Dr Sue Kageler serves as a Senior Lecturer in Forensic Data Analysis within the Department of Applied Sciences at the University of the West of England (UWE Bristol), operating under the Faculty of Health and Applied Sciences. Her academic role focuses on bridging data science methodologies with forensic investigation practices in the UK criminal justice context. Her research expertise centers on Forensic Data Analysis, specializing in digital evidence processing, pattern recognition in crime datasets, and development of analytical frameworks for law enforcement. This interdisciplinary field integrates machine learning algorithms, statistical modeling, and cybercrime investigation protocols to address complex challenges in digital forensics. Her work demonstrates particular relevance to cybercrime units and forensic laboratories requiring advanced data interpretation capabilities. While the profile indicates publication activity (noted by JavaScript-dependent publication listings), no specific awards, student supervision records, or research grants were documented in the available materials. Contact information includes a university telephone line (+4411732 82120) but no email address was provided in the source text.
Dr. Olga Kellert serves as a Lecturer in the Department of Romance Philology within the Faculty of Humanities at the University of Göttingen, where she conducts interdisciplinary research at the intersection of sociolinguistics, computational linguistics, and historical Romance linguistics. Her work bridges traditional linguistic analysis with cutting-edge digital methodologies, particularly focusing on language variation in social media contexts and diachronic syntactic change. Her research encompasses three primary domains: (1) Sociolinguistic analysis of code-switching and language variation using geolocated social media data, particularly examining Spanish-French interactions in Quebec and Spanish-Italian dynamics in South America; (2) Computational approaches to sentiment analysis and linguistic modeling, with emphasis on syntax-aware NLP systems; (3) Historical evolution of quantificational structures in Romance languages, especially indefinites and free choice items across Old and Modern Italian, Spanish, and Catalan. Current projects include the sociocomputational assessment of belief states among vulnerable indigenous groups in Latin American crises (funded through international collaboration with Mexico, Ecuador, Peru, and Austria) and geospatial analysis of urban linguistic variation using Twitter data. Her publication trajectory reveals a strategic evolution from foundational work in Romance syntax and prosody toward increasingly computational methodologies, with recent publications heavily featuring NLP applications, geotagged linguistic analysis, and interdisciplinary crisis communication research. This shift reflects broader trends in digital humanities while maintaining deep roots in Romance linguistic theory. Habilitation Completion Grant from the Faculty of Humanities, University of Göttingen Dr. Kellert actively supervises student project work on language mixing in social media contexts and collaborates extensively through the University of Göttingen's CRC 'Textstrukturen' research center. Her grant portfolio demonstrates significant international collaboration, particularly with Latin American institutions on crisis communication projects and European partners on historical linguistics initiatives. Current funding includes DFG support for 'Quantification in Old Italian' and international partnerships for sociolinguistic crisis response research. Her research operates within the Collaborative Research Center 'Textstrukturen' at the University of Göttingen, where she contributes to interdisciplinary teams combining linguistic theory, computational methods, and sociocultural analysis. Recent projects involve cross-institutional teams spanning Mexico, Ecuador, Peru, Austria, France, and Italy, with particular emphasis on community-engaged research with indigenous populations in Latin America.
Prof. Dr. Steffen Goebbels is a Professor of Mathematics and Computer Science at Niederrhein University of Applied Sciences, Faculty of Electrical Engineering and Computer Science in Krefeld, Germany. He maintains an office in room F 202 and is actively involved in teaching and research. His academic work spans multiple disciplines with a strong focus on applied mathematics and computer science. His research interests center around 3D city modeling, mathematical optimization, and computer graphics. He has made significant contributions to the field of CityGML data processing and has developed algorithms for calculating 3D building models from land registry data and laser scan data. His work with the iPattern Institute has led to practical applications in cities like Krefeld, Leverkusen, and Dortmund. He has also contributed to neural network approximation theory and various optimization problems. Prof. Goebbels has published extensively in recent years, with publications spanning computer graphics, mathematical optimization, and machine learning. His research shows a consistent pattern of applying mathematical techniques to solve practical problems in 3D modeling and computer vision. He has also co-authored several influential textbooks on mathematics for computer science students. He has received recognition for his work through publications in reputable journals and conference proceedings, though specific awards are not mentioned in the available information. His research has practical applications in urban planning, architectural visualization, and manufacturing processes. Prof. Goebbels is actively involved in teaching mathematics courses (Mathematics 1-3), Numerical Analysis, Logic Programming, Functional Programming, and Scientific Computing. He has developed teaching materials including online courses and textbooks that are widely used in his institution.
