Oliver Deussen is a Professor of Visual Computing at the University of Konstanz, recognized by the German Informatics Society (GSI) as a Fellow for his contributions to computer science. His research focuses on visualization, robotics, and environmental modeling, particularly in plant and landscape representation. He has pioneered methods in image manipulation and robotic painting, emphasizing digitalization's societal impacts. His work spans computational biology (e.g., schooling fish behavior) and AI-driven creative technologies. Research Interests: Visualization techniques, swarm behavior analysis, robotic creativity, and interdisciplinary applications of computer science. He explores how computational methods can model natural systems and enhance human-machine interaction. Awards: Fellow of the German Informatics Society (GSI) His research often bridges theory and practice, with contributions to SLAM frameworks, style transfer algorithms, and uncertainty visualization tools. Collaborations in robotics and biology reflect his commitment to applied computational research.
Dr. Sen Wang is an Associate Professor in Robotics and Autonomous Systems at Imperial College London's Department of Electrical and Electronic Engineering, affiliated with I-X (Imperial's AI initiative), the Grantham Institute, and the Robotics Forum. He directs the Sense Robotics Lab and founded the MSc in Artificial Intelligence Applications and Innovation. His research focuses on advancing robotic autonomy through probabilistic and machine learning methods, addressing challenges in unstructured environments such as underwater infrastructure inspection and climate change solutions. Key projects include leading the £18M UKRI ORCA Hub, developing underwater robotics for offshore energy infrastructure inspection, and achieving the first autonomous wind farm foundation inspection at EDF's Blyth site. He holds editorial roles at IEEE Transactions on Robotics and other journals. Research interests span robotics, computer vision, SLAM, and AI applications, with recent work emphasizing underwater systems, sensor fusion, and safety-critical autonomy. His publications bridge theoretical advancements with real-world deployments in marine robotics and environmental monitoring. Awards: 2024 AI Most Influential Scholar Award Honourable Mention Grants: £18M ORCA Hub funding (UKRI) Labs: Sense Robotics Lab, I-X AI Initiative
Dr. Frances Yung is a Postdoctoral Researcher at Saarland University's Department of Language Science and Technology within the Department of Computer Science. She has been affiliated with Prof. Vera Demberg's research group since April 2017 and is currently working on the DFG-funded SFB-1102 project "Information Density and Linguistic Encoding," specifically on project B2 "Cognitive modelling of information density for discourse relations." She is pursuing her habilitation, indicating career progression toward a higher academic position in the German university system. Dr. Yung's research focuses on discourse relations at the intersection of NLP, corpus linguistics, and experimental psycholinguistics. Her work explores how information density affects discourse relation marking through cognitive modeling approaches. She has developed expertise in discourse parsing, resource construction, annotation aggregation, and experimental pragmatics, with particular attention to multilingual aspects of discourse phenomena. Her research combines computational modeling with experimental methods to understand how speakers produce and comprehend discourse relations. Analysis of Dr. Yung's recent publications reveals a strong focus on discourse relation resources, particularly multilingual corpora like DiscoGeM 2.0 covering English, German, French, and Czech. Her work increasingly incorporates crowdsourcing methodologies and examines how large language models can be leveraged for discourse annotation tasks. She has made significant contributions to understanding the challenges of implicit discourse relation annotation and the biases introduced by different task designs in crowdsourcing environments. Active reviewer for major computational linguistics conferences (ACL, EMNLP, NAACL, EACL, COLING, IJCNLP) and workshops since 2016 Served as area chair for Sigdial 2024 Regular service on program committees for discourse-related workshops Dr. Yung has supervised multiple Master's theses on topics related to discourse relations, implicit relation identification, and domain adaptation. Her teaching portfolio includes courses on crowdsourcing linguistic annotations, discourse relations from cognitive and NLP perspectives, and recent advances in discourse processing. She has also served as a teaching assistant for data science and AI courses, demonstrating her commitment to interdisciplinary education at the intersection of computer science and linguistics.
