Daniel Sage is a Lecturer and Scientific Advisor at École polytechnique fédérale de Lausanne (EPFL) , affiliated with the Biomedical Imaging Laboratory (LIB) under the College of Engineering (STI) and School of Life Sciences (SV) . He specializes in bioimage informatics , structured-illumination microscopy , and deep learning applications for biomedical imaging. His work spans algorithm development for single-molecule localization microscopy (SMLM) , fluorescence imaging , and 3D reconstruction . His research group has developed open-source tools like FlexSIM for light inhomogeneity correction, DeepImageJ for integrating deep learning in ImageJ, and Steer'n'Detect for orientation-accurate template detection. His publications focus on correcting multiple-blinking artifacts in PALM, optimal transport metrics for SMLM evaluation, and contextual feature analysis for xenograft cell classification. He mentors PhD students and contributes to interdisciplinary education through courses such as Bioimage Informatics and Fundamentals of Image Analysis , emphasizing practical software solutions and Java programming for bioimage processing. His collaborations include institutions like Howard Hughes Medical Institute and Centre National de la Recherche Scientifique (CNRS) .
Federico Miretti is an Assistant Professor at the Polytechnic University of Turin, affiliated with the Department of Energy and the interdepartmental center Cars@PoliTo. His research focuses on hybrid and electric vehicles, with emphasis on energy management strategies, battery state estimation, and sustainable transport solutions. PhD in Energetics from Polytechnic University of Turin Research areas include: Optimization-based Energy Management Strategies for hybrid propulsion systems Simulation and digital twinning of hybrid systems Thermal management for electrified vehicles Techno-economic assessment of mobility solutions Recent publications highlight advancements in battery temperature anomaly detection, wireless power transfer feasibility, and control algorithms for energy efficiency. His work aligns with SDGs 7 (Clean Energy) and 9 (Innovation). Teaching roles include: Fluid Machinery (2019-2025) Energy Management in Hybrid/Electric Vehicles (2021-2025) Projects: PRoSIT (2025): Predictive thermal management demonstrator Consulting contract with MIDAC SpA (2025): Battery Digital Twin development EBOAT project (2025-2026): Technical support for Vulkan
Dimitris N. Metaxas is a Professor in the Department of Computer Science within the School of Arts and Sciences at Rutgers University. His research spans computer vision, medical image analysis, and artificial intelligence, with a particular focus on medical applications including cardiac MRI analysis and foundation models for healthcare. Dr. Metaxas's research interests encompass medical image analysis, computer vision, deep learning, and artificial intelligence. His work demonstrates a strong emphasis on applying advanced machine learning techniques to medical imaging problems, particularly in cardiac analysis. He has made significant contributions to diffusion models, multimodal learning, and efficient AI techniques for medical applications. His research bridges the gap between theoretical computer vision and practical healthcare solutions, with numerous publications in top-tier conferences and journals. His recent publications show a clear trend toward foundation models for medical image analysis, with significant contributions to cardiac MRI segmentation, diffusion models, and multimodal learning. The research spans both theoretical advancements in AI techniques and practical applications in healthcare, particularly focused on improving medical diagnostics through computer vision. His work demonstrates expertise in adapting cutting-edge AI techniques like diffusion models and large language models for specialized medical applications. Dr. Metaxas has mentored numerous students and researchers, as evidenced by his extensive publication record with multiple co-authors across various institutions. His work has received significant attention in the research community, with numerous publications in top venues including CVPR, ICCV, MICCAI, and Medical Image Analysis. His research group focuses on medical image computing, computer vision, and machine learning applications in healthcare. The team works extensively with cardiac MRI data, developing advanced techniques for segmentation, reconstruction, and analysis of 4D cardiac imaging. They are particularly known for their contributions to foundation models in medical imaging and efficient adaptation techniques for specialized medical tasks.