Mario Ghossoub is an Associate Professor and Sun Life Research Fellow at the University of Waterloo, specializing in Actuarial Science. His research focuses on optimal risk-sharing mechanisms, reinsurance markets, game-theoretic models of insurance, and behavioral economics. He investigates topics such as Pareto optimality, distortion risk measures, and decision-making under uncertainty, particularly in contexts involving heterogeneous beliefs and risk preferences. Key research areas include multi-armed bandit problems under mean-variance frameworks, Nash equilibria in large reinsurance markets, and the design of efficient peer-to-peer insurance systems. His work bridges actuarial science with economics, finance, and decision theory to address challenges in risk management, contractual design, and market efficiency. Recent contributions analyze counter-monotonic risk allocations, robust distortion risk measures, and Stackelberg equilibria in multi-agent systems. His articles often explore the interplay between behavioral biases (e.g., rank-dependent utility) and traditional actuarial principles, with applications to flood risk management and decentralized exchange markets. While no specific scientific awards are listed, his extensive publication record reflects significant contributions to theoretical and applied aspects of risk and insurance. His work is characterized by rigorous mathematical modeling and relevance to practical policy design in financial and insurance sectors.
Jingyi Jessica Li is a Professor at the University of California Los Angeles (UCLA), holding joint appointments in the Departments of Statistics and Data Science, Human Genetics, Computational Medicine, and Biostatistics. Her research focuses on developing statistical and computational methods for analyzing high-throughput genomic data, with applications in transcriptomics, single-cell analysis, and epigenetics. She earned her B.S. in Biological Sciences from Tsinghua University (2007) and her Ph.D. in Biostatistics from UC Berkeley (2013). Her work emphasizes methodological advancements in bioinformatics, including tools for single-cell RNA-seq analysis (e.g., scDesign, scImpute), contamination detection (scCDC), and differential expression (PseudotimeDE). She has contributed significantly to understanding transcriptional regulation, epigenetic mechanisms, and translational control in biological systems. Research Themes: Single-cell genomics, statistical methodology, computational biology, epigenomics, and systems biology Labs/Teams: jsb_ucla Lab Dr. Li has received numerous accolades, including the COPSS Emerging Leader Award (2023), ISCB Overton Prize (2023), and a Radcliffe Fellowship (2022–2023). Her work bridges statistical rigor and biological insight, addressing challenges in large-scale genomic data interpretation.
Quan Vu is a Postdoctoral Fellow in Statistics at the Australian National University (ANU), specifically within the Research School of Finance, Actuarial Studies & Statistics. His research focuses on advancing statistical methodologies for spatial and spatio-temporal data, with applications in environmental and ecological contexts. Key areas of interest include Gaussian processes, basis function models, and neural networks for regression, prediction, and uncertainty quantification. His work addresses challenges in modeling complex dependencies in data, such as clustered, spatial, and spatio-temporal structures, and translates these methods into real-world solutions through collaborations with domain experts. Recent research trends emphasize nonstationary covariance modeling, compositional warpings for large datasets, and gradient-enhanced surrogate models for intractable likelihoods. Quan has contributed to high-impact journals such as Methods in Ecology and Evolution and the Journal of the American Statistical Association , with notable applications in species distribution modeling and environmental data analysis. He is actively involved in supervising research students and remains engaged in the statistical community through discussions on methodological advancements.
