Simon Colton is a Professor of Computational Creativity, Games, and Artificial Intelligence at Queen Mary University of London, affiliated with the School of Electronic Engineering and Computer Science. His research focuses on AI-driven creativity in domains like music, game design, and visual arts, emphasizing generative techniques and philosophical aspects of creative systems. He leads projects in automated composition, procedural content generation, and interdisciplinary AI applications. Education: Extensive background in AI and computational creativity, with a focus on generative systems. Research Interests: Computational Creativity, Generative AI, Music Generation, Game Design Automation, and Neuro-Symbolic Systems. His work explores how AI can autonomously create artistic content, including music, games, and visual art, with notable contributions to frameworks like ANGELINA for game design and The Painting Fool for automated art. Recent projects include AI-driven music composition and tools for creative support (e.g., Gamika, Danesh). Key grants include the £3M EPSRC-funded IGGI2 Centre for Doctoral Training, focusing on Intelligent Games and Game Intelligence. His lab, the Centre for Multimodal AI, advances research in cross-modal AI creativity.
Diogo Bolster is the Frank M. Freimann Professor of Hydrology and Henry Massman Department Chair in Civil and Environmental Engineering and Earth Sciences at the University of Notre Dame. His research focuses on environmental fluid flows, contaminant transport, and porous media applications spanning groundwater systems to atmospheric flows. Education includes a Ph.D. from UC San Diego, M.S. from UC San Diego, B.S. from University College Dublin, and postdoctoral work at Polytechnic University of Cataluna. Research explores flow and transport phenomena in heterogeneous environments with applications to sustainable water management and hazard mitigation. Work integrates theoretical modeling, laboratory experiments, and field studies to address challenges in hydrology and environmental engineering. Recent publications demonstrate strong focus on pore-scale processes, coastal flooding, antibiotic resistance transport, and computational methods for environmental systems, reflecting interdisciplinary approaches to water-related challenges. Awards include the 2023 Polubarinova-Kochina Hydrologic Sciences Mid-Career Award from the American Geophysical Union for contributions to the field. Leads an active research group with multiple PhD students and postdocs, supported by NSF and other grants. Collaborations span institutions internationally and include field studies at Notre Dame's environmental facilities.
Chenglong Ma is a Research Fellow at RMIT University's School of Computing Technologies, located at the City Campus in Australia. His research focuses on advancing recommender systems through innovative approaches to user behavior modeling and algorithm evaluation. His primary research interests span Graphics, Augmented Reality, Games , and particularly Recommender Systems . Ma's work investigates how user personality traits influence recommendation outcomes, how conformity behavior affects recommendation quality, and how to better evaluate recommendation algorithms through novel theoretical frameworks. His research bridges computer science with social science, as evidenced by his development of the Classification Algorithm for Skin Color (CASCo). Analysis of his publication record reveals a strong focus on addressing fundamental challenges in recommender systems, with recent work (2024-2025) concentrating on LLM-enhanced user behavior simulation, personality-driven modeling, and novel evaluation frameworks. His research demonstrates a progression from studying population-scale concept drift during events like the pandemic toward more sophisticated modeling of individual user characteristics and behaviors. As a supervisor, Ma is involved in research projects including 'Between activism and literacy: The mediatization of data journalism in Indonesia' (2025), indicating his interest in the intersection of technology, media, and society.