Prof. Andreas Farnleitner is a Professor at TU Wien, affiliated with the Environmental Microbiology and Molecular Diagnostics Research Group. His research focuses on microbial water quality, molecular diagnostics, and environmental microbiology. Key areas include PCR-based methods for fecal pollution tracking, antimicrobial resistance in aquatic environments, and climate change impacts on waterborne pathogens. He has contributed to advancements in DNA aptamer technology for rapid water quality monitoring and employs machine learning for predictive modeling in hydrology. His work spans collaborations across Europe, addressing One Health challenges and wastewater-based epidemiology. Recent projects include studies on Vibrio cholerae in Austrian bathing waters and the biostability of drinking water resources. Prof. Farnleitner leads interdisciplinary initiatives to improve water safety and environmental management. Publications highlight his expertise in microbial source tracking, climate change effects on infection risks, and antibiotic resistance patterns. His team develops innovative diagnostic tools and models for environmental monitoring, with applications in both academic and applied settings. Current projects emphasize the integration of molecular biology and engineering to address global water quality challenges.
Dr. Lateef Akanji is a Senior Lecturer in the Department of Petroleum Engineering at the School of Engineering, University of Aberdeen, where he has been contributing since 2014. He previously served as Lecturer and Head of the Petroleum Technology Research Group at the University of Salford, Assistant Professor at King Saud University, and Visiting Lecturer at the University of Leoben. His academic journey includes a PhD from Imperial College London and degrees from the University of Ibadan. University: University of Aberdeen School: School of Engineering Position: Senior Lecturer, Petroleum Engineering Email: l.akanji@abdn.ac.uk Education: PhD, Petroleum Engineering, Imperial College London M.Sc., Petroleum Engineering, University of Ibadan B.Sc. (Honours), Petroleum Engineering, University of Ibadan DIC (Diploma of Imperial College) Research Interests: Dr. Akanji's research centers on multiphase flow in porous and permeable media, with applications in enhanced oil recovery (EOR) in clastic, carbonate, and unconventional shale reservoirs. His work integrates theoretical, experimental, and computational fluid dynamics, utilizing platforms like Python, C++, and Fortran. He is pioneering the application of artificial intelligence in petroleum engineering, particularly in EOR screening and production optimization. His research includes pore-scale modeling, gas-lift systems, and nuclear reactor flow dynamics. Publication Trends: His recent publications (2025–2021) reflect a strong focus on fluid displacement in porous media, shale reservoir characterization, AI applications in energy, and nuclear safety. Notable themes include computational modeling of multiphase flow, biosurfactant EOR, and advanced numerical methods for reservoir simulation. Scientific Awards and Honors: Fellow of the Higher Education Academy (FHEA) Chartered Engineer (CEng) Chartered Petroleum Engineer European Engineer (Eur Ing) Member of the Energy Institute (MEI) Advising and Grants: Dr. Akanji supervises numerous PhD students in areas such as AI-based production optimization, permeability upscaling, and biosurfactant EOR. He leads research funded by PTDF, TETFUND, Sonangol, and Elphinstone, focusing on high-pressure high-temperature flow loops, gas-lift pilot rigs, and neuro-fuzzy screening systems. His collaborative projects involve institutions in the UK, Austria, and Australia. Laboratories and Research Platforms: He contributes to the development of the Complex System Modelling Platform (CSMP++), a C++-based API for simulating multi-physics flow in porous systems, co-developed with ETH Zurich and Montanuniversität Leoben. He also leads a technology innovation platform for EOR, including experimental rigs for biosurfactant screening and gas-lift stability testing.
Robert Berman is a Professor in the Department of Mathematical Sciences at the University of Gothenburg. He is affiliated with the Algebra and Geometry division and can be reached at robertb@chalmers.se. His research focuses on advanced areas of mathematics including algebraic geometry, complex geometry, and geometric analysis, with a particular emphasis on Kähler metrics, Monge-Ampère equations, and probabilistic methods in geometry. His work bridges pure mathematics with applications in theoretical physics and numerical analysis. Key research themes include the study of K-stability in algebraic geometry, the interplay between statistical mechanics and geometric structures, and the analysis of discretized Monge-Ampère equations in optimal transport. Recent publications (2023–2025) address Hölder inequalities in Kähler geometry, Manin-Peyre conjectures, and probabilistic approaches to Kähler-Einstein metrics. Berman collaborates extensively with leading researchers such as Bo Berndtsson and Sébastien Boucksom. His contributions span foundational results in geometric analysis and their implications for birational geometry and arithmetic geometry. No awards or grants are explicitly listed in the provided materials.