Sebastian Wohner is a researcher at the Chair of Computer Graphics and Visualization (Prof. Westermann) at the Technical University of Munich. His work focuses on advanced visualization techniques, machine learning applications in graphics, and GPU-accelerated algorithms for 3D design and simulation. He actively contributes to projects such as the NVIDIA CUDA Research Center and ERC-funded initiatives like SaferVis and realFlow, emphasizing real-time liquids and safer visualization systems. His research interests span 3D Gaussian splatting, topology optimization, neural fields for statistical dependencies, and spatio-temporal flow visualization. He has pioneered methods for compressing meteorological ensembles and accelerating novel view synthesis in consumer devices. Wohner also explores GPU-based linear algebra optimizations and efficient rendering techniques for ribbons and twisted lines. In teaching, he leads courses on game physics, visual data analytics, deep learning in computer graphics, and topology optimization. Notable contributions include the development of the Particle Engine and Bunny Demo applications, as well as advancements in differentiable rendering and robotic perception systems. His work bridges theoretical foundations with practical implementations in both academia and industry.
Mark Hasegawa-Johnson is a Professor of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign, where he has been faculty since 1999. He holds affiliations with the College of Engineering and leads the Statistical Speech Technology Group. His academic roles include serving as Editor-in-Chief of the IEEE Transactions on Audio, Speech and Language, and membership in the ISCA Diversity Committee. Education: PhD in Electrical Engineering and Computer Science from MIT (1996), postdoctoral research at UCLA (1996-1999). Research interests span automatic speech recognition, machine learning applied to phonetics and prosody, and accessibility technologies for under-resourced languages and speech disorders. Key projects include the Speech Accessibility Project, which improves speech recognition for individuals with dysarthria, and international competition successes in audio event detection and multilingual broadcast retrieval. Scientific achievements include Fellowships from the IEEE (2020), Acoustical Society of America (2011), and ISCA. Awards also include NIH’s National Research Service Award (1998-1999) and the Frederic Vinton Hunt Post-Doctoral Fellowship (1996-1997). Teaching focuses on courses like Artificial Intelligence, Multimedia Signal Processing, and Speech Processing. Research supervision emphasizes undergraduate projects in signal processing and speech recognition, with notable student contributions to prosody-dependent speech recognition and audio source separation. Labs/Teams: Leads the Speech Accessibility Project and collaborates with interdisciplinary teams on projects like Mandarin language education tools and audio-visual speech models. Current work explores unsupervised learning, cross-lingual speech recognition, and AI-driven accessibility solutions.
Andi Han is a Lecturer in Data Science at the School of Mathematics and Statistics, University of Sydney . He earned his PhD in Business Analytics from the University of Sydney Business School in 2023 and served as a postdoctoral researcher at RIKEN AIP’s Continuous Optimization Team until 2025. Research Interests: Large generative models (diffusion models, large language models) Optimization on manifolds Efficiency of foundation models Graph neural networks for biology and chemistry Awards: DAAD AInet Fellowship (2025) PhD Completion Award (USYD, 2023) Best Paper Award (IEEE SCCI, 2022) University Medal (USYD, 2019) Business Analytics Prize (USYD, 2018) Teaching: STAT5002: Introduction to Statistics (Unit Coordinator & Lecturer, S2 2025) MATH1061: Mathematics 1A (Lecturer, S2 2025) His recent publications focus on Riemannian optimization techniques, diffusion models, and graph neural networks (GNNs), with applications in protein sequence generation, transformer optimization, and AI for science. Collaborative work spans institutions like RIKEN, Zhejiang Lab, and A*STAR. He actively organizes workshops, including Deep Generative Model in Machine Learning: Theory, Principle and Efficacy at ICLR 2025.
