Gualberto Asencio Cortés is Associate Professor in Computer Science at Universidad Pablo de Olavide. He holds Ph.D. in Computer Science from UPO (2013) and Executive Master in Innovation from EOI (2016). Research focuses on developing advanced algorithms for: Time series forecasting in energy, agriculture, and environmental domains Feature selection methods for deep learning Explainable AI for predictive modeling Big data stream processing Developed innovative tools like FS-Studio for feature selection experimentation. Recent publications demonstrate strong focus on agricultural applications (pest forecasting, phenology prediction) and energy consumption modeling using hybrid deep learning approaches.
Wei Jia is a Professor at the School of Computer and Information, Hefei University of Technology, China. Their research focuses on artificial intelligence, machine learning, computer vision, and robotics, with contributions to knowledge graphs, biometric systems, and autonomous systems. They have co-authored over 130+ publications in top-tier journals and conferences, including venues like IEEE Transactions, CVPR, and AAAI. Research interests span deep learning techniques, graph neural networks, and optimization for large-scale systems. Notable work includes entity extraction frameworks, safety analysis in engineering systems, and swarm control algorithms for unmanned vehicles. Contributions also extend to data management systems, such as the TierBase key-value store and the OVERLORD data loader for foundation models. Publications highlight interdisciplinary applications in cybersecurity, robotics, and biomedical imaging. Their work often bridges theoretical advancements with practical implementations, addressing challenges in both software and hardware systems. No specific awards or grants are listed in the provided text.
Andreas Fischer is an Ordentlicher Professor at the Fribourg School of Engineering and Architecture (HES-SO), specializing in Pattern Recognition, Machine Learning, and Document Analysis. His research focuses on handwriting recognition, graph-based methods, and applications in cultural heritage preservation and medical imaging. Education: BSc in Computer Science from Fribourg School of Engineering and Architecture Research Interests: Graph Neural Networks for automata universality analysis Hybrid systems for Vietnamese stele keyword spotting Medical image analysis (colorectal cancer, tumor budding) Large language models for post-OCR correction Key Projects: TAINA Technology (handwriting validation for tax forms) Swisscom (Swiss German to High German translation) Hasler Foundation (Vietnamese stele graph-based analysis) Publications: Over 30 peer-reviewed articles in top journals/conferences (IEEE Access, Medical Image Analysis, Pattern Recognition, etc.), with focus on graph-based methods, handwriting recognition, and medical applications. Grants & Roles: Principal Applicant for multiple industry-funded projects (TAINA, Swisscom) Co-developer of DIVA-DAF deep learning framework
Dr. Benedikt Holtmann is a Research Fellow at the Faculty of Biology, Ludwig Maximilian University of Munich. His research focuses on behavioral ecology and evolutionary biology, particularly studying how social environments influence behavior-related settlement decisions in wild passerine birds. Funded by the German Research Foundation (DFG), his work examines the non-random assortment of behavioral phenotypes and its implications for individual fitness and population dynamics. Research interests center on animal personality, behavioral syndromes, and evolutionary ecology in avian systems. His investigations utilize model species including great tits ( Parus major ) and dunnocks ( Prunella modularis ) to understand: Social environment effects on settlement patterns Fitness consequences of behavioral phenotypes Personality-matching habitat selection Genetic basis of animal behavior Reproductive strategies and sexual selection Recent publications demonstrate strong emphasis on: Integrative studies of animal personality traits Social structure dynamics in corvids and passerines Reproductive success determinants Long-term ecological monitoring approaches Physiological and genetic mechanisms underlying behavior with research conducted across European and New Zealand ecosystems. Current research infrastructure includes access to the Biozentrum Martinsried facilities and field sites across Germany supporting avian population studies through capture-mark-recapture methodologies and behavioral observation systems.
