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Xian YuView profile

Assistant Professor

• Hepatology
• Epidemiology
• Health Services Research
• +3 more

Dr. Xian Yu is an Assistant Professor in the Department of Medicine-Health Services Research at Baylor College of Medicine (Houston, Texas). With a PhD in Statistics from The University of Texas at Dallas and master's degrees in Statistics (University of Alaska Fairbanks) and Economics (Texas A&M University), Dr. Yu specializes in epidemiological modeling and health outcomes research. His research focuses on: Developing risk-prediction models for liver diseases (e.g., hepatocellular carcinoma, cirrhosis) Analyzing metabolic dysfunction-associated steatotic liver disease (MASLD) progression Investigating therapeutic outcomes of diabetes and antiviral medications Studying health disparities and social determinants in chronic disease management Recent publications (2020-2024) demonstrate strong emphasis on: Serum biomarker validation for liver cancer risk stratification Comparative effectiveness of GLP-1 agonists in metabolic liver diseases Health services research within Veterans Affairs populations Social determinants of health in immigrant and pediatric care No information about research grants, supervised students, laboratory leadership, or scientific awards was available in the source materials.

• Data Mining
• Pattern Mining
• Database Systems
• +3 more

Arnaud Giacometti is a Professor at the University of Tours, affiliated with the Faculty of Science and Technology and the Computer Science Department. He is also the President of the University and leads research at the Tours Fundamental and Applied Computer Science Laboratory (LIFAT). Research Interests: His work focuses on data mining, pattern discovery, and knowledge graph analysis. He has developed innovative methods for frequent pattern mining, sequential pattern sampling, and constraint-based clustering, emphasizing efficiency and scalability in large datasets. Recent Publications: His research trends include trie-based sampling, reservoir pattern sampling in data streams, and semantic web-driven comparison tables. Key subfields span distributed databases, knowledge graph representativeness, and memory-aware parallelization for big data. Scientific Roles: As President of the University of Tours, he oversees institutional leadership while maintaining active research contributions. His laboratory (LIFAT) serves as a hub for fundamental and applied computer science research.

Jeffrey KwanView profile

Lecturer

• Probability Theory
• Stochastic Processes
• Statistical Inference
• +3 more

Dr. Jeffrey Kwan is a Lecturer in Statistics at the School of Mathematics and Statistics, University of New South Wales (UNSW Sydney). His research focuses on probability theory and stochastic processes, particularly Hawkes processes and their asymptotic behavior. He completed his PhD in 2023, specializing in ergodicity and applications for non-stationary and non-exponential Hawkes models. PhD (UNSW Sydney, 2023): Ergodicity of Hawkes processes Jeffrey's research spans probability theory, stochastic processes, financial data modeling, and data-driven legal analysis. He has published extensively on Hawkes processes, including parametric inference, ergodicity, and applications in terrorism modeling, crime analysis, and public policy. His scientific awards include the Excellence in Postgraduate Research (Statistical Society of Australia, NSW Branch, 2022), University Medal (UNSW Sydney, 2017), and multiple Dean's List recognitions. He has taught undergraduate and postgraduate courses in statistics, probability, and data science, including roles in the Business School and Faculty of Science. 2025: Chair, School of Mathematics and Statistics EDI Committee 2024: Secretary, Statistical Society of Australia (NSW Branch) 2023: Statistical Consultant at Stats Central (Mark Wainwright Analytical Centre)