Arlei Silva is an Assistant Professor of Computer Science at Rice University. He holds a Ph.D. from the University of California, Santa Barbara (UCSB), and M.Sc. and B.Sc. degrees from Universidade Federal de Minas Gerais, Brazil. His research focuses on data science, network science, machine learning, and graph algorithms, with applications in flood prediction, graph anomaly detection, and temporal graph analysis. He has held visiting roles at Rensselaer Polytechnic Institute and has contributed to projects like trajectory compression and network process discovery. Education: Ph.D. in Computer Science, University of California, Santa Barbara (2019) M.Sc. in Computer Science, Universidade Federal de Minas Gerais, Brazil (2011) B.Sc. in Computer Science, Universidade Federal de Minas Gerais, Brazil (2008) Research Interests: His work integrates machine learning and graph theory to address challenges in network dynamics, spatiotemporal modeling, and anomaly detection. Recent projects include FloodGNN-GRU for flood prediction, DOPPLER for dataflow optimization, and Pole for signed network embeddings. Awards: SNAKDD Best Paper Runner-up (2013) Best M.Sc. Thesis (Brazilian Computer Society, 2011) Best Undergraduate Research (top 6, Brazilian Computer Society, 2009) Labs/Teams: Engages in interdisciplinary projects at Rice, focusing on environmental informatics and network science. His GitHub repositories include work on trajectory compression, anomaly detection, and spectral algorithms for graph cuts.
Christopher Horvat is an Assistant Professor in the Department of Earth, Environmental & Planetary Sciences at Brown University. He leads the Antipodal Oceanography Group, focusing on rapid climate change in polar and tropical regions. His research integrates applied mathematics, physical oceanography, and remote sensing to study sea ice dynamics, ocean turbulence, and climate modeling. Key areas include sea ice fracture mechanics, wave-ice interactions, and machine learning applications in climate science. Horvat's work leverages satellite data (e.g., ICESat-2) and computational models to address challenges in polar and tropical climate systems. His recent studies emphasize improving sea ice concentration estimates, analyzing extreme climate events, and understanding ecological responses to environmental shifts. His methodologies include novel parameterizations of ocean turbulence, floe size distribution models, and algorithm development for satellite data interpretation. His research group collaborates on projects like the NASA ABoVE campaign and utilizes advanced tools such as WIFF1.0—a machine learning-based sea-ice fracture parameterization. While his publications highlight interdisciplinary approaches, his current efforts aim to bridge observational data with predictive climate models to inform Arctic and Antarctic resilience strategies.
Masoud Barati is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Pittsburgh's Swanson School of Engineering. Previously, he held similar roles at the University of Houston and Louisiana State University. He earned his PhD in Electrical Engineering from the Illinois Institute of Technology (2009-2013) and completed a postdoctoral fellowship at the University of Chicago Booth School of Business in 2014. His research bridges pure and applied mathematics with electric power systems, focusing on resilience, quantum computing, and control systems. Barati’s academic journey includes a diverse range of studies: his doctoral work at Illinois Tech focused on electrical engineering, while his postdoctoral research at the University of Chicago explored business-related systems through a computational lens. His current role at the University of Pittsburgh allows him to advance interdisciplinary research in energy systems. Research interests span algebraic geometry and category theory for computational problem-solving in power systems, alongside applied areas like optimization, quantum computing, Bayesian statistics, and deep learning. He investigates resilience in power grids under natural hazards and cyber-attacks, with specific attention to AC optimal power flow, energy justice, and multi-interdependent infrastructure management. His work integrates smart city technologies, blockchain for energy trading, and data-driven methods such as kernel regression and neural networks for real-time grid analysis. His recent publications emphasize quantum protocols for entanglement detection, smart city energy infrastructure resilience, and machine learning-augmented approaches to power system control. These trends reflect a focus on leveraging advanced computational tools to address modern energy challenges. Barati has contributed to collaborative research projects, including a global algorithm for AC-OPF optimization funded by the National Science Foundation (NSF). While no scientific awards are explicitly listed, his research portfolio demonstrates significant contributions to energy systems and quantum computing. His advisory work includes various projects on grid resilience, though formal student advisee names are not provided in the text. Grants and funding are associated with his collaborative efforts in developing scalable optimization algorithms for power systems. Research activities involve partnerships with institutions like IBMQ for quantum simulations and local case studies in Pittsburgh. He explores innovative solutions such as mobile charging stations for EVs and blockchain-based energy trading models, reflecting a commitment to both theoretical and applied advancements in energy engineering.
