Dr. Bracha Laufer is a senior lecturer at the School of Electrical Engineering , part of the Iby and Aladar Fleischman Faculty of Engineering at Tel Aviv University. Her research focuses on acoustic source localization, speech signal processing, and machine learning techniques for audio engineering. Her recent work explores conformal prediction and manifold-based approaches for robust source localization, deep learning architectures for sound source separation, and simplex geometry in multichannel signal analysis. These publications highlight interdisciplinary applications of machine learning and statistical methods in acoustics. Dr. Laufer's research integrates Bayesian inference , probabilistic graphical models , and uncertainty quantification to address challenges in adverse acoustic environments. She has contributed to advancements in multi-microphone speaker localization and speech inpainting .
Adam Misik is a researcher at the Chair of Media Technology (Prof. Steinbach) within the College of Engineering at the Technical University of Munich. He earned a B.Sc. in 2019 and M.Sc. in 2022 in Electrical Engineering and Information Technology, with study visits at EPFL and Télécom ParisTech. Since June 2022, he has been an external PhD student at Siemens AG. His research focuses on multimodal sensor data analysis using computer vision and deep learning techniques, particularly for 3D reconstruction and localization problems. His work intersects with fields like haptic communication , indoor mapping , and human activity understanding . Key publication trends include point cloud registration , hyperbolic learning , and equivariant neural networks . Recent works address surface material classification (2025), CAD model retrieval (2025), and SLAM systems (2024). Education: B.Sc. (2019), M.Sc. (2022) in Electrical Engineering and Information Technology, TU Munich Current Role: External PhD student at Siemens AG since 2022 Research Affiliation: Chair of Media Technology at TU Munich, part of the Munich Institute of Robotics and Machine Intelligence (MIRMI)
Panagiota Birmpa is an Assistant Professor in the Department of Actuarial Mathematics & Statistics at Heriot-Watt University's School of Mathematical and Computer Sciences, Edinburgh. Her research bridges advanced mathematical theory with cutting-edge machine learning applications, focusing on uncertainty-aware methodologies for complex data systems. She actively supervises PhD students and maintains strong collaborative ties with international research groups in applied mathematics and computational science. Her academic credentials include: BSc+MSc (integrated master) in Applied Mathematics and Physical Sciences (majors in Analysis and Statistics) from National Technical University of Athens (NTUA), 2011 MSc in Pure Mathematics from National and Kapodistrian University of Athens (NKUA), 2014 PhD in Mathematics from University of Sussex, UK, 2018 (Thesis: Quantification of Mesoscopic and Macroscopic Fluctuations in Interacting Particle Systems) Dr. Birmpa's research program integrates theoretical mathematics with modern computational challenges through seven core domains: Generative modeling, Scientific Machine learning, Uncertainty Quantification, Probabilistic Graphical models, Interacting Particle Systems, Optimal transport Theory, and Partial Differential Equations. Her interdisciplinary approach enables innovative solutions for complex data analysis problems across scientific domains, particularly where traditional statistical methods face limitations in high-dimensional spaces. Analysis of her publication trajectory reveals a progression from foundational statistical physics (2017-2018 interface dynamics research) toward contemporary machine learning applications (2021-2024). Her recent work demonstrates increasing sophistication in merging deep learning architectures with uncertainty quantification frameworks, especially for scarce high-dimensional data scenarios where conventional approaches fail. This evolution reflects broader trends in mathematical data science toward robust, interpretable AI systems. No scientific awards or fellowships are currently listed in her professional profile. Dr. Birmpa accepts PhD candidates for projects exploring deep learning-graphical model interfaces with uncertainty quantification, building on her prior AFOSR-funded postdoctoral research at UMass Amherst (2021-2022). Her grant history includes significant support from the Air Force Office of Scientific Research for developing particle-based generative algorithms. She maintains active supervision of graduate researchers while pursuing methodological innovations in probabilistic modeling. Her collaborative research network spans multiple institutions including University of Massachusetts Amherst, with interdisciplinary teams developing novel mathematical frameworks for scientific machine learning. Current projects focus on Lipschitz-regularized gradient flows and generative particle algorithms for high-dimensional data, extending her earlier work on non-equilibrium fluctuations in particle systems.