• Bayesian time series analysis
• forecasting
• finance
• +4 more

Dr Kostas Triantafyllopoulos serves as Senior Lecturer in Statistics and MSc Statistics Course Director at the University of Sheffield's School of Mathematical and Physical Sciences. He joined Sheffield in February 2005 after academic positions at Newcastle University (2002-2004) and a post-doctoral appointment at Bristol University (2001-2002). His educational background includes: BSc in Mathematics from Aristotle University of Thessaloniki (1996) MSc in Quality Management from Napier University of Edinburgh (1998) PhD in Statistics from the University of Warwick (2002) Triantafyllopoulos specializes in Bayesian time series analysis and forecasting , with significant applications across finance, bioinformatics, medicine, signal processing, and quality control. His methodological innovations include multivariate stochastic volatility modeling using Wishart processes, real-time covariance estimation for state-space models, and particle filter applications for count time series. Recent work (2019-2022) demonstrates strong focus on medical decision making through flexible survival models and pandemic impact analysis using hybrid Bayesian-neural network approaches for shipping economics. Publication trends reveal a strategic evolution from foundational Bayesian state-space work (2006-2012) toward high-impact interdisciplinary applications. His 30+ journal articles show consistent emphasis on solving autocorrelation challenges in control charts, developing non-parametric monitoring systems for environmental data, and creating flexible parametric frameworks for survival data extrapolation in healthcare contexts. Research funding includes Nuffield Foundation support as Principal Investigator for "The multivariate general steady model" and EPSRC collaboration as Co-investigator on "Simulation Tools for Automated and Robust Manufacturing". He actively contributes to the Mathematical and Statistical Modelling research group, with current work bridging theoretical statistics and real-world industry/healthcare challenges.

• Reinforcement Learning
• Machine Learning Theory
• Algorithms
• +4 more

Ruosong Wang is an Assistant Professor in the Machine Learning Department within the School of Computer Science at Carnegie Mellon University. His research focuses on theoretical aspects of machine learning, particularly reinforcement learning, bandit problems, and algorithmic foundations. Wang's research interests span several key areas in theoretical machine learning. He has made significant contributions to reinforcement learning theory, particularly in understanding sample complexity, horizon dependence, and function approximation. His work on bandit algorithms explores variance-aware and sparse linear settings, while his research on subspace embeddings contributes to fundamental algorithmic techniques. Wang's approach combines rigorous theoretical analysis with practical implications for learning algorithms. His publication record shows a strong focus on theoretical guarantees and tight bounds for various learning problems. Wang's research demonstrates a progression from foundational algorithmic work to increasingly sophisticated analyses of reinforcement learning systems. His papers often address fundamental questions about the limits and possibilities of learning algorithms in complex environments. Wang has published extensively at top-tier machine learning and theoretical computer science venues including ICML, NeurIPS, ICLR, and STOC. His collaborative work with leading researchers in the field demonstrates his integration into the theoretical machine learning community. As an advisor and researcher, Wang contributes to advancing the theoretical foundations of machine learning, with particular emphasis on understanding the fundamental limits and possibilities of learning algorithms in sequential decision-making contexts.

• Applied Statistics
• Statistical Analysis
• Fuzzy Logic
• +1 more

Zeynep GÖKKUŞ is a Lecturer at Kastamonu University , specifically within the Computer Programming Program of the Kastamonu Vocational School . She has held this full-time position since 2023, previously working at the same institution's Computer Technology and Programming Department in the İnebolu Vocational School from 2009 to 2021. Her academic career has been consistently focused on Applied Statistics and Statistical Analysis , with additional expertise in Fuzzy Logic and Biostatistics . PhD in Statistics from Ondokuz Mayıs University (2018) MSc in Mathematics from Kafkas University (2008) BSc in Mathematics from Atatürk University (2005) Research Interests : Her work spans Statistical Modeling for agricultural and aquacultural applications, including Fuzzy ANOVA , Cox Regression , and Group Sequential Testing . She has developed innovative approaches in Multi-Criteria Decision Making for environmental assessments and contributed to Nanofiber Synthesis and heavy metal removal studies. Article Trends : Her publications (2012–2025) show increasing focus on Fuzzy Logic integration with traditional statistical methods (ANOVA, AAS), alongside applications in Aquaculture Survival Analysis and Environmental Remediation . Recent works emphasize Circular Intuitionistic Fuzzy Control Charts and Geospatial Agricultural Quality Rankings . Scientific Impact : According to Google Scholar metrics, she has 10 citations and an h-index of 2 . Her research has been published in journals like Aquaculture (SCI) and Turk Tarım ve Doğa Bilimleri Dergisi (TR Dizin), with conference presentations at international events including INFUS 2023–2024 and the International Sport Sciences Congress.