Professor David J.A. Evans is a distinguished glacial geomorphologist at Durham University's Department of Geography. His research spans three interconnected themes at the interface of glacial geology and Quaternary science, focusing on palaeoglaciology and the reconstruction of former glaciers and ice sheets through time. With extensive field experience across the Arctic, North Atlantic, and Southern Hemisphere, he has contributed significantly to the understanding of glacial landscapes and processes globally. BA (Hons), St. David's University College, University of Wales, Lampeter, 1982 MSc, Memorial University of Newfoundland, Canada, 1984 PhD, University of Alberta, Canada, 1988 Professor Evans' research centers on glacial landsystems, glacial sedimentology, and Quaternary palaeoenvironments of glaciated basins. His work develops conceptual landsystems models for glacial process-form relationships, with field sites ranging from the Canadian High Arctic to mid-latitude mountains. He has pioneered research connecting subglacial process-form relationships to landform development models, particularly through his studies of active temperate glacier margins in Iceland. His sedimentological work has established a genetically framed classification scheme for tills and glacitectonites. Evans has contributed to major ice sheet reconstructions worldwide, including the British and Irish Ice Sheet (through the BRITICE-CHRONO project), Laurentide Ice Sheet, and Arctic Norway. His extensive publication record demonstrates consistent research activity with 15 recent articles focusing on ice sheet dynamics, glacial landform evolution, and Quaternary environmental reconstruction across multiple continents. These works reveal a strong emphasis on methodological innovation in mapping and analyzing glacial landscapes, with increasing integration of remote sensing and quantitative approaches to understand both contemporary glacier behavior and past ice sheet dynamics. Scientific Recognition: Busk Medal (Royal Geographical Society), 2017, for excellence and originality in the study of glacial landscapes and processes and empowering the next generation Professor Evans has collaborated extensively with international research teams on major projects including BRITICE-CHRONO and has contributed to engineering geology applications through technical guides for glaciated terrains. His work bridges pure academic research with practical applications in engineering geology and environmental management. He has supervised numerous field guides and edited influential publications in glacial geomorphology. His research involves extensive fieldwork in Iceland (particularly on the Vatnajökull ice cap), the British Isles, Canadian Arctic, and other glaciated regions, often using modern glacier systems as analogues for reconstructing past ice sheets. Evans has developed significant expertise in glacial landsystem mapping and interpretation, contributing to both academic understanding and practical engineering applications in glaciated terrains.
Prof. Dr. Bernd Skiera is a leading Marketing Professor at Goethe University Frankfurt since 1999 and a member of the managing board of the efl - The Data Science Institute. His work bridges information systems and marketing, with a focus on data-driven decision making and digital transformation.
Aniello Murano is a Professor of Computer Science at the Department of Electrical Engineering and Information Technologies , University of Naples Federico II. He serves as Scientific Director of the ASTREA (Automated Strategic Reasoning) Laboratory and leads cutting-edge research in Artificial Intelligence, Strategic Reasoning, Multi-Agent Systems , and Formal Verification . Research Interests : Strategic reasoning under perfect/imperfect information, specification/verification/synthesis of reactive systems, temporal/modal logics, automata theory, parity games, game theory, mechanism design, and formal languages. Notable Projects : PNRR Research Unit Coordinator (2023-2025) on Resilient AI, PRIN 2020 Unit Coordinator (RIPER: Resilient AI-Based Self-Programming and Strategic Reasoning), H2020-MSCA SEAL (Principal Coordinator). Awards & Honors : JPMorgan Faculty Research Award (2022), Royal Society Award (2016), Best Paper PRIMA (2015), INDAM Project Leader (2023), Italian Scientific Habilitation (2017-2018). Students & Postdocs : Supervised 6 PhD students (e.g., Silvia Stranieri, Vadim Malvone) and mentored postdocs such as Munyque Mittelmann and Bastien Maubert. Laboratory : Leads ASTREA Lab, focusing on automated strategic reasoning and resilient AI systems.
Kjell Jorner is an Assistant Professor of Digital Chemistry in the Institute for Chemical and Bioengineering at ETH Zurich's Department of Chemistry and Applied Biosciences. His research group focuses on integrating computational methods and machine learning to address challenges in chemical synthesis, materials design, and reaction prediction. Education: PhD from Uppsala University (Photochemistry of aromatic compounds) Postdoctoral studies at AstraZeneca UK (Reaction prediction using computational chemistry and ML) Postdoctoral studies at University of Toronto (Molecular design of catalysts and organic electronic materials) Research Interests: Professor Jorner's work bridges computational chemistry, machine learning, and experimental design. Key areas include: Development of quantum mechanics-machine learning hybrid approaches for reaction feasibility prediction Inverse molecular design of functional materials (e.g., singlet-fission systems) Computational catalyst optimization and high-throughput screening methods Digital tools for chemical education and cheminformatics Publication Trends (2023-2025): Recent articles demonstrate a strong focus on machine learning applications in chemistry, including reaction prediction algorithms, catalyst design frameworks, and automated molecular generation. A recurring theme is the development of computational tools to accelerate materials discovery and optimize chemical processes. Laboratory & Team: Leads the Digital Chemistry research group at ETH Zurich (HCI E 137) exploring computational approaches to chemical challenges.
Dr. Gabriel Wainer is a Professor in the Department of Systems and Computer Engineering at Carleton University's Faculty of Engineering and Design. He leads the Advanced Real-Time Simulation Lab and specializes in modeling and simulation methodologies, particularly focusing on discrete event systems, real-time modeling, cellular automata, and DEVS formalism. Research Interests: Discrete event systems, DEVS formalism, cellular automata, real-time simulation, IoT applications, and parallel/distributed simulation Affiliation: Carleton University Recent publications highlight his work in advanced simulation frameworks, energy-efficient 5G systems using deep reinforcement learning, and pandemic modeling with cellular automata. His lab develops tools like PROMETHEUS and Devsmap for standardized DEVS model representation, while also exploring applications in wireless communication, building energy systems, and behavioral epidemiology.
Mannes Poel is a researcher specializing in Datamanagement & Biometrics with a focus on applied machine learning. His work spans healthcare analytics, sensor technology optimization, and meta-learning frameworks for missing data. ORCID: 0000-0002-3813-9732 Active Domains : Artificial Intelligence, Medical Predictive Modeling, Industrial Sensor Analytics Collaboration Network : Interdisciplinary work with institutions in healthcare (e.g., Diagnostics journal) and engineering (e.g., IEEE MEMS conference) Scientific Achievements : Best paper award at Intetain 2107 (2017) Research Trends : His recent publications (2024-2025) emphasize explainable AI for missing data in clinical contexts and machine learning-enhanced sensor systems for industrial fluid dynamics. These works combine traditional ML with real-time data processing and cross-domain model adaptation.
Talia Ringer is an Assistant Professor in the Department of Computer Science at the University of Illinois, where she is a member of the PL/FM/SE (Programming Languages/Formal Methods/Software Engineering) research group. She leads the Illinois Theorem Provers (ITP) lab, which focuses on advancing proof engineering technologies to make formal verification accessible to programmers of all skill levels across all domains. Research Interests Dr. Ringer's research spans multiple aspects of proof engineering with a strong focus on integrating techniques from dependent type theory, program transformations, and neural proof synthesis to solve real-world verification challenges. Her work addresses how to build systems that allow programmers to prove the absence of costly or dangerous bugs in software. She is particularly interested in proof repair, machine learning for proofs, and developing new methodologies that can drive the creation of large, secure, and robust verified software and hardware systems. Research Trends Dr. Ringer's recent publications demonstrate a strong shift toward integrating machine learning with formal verification, particularly in proof repair and synthesis. Her work explores how large language models can assist with theorem proving, how reinforcement learning can automate verification processes, and how to make proof engineering more practical for real humans. Many publications involve collaborations with students and researchers from multiple institutions, reflecting her commitment to interdisciplinary research. Awards and Recognition Distinguished Paper Award at ESEC/FSE 2023 for "Baldur: Whole-Proof Generation and Repair with Large Language Models" ACM SIGPLAN Distinguished Service Award in 2023 Mentoring and Service Dr. Ringer is a dedicated mentor who has advised numerous undergraduate and graduate students. She is the founder and president of the Computing Connections Fellowship, which provides transitional funding for computer science PhD students needing to escape unhealthy environments. She is also the founder and previous chair of the SIGPLAN Long-Term Mentoring Committee (SIGPLAN-M), which connects more than 200 mentors and 300 mentees across more than 44 countries. Her service work was formally recognized with the 2023 ACM SIGPLAN Distinguished Service Award. Laboratory and Collaborations Dr. Ringer leads the Illinois Theorem Provers (ITP) lab with current members including postdocs, PhD students, masters students, and undergraduates. She collaborates extensively with researchers at the University of Washington, UMass Amherst, Google Research, Galois, and other institutions on various proof engineering projects.
Michael Pyrcz is a Professor in the Hildebrand Department of Petroleum and Geosystems Engineering and holds the rank of Associate Professor in the Jackson School of Geosciences at the University of Texas at Austin. He is the recipient of the B. J. Lancaster Professorship in Petroleum Engineering and the George H. Fancher Centennial Teaching Fellowship in Petroleum Engineering. His research focuses on subsurface data analytics, geostatistics, and machine learning applications in energy systems and CO2 sequestration. Pyrcz teaches widely, including through online lectures and GitHub workflows, and has authored over 50 peer-reviewed publications and a textbook on spatial data analytics. His work integrates machine learning with geoscience challenges, such as uncertainty quantification in reservoir modeling and CO2 storage site evaluation. He leads initiatives in energy data analytics through the Freshman Research Initiative and collaborates with industry on workflow development. Key research areas include generative AI for subsurface models, stochastic methods for fracture networks, and anomaly detection in geologic monitoring. Education: Background in petroleum engineering and geosciences (details not explicitly provided). Grants/Advising: Extensive industry collaboration and mentorship roles at Chevron prior to UT Austin. Labs/Teams: Maintains active GitHub repositories (GeostatsGuy), YouTube lecture series (GeostatsGuyLectures), and social media outreach (X/GeostatsGuy).
Yan Chen is a Professor of Computer Science at Northwestern University's Robert R. McCormick School of Engineering and Applied Science. He leads the Northwestern Lab for Internet and Security Technology (LIST) and the Center for Ultra-scale Computing and Information Security. His research focuses on cybersecurity, network measurement, and distributed systems security. Chen holds a Ph.D. from UC Berkeley (2003), M.S. from SUNY Stony Brook, and B.E. from Zhejiang University. Research interests include securing networking systems, intrusion detection, cloud-native platforms, and mobile security. Notable awards include the DOE Early CAREER Award (2005), Air Force Young Investigator Award (2007), and ACM ASPLOS'18 Most Influential Paper Award. His work has been cited over 17,000 times with an h-index of 62 (2024). Key contributions include the LIST lab's advancements in APT detection, provenance tracking in microservices, and security frameworks like FlowCog. He advises numerous Ph.D. students and has graduated over 20 researchers now in academia and industry.
John Bagterp Jørgensen is a Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). His research focuses on computational methods for Model Predictive Control (MPC), numerical optimization, and dynamic optimization, with applications in industrial processes, biomedical systems, and sustainable energy. He holds leadership roles in 2-control ApS, a company developing advanced control solutions for industries such as cement production and oil recovery. Education: PhD and M.Sc. in Technical Sciences from DTU (1997–2005 and 1991–1997). Professional experience includes roles as an Assistant Professor at DTU and CTO/CEO at 2-control ApS. Research interests span MPC algorithms, numerical methods for differential equations, and system identification. His work bridges academia and industry, addressing challenges in energy efficiency, vaccine manufacturing, diabetes treatment, and cement production processes. His recent articles emphasize industrial applications of control systems, including cement rotary kiln dynamics, vaccine production optimization, and dual-hormone artificial pancreas development. He has received the Nordic Energy Research Award (1994) and contributed to UN Sustainable Development Goals related to affordable energy and industrial innovation. Advising and grants: Supervises multiple PhD projects on topics like electrification of industrial processes and sustainable SCP production. Collaborates with global institutions on energy and biomedical research. Labs/teams: Leads teams in DTU’s Scientific Computing and Center for Energy Resources Engineering, with active partnerships in industry and academia.
Shaowu Pan is an Assistant Professor of Aerospace Engineering at Rensselaer Polytechnic Institute (RPI), affiliated with the Future of Computing Institute (FOCI) and the Scientific Computation Research Center (SCOREC). He holds a Ph.D. in Aerospace Engineering and Scientific Computing from the University of Michigan and completed a postdoctoral fellowship at the University of Washington’s AI Institute in Dynamic Systems. Education: Ph.D., University of Michigan, 2021 M.S., University of Michigan, 2015 B.E. & B.S., Beihang University, 2013 Research Interests: His work focuses on the intersection of computational fluid dynamics, data-driven modeling, and scientific machine learning. Key areas include operator-theoretic modeling of fluid flows, generative AI for physical systems, and physics-informed neural networks. He develops novel algorithms for reduced-order modeling and stability-preserving surrogate models, with applications in turbulence, plasma physics, and aerodynamics. Key Contributions: Developed PyKoopman , an open-source Python package for Koopman operator approximation. Pioneered mesh-agnostic representation methods like Neural Implicit Flow for spatio-temporal data. Advanced physics-informed neural networks for solving Grad-Shafranov equations and plasma equilibrium problems. Awards & Recognition: John Tichy Junior Faculty Travel Grant (2024) Chinese Outstanding Student Abroad Award (2021) Richard and Eleanor Towner Prize Nominee (2019) Teaching & Mentorship: He teaches courses like MANE 2110: Numerical Methods and Programming for Engineers and mentors multiple Ph.D., master’s, and undergraduate students. His doctoral committee involvement spans interdisciplinary projects in fluid dynamics and AI. Grants & Software: Lead PI for NSF-funded projects on neural representation learning for turbulent flows. Developed software tools like spKDMD and Warp-DG for dynamics analysis and CFD simulations. Labs & Collaborations: Collaborates with institutions like Los Alamos National Laboratory and actively participates in conferences (e.g., AIAA SciTech, SIAM). His research bridges computational science, machine learning, and fluid dynamics to address complex nonlinear systems.
Professor Albert Cheng is a faculty member in the Department of Computer Science at the University of Houston. His research focuses on real-time systems, cyber-physical systems, smart cities, and embedded systems with societal impacts. He has authored over 270 publications and a textbook on real-time systems. Cheng holds roles as an Associate Editor for the IEEE Transactions on Knowledge and Data Engineering and ACM Computing Surveys. His research interests span real-time scheduling, machine learning applications, and systems optimization. Recent work includes vehicular traffic modeling for epidemiological risk reduction, quantum computing response time analysis, and satellite mission planning. Awards include Fulbright Specialist, Distinguished ACM membership, and IEEE Senior Member status. Cheng’s articles demonstrate expertise in real-time scheduling algorithms, cyber-physical systems development, and smart city infrastructure. His contributions bridge theoretical computer science with practical implementations in transportation, healthcare, and aerospace domains. Ongoing efforts include fault-tolerant systems, energy-efficient scheduling, and CPS education initiatives. Awards: Fulbright Specialist, ACM Distinguished Member, IEEE Senior Member, Institute of Physics Fellow Labs/Teams: Hewlett Packard Enterprise Data Science Institute (HPE DSI), Research Computing Data Core (RCDC)