Roland Badeau is a Full Professor in the Signal, Statistics and Learning (S2A) team within the Image, Data, Signal (IDS) Department at Télécom Paris, Institut Polytechnique de Paris. His primary affiliation is with the Information Processing and Communication Laboratory (LTCI). Research Interests: Badeau specializes in statistical modeling of non-stationary signals, with core expertise in adaptive high-resolution spectral analysis and Bayesian extensions to Non-negative Matrix Factorization (NMF). His work spans room acoustics (stochastic reverberation models), data representation (dimensionality reduction, time-frequency analysis), probabilistic latent variable modeling, and algorithm development (Bayesian estimation, optimization methods, fast adaptive algorithms). Applications focus on audio/music processing including source separation, denoising, dereverberation, multipitch estimation, and automatic music transcription, with extensions to biomedical data analysis and digital communications. Key Trends in Publications: Recent work centers on Statistical Wave Field Theory, establishing mathematical frameworks for reverberation modeling using energy-stress tensor formalism and Riemannian geometry. His publications demonstrate a progression from foundational signal processing algorithms (e.g., YAST, ESPRIT) to physics-informed approaches for polyhedral rooms and frequency-dependent attenuation, with strong emphasis on Bayesian and alpha-stable distribution methods for robust audio separation. Academic Leadership: Badeau supervises doctoral and master’s theses while leading teaching units at Télécom Paris. He serves as the TSIA study track supervisor (Signal Processing for Artificial Intelligence) and Master ATIAM correspondent. His team (S2A) develops tools like DESAM for joint source separation and multi-track coding.
Walter Briec is a Professor at the University of Perpignan, affiliated with the INSTITUT IAE and the LAMPS (Multidisciplinary Modeling and Simulation Laboratory). His research spans Mathematical Economics, Optimization, Operations Research, and Tropical Mathematics, with applications in productivity measurement, equilibrium concepts, and portfolio efficiency assessment. Section CNU 05, Economic Sciences Thematic axes: Nonlinear Analysis and Optimization, Physics of Complex Systems, Mathematical and Numerical Modeling for Mechanics His research focuses on distance functions and their applications, including productivity indicators, game theory (cooperative and non-cooperative), and tropical mathematics. He explores duality-based methods, non-convexity in economic models, and convexity structures in financial portfolio selection. Projects like LAWBOT (Deep Learning for juridical outcomes) and BPS Customer Eco-Responsibility Analysis highlight his interdisciplinary work. Article trends include non-associative algebraic structures, productivity growth analysis, game-theoretic approaches, and tropical mathematics. Collaborative works with researchers like Arnaud Abad, Kristiaan Kerstens, and Stéphane Mussard emphasize methodological innovations and real-world applications in economics and finance.
Stefano Scanzio is a Senior Researcher at the National Research Council of Italy (CNR-IEIIT) and teaches computer science courses at Politecnico di Torino . With over 60 publications in wireless networks, real-time communication, and industrial IoT, he serves as Associate Editor for Ad Hoc Networks , IEEE Access , and Electronics journals. Ph.D. in Computer Science (Politecnico di Torino, 2008) Laurea in Computer Science (Politecnico di Torino, 2004) His research focuses on industrial wireless networks , particularly Wi-Fi and IEEE 802.15.4 TSCH, with emphasis on: Energy-saving mechanisms in wireless sensor networks Predictive models for Wi-Fi channel quality Reliable communication through seamless redundancy Machine learning integration for network optimization Clock synchronization under non-Gaussian noise Recent publications analyze Wi-Fi 7's multi-link operation, executable QR codes for IoT, and AI-driven network self-configuration. His work has been recognized with multiple best paper awards .