Jean F. Honorio Carrillo is an Adjunct Professor at Purdue University's Department of Computer Science and a Senior Lecturer at the University of Melbourne's School of Computing and Information Systems. He specializes in machine learning theory, optimization, and their applications to combinatorial and non-convex problems. His research focuses on developing algorithms with theoretical guarantees for structured prediction, robustness, fairness, and federated learning. He has advised numerous students across multiple institutions and holds adjunct roles at Purdue's Statistics Department and MIT CSAIL. Roles: Senior Lecturer (Melbourne), Adjunct Professor (Purdue), Adjunct at MIT CSAIL Research Areas: ML Theory, Non-Convex Optimization, Fairness, Federated Learning Key contributions include breakthroughs in exact inference for structured prediction, optimization frameworks for NP-hard problems, and theoretical foundations for modern ML challenges. His work has been published in top venues like NeurIPS, ICML, and JMLR. He has secured grants from NSF and industry partners, including a 2021 NSF DMS grant for deep learning research. His students have gone on to postdoctoral roles at NUS and UChicago/CMU.
Kevin Ford is a Professor of Mathematics at the University of Illinois at Urbana-Champaign, affiliated with the Department of Mathematics within the College of Liberal Arts & Sciences. His research focuses on Number Theory, including prime number theory, divisor theory, probabilistic methods, and sieve theory, with notable contributions to the Riemann zeta function and divisor distribution. Ford is also an editor for Research in Number Theory and the Bulletin and Journal of the London Mathematical Society . His recent work explores topics such as large gaps between primes, composite polynomial values, and the concentration of divisors. Ford’s publications often bridge analytic and probabilistic techniques to address classical number-theoretic problems. Despite extensive contributions, no specific academic awards are explicitly listed, though his editorial roles highlight his influence in the field. He advises no listed students, though his research group and collaborations with leading mathematicians like Terence Tao and Dimitris Koukoulopoulos are evident in his publications. Ford’s academic presence includes organizing the Illinois Number Theory Seminar and maintaining an active research agenda in number theory and combinatorics.
Meng Xu is an Assistant Professor in the Cheriton School of Computer Science at the University of Waterloo, Canada. He is affiliated with the Cryptography, Security, and Privacy (CrySP) group and the Cybersecurity and Privacy Institute (CPI). His research focuses on system and software security, emphasizing secure-by-design languages (e.g., Rust, Move), automated program analysis, and runtime defense techniques. Education : Ph.D., Computer Science (2020), Georgia Institute of Technology B.Eng. and B.Business (First Class Honors), Nanyang Technological University (2014) Research Interests : Secure-by-design languages Automated security analysis (fuzzing, symbolic execution) Runtime defense mechanisms (moving target defense, secure hardware) Key Awards : EAPLS Best Paper Award (2022) USENIX Security Distinguished Paper Award (2018) Grants & Funding : BlackBerry Research Grant (CAD $200,000) Amazon Research Award (USD $60,000) NSERC Discovery Grant (CAD $170,000) Labs & Collaborations : CrySP (Cryptography, Security, and Privacy Group) Cybersecurity and Privacy Institute (CPI)
Dr. Chao Fan is an Assistant Professor in Civil Engineering and Environmental Engineering and Earth Sciences at Clemson University, affiliated with the Glenn Department of Civil Engineering. His research focuses on climate change adaptation, socio-environmental systems dynamics, and urban resilience, leveraging AI and data science. He holds a Ph.D. from Texas A&M University (2020), an M.S. from UC Davis (2017), and a B.S. from China University of Mining and Technology (2016). Dr. Fan's work integrates interdisciplinary approaches to address challenges in disaster management, smart cities, and environmental justice. Key interests include social sensing for infrastructure disruptions, equity in urban mobility networks, and leveraging digital twins for resilience planning. His recent publications explore topics like wildfire impacts, PM2.5 exposure inequity, and carbon market mechanisms for infrastructure adaptation. Professional memberships include ASCE, ACM SIGKDD, AGU, and AAAS. His lab (fanchaolab.com) develops innovative solutions for climate adaptation and equitable urban systems, emphasizing fairness in AI-driven models and network analysis.