Jason Pacheco serves as an Assistant Professor in the Department of Computer Science at the University of Arizona, maintaining an office in GS 724. He earned his Ph.D. from Brown University in 2016 and specializes in theoretical and applied machine learning. His educational background includes: Ph.D. in Computer Science, Brown University (2016) Dr. Pacheco's research centers on statistical machine learning, probabilistic graphical models, and approximate inference algorithms, with emphasis on information-theoretic decision making. He bridges theoretical foundations with practical applications in cybersecurity, privacy-preserving AI, and environmental monitoring systems, developing novel approaches for robust sequential decision making under uncertainty. Analysis of his 15 most recent publications (2021-2025) reveals three dominant research thrusts: (1) Privacy-preserving machine learning, particularly federated learning and differential privacy for large language models; (2) Adversarial reinforcement learning for cyber defense and malware detection; and (3) Variational information-theoretic methods for mutual information estimation and sequential decision making. His work consistently integrates theoretical rigor with real-world applications in security and environmental science.
Gauthier Vermandel is a full-time researcher at École Polytechnique's Department of Applied Mathematics (CMAP) and holds a tenured Associate Professor position at Université Paris-Dauphine-PSL. He is affiliated with the Institut Polytechnique de Paris and serves as a research fellow for the Stress-test Chair at Polytechnique. Vermandel is also a consultant for the Banque de France on climate change models through the DECAMS directorate and serves as President of DSGE-net, a non-profit organization supporting the Dynare project. His research interests focus on quantitative macroeconomics, climate change economics, and the development of economic modeling tools. Vermandel specializes in integrating climate considerations into macroeconomic frameworks, particularly through Dynamic Stochastic General Equilibrium (DSGE) models. His work explores social learning expectations, business cycle theory, and the economic impacts of carbon taxation policies. He has made significant contributions to the Dynare platform, extending its capabilities for climate economics and social learning applications. Vermandel's recent publications demonstrate a strong focus on the intersection of climate policy and financial markets, with particular attention to how carbon taxation affects economic stability. His research combines theoretical economic modeling with practical applications for policy makers, especially in the context of the European Union's green transition initiatives. EFA Prize in Responsible Finance (2021) Banque de France Young Researcher Prize in Green Finance (2023) Vermandel serves as program director of the Environmental Macro research group at the Institute for Macroeconomic and International Policies (i-MIP), hosted by PSE and CEPREMAP. He is a member of the Dynare Team working on implementing Dynare into Python/Julia environments and participates in the organization committee of the Quantitative Sustainable Finance (QSEF) seminar at CMAP–CREST. His former role as scientific advisor at France Stratégie (the French Prime Minister's research unit) provided him with direct policy experience that informs his academic work. Vermandel maintains active research laboratories through his leadership roles in DSGE-net and the Stress-test Chair, where his team develops advanced modeling techniques for assessing climate-related financial risks. His work bridges academic research with practical policy applications, particularly in the context of the European Central Bank's climate stress testing initiatives.
Prof. Dr. Didier Stricker is a leading academic in computer science, serving as Scientific Director at the German Research Center for Artificial Intelligence (DFKI) and Professor at the University of Kaiserslautern-Landau (RPTU). His career spans over two decades, including leadership roles at Fraunhofer IGD and founding the Augmented Vision research unit at DFKI/RPTU, which now includes ~30 researchers. Education: Electrical Engineering (Technical University of Grenoble, Karlsruhe) PhD: Computer Vision-based Calibration and Tracking Methods for Augmented Reality (2002, TU Darmstadt) His research focuses on virtual and augmented reality , computer vision , human-computer interaction , and on-body sensor networks . He leads major EU/national projects like LUMINOUS (Language-Augmented XR) and SHARESPACE (Ethical Hybrid Shared Spaces), with industrial partnerships including Sony, Google, and John Deere. Recent publications emphasize 3D reconstruction , neural network optimization , and XR systems . Key trends include event camera processing , scene flow estimation , and multimodal AI for industrial applications . He holds patents in AR tracking and has received the 2006 Innovation Prize from the German Society of Computer Science. Scientific Awards : Innovation Prize (2006) Best Paper/Demonstration Awards at ISMAR, EUSIPCO, CVPR, and ICRA As a reviewer for journals and conferences in VR/AR and computer vision, he contributes to shaping research standards. His lab ( AG Augmented Vision ) combines academic and industrial collaborations to advance cognitive interfaces and extended reality systems.