Alberto Vela Martin serves as an Assistant Professor in the Department of Aerospace Engineering at Charles III University of Madrid (UC3M), affiliated with the Aerospace Engineering Research Group. His academic profile spans Aeronautics and Physics, with core expertise in fluid dynamics and turbulence phenomena. Contact is maintained via alvelam@ing.uc3m.es, and his scholarly identity is verified through ORCID (0000-0003-4561-8683). His research program critically investigates predictability in complex fluid systems, specializing in isotropic turbulence modeling and extreme-event forecasting in turbulent media. Key methodologies include massive ensemble forecasting techniques and computational analysis of chaotic fluid behavior, bridging theoretical physics with aerospace engineering applications. This work addresses fundamental challenges in statistical mechanics of turbulence and nonlinear dynamics, with implications for weather prediction systems and industrial fluid management. Recent 2024 publications in Physical Review Fluids demonstrate his focus on turbulence predictability limits and computational complexity in extreme-event scenarios. These studies reveal critical thresholds in forecasting accuracy for isotropic systems and quantify the algorithmic challenges in predicting rare turbulent phenomena, establishing new frameworks for ensemble-based fluid dynamics modeling. Within UC3M's Aerospace Engineering Research Group, Dr. Vela Martin contributes to advanced computational fluid dynamics projects, utilizing high-performance simulation environments to analyze turbulent flow structures. His collaborative work emphasizes cross-disciplinary approaches integrating physics-based modeling with data science techniques for complex fluid behavior prediction.
Roberto Gil Pita is a Professor at the University of Alcalá (Spain), affiliated with the Department of Signal Theory and Communications. He leads the AES3 research group focusing on acoustic and electromagnetic smart sensor networks and signal processing applications. His doctoral work (2006) centered on radar target classification using statistical and AI methods under the supervision of Dr. Manuel Rosa Zurera. His research spans signal processing, machine learning, and their applications in aerospace, biomedical systems, and smart cities. Key areas include UAV detection, emotion recognition from speech, and acoustic localization using microphone arrays. His academic background includes a doctorate from the University of Alcalá and extensive contributions to wireless acoustic sensor networks, hearing aid signal processing, and bioimpedance spectroscopy. He has developed energy-efficient algorithms for real-time audio analysis, acoustic violence detection systems, and robust methods for speech enhancement in noisy environments. His work bridges theoretical signal processing with practical engineering solutions for defense, healthcare, and urban monitoring. Research interests extend to aeroelastic flutter analysis in aviation, wearable biomedical sensors for stress assessment, and data-driven approaches for sound environment classification. He has pioneered the use of deep learning in flutter testing and acoustic event classification, contributing to datasets like REALISED for benchmarking machine learning models. Notable projects include acoustic localization of drones using microphone arrays, real-time emotion detection systems, and collaborative research in smart healthcare technologies. His work emphasizes computational efficiency and energy conservation, particularly for embedded systems and battery-operated devices.
Besat Kassaie is a Postdoctoral Researcher collaborating with Renée Miller. His research focuses on information extraction, natural language processing, and data privacy. Key areas include improving unstructured data quality through updatable extracted views, mathematical information retrieval (MathIR), and ontology matching techniques. His work spans theoretical advancements in information systems and practical applications in healthcare analytics. Notable contributions include frameworks for automated view maintenance, differential privacy methods for text data, and systems for detecting math answers using neural networks like Tangent-L. Kassaie's articles demonstrate a strong emphasis on database systems optimization, dynamic data processing, and the intersection of AI with mathematical problem-solving. His research bridges foundational computer science principles with real-world challenges in data privacy and interpretability.
Ilgar Şafak is an Associate Professor in the Department of Civil Engineering at Istanbul Bilgi University, Faculty of Engineering and Natural Sciences. He holds a Ph.D. in Civil Engineering from the University of Florida and has extensive research experience in coastal and ocean hydrodynamics through fellowships at the United States Geological Survey (USGS) and postdoctoral work at the University of Virginia. Education: Ph.D., Civil Engineering, University of Florida, USA (2006–2010) M.Sc., Civil Engineering, Middle East Technical University, Turkey (2004–2006) B.Sc., Civil Engineering, Middle East Technical University, Turkey (1999–2003) Mendenhall Fellow, USGS, Woods Hole, MA (2012–2016) Post-Doc, Coastal and Ocean Hydrodynamics, University of Virginia, USA (2010–2012) His research focuses on coastal and ocean physics , including waves, hurricanes, currents, tides, sediment transport, turbulence, and morphodynamic evolution. He specializes in numerical modeling and simulation of hydrodynamic systems and developing nature-based solutions such as living shorelines to adapt aquatic systems to climate change and anthropogenic stressors. His work integrates field experiments, data analysis, and advanced computational models. The recent publications highlight a strong trend in coastal resilience , storm impact modeling , and ecological engineering . His research spans hurricane impacts, shoreline change prediction, tidal flat stability, and sustainable coastal infrastructure, reflecting a multidisciplinary approach combining civil engineering, oceanography, and environmental science. Scientific Awards and Recognitions: Mendenhall Research Fellowship, USGS Project Investigation Award, USGS Coastal and Marine Geology Program Outstanding Reviewer Award, Journal of Ocean Modelling Outstanding Reviewer Award, Continental Shelf Research NAUI SCUBA Diver Certification (Open Water, Advanced, Rescue, Nitrox, Scientific Research) Dr. Şafak has been actively involved in advising and securing research grants from major national and international agencies including NSF, USGS, ONR, TÜBİTAK, and Florida FWC . He has served on Ph.D. committees, reviewed proposals for NSF and TÜBİTAK, and contributed to academic accreditation through MÜDEK. His leadership roles include Vice Dean and member of strategic planning boards, reflecting strong institutional engagement. He collaborates with leading institutions such as Woods Hole Oceanographic Institution, Scripps Institution of Oceanography, University of Florida, and Yildiz Technical University . He leads and participates in field and modeling projects on living shorelines, hurricane impacts, and sediment dynamics, often involving interdisciplinary teams and long-term ecological research initiatives.
Prof. Dr. Matthias Grabmair is a Professor of Legal Tech at the TUM School of Computation, Information and Technology, part of the Technische Universität München (TUM). His research focuses on advancing computational methods for legal analysis, particularly leveraging natural language processing (NLP) to enhance legal document summarization, judgment prediction, and AI-driven legal systems. He leads the JUSMOD organization and has contributed to datasets like ECtHR-PCR for precedent understanding. Key research areas include automated legal reporting (e.g., LexGenie), cross-jurisdictional analysis, and ethical AI applications in privacy policies (PrivaT5). His work emphasizes improving judicial decision-making transparency through models like Hiculr for rhetorical role labeling and Chronoslex for temporal generalization. Prof. Grabmair also explores adversarial robustness in legal AI systems and transfer learning across legal domains. Publications span 2005–2025, with recent emphasis on generative AI for legal texts, explainable AI in judgment prediction, and curriculum learning for legal document analysis. His research bridges computational methods with legal practice, addressing challenges in fairness, reliability, and scalability of AI applications in law.
Maria Hybinette is an Associate Professor of Computer Science at the University of Georgia's School of Computing. Her research focuses on high-performance simulation systems, particularly in multi-agent behavior modeling and financial market simulation. She emphasizes hybrid (micro/macro) simulations, optimization for parallel processing, and usability improvements in simulation frameworks. Her work spans over two decades, with notable contributions to agent-based simulation frameworks like ABIDES, Pickle, and SASSY. She has also pioneered methods for parallelization in discrete event simulations, including latency reduction techniques and adaptive caching strategies. Key research areas include stock market dynamics modeling, autonomous agent coordination, and real-time multi-target tracking systems. Her publications consistently explore scalability, computational efficiency, and validation methodologies in simulation systems. Awards and recognition are not explicitly listed in the provided materials. She is affiliated with the Boyd Research and Education Center in Athens, GA, and maintains active collaborations in computational finance and robotics.
Steve Poole is a Professor of History and Heritage at the University of the West of England (UWE), affiliated with the Faculty of Arts, Creative Industries, and Education (ACE). He directs the Regional History Centre and leads an ESRC-funded project exploring intergroup dynamics in the 1831 Reform Riots. His research spans 18th- and 19th-century English history, focusing on protest, crime, tidal landscapes, and digital heritage technologies. Collaborations include the National Trust, Bristol Initiative Trust, and heritage design firms like Splash & Ripple and Satsymph. Notable projects include Ghosts in the Garden (a locative game at the Holburne Museum) and Romancing the Gibbet (an immersive soundscape on 18th-century crime). Poole’s academic work emphasizes histories 'from below,' particularly in Bristol and the Southwest. He co-authored Bristol From Below: Law, Authority and Protest in a Georgian City and chairs the John Thelwall Society. His heritage projects integrate digital media to reinterpret historical narratives, such as using empathy-driven soundscapes for heritage experiences. He also advises on urban waterfront histories and serves on committees for the Southern History Society and Bristol Record Society. His publications span scholarly articles and edited volumes, with a focus on riots, regicide, and radical press (e.g., Bath Spark ). Recent work examines the social psychology of collective action in 19th-century riots and the legacy of monuments in contemporary urban spaces. Poole’s interdisciplinary approach bridges traditional history with modern technologies, aiming to democratize historical interpretation through accessible, immersive formats.
Amy McGovern is a Lloyd G. and Joyce Austin Presidential Professor at the University of Oklahoma, holding dual professorships in the School of Computer Science and School of Meteorology . She directs the NSF-funded AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES), a multi-institutional initiative advancing AI for environmental decision-making. Education: Ph.D. in Computer Science, University of Massachusetts Amherst (2002) M.S. in Computer Science, University of Massachusetts Amherst (1998) B.S. (Honors) in Computer Science, Carnegie Mellon University (1996) Research Interests: Machine learning for severe weather prediction (tornadoes, hail, lightning) Trustworthy AI in environmental science Deep learning for weather data analysis and visualization Broadening participation in STEM through AI education Funding & Leadership: NSF AI Institute (AI2ES): $20M+ directorship NOAA-funded projects on tornado prediction (2020-2025) NASA grants for severe storm detection (2020-2023) Awards: American Meteorological Society Fellow (2020) NSF CAREER Award (2008-2015) OU Vice President for Research Award (2019) Outreach & Education: Developing AI curricula for HSI/MSI institutions K-12 STEM outreach programs IDEA Lab (Interaction, Discovery, Exploration, and Adaptation)
Dr. Yan Zhang is a Presidential Professor in the School of Electrical and Computer Engineering at the University of Oklahoma, affiliated with the Advanced Radar Research Center (ARRC) and leading the Intelligent Aerospace Radar and Radio Team (IART). He holds a PhD from the University of Nebraska-Lincoln (2004) and prior roles include research scientist at Intelligent Automation Inc. (2004–2006). His research focuses on intelligent radar systems, phased array antennas, and weather surveillance radar technologies, with emphasis on machine learning integration for improved data analysis and disaster prediction. Education: B.S. and M.S. in Electrical Engineering from Beijing Institute of Technology (1998, 2001); PhD in Engineering from University of Nebraska-Lincoln (2004). Research Interests: Multi-mission airborne/spaceborne radars for weather and air-traffic monitoring, Sense-and-Avoid systems for UAVs, counter-drone technologies, and ultra-wideband radar solutions. His work bridges radar engineering with meteorology, addressing challenges in phased array antenna design, polarimetric data quality, and real-time FPGA/DSP implementations. His recent articles emphasize advancements in polarimetric radar data assimilation, machine learning-driven meteorological modeling, and cylindrical phased array configurations. Collaborations include NASA and NOAA for weather radar modernization. He leads the Radar Innovations Lab at OU, advancing radar hardware and algorithmic innovations.