Nathaniel Daw holds the Huo Professorship in Computational and Theoretical Neuroscience at the Princeton Neuroscience Institute , Princeton University. His research integrates computational, neural, and behavioral approaches to study decision-making through trial-and-error learning and reward/punishment processing. Research Interests: Computational Neuroscience Decision-Making Under Uncertainty Model-Based and Model-Free Learning Neural Mechanisms of Self-Control Publications (2025-2019) explore intersections of machine learning and neuroscience, focusing on reward-guided behavior, memory systems, and psychiatric implications like anorexia nervosa and obsessive-compulsive disorder. Key themes include neural replay, cognitive effort allocation, and predictive modeling of human and animal learning. Scientific Awards: Princeton-Rutgers $16M Research Grant for mental illness studies Grants and Collaborations: Highlighted by a major interdisciplinary grant with Rutgers University to advance understanding of mental illness through computational frameworks. Labs and Teams: Leads the Daw Lab at Princeton Neuroscience Institute, focusing on neurocomputational models of decision-making and self-control.
Dr. Sumit Kothari is a Research Fellow at University College London's Bartlett School of Environment, Energy and Resources, specializing in climate finance and low-carbon transition dynamics. His work bridges financial systems analysis with environmental sustainability, leveraging industry experience from Morgan Stanley and entrepreneurial background in Indian investment startups to address real-world climate finance challenges. His educational foundation includes: PhD in Sustainable Resources and Climate Policy (UCL, 2019-2024) MSc Economics and Policy of Energy and the Environment (UCL, 2016-2017) Post Graduate Diploma in Management (IIM Calcutta, 2002-2004) Bachelor of Commerce (University of Mumbai, 1998-2001) Dr. Kothari's research centers on Climate Finance and Energy Transition Dynamics , employing Complexity Economics to analyze investment networks and risk underwriting in developing countries. His work on Technology Scaling examines financial mechanisms for accelerating renewable adoption, while his expertise in Computational Statistics informs analysis of environmental resource economics and policy design. Key contributions address financial tipping points, equity in climate finance flows, and fossil fuel finance phase-out challenges. His publication record (2021-2025) reveals three dominant trends: 1) Systemic analysis of financial mechanisms driving sustainability transitions, 2) Critical examination of equity gaps in international climate finance, particularly for developing nations, and 3) Application of complexity theory to low-carbon finance markets. These works, published in Nature Communications, Nature Climate Change, and Earth System Dynamics, demonstrate increasing impact with multiple papers exceeding 100 citations. No major scientific awards were identified in the source materials, though his research has influenced policy discussions and been covered by numerous news outlets including Financial Times and Bloomberg. Dr. Kothari's advisory activities focus on sustainable development goals 7, 11, and 13, with collaborations spanning UCL, University of Strathclyde, and industry partners. Current research grants investigate: Financial tipping points for accelerated decarbonization Equity frameworks for international climate finance Complexity modeling of low-carbon investment networks He contributes to UCL's Climate Finance and Technology Scaling research cluster, collaborating with environmental economists and complexity scientists to develop practical financial instruments for the low-carbon transition while maintaining industry connections through his private investment startup cofounding role.
Dr Rand Low is an Associate Professor of Quantitative Finance at Bond Business School , with an Honorary Associate Professor role at the University of Queensland. He works at the Centre for Data Analytics and holds a Chartered Professional Engineer designation from Engineers Australia. PhD in Finance, University of Queensland (2009-2013) Bachelor's degrees in Engineering and Computer Science, University of Melbourne (2001-2005) Graduate Diploma in Project Management, University of New England His research focuses on portfolio optimization , risk management , and machine learning applications in finance, particularly for corporate bonds , digital assets , and commodities . He has published in top journals including Journal of Banking & Finance and Energy Economics . Dr Low's work has been recognized through awards like the Australia Awards - Endeavour fellowship and Dean's Award for Research Higher Degree Excellence . He actively supervises HDR students and serves on editorial boards for journals with Q1 rankings. Industry experience includes leadership roles at Bank of America Merrill Lynch and BlackRock in New York, where he developed quantitative models for market risk , structured products , and model governance . He currently works on the RBA's CBDC Pilot for blockchain-based corporate bond settlement.