Peter MicalosView profile

Senior Lecturer

• Anatomy
• Physiology
• Pain Research
• +7 more

Dr. Peter Micalos is a Senior Lecturer in Anatomy and Physiology at Charles Sturt University's School of Dentistry and Medical Sciences, based at the Port Macquarie campus. With extensive experience in teaching anatomy, pathophysiology, and exercise science, he applies engaging pedagogical approaches across various learning levels. As the School Honours Advisor for the B.Sci (Honours) program, he plays a significant role in academic guidance at the institution. Dr. Micalos's educational background includes a PhD in Neurosensation, Pain, and Exercise, completed between 2005-2012, with research focused on "The effect of aerobic exercise on pain and neurosensory modulation in healthy participants and with chronic pain disorder." His teaching philosophy emphasizes analytical reasoning through observation and interpretation, utilizing scaffolded and sequential learning strategies that foster lifelong learning. His research spans diverse areas of human health including pain mechanisms, exercise science, cancer, nutrition, the human microbiome, and Parkinson's disease. Dr. Micalos has secured over $80,000 in external research funding and has published more than 20 scientific articles, literature reviews, an edited book, and a book chapter, accumulating over 500 citations in Google Scholar with an h-index of 9. His recent publications demonstrate a strong focus on rice bran arabinoxylan compounds in cancer treatment, Parkinson's disease in regional Australia, and prehospital emergency care. Dr. Micalos has received multiple awards including the CRGT Completion Award (2012) and School of Biomedical Sciences Teaching Awards (2017, 2019). His significant research contributions include collaborations with Westmead Hospital's Department of Radiology, the Brain Dynamics Centre at CSU, the University of Aalborg in Denmark, and the University of Technology Sydney. His work on pain measurement employs sophisticated techniques including nociceptive withdrawal reflex, sensory perceptual ratings, somatosensory cortical evoked potentials, and functional magnetic resonance imaging.

Karen AzariView profile

Assistant Professor

• Cryptography
• Security Proofs
• Secure Group Messaging
• +4 more

Karen Azari is an Assistant Professor at the University of Vienna, where she is a member of the Research Group Theory and Applications of Algorithms within the Faculty of Computer Science. Her office is located at Währinger Straße 29, 1090 Wien, and she can be reached at karen.azari@univie.ac.at. Dr. Azari's academic background includes: PhD studies at the Institute of Science and Technology Austria (ISTA), advised by Krzysztof Pietrzak Postdoctoral work at ETH Zurich under the guidance of Dennis Hofheinz Her primary research area is cryptography, specializing in formal security definitions and security proofs of cryptographic protocols. She focuses on secure group messaging, proof systems for decentralized systems, and the foundations of resource-restricted cryptography. Her work combines mathematical rigor with practical implementation considerations, developing protocols with provable security guarantees that address real-world security challenges. Dr. Azari approaches cryptographic problems with precision, ensuring her contributions advance both theoretical foundations and practical applications of cryptographic protocols. Analysis of Dr. Azari's recent publications reveals a consistent focus on strengthening the theoretical underpinnings of cryptographic protocols while addressing practical implementation challenges. Her work spans multiple subfields including secure multi-party computation, group key agreement protocols, and cryptographic primitives based on lattice problems. A distinctive trend in her research is the emphasis on adaptive security models, which provide stronger guarantees against sophisticated adversaries who can adapt their attacks based on observed protocol executions. Her scientific contributions have been recognized with notable honors: Best Student Paper award at ACNS 2025 for "Tighter provable security for TreeKEM" As an educator and mentor, Dr. Azari currently supervises PhD student Maximilian von Consbruch (since 2025). She teaches core computer science courses including "Algorithms and Data Structures" and "Foundations of Cryptography" in the Spring semester of 2025. Her service to the academic community includes participation in prestigious program committees for major cryptography conferences including CFAIL 2025, ASIACRYPT 2024, CFAIL 2024, CRYPTO 2024, EUROCRYPT 2023, and TCC 2022. Dr. Azari is a principal investigator within the Theory and Applications of Algorithms research group at the University of Vienna's Faculty of Computer Science, where she leads a research team focused on developing theoretically sound yet practically implementable cryptographic solutions for emerging security challenges in communication systems and decentralized applications.