Dr. Grace Wenling Cao is an Assistant Professor in Linguistics (Phonetics and Phonology) at the School of Languages, Cultures and Linguistics, University College Dublin. She holds a PhD from the University of York and an MSc from the University of Edinburgh. Prior to her current position, she served as a Hong Kong RGC Postdoctoral Fellow and lecturer at the Chinese University of Hong Kong and the Hong Kong University of Science and Technology. PhD in Linguistics – University of York MSc in Developmental Linguistics – University of Edinburgh Her research focuses on sociophonetics , forensic phonetics , and language attitudes in multilingual contexts, particularly in Hong Kong. She investigates phonetic convergence, cross-language speaker identification, the impact of visual cues on speech perception, and identity-related language changes in Cantonese and Hong Kong English. Her work bridges theoretical linguistics with real-world applications in forensic science and human-AI interaction. Recent publications span high-impact journals and conferences, showing a strong trend in using acoustic-phonetic analysis to understand social biases, speaker identity, and forensic challenges in trilingual environments. Her work increasingly integrates AI and machine learning approaches to speaker recognition, especially in cross-language settings using filled pauses as biometric markers. She has received multiple scientific awards and grants, including the prestigious Hong Kong RGC Postdoctoral Fellowship and funding from the Worldwide Universities Network. Her current research includes a major project on human-AI speech accommodation. Hong Kong RGC Postdoctoral Fellowship Worldwide Universities Network Research Mobility Program CUHK Global Scholarship Program Linguistic Association of Great Britain Conference Fund University of York Humanities Research Centre Grant Dr. Cao actively supervises graduate students, having guided 14 Master’s theses and one undergraduate intern. She coordinates key modules such as Phonology (MA), Phonology 2, and Sounds in Language at UCD. She has also taught a wide range of undergraduate and postgraduate courses at CUHK and HKUST, including sociolinguistics, phonetics, and bilingualism. She leads research on trilingual speech in Hong Kong, particularly focusing on forensic applications and sociolinguistic identity. Her team explores how filled pauses, accent perception, and visual cues affect intelligibility and speaker identification, with implications for both legal and technological domains.
Elsa L Gunter is a Research Professor and Senior Lecturer at the University of Illinois at Urbana-Champaign's Department of Computer Science. Her academic background includes a Ph.D. in Mathematics from the University of Wisconsin, Madison. She leads research in formal methods, programming languages, and human-computer systems. Research Interests: Her work spans formal verification, programming language semantics, automated theorem proving, and security. She develops tools for compiler optimization verification, human-automation system safety, and concurrent program analysis. Key projects include the VeriF-OPT framework for parallel program transformations and Tutela for human-computer system protection analysis. Publications Focus: Her recent research emphasizes compiler verification, concurrency models, and human-system interaction. Work includes symbolic analysis for CSP, dependently-typed session systems, and robustness verification for safety-critical interfaces. Awards: Most Influential 10 Year Paper award at Requirements Engineering (RE 2010) EASST Best Software Science Paper at ETAPS 2001 Best Paper award at Fourth International Conference on Requirements Engineering (2000) Funding and Labs: Secured NSF grants for projects on parallel program verification ($450K) and human task analysis ($500K). Leads the Formal Methods and Verification Lab, collaborating with NASA on NextGen aviation systems. Student Advising: Mentored 7 PhD graduates and 12+ Master's students. Current PhD candidates work on secure distributed programming (Dennis Griffith) and formal methods for concurrent systems (Liyi Li, Susannah Johnson).
Margarida Carvalho is an Associate Professor in the Department of Computer Science and Operations Research at Université de Montréal, where she holds the FRQ-IVADO Research Chair in Data Science for Combinatorial Game Theory. She is also an Associate Academic Member at Mila (Quebec AI Institute), contributing to their research in AI for Humanity. Her academic journey spans from Portugal to Canada, where she has established herself as a leading researcher at the intersection of operations research and game theory. Carvalho earned her bachelor's and master's degrees in mathematics from the Faculty of Sciences of the University of Porto (FCUP), followed by a PhD in Computer Science from the same institution in 2016. Her doctoral work, which focused on game theory applications for kidney exchange programs, earned her the prestigious 2018 EURO Doctoral Dissertation Award, making her the first Portuguese woman to receive this honor. After completing her PhD, she worked as an IVADO Postdoctoral Fellow at Polytechnique Montréal before joining Université de Montréal as an Assistant Professor in 2018. Her research focuses on combinatorial optimization and algorithmic game theory, with applications spanning healthcare (kidney exchange programs, hospital operations), sustainable development (electric vehicle infrastructure, urban planning), and education (school choice systems). She develops novel mathematical programming approaches to model and solve problems involving multiple decision-makers with potentially conflicting objectives. Her work bridges theoretical advances in optimization with practical implementations that address real-world challenges in resource allocation and decision-making under uncertainty. Notably, her research on fairness in kidney exchange programs has contributed to more equitable organ allocation policies. Her 15 most recent publications reveal a strong trend toward integrating game-theoretic concepts with practical optimization challenges, particularly in healthcare and sustainable infrastructure. She has pioneered approaches that balance utilitarian objectives with fairness considerations, developed novel formulations for bilevel and multilevel optimization problems, and created learning-based frameworks for complex decision environments. Her work consistently demonstrates how mathematical rigor can inform practical policy decisions in critical domains. 