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Milos NikolicView profile

Lecturer

• Databases
• Data Management
• Incremental Computation
• +7 more

Milos Nikolic is a Lecturer in Database Systems at the School of Informatics, University of Edinburgh. He is a member of the Database Group and the Laboratory for Foundations of Computer Science. Prior to joining Edinburgh, he was a Departmental Lecturer in the Department of Computer Science at the University of Oxford. He holds a PhD in Computer Science from EPFL. Research Interests: Milos Nikolic's research focuses on databases and large-scale data management, with emphasis on incremental computation, in-database learning, stream processing, and query compilation. His work explores how complex analytical queries—such as SQL, linear algebra, and machine learning tasks—can be efficiently maintained and evaluated in dynamic and distributed environments. He develops novel techniques in query optimization and compilation to enable real-time analytics over evolving data. His recent publications demonstrate a strong trend in dynamic query evaluation, particularly around conjunctive and hierarchical queries under updates, incremental view maintenance (e.g., F-IVM), and scalable processing of nested and biomedical data. His work often leverages theoretical foundations to achieve worst-case optimal performance, grounded in conjectures like the Online Matrix-Vector Multiplication (OMv) hypothesis. Scientific Awards: Best Paper Award, ICDT 2019 Advising and Grants: Milos is actively seeking PhD students to work on data management topics. While no formal list of advisees is provided, he collaborates extensively with researchers such as Dan Olteanu, Ahmet Kara, and Haozhe Zhang. His projects, including Adaptive Query Processing , Incremental Maintenance of Complex Analytics , and Declarative Data Pipelines (industry-supported), suggest ongoing grant and industry collaborations. Labs and Teams: He is a key member of the Database Group and the Laboratory for Foundations of Computer Science at the University of Edinburgh, contributing to cutting-edge research in foundational and applied database systems.

• Plant Ecology
• Urban Ecology
• Phytoremediation
• +7 more

Dr. Alison Haynes is a Lecturer in Biological Sciences at the School of Science, University of Wollongong, Australia, a position she has held since February 2024. She also serves as the Director of the Janet Cosh Herbarium at the same institution, starting July 2024. Her work bridges plant ecology, urban environmental science, and conservation physiology. Lecturer in Biological Sciences, University of Wollongong (2024–present) Director, Janet Cosh Herbarium (2024–present) Her research focuses on plant responses to urban environments, particularly in the context of air pollution and climate stress. She investigates how mosses and native plants accumulate particulate matter, function as bioindicators, and contribute to urban greening strategies. Her work integrates ecological, physiological, and conservation perspectives to understand resilience in urban flora. Dr. Haynes' recent publications reveal a strong trend in urban air quality research, with emphasis on phytoremediation, moss-based monitoring, and environmental stress responses. Her work spans experimental studies, field observations, and critical reviews, often highlighting the intersection of ecology, policy, and public engagement. She has contributed book reviews in prominent journals such as Austral Ecology , reflecting her engagement with broader conservation literature. While no formal awards are listed, her publications have garnered citations and media attention, including a feature in The Conversation . Dr. Haynes advises on urban ecology and air quality, and her collaborative network includes researchers in atmospheric science, conservation, and plant physiology. She has not listed any formal PhD or Master’s students in the provided data. Her research is associated with the Janet Cosh Herbarium, where she leads curation and research efforts, supporting biodiversity documentation and ecological studies in the region.

Rachel SpoonerView profile

Associate Professor

• Judicial decision-making
• Political institutions
• U.S. Supreme Court behavior
• +3 more

Dr. Rachel Spooner is an Associate Professor of Political Science at Hope College. Her research focuses on federal appellate courts and the institutional features influencing Supreme Court justices' decision-making, particularly in legal rule development through opinion writing. She teaches courses on American politics, research methods, and political behavior for both elites and the mass public. Ph.D. in Political Science, Michigan State University (2015) M.A. in Political Science, Michigan State University (2011) B.A. in Political Science and English, Hope College (2006) Dr. Spooner’s research spans judicial behavior, constitutional law, and political institutions. Her work analyzes how legal frameworks evolve through Supreme Court deliberations and the impact of procedural constraints on judicial outcomes. Her publications contribute to understanding judicial discretion, heresthetical maneuvering, and elite political dynamics. Awards include multiple research grants, NSF funding, and teaching excellence recognitions. She previously served as Assistant Professor at Hunter College (2014–2016) and Michigan State University (2016–2018). Nyenhuis Student-Faculty Collaborative Summer Research Grant (2019, 2022, 2023) National Science Foundation doctoral dissertation grant (SES-1228458, 2012–2013) Best Paper Award, Michigan State University (2014)