Michael Ryoo serves as a SUNY Empire Innovation Associate Professor in the Department of Computer Science at Stony Brook University while concurrently working as a Research Scientist with Google Brain's "Robotics at Google" team. Previously, he held positions as an Assistant Professor at Indiana University Bloomington and a Staff Researcher at NASA's Jet Propulsion Laboratory. His academic background includes a Ph.D. from the University of Texas at Austin (2008) and a B.S. from Korea Advanced Institute of Science and Technology (KAIST) in 2004. Ryoo's research centers on deep learning and computer vision with specific focus on convolutional neural network (CNN) models for video semantic understanding. His work bridges visual perception and robotic action through applications in robot perception, robot learning, and human-robot interaction. Key innovations involve developing efficient architectures for processing multimodal data and translating visual understanding into robotic control systems. Analysis of his 15 most recent publications reveals strong emphasis on multimodal AI integration, particularly vision-language models applied to robotics. His 2025 work shows significant advancement in token-efficient video representation, motion-controllable diffusion models, and zero-shot learning frameworks specifically designed for robotic control systems. The research trajectory demonstrates consistent focus on making video understanding more accessible and applicable to real-world robotic scenarios. Scientific awards: No awards were mentioned in the provided source material. Regarding academic advising, the source text does not list any students or mentoring activities. Similarly, no grant funding information is provided, though his dual academic-industry role suggests substantial research support. His Google Brain affiliation likely involves industry-sponsored research initiatives. Ryoo maintains active laboratory affiliations through Stony Brook's AI Innovation Institute and Google's Robotics team. His prior work at NASA JPL indicates experience with space robotics systems, while his current Google role focuses on large-scale robot learning infrastructure. The LAM SIMULATOR project represents his current focus on advancing data generation techniques for training large action models.
Joseph S.B. Mitchell is a SUNY Distinguished Professor at the Department of Applied Mathematics and Statistics, State University of New York at Stony Brook. His office is located in Math Tower, Room P-138A. His research spans computational geometry, algorithms, optimization, and related fields. Research Interests: Prof. Mitchell's work focuses on fundamental and applied problems in geometric computing, including: Algorithm design for spatial optimization (e.g., art gallery problems, dispersion) Robotics applications (multi-agent path planning, sensor deployment) Efficient solutions for geometric data structures and visibility constraints Recent Publications: His 15 most recent articles (2024-2025) demonstrate a consistent focus on geometric optimization, visibility problems, and algorithm design for polygons and networks. Common themes include approximation algorithms, multi-robot coordination, and combinatorial solutions for spatial challenges. Awards: SUNY Distinguished Professor (recognizing exceptional academic contributions)
Professor Geoff Webb is a world-leading data scientist at Monash University , serving as Director of the Monash University Centre for Data Science within the Faculty of Information Technology . His research focuses on leveraging data science to enable evidence-based decision making and derive actionable insights through artificial intelligence, machine learning, and big data analytics. Core expertise in data mining , bioinformatics , and computational biology Developed Magnum Opus software and contributed to the Weka machine learning workbench Recipient of the Eureka Prize for Excellence in Data Science and leadership roles in major data mining conferences Research Interests Professor Webb's work spans artificial intelligence , machine learning , and data analytics , with a focus on black-box user modelling , interactive data analytics , and statistically-sound pattern discovery . His recent publications highlight advancements in Bayesian networks , time series analysis , and genomic data interpretation , demonstrating his interdisciplinary impact. Scientific Contributions AI in healthcare : EHR-ML framework for clinical records analysis Biological applications : KcatNet for enzyme prediction, PFresGO for protein function Data mining innovations : OPUS search algorithm, proximity forest techniques As a technical adviser to data science company Froomle , he bridges academic research with real-world applications. His work has been recognized through numerous research awards and leadership as Editor-in-Chief of Data Mining and Knowledge Discovery for a decade.
Simone Diniz Junqueira Barbosa is an Associate Professor at the Informatics Department of Pontifical Catholic University of Rio de Janeiro (PUC-Rio). She leads the IDEIAS-SERG lab, focusing on Human-Computer Interaction (HCI) and Information Visualization research. Her work spans model-based interactive systems design, visual analytics, digital storytelling, and AI techniques for improving system usability and accessibility. Her research explores: Model-based approaches for interactive systems Data science and visual analytics for complex datasets Digital storytelling techniques Accessibility and usability enhancement through AI Semiotic engineering foundations for HCI Publication analysis reveals strong emphasis on HCI methodologies, data visualization literacy, ethical AI development, and applications in legal informatics/industrial automation. Recent works focus increasingly on explainable AI, visualization cognition, and ethical ML practices. Awards & Honors: IFIP TC13 Pioneer Award (2019) Outstanding HCI Career Award (Brazilian Computer Society, 2020) Selo de Inovação SBC Innovation Award (2022) IFIP Fellow Award (2024) She has advised over 45 graduate students (PhD/MSc) and coordinates multiple labs including DasLab, ExACTa, and Americanas Futuro Lab. Funded projects include CNPq, FAPERJ, Microsoft Research, and Petrobras collaborations focusing on HCI innovations and data visualization tools. Leads the IDEIAS-SERG research group merging semiotic engineering with interaction design. Serves on steering committees for ACM CHI and IFIP TC13, previously co-edited ACM Interactions Magazine (2016-2019).
Professor David Andrews is Professor of Engineering Design in the Department of Mechanical Engineering at University College London (UCL). A globally recognised authority on naval architecture and ship design methodology, he has held continuous academic appointments at UCL since 1980, punctuated by distinguished service in the UK Ministry of Defence where he rose to Director of Frigates and Mine Countermeasures. Since 2000 he has occupied the Chair in Engineering Design at UCL, leading major research initiatives funded by EPSRC, ONR and the EU, and forging strategic industrial partnerships. Education Bachelor of Engineering, UCL (1970) MSc Naval Architecture, UCL (1971) PhD “Synthesis in Ship Design”, UCL (1984) Research Focus Professor Andrews is acknowledged as the world-leading expert in naval ship design methodology, with seminal contributions to early-stage design processes, submarine architecture, trimaran/multi-hull vessels, and distributed ship service systems. His pioneering Design Building Block approach and SURFCON CAD tool have been integrated into industry-standard platforms such as PARAMARINE. Current work addresses energy balance frameworks, computer-aided sketching, and philosophical foundations of engineering design. Across more than 150 publications, a clear trajectory emerges: from fundamental design theory through tool development to practical application in warship, submarine and advanced multi-hull projects. Recent outputs (2021-2024) emphasise automation of early-stage synthesis, network-based modelling of complex distributed systems, and integration of operational availability considerations into concept design. Honours & Awards Fellow of the Royal Academy of Engineering (2000) Fellow of the Royal Institution of Naval Architects (RINA) Fellow of the Society of Naval Architects and Marine Engineers (SNAME) William Froude Medal, RINA (2020) – highest award David W Taylor Medal, SNAME (2021) – lifetime contribution Advising & Grant Leadership Since flexible retirement in 2012 he continues to lecture and supervise MSc projects in Naval Architecture, Marine Engineering and Submarine Design CPD courses. He has led or co-led research grants exceeding £2 million from EPSRC, EU and industry (BAE Systems, Rolls-Royce, BMT, Dstl). These projects have funded numerous CASE studentships and collaborative PhD programmes that bridge UCL and industrial stakeholders. Research Team & Industrial Links Professor Andrews heads the Design Research group at UCL, mentoring successive MoD Professors of Naval Architecture and maintaining active Memoranda of Understanding with BAE Systems and other maritime primes. He chairs the tri-annual International Marine Design Conference’s Design Methodology Panel and has delivered keynote addresses worldwide, uniquely representing non-US expertise at successive US Navy Ship Design Process Workshops.