2018 EURO Doctoral Dissertation Award for her PhD thesis on game theory applications for kidney exchange programs Mathematical Programming 2024 Meritorious Service Award Teaching Excellence Award from Université de Montréal Supervised student Maria Bazotte receiving the Dupačová-Prékopa Best Student Paper Prize in Stochastic Programming Carvalho actively advises graduate students, with Marylou Fauchard (Master's) and William St-Arnaud (PhD) among her current advisees. Her research is supported by grants from Hydro-Québec, the Natural Sciences and Engineering Research Council of Canada (Discovery grant 2017-06054 and Collaborative Research and Development Grant CRDPJ 536757–19), and FRQ-IVADO. She serves as an associate editor for INFORMS Journal on Computing, OR Spectrum, and Dynamic Games and Applications, and is a founding board member and treasurer of the Bilevel Optimization Society. She teaches courses in Mathematical Programming, Operational Research Models, and Discrete Mathematics at Université de Montréal. Carvalho is affiliated with Mila (Quebec AI Institute), where she contributes to research initiatives focused on AI for Humanity, particularly in the areas of algorithmic fairness and sustainable development. Her FRQ-IVADO Research Chair supports her work on combinatorial game theory applications, and she collaborates with researchers across disciplines through the IVADO research community. She has been instrumental in establishing the Bilevel Optimization Society, creating a dedicated forum for researchers working on hierarchical decision-making problems.
Dr Raj Mehrotra is a Senior Research Fellow at the Water Research Centre, School of Civil and Environmental Engineering, University of New South Wales. He holds a PhD from UNSW, M.E. from Indian Institute of Technology Roorkee, and B.E. from Jiwaji University. His work focuses on statistical applications in hydrology and hydroclimatology, particularly on multivariate bias correction and stochastic downscaling of climate model simulations. Education: PhD: University of New South Wales M.E.: Indian Institute of Technology, Roorkee B.E.: Jiwaji University, Gwalior Raj develops innovative methodologies for climate data processing, including the Multivariate Bias Correction (MBC) and Multisite Rainfall Downscaling (MRD) software. His research addresses climate change impacts on water resources, reservoir management, and flood/drought risk assessment, with applications in Australia, India, and Thailand. Recent publications reveal a focus on climate projections, bias correction frameworks, and hydrological modeling. Key topics include CMIP5 decadal predictions, stochastic rainfall generation, and uncertainty quantification in climate simulations. His work has been supported by ARC Linkage projects, WaterNSW, Bureau of Meteorology, and international collaborations like the Australia-India Strategic Research Fund. He contributes to climate-hydrology software development and has advised on projects related to water infrastructure, agricultural productivity, and groundwater sustainability. Current projects include multivariate bias correction of regional climate simulations, development of the MINBC package, and ensemble modeling for ungauged catchments.
Paul Townend is an Associate Professor at Umeå University , Sweden, with a Docent qualification. He leads the Green Distributed Computing group and serves as Research Leader for the Autonomous Distributed Systems (ADSLab) lab. Scientific Coordinator for Horizon Europe COGNIT Co-Manager & Scientific Advisor for WASP WARA-Ops Member of WASP Graduate School Management Team His research focuses on energy-efficient and sustainable distributed systems , with a current emphasis on Edge-Cloud integration . Additional areas include fault tolerance, provenance, data center optimization, and simulation . He has supervised 5 PhD students and 2 postdocs , with expertise in IoT, machine learning for cloud management, and microservices . Best Paper Awards at SOSE 2013 and ISORC 2012 Principal Investigator for grants totaling over $5.8M , including projects like Adaptive Monitoring of Streaming Data and Energy-Aware Cloud-Edge Management
Kristy Hwang is an Assistant Professor in the Department of Neurology at the University of California, Irvine School of Medicine. Her research focuses on the genetic and molecular mechanisms underlying Alzheimer's disease, particularly the interaction between apolipoprotein E (apoE) plasma levels and brain amyloidosis. Alzheimer's disease Genetic risk factors Neuroinflammation Brain imaging Key findings from her work include the modulation of apoE-brain amyloidosis associations by specific Alzheimer's risk genes (BIN1, CD2AP, CR1). Her 2015 study demonstrated that low plasma apoE correlates with increased amyloid deposits in most cortical regions, except sensorimotor and entorhinal areas, depending on genetic variants. Scientific contributions span collaborations with the Alzheimer's Disease Neuroimaging Initiative. Research grants from the NIH/NIA (P30 AG010124) support her investigations into epigenetic and downstream interactions in neurodegenerative pathways.
