenbach's publications demonstrate consistent advancement in self-supervised RL methodologies, with recent work focusing increasingly on temporal abstraction and representation learning theory. His research shows progression from foundational contrastive RL frameworks toward more sophisticated analyses of generalization properties and uncertainty quantification. The 2025 works indicate expanding investigation into hierarchical control, probabilistic alignment, and hyper-deep network architectures. Eysenbach has been recognized with prestigious awards including the Hertz Fellowship and NSF Graduate Research Fellowship, supporting his doctoral research in self-supervised RL methodologies. His work has been presented at top machine learning conferences including NeurIPS, ICML, and ICLR. As director of the Princeton Reinforcement Learning Lab, Eysenbach oversees research initiatives in self-supervised RL, including projects on intention-conditioned modeling, horizon generalization, and contrastive learning frameworks. He has secured funding from the Princeton AI Lab to study neural correlates of temporal contrast in decision-making. Eysenbach teaches courses in reinforcement learning and has developed new benchmarks like JaxGCRL to accelerate research in goal-conditioned RL.
Federico Toschi is a Full Professor at Eindhoven University of Technology (TU/e), holding joint appointments in Applied Physics and Mathematics and Computer Science departments. His research focuses on multi-scale transport phenomena, combining statistical physics, fluid dynamics, and computational methods. He leads projects in the 4TU Centre for Multiscale Phenomena and EAISI. Education: PhD in Physics (University of Pisa, 1998) and academic background at Scuola Normale Superiore di Pisa. Interdisciplinary expertise in fluid dynamics turbulence, Lagrangian turbulence, crowd dynamics, and Lattice Boltzmann methods. Recipient of APS Fellow (2015), Euromech Fluid Mechanics Fellow (2012), and Ig Nobel Prize for Physics (2021). Research emphasizes turbulence modeling, pedestrian dynamics, and active matter, with applications in environmental flows and crowd management. His work bridges computational innovations with experimental validations. Recent articles explore kinetic data-driven turbulence modeling, pedestrian flow optimization, and turbulence effects in biological systems. Projects include digital twins for seismicity modeling and rarefied gas dynamics. Teaches fluid mechanics, computational physics, and chaos theory courses. Founded Flow Matters Holding BV, applying research to practical solutions.
Luís B. Elvas is an Assistant Professor at ISCTE-University Institute of Lisbon's Department of Social and Business Sciences (SINTRA) and a Research Assistant at ISTAR-Iscte Research Center. He holds qualifications including a Technical Specialization in TensorFlow for AI (Coursera, 2021) and certifications in IoT/Blockchain from ISCTE and cybersecurity from Palo Alto Networks. His research spans artificial intelligence, healthcare informatics, smart cities, and blockchain, with applied work in medical imaging, data sharing, and urban analytics. Research interests include: Healthcare AI : Developing deep learning models for cardiac diagnostics, medical imaging analysis, and blockchain-based health data systems Smart Cities : Implementing IoT solutions for urban mobility optimization, disaster management, and sustainable transportation Data Science : Creating predictive analytics frameworks for clinical decision support and urban planning His publications demonstrate a strong focus on AI-driven healthcare solutions (67% of recent works) and smart city technologies (33%), with emerging interests in blockchain and NLP. Research consistently targets real-world applications in clinical settings and urban environments. Awards: Award for best internship, Order of Engineers (2022) Distinction for best internship, Order of Engineers (2021) He leads/contributes to multiple EU research consortia including AMR-EDUCare (antimicrobial resistance education), NEEM (e-health in Nepal), and Blockchain.PT. Coordinates the IEEE Computational Intelligence Society Student Branch Chapter at ISCTE and developed the ManagiDiTH master's program in digital health transformation.