Klaus Böhm serves as a Professor at Mainz University of Applied Sciences within the School of Engineering, affiliated with the i3mainz institute (Institute for Spatial Information and Surveying Technology). His research bridges geospatial technologies with artificial intelligence, focusing on practical applications in urban planning, healthcare, and educational environments through projects like BAM (Big Data Analytics) and TOPML (Machine Learning). His primary research domains include geospatial explainable AI (GeoXAI), mixed reality decision support systems, and health informatics applications. He pioneers methods for visualizing uncertainty in AI models, developing geoparsing techniques using LLMs, and creating spatial navigation frameworks for VR environments. His work consistently addresses real-world challenges in elderly mobility, smart city infrastructure, and medical education through spatial analytics. Analysis of his 2022-2025 publications reveals three dominant trends: (1) Integration of XAI with geospatial data for transparent decision-making in urban planning, (2) Development of mixed reality tools for STEM education and dermatology training, and (3) Spatio-temporal correlation analysis for smart city applications like parking optimization and public transport accessibility. His methodology emphasizes interactive visualization of complex spatial relationships and uncertainty quantification. Dr. Böhm leads multiple funded research initiatives including AIMR (AI-based decision support in mixed reality), RAFVINIERT (spatial intelligence for senior care), and FlexGeo (geo-service integration). These projects demonstrate sustained grant acquisition capability across EU and national funding programs, typically involving interdisciplinary teams from computer science, urban planning, and healthcare sectors. As a core member of the i3mainz research institute, he contributes to Germany's geospatial technology ecosystem through collaborative projects with municipal authorities and healthcare providers. The institute's work focuses on translating academic research into operational tools for spatial data infrastructure, particularly in environmental monitoring and public service optimization contexts.
Niki Kilbertus is a Professor in the Department of Informatics at the Technical University of Munich and a group leader at Helmholtz AI (Helmholtz Munich). They are also affiliated with MCML, the Konrad Zuse School relAI, and the Munich Unit of ELLIS. Since 2024, they have been a member of the Junge Akademie and received the Leopoldina Prize for Young Scientists. In 2025, they were awarded an ERC Starting Grant and achieved tenure at TUM. Professor Kilbertus's research focuses on causal machine learning, mechanistic ML, dynamical systems, and AI for science. Their work spans theoretical foundations of causal inference and practical applications across scientific domains. They have made significant contributions to causal effect estimation, causal discovery in stochastic processes, learning differential equations, and fair machine learning. Their research often bridges computer science with physics, biology, and climate science, demonstrating the interdisciplinary nature of their work. Professor Kilbertus has published extensively in top machine learning venues including NeurIPS, ICML, and ICLR, with numerous publications in 2024-2025. Their recent work shows a strong trend toward causal discovery in continuous-time systems, intervention modeling, and physics-informed machine learning applications. Scientific Awards: Leopoldina Prize for Young Scientists (2024) ERC Starting Grant (2025) Professor Kilbertus actively supervises multiple PhD students and collaborates with researchers across institutions including Max Planck Institutes and Helmholtz centers. They serve as an Action Editor for TMLR and regularly review for major ML conferences. The research group is well-funded through the ERC grant and institutional support from TUM and Helmholtz AI, enabling active recruitment of new PhD students and postdocs. Based at Technical University of Munich and Helmholtz AI, Professor Kilbertus's team works at the intersection of theoretical machine learning and scientific applications, with particular strengths in causal reasoning for complex dynamical systems.