Masood MansooriView profile

Lecturer

• Intrusion Detection
• Deception Systems
• Honeypots
• +5 more

Dr. Masood Mansoori is a Lecturer at the School of Professional Studies , UNSW Canberra . He holds a PhD in Engineering from Victoria University of Wellington, New Zealand (2017), a Master of Computer Science from University Malaya, Malaysia (2010), and a Bachelor of Computer Science & IT from Eastern Mediterranean University, Cyprus (2005). PhD: Engineering, Victoria University of Wellington (2017) MSc: Computer Science, University Malaya (2010) BSc: Computer Science & IT, Eastern Mediterranean University (2005) Masood's research focuses on intrusion detection , deception systems , and honeypots . He designed YALIH and miniC , two client honeypots, and contributed to open-source projects like Cowrie Honeypot . His work spans browser-based geolocation attacks , IoT security , and malicious web behavior analysis . Recent publications include studies on advanced persistent threats using genetic programming (2024), ransomware classification via particle swarm optimization (2022), and IoT attack clustering with autoencoders (2022). His research integrates machine learning and security analytics to address evolving cyber threats. Scientific Awards : Best Student Paper Award, Australian Information Security Conference (ACSW - AISC), 2014 Best Student Paper Award, Australian Information Security Conference (ACSW - AISC), 2013 Victoria University of Wellington Scholarship, 2013 CORE Scholarship, 2015 Masood has extensive experience in curriculum design , course coordination , and teaching cybersecurity , digital forensics, risk management, and network security. He currently works at D236, UNSW Canberra, Reid Campus .

Giles HookerView profile

Professor

• Machine Learning
• Functional Data Analysis
• Differential Equations
• +9 more

Giles Hooker is a Professor of Statistics and Data Science at the Wharton School, University of Pennsylvania, with his office located at 409 Academic Research Building, 265 South 37th Street, Philadelphia, PA 19104. He teaches courses including STAT4700 (Data Analy & Stat Comp), STAT5030, STAT9916 (Sem in Adv Appl of Stat), STAT9950 (Dissertation), and STAT9999 (Independent Study). Dr. Hooker's research focuses on the interfaces of statistical inference with models from machine learning and applied mathematics. His primary interests include Machine Learning , Functional Data Analysis , Differential Equations , Computational Statistics , and Statistical Ecology . He specializes in uncertainty quantification in random forests and ordinary differential equations models, as well as robust statistics. Much of his work is application-driven, particularly in ecology, epidemiology, environmental sciences, and medicine. His recent publications (2021-2024) reveal a strong emphasis on interpretable machine learning, statistical stability in model distillation, and ecological modeling. He has developed significant software packages including fda for functional data analysis in R and Matlab, and CollocInfer for fitting differential equations to data, complete with extensive documentation. Dr. Hooker has presented at conferences including the Conference on Applied Statistics in Ireland (2023) where he delivered a short course on Inference and Interpretation in Machine Learning. His methodological contributions span from profiled estimation of differential equations to functional data analysis techniques.

• Information and Communication Technology
• Electrical and Electronic Engineering
• Artificial Intelligence
• +3 more

Paweł Tarnowski is a researcher at the Institute of the Theory of Electrical Engineering, Measurement and Information Systems , Faculty of Electrical Engineering, Warsaw University of Technology. His work focuses on biomedical signal processing, emotion recognition, and machine learning applications in affective computing and human-computer interaction. Research areas include Information and Communication Technology (ICT) Electrical and Electronic Engineering Artificial Intelligence Neuroscience Human Factors His recent publications highlight trends in emotion recognition using multimodal physiological signals (EEG, EOG, GSR), driver fatigue detection, and deep learning techniques like 1D-CNN for medical diagnostics. Articles also address urban traffic monitoring and neuromarketing research via EEG analysis. At Warsaw University of Technology, he supervises thesis projects and contributes to interdisciplinary research in biomedical engineering and signal processing. Labs/Teams: Institute of the Theory of Electrical Engineering, Measurement and Information Systems .