Prof. Dr. Roland Langrock holds the Chair of Statistics and Data Analysis at the Faculty of Economics, University of Bielefeld . He is a spokesperson for the Center for Statistics and a subproject manager in the Transregio 212 NC³ collaboration. His research spans ecological statistics, sports analytics, and time series modeling. 2026–present: Principal investigator for "Data-based indication of fraud in live betting" (DFG) 2025–present: Subproject manager D06 in TRR 212 NC³ 2021–present: ERASMUS representative for Master of Statistical Sciences Research Interests: His work focuses on hidden Markov models for analyzing animal movement, sports performance, and commercial data. Key applications include marine predator behavior , football match dynamics , and fraud detection in betting . He develops flexible statistical frameworks for state-switching processes across domains. Scientific Awards: Multiple German Research Foundation grants (2017–2026) and participation in EU-funded projects. Notable publications in Journal of the Royal Statistical Society , Ecology Letters , and Science . Additional Roles: Member of the Bielefeld Graduate School in Theoretical Sciences, organizer of advanced statistical methods courses, and contributor to software packages like moveHMM . His collaborations extend to marine biology (blue whales), subterranean rodent studies, and retail demand forecasting.
Mr. Shuang Ao is a Postdoctoral Research Fellow at the School of Computer Science and Engineering, University of New South Wales (UNSW Sydney). He earned his PhD from the University of Technology Sydney in January 2024. His research focuses on machine learning, reinforcement learning, and curriculum learning, with applications in robotic control and antibody drug discovery. Research Interests: Machine Learning Reinforcement Learning Curriculum Learning Graph Algorithms Optimization Techniques Recent Publication Trends: Shuang's work spans large language models for location-based recommendations, spatio-temporal forecasting, reinforcement learning frameworks, and graph algorithm optimizations. His articles address both theoretical advancements and practical applications in scalable systems and data analysis. Contact: Email: shuang.ao@unsw.edu.au
José Enrique del Moral García is a Researcher in the Faculty of Education specializing in Physical Education and Sport at an institution in Castile and León, Spain. He is an active member of the GIADES research group (Research in physical activity, sport, and health), a Consolidated Research Unit of the regional government focusing on evidence-based health promotion through physical activity. His academic foundation includes a PhD from the University of Jaén (2010) with the thesis "Physical Activity and Body Composition in Andalusian Schoolchildren Aged 13-16. Analysis of Quality of Life and Motivations for Participating in Physical and Sports Activities," supervised by Dr. Emilio J. Martínez López. This research established his focus on youth health behaviors and motivational dynamics in physical activity contexts. His scholarly work centers on the psychological dimensions of physical education, particularly motivation drivers in school settings and their interplay with healthy lifestyle factors. He investigates critical contemporary issues including the relationship between physical activity and bullying/cyberbullying, Mediterranean diet adherence in adolescents, and digital media's impact on youth well-being. His research consistently bridges theoretical frameworks with practical educational applications, emphasizing school-based interventions for promoting holistic adolescent development. Analysis of his 2023-2025 publications reveals a sophisticated interdisciplinary approach integrating sports psychology, public health, and educational theory. His work demonstrates growing emphasis on digital behavior-physical activity interactions, with systematic reviews establishing causal pathways between screen time, physical inactivity, and social problems like bullying. The Mediterranean diet emerges as a recurring protective factor in his studies, examined through gender-specific and age-stratified lenses. As a core contributor to the GIADES research group, he participates in collaborative projects translating scientific findings into classroom practices. His methodological repertoire spans quantitative surveys, experimental interventions, and systematic reviews, with consistent focus on actionable outcomes for educators and policymakers seeking to enhance youth health through physical education.