Kevin LiuView profile

Associate Professor

• Phylogenetics
• Computational Biology
• Genomics
• +3 more

Kevin Liu is an Associate Professor at Michigan State University, affiliated with the Genetics & Genome Sciences Program and the Ecology, Evolution & Behavior Program . His research focuses on computational biology, phylogenetics, and genomics, particularly developing statistical methods for evolutionary analysis. Institution: Michigan State University Programs: Genetics & Genome Sciences Program, Ecology, Evolution & Behavior Program Kevin Liu’s work centers on advancing phylogenetic reconstruction techniques, addressing challenges in species tree estimation, multiple sequence alignment, and handling non-tree-like evolutionary histories. He employs coalescent-based models, hidden Markov models, and resampling methods to improve accuracy in genomic data analysis. Recent trends in his research include developing scalable algorithms for phylogenetic network inference, integrating statistical resampling techniques, and exploring the impact of alignment and tree estimation errors on evolutionary studies. His publications highlight collaborations in computational method development for large-scale genomic datasets.

Philip ClemsonView profile

Research Fellow

• Time Series Analysis
• Complex Systems
• Chaotic Systems
• +7 more

Dr. Philip Clemson is a Senior Research Associate in the Department of Physics at Lancaster University, specializing in the time series analysis of complex systems. His work focuses on chaotic and stochastic data, particularly from non-autonomous (time-dependent) systems, with significant applications in biomedical physics and living systems. He has extensive experience in industry, having developed algorithms for medical devices and wearable technology. His research also encompasses numerical Bayesian statistical techniques using Markov chain Monte Carlo and sequential Monte Carlo methods. Current projects include phase coherence analysis for autonomic neuropathy diagnosis and network dynamics-based standards for endothelial health, reflecting his interdisciplinary approach between physics and biomedical applications. 2024: Phase coherence in autonomic neuropathy diagnosis 2023-2024: Network dynamics for endothelial health standards His recent publications demonstrate expertise in time-localized analysis, cardiovascular dynamics, and chronotaxic systems. Dr. Clemson is part of the Nonlinear and Biomedical Physics research group within the Physics Department.

Michael N. ShadlenView profile

Professor

• Neuroscience
• Decision Making
• Computational Neuroscience
• +7 more

Michael N. Shadlen, MD, PhD , is a Professor of Neuroscience at Columbia University’s Vagelos College of Physicians and Surgeons , a Principal Investigator at the Zuckerman Institute, and an HHMI Investigator since 2000. His work bridges neurophysiology and computational modeling to study decision-making mechanisms in the parietal cortex. Key Affiliations: Vagelos College of Physicians and Surgeons, Columbia University Mortimer B. Zuckerman Mind Brain Behavior Institute Howard Hughes Medical Institute (HHMI) The Kavli Institute for Brain Science Grossman Center for the Statistics of Mind Research Focus: Dr. Shadlen investigates how neural circuits integrate sensory evidence , compute costs/benefits , and enable temporal anticipation . His studies link higher cognition (e.g., language, consciousness) to fundamental neural building blocks, aiming to address disorders like schizophrenia , autism , and dementia . Scientific Contributions: 2023: National Academy of Sciences Fellow 2017: Karl Spencer Lashley Award (American Philosophical Society) 2015: AAAS Fellow 2014: National Academy of Medicine Member 2012: Golden Brain Award (Minerva Foundation) Recent Work Trends: His 2025 Nature Methods paper introduces DREDge , a tool for motion correction in neural recordings . Earlier studies (2016–2023) explore evidence integration , confidence dynamics , and time-sensitive neural processes , emphasizing computational parallels in cognition.

Ada KedingView profile

Lecturer

• Clinical Trials
• Orthopaedic Statistics
• Medical Statistics
• +3 more

Ada Keding is a Lecturer and Researcher at the York Trials Unit , University of York, since 2012. She specializes in statistical analysis for orthopaedic clinical trials and supports trial design across diverse health domains, including surgical interventions and public health. Her educational background includes a BSc in Psychology (York), MSc in Human-Computer Interaction (UCL), and MSc in Statistics (Sheffield). Research Roles: Deputy Chair (Dissertation Development Committee), Deputy Chair (Library Committee), Member (Departmental Research Governance Committee) Key Collaborations: NIHR Health Technology Assessment Programme, TB & Tobacco Consortium, DiaDeM Global Health Research Group Research Focus: Ada’s work centers on randomized controlled trials (RCTs), particularly longitudinal studies in orthopaedics. She has contributed to trials like ACTIVE , PROFHER , UK FROST , and DiaDeM , addressing clinical effectiveness, cost-utility, and biases in trial design. Her methodological expertise includes Bayesian sequential models , patient-reported outcomes