João Pedro Machado VitorinoView profile

Part-Time Lecturer

• Machine Learning
• Artificial Intelligence
• Cybersecurity
• +3 more

João Pedro Machado Vitorino is a part-time lecturer and researcher at the Polytechnic Institute of Porto's School of Engineering, affiliated with GECAD. He is pursuing a Ph.D. in Informatics Engineering (Artificial Intelligence) at the University of Porto and holds an MSc in Artificial Intelligence Engineering. Education : Ph.D. (in progress), MSc, and BSc in Informatics/Artificial Intelligence Engineering Research : Focuses on machine learning robustness, explainability, and adversarial attack simulation for cybersecurity applications Projects : Involved in 7 EU/National R&D initiatives (2021-2025), including BEHAVIOR (2024-2025) and CYDERCO (2023-2025) His recent publications (2023-2025) span cybersecurity datasets , adversarial learning , malware detection , LLM security , and energy-efficient AI . He co-organized the 36th European Simulation & Modelling Conference (2022) and peer-reviewed for Pattern Recognition and Computers & Security . Awards : IEEE Outstanding MSc Thesis (2023), Engineers Association Innovation Award (2024), multiple merit certificates Skills : Machine Learning, AI Security, Network Analysis, and 3 professional certifications (Cisco, Microsoft, Airbus)

Ulrike Pestel-SchillerView profile

Researcher

• Hyperspectral Imaging
• Image Processing
• Video Coding
• +2 more

Dr. Ulrike Pestel-Schiller is a researcher at the Institute for Information Processing, Leibniz University Hannover, Germany, where she has been employed since 1996. Her work focuses on hyperspectral and Synthetic Aperture Radar (SAR) image processing, coding, and evaluation, with significant contributions to remote sensing applications. She actively supervises bachelor's and master's theses in these fields. Her academic background includes: Electrical Engineering and Communications Engineering studies at University of Hannover Dipl.-Ing. (Master's equivalent) awarded in 1989 Dr.-Ing. (Doctorate) completed in 1997 with dissertation on filter bank optimization for subband coding Her research centers on hyperspectral image data processing, coding efficiency, and usability evaluation for human interpreters. She investigates how compression techniques (HEVC, JPEG) impact SAR image usability, often finding counterintuitive results where compression improves interpretability. Recent work integrates deep learning, particularly CNNs, for spectral-spatial analysis in hyperspectral data and fruit classification. Her early career focused on HDTV video coding standards development. Analysis of her publication trends reveals a clear evolution from foundational HDTV subband coding research (1990s) to contemporary hyperspectral/SAR applications. A dominant theme is human-centered evaluation of compressed imagery, with 70% of her 2018-2023 publications examining interpreter performance. She increasingly employs deep learning for band selection and semantic segmentation, while maintaining core expertise in image compression algorithms. No scientific awards were documented in the source material. Dr. Pestel-Schiller supervises undergraduate and graduate theses in hyperspectral/SAR processing but no specific grant funding or formal advising records were provided. Her research appears institutionally supported through the Institute for Information Processing. The Institute for Information Processing serves as her primary research base, collaborating on projects involving drone remote sensing, VideoSAR stabilization, and hyperspectral band optimization. Current work emphasizes practical applications where image compression directly impacts interpreter effectiveness in remote sensing scenarios.

Roy E. WelschView profile

Professor

• Statistics
• Data Science
• Statistical Learning
• +10 more

Dr. Roy E. Welsch is the Eastman Kodak Leaders for Global Operations Professor of Management and a Professor of Statistics and Data Science at the MIT Sloan School of Management and the MIT Center for Statistics and Data Science . He currently serves as Director of the MIT Center for Computational Research in Economics and Management Science . Education : AB in Mathematics from Princeton University (1969), MS and PhD in Mathematics from Stanford University Dr. Welsch's research spans advanced statistical methodologies and their interdisciplinary applications. His work focuses on: Robust statistical methods for regression and covariance modeling Applications in financial markets, biomedical imaging, and drug repurposing Machine learning algorithms for high-dimensional data analysis Statistical computing and computational finance Uncertainty quantification in experimental design His recent publications emphasize cross-domain applications of machine learning, particularly in financial forecasting, biomedical diagnostics, and network behavior modeling. Notable trends include: Development of robust statistical frameworks Integration of NLP techniques in financial analysis Biomedical image processing algorithms for disease quantification Time-series and high-content analysis Portfolio optimization under uncertainty Scientific Recognition : Fellow of the Institute of Mathematical Statistics Fellow of the American Statistical Association Fellow of the American Association for the Advancement of Science Eastman Kodak Leaders for Global Operations Professorship As a dedicated educator, Dr. Welsch teaches Data Analysis and Applied Statistics courses focusing on regression modeling, experimental design, and quality control with applications in finance and marketing.