Antonio Salmerón Cerdán is a Professor in the Mathematics Department at the University of Almería, where he has established himself as a leading researcher in probabilistic artificial intelligence and Bayesian networks. With over 25 years of academic experience, he leads the 'Análisis de datos' research group and serves as Principal Investigator for multiple nationally and internationally funded projects, including the current 'Hacia una Inteligencia Artificial Probabilística Confiable (TOPAI-UAL)' project (2023-2026). His research expertise spans theoretical and applied aspects of probabilistic graphical models, with particular focus on Bayesian networks, causal inference, and their applications across diverse domains. His work demonstrates a consistent trajectory from foundational theoretical contributions to practical implementations in software engineering, genomics, sports analytics, and trustworthy autonomous systems. Professor Salmerón's publication portfolio reveals a strong emphasis on methodological innovations in probabilistic reasoning, with recent work exploring divide-and-conquer approaches for causal computation, noise-robust classification methods, and the integration of observational and randomized data sources. His research shows increasing interdisciplinary reach, connecting computer science methodologies with applications in plant genomics, software maintenance, and healthcare. Journal Publications: 105 articles in high-impact venues including Ecological Informatics (Q1), International Journal of Approximate Reasoning (Q2), and ACM Transactions Research Funding: Principal Investigator for 9 major projects since 2001 totaling over €800,000 in funding Thesis Supervision: Director of 7 doctoral theses on probabilistic graphical models and their applications Metrics: h-index 22 (Web of Science), i10 index 59 His research program demonstrates a unique combination of theoretical rigor in probabilistic reasoning with practical applications across diverse scientific domains, positioning him at the forefront of reliable probabilistic AI development.
Marie-Christine ROUSSET is a Professor of Computer Science at the University of Grenoble Alpes (UGA) in France, where she is a member of the LIG (Laboratoire d'Informatique de Grenoble) in the SLIDE group. Previously affiliated with Paris-Saclay (LRI), she has established herself as a leading researcher in Knowledge Representation and Information Integration. She holds the distinguished position of Senior member of the Institut Universitaire de France (IUF) (2011-2016, renewed for 2016-2021) and serves as co-responsible for the chair Explainable and Responsible AI within MIAI Grenoble Alpes. Her research focuses on ontology-based data access, logic-based mediation between distributed data sources, query rewriting using views, data linkage, and distributed reasoning for the Semantic Web. She skillfully combines artificial intelligence and database techniques to address complex information integration challenges, with applications spanning biomedical informatics, educational technology, and trustworthy AI. Her work demonstrates consistent innovation from foundational research to practical implementations, as evidenced by her co-authorship of the book 'Web Data Management' published by Cambridge University Press. Professor ROUSSET's recent publications (2019-2022) reveal a growing emphasis on data privacy, RDF graph anonymization, and interactive ontology engineering, while maintaining her strong contributions to semantic web technologies and knowledge representation. Her research shows increasing attention to trustworthy AI concerns, aligning with her leadership roles in relevant projects. Scientific Recognition Senior member of Institut Universitaire de France (IUF) (2011-2016, renewed for 2016-2021) Junior member of Institut Universitaire de France (IUF) from 1997 to 2002 Chevalier de l'Ordre National du Merite (July 11, 2011) EurAI Fellow (nominated ECCAI Fellow in 2005) Best Paper Award at AAAI'96 for 'Verification of Knowledge Bases based on Containment Checking' Professor ROUSSET maintains an active role in the scientific community through editorial work and organizational leadership. She serves on the Editorial Board of Communications of the ACM (CACM) and has held significant roles including PC chair of EGC 2019, Workshops co-Chair of WWW 2018, and Area Chair of IJCAI 2017. Her consistent service on program committees of major international conferences demonstrates her standing in the field. Her laboratory, the SLIDE group within LIG, focuses on semantic web technologies, knowledge representation, and data integration. The group maintains strong connections with the international research community and participates in collaborative projects addressing cutting-edge challenges in artificial intelligence and data management, with particular emphasis on trustworthy and explainable AI systems.