Prof. Dr. Karsten Borgwardt is Director of the Research Department of Machine Learning and Systems Biology at the Max Planck Institute of Biochemistry in Martinsried, Germany. A leading figure in the intersection of machine learning, bioinformatics, and systems biology, he heads a multidisciplinary team that develops novel computational methods to extract knowledge from large biomedical data sets. Research Mission: The Borgwardt lab converges big data analytics and biomedical research . Two overarching goals drive their work: (1) Automatically generating new biological and medical knowledge from massive data via state-of-the-art machine-learning algorithms. (2) Understanding the molecular underpinnings of biological system function, with emphasis on personalized medicine and biomarker discovery. Their methodological toolbox spans graph neural networks, kernel methods, conformal prediction, deep learning on sequences and structures, and topological data analysis . Application domains include antimicrobial resistance prediction, protease engineering, acute-kidney-injury forecasting, coronary-artery-disease diagnostics, single-cell spatial proteomics, and Long-COVID immune profiling. Recent Publication Landscape (2023-2025): The group’s latest articles demonstrate a clear trend toward translationally relevant machine learning . High-impact venues such as Nature Communications , Science , ICLR , and RECOMB feature their work on: Data-driven protein engineering using DNA-recorded deep mutational scanning. Guaranteed antimicrobial resistance detection from MALDI-TOF spectra via conformal prediction. Graph-based biomarker discovery with theoretical guarantees. Deep phenotyping of human iPSC-derived neuronal networks to study disease mutations. Multi-modal learning that fuses genomics, proteomics, and clinical data for patient stratification. These contributions collectively advance both the theoretical foundations and real-world deployment of machine learning in medicine. Scientific Awards & Honors: While no explicit award list is provided, the breadth and impact of publications, invited book chapters, and keynote-level conference presentations (ICLR, RECOMB, ISMB/ECCB) testify to sustained international recognition. Laboratory & Collaboration Ecosystem: The Borgwardt lab operates at the Max Planck Institute of Biochemistry —a world-leading biomedical research campus. Collaborations span multiple Max Planck centers, university hospitals across Europe, and international consortia such as the EyeConic study on optogenetics therapy. The lab’s open-source footprint includes the Multi-SConES R package for multi-task network-regularized feature selection, fostering reproducible science across the community.
Dr. Kenneth Fuld serves as Dean of the College of Liberal Arts at the University of New Hampshire, holding a faculty position within this academic unit. His administrative leadership complements an extensive research career spanning over four decades in vision science and ophthalmology, with publications appearing in premier journals including Investigative Ophthalmology and Visual Science and Vision Research . His educational foundation includes: B.A. from Northeastern University Ph.D. in Psychology from Dartmouth College Dr. Fuld's research program centers on the physiological and perceptual mechanisms of human vision, with particular emphasis on macular pigment function and photophobia phenomena. His work has systematically investigated the spatial distribution of macular pigment optical density, its relationship to dietary carotenoids, and protective effects against light-induced visual discomfort. The longitudinal nature of his publications reveals an evolving research trajectory from foundational color perception studies in the 1970s-1980s toward specialized investigations of retinal photoprotection mechanisms in later decades. His 2019 marine debris visual identification study represents a notable interdisciplinary extension of his visual assessment expertise into environmental science applications. Analysis of his publication trends indicates consistent focus on three interconnected domains: (1) macular pigment biochemistry and topography, (2) photophobia mechanisms and spatial characteristics, and (3) color perception fundamentals. This research has generated important insights into retinal protection strategies, visual discomfort thresholds, and the physiological basis of color vision. While no specific scientific awards are documented in the available materials, his sustained publication record in high-impact vision science journals demonstrates significant scholarly contribution to the field. His 2014 chapter on psychology faculty preparation suggests active engagement in academic development initiatives beyond his research program. As Dean of the College of Liberal Arts, Dr. Fuld oversees academic programming and faculty development across multiple disciplines. His administrative leadership appears integrated with his scholarly identity, as evidenced by publications addressing both vision science and academic training. No information is available regarding current grant funding or laboratory facilities under his direct supervision.