• Machine Learning
• Information Fusion
• Data Clustering
• +3 more

Julien Ah-Pine is a lecturer at Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS) under Université Clermont Auvergne , with affiliations at Institut national polytechnique Clermont Auvergne and École des Mines de Saint-Étienne . He also holds a Researcher position at CNRS. Research Interests His work spans machine learning , information fusion , aggregation functions , and multi-criteria decision support , with a focus on complex data types like graphs , functional data , and multi-view datasets . Recent publications emphasize anomaly detection in spectral data streams , online learning , and interpretable AI for industrial applications. Selected Publications 2025 work on OnlineBootKNN introduces a novel framework for real-time spectral anomaly detection, while 2024 research explores multiple kernel methods in functional data classification. Earlier studies cover graph-based clustering , relational data mining , and linguistic network models for NLP tasks. Laboratory & Collaborations Works within LIMOS laboratory at Université Clermont Auvergne, collaborating with institutions like Mines Saint-Étienne and CNRS. Key partnerships include Nicolas Rojas Varela and Engelbert Mephu Nguifo on data stream analysis projects.

• Data science
• Machine learning
• Artificial intelligence
• +10 more

Dr. Xiaobai (Bob) Li is a Professor in the Department of Operations and Information Systems at the Manning School of Business, University of Massachusetts Lowell. His work bridges data science, machine learning, and business analytics with specialized focus on data privacy and information economics. Ph.D. in Management Science, University of South Carolina MBA, University of New Hampshire BS in Civil Engineering, Chongqing University Research Interests span data science, machine learning, business analytics, and data privacy. His work explores Privacy-preserving analytics Healthcare data anonymization Information economics Optimization techniques Big Data applications Text analytics with particular emphasis on balancing data utility and privacy protection. Scientific Awards include: Manning Research Excellence Award (2023) Best Associate Editor Award (2021) Best Paper Awards at NEAIS, WITS, and ICIS Teaching Excellence Awards Grants from NIH and NSF supported his work on Consumer Electronic Privacy Protection and New Technology to Preserve Patient Privacy . He has served as Associate Editor for journals like Information Systems Research and Decision Support Systems .

Ayush BhartiView profile

Research Fellow

• Probabilistic Machine Learning
• Bayesian Statistics
• Simulation-based Inference
• +3 more

Ayush Bharti is an Academy Research Fellow (Research Fellow) in the Department of Computer Science at Aalto University, Finland. His primary affiliations include the Probabilistic Machine Learning research group and the Academy Professorship led by Samuel Kaski . His research centers on probabilistic machine learning with dual focus areas: advancing simulation-based inference methodologies and applying statistical techniques to stochastic radio channel modeling. Key interests include Bayesian statistics, robustness under model misspecification, handling missing data, and developing efficient approximate Bayesian computation frameworks. His work bridges theoretical machine learning with practical wireless communication challenges. Analysis of his 15 most recent publications (2021-2025) reveals a consistent trajectory in simulation-based inference innovation, featuring neural processes, diffusion models, and multilevel architectures. Approximately 60% of his work targets general statistical challenges (e.g., robustness, cost-awareness), while 40% addresses radio channel modeling applications. Notable trends include integration of domain expertise in inference loops and development of context-aware optimization techniques. No information is available regarding student supervision or research grants. Bharti operates within Aalto University's Probabilistic Machine Learning ecosystem, contributing to the Academy Professorship project under Samuel Kaski. This collaborative environment focuses on developing scalable inference algorithms for complex real-world systems, with particular emphasis on uncertainty quantification in simulation-driven scientific discovery.

S. Jon ChapmanView profile

Professor

• Industrial mathematics
• Mathematical modelling
• Matched asymptotic expansions
• +16 more

Professor S. Jon Chapman is a faculty member at the Mathematical Institute, University of Oxford, holding the position of Professor of Mathematics and its Applications. He is affiliated with the Oxford Centre for Industrial and Applied Mathematics research group. His educational background includes a DPhil, MA, and BA. Research interests span diverse areas of applied mathematics and scientific modeling: Industrial mathematics and mathematical modeling Partial differential equations and asymptotic methods Fluid dynamics and turbulence theory Biophysical applications including tumor growth and tissue modeling Electromagnetic scattering and superconductivity Materials science and energy systems Publication analysis reveals two primary trends: Recent work (2025) focuses on electrochemical systems (battery modeling, gas-induced bulging) and biological applications (organoid models). Earlier influential publications established expertise in pattern formation, fluid dynamics (ship waves, spiral waves), and transport phenomena in biological systems. Mathematical techniques consistently feature multiscale analysis, asymptotic methods, and nonlinear modeling. Awards and honors recognizing scholarly contributions: Naylor Prize (2015) Julian Cole Prize (2002) Whitehead Prize (1998) Richard C. DiPrima Prize (1994) Johnson Mathematical Prize (1992) No information is available regarding student advising, grants, or laboratory affiliations.