Florian Buettner is Professor for Bioinformatics in Oncology at Goethe University Frankfurt, with affiliations at the German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ). His research integrates multi-omics data with machine learning for cancer research. Research focuses on: Multi-omics bioinformatics AI for precision oncology Probabilistic modeling Single-cell analysis Uncertainty quantification Buettner received an ERC Consolidator Grant to develop trustworthy AI models for cancer diagnosis. His methodological innovations include techniques for single-cell RNA sequencing analysis and model calibration.
Maria José Serna Iglesias is a Full Professor at the Departament de Ciències de la Computació of the Universitat Politècnica de Catalunya (UPC), Barcelona Tech . She leads the research group ALBCOM (Algorithmics, Bioinformatics, Complexity and Formal Methods) and coordinates doctoral programs in Computing. Her research focuses on algorithmics, computational complexity, social network analysis, and game theory. She actively participates in conferences such as SEA 2023-2025 , CIAC , and Algorithmic Decision Theory . Teaching includes advanced algorithmics courses for undergraduate and master’s programs. Current projects include the MOTION initiative (PID2020-112581GB-C21) on large-scale data processing. Past projects involve EU initiatives like WISEBED and DELIS . Her work bridges theoretical computer science with applications in networks and social systems, emphasizing algorithmic solutions for complex problems.
Gaofeng Jia is Associate Professor in Civil and Environmental Engineering at Colorado State University's Walter Scott, Jr. College of Engineering. He specializes in natural hazard risk assessment, infrastructure resilience, and uncertainty quantification. Research integrates simulation-based approaches with high-performance computing for assessing complex engineering systems. Core areas include infrastructure deterioration modeling, wave energy converter optimization, tsunami evacuation planning, and surrogate modeling techniques. Recent work emphasizes physics-informed machine learning for engineering applications. Publications demonstrate consistent methodological innovation in uncertainty quantification across domains including seismic engineering, coastal hazards, renewable energy systems, and structural reliability. Articles employ advanced computational statistics, Bayesian methods, and multi-fidelity modeling. Young Researcher Best Paper Award Best Student Paper Award
Charles Ling is a Professor of Computer Science at Western University, holding the title of Science Distinguished Research Professor. He also serves as Director of the Data Mining and Business Intelligence Lab and Associate Scientist at the Lawson Health Research Institute. His academic background includes a B.Eng. (CS and EE) from Shanghai Jiao Tong University and MSc/PhD from the University of Pennsylvania (UPenn). Research interests span machine learning, deep learning, AI, and healthcare informatics, with notable contributions to the GlucoGuide diabetes management system. He has authored over 220 peer-reviewed papers and a book titled Crafting Your Research Future , focusing on academic career development. Awarded Fellow of the Canadian Academy of Engineering (CAE) and recipient of the First Prize for Best Clinical Research Presentation (2011). Active in grants (NSERC, FedDev, Mitacs) and organizational roles in top conferences (KDD, ICDM). Supervises 5 PhD and 4 MSc students, with notable advisees including Harry Zhang and Victor Sheng. Leverages AI in education to enhance children's cognitive abilities through video-based programs like Power Thinking , approved by Curriculum Services Canada. His work integrates machine learning with healthcare, finance, and software engineering.
Dr. Liangping Li is an Associate Professor in the Department of Geology and Geological Engineering at South Dakota School of Mines & Technology. He holds a Ph.D. from Technical University of Valencia and an M.S. from China University of Geoscience, with expertise in hydrogeology, groundwater modeling, and geothermal energy systems. Education: M.S., China University of Geoscience; Ph.D., Technical University of Valencia His research focuses on integrating machine learning with groundwater modeling, data assimilation, geostatistics, and optimization of geothermal energy systems. He has pioneered methods combining generative adversarial networks (GANs) and ensemble smoother techniques for inverse modeling in complex aquifers. Recent publications highlight his work on extremal optimization for well placement, progressive growing GANs for facies modeling, and stochastic inversion of fracture networks. His research trends emphasize computational innovation in subsurface flow simulation and sustainable groundwater management. Scientific Awards: NSF RII Track-4 Grant, NSF REU Site Grant, BLM Environmental Monitoring Grant, and appointments as Associate Editor for Advances in Water Resources and Mathematical Geosciences . Dr. Li teaches courses in groundwater engineering, statistical methods, and environmental field camp, while mentoring graduate and undergraduate researchers in subsurface energy and water resource projects.