• Cognitive Psychology
• Experimental Psychology
• Applied Data Science
• +4 more

Baptist Liefooghe is an Associate Professor in the Social, Health and Organisational Psychology department at Utrecht University's Faculty of Social and Behavioural Sciences. His research bridges cognitive psychology with human-centered artificial intelligence, focusing on how humans interact with and trust AI systems while examining fundamental cognitive processes like task switching and instruction-based learning. His primary research interests include Cognitive Psychology , Experimental Psychology , Applied Data Science , and Human-centered Artificial Intelligence . Dr. Liefooghe investigates how humans process instructions, switch between tasks, and develop trust in artificial intelligence systems. His work demonstrates how cognitive processes influence human-AI interactions, particularly examining how people judge AI morality and competence, how social presence affects trust in algorithms, and how cognitive control mechanisms operate during task switching. Analysis of his recent publications reveals a strong trend toward interdisciplinary research combining cognitive psychology with AI applications. His work spans from fundamental cognitive processes (task switching, instruction implementation) to applied human-AI interaction research (trust in AI, facial perception of artificial agents, health behavior change through AI). This integration of basic cognitive research with practical AI applications represents a distinctive feature of his scholarly contributions. Dr. Liefooghe has published extensively in top-tier journals including Journal of Cognition, Cognitive Research, Cortex, and Frontiers in Artificial Intelligence. His research has garnered significant attention in the field, with multiple highly cited publications examining the cognitive mechanisms underlying human interaction with technology. His methodological expertise includes experimental design, statistical analysis using R programming language, and cognitive modeling. This technical proficiency enables him to conduct rigorous investigations into complex cognitive phenomena while applying these insights to real-world human-AI interaction scenarios.

René VidalView profile

Professor

• Machine Learning
• Computer Vision
• Biomedical Data Science
• +3 more

René Vidal is the Rachleff & Penn Integrates Knowledge (PIK) University Professor at the University of Pennsylvania, with appointments in the Departments of Electrical and Systems Engineering, Radiology, Computer and Information Science, and Statistics and Data Science. He also serves as Director of the Center for Innovation in Data Engineering and Science (IDEAS) and the NSF-Simons Collaboration on the Mathematical Foundations of Deep Learning (THEORINET). A dual faculty member at Johns Hopkins University in Biomedical Engineering, Computer Science, and other departments, Vidal is an Amazon Scholar and Affiliated Chief Scientist at NORCE. PhD, Electrical Engineering and Computer Sciences, University of California, Berkeley (2003) M.S., Electrical Engineering, University of California, Berkeley (2000) B.S. (valedictorian), Electrical Engineering, Pontificia Universidad Catolica de Chile (1997) His research spans the mathematical foundations of deep learning, focusing on non-convex optimization , learning dynamics , and overparametrization . Key contributions include Sparse Subspace Clustering , Kernel GPCA , and Low-Rank Matrix Factorization , with applications in autism diagnosis , robotic surgery , and cardiac imaging . Recent work explores continual learning , adversarial robustness , and trustworthy AI in biomedical contexts. His 15 most recent publications highlight advances in medical imaging , language models , and robust computer vision , spanning topics from chest X-ray analysis to motor imitation tasks in autism . Articles like Geometric Analysis of Nonlinear Manifold Clustering underscore his theoretical contributions, while works on KDA: Knowledge-Distilled Attacker and Conformal Information Pursuit address practical AI safety and interpretability. Scientific accolades include: 2022 ACM Fellow 2021 IEEE McCluskey Technical Achievement Award 2017 Jean D’Alembert Fellowship 2012 J.K. Aggarwal Prize 2009 Sloan Research Fellow 2005 NSF CAREER Award His lab mentors 11 current PhD students across Johns Hopkins and University of Pennsylvania , with alumni contributing to institutions like Meta , Amazon , and GE Research . Vidal’s interdisciplinary work bridges mathematics , engineering , and healthcare , supported by grants from the DoD , NSF , and ONR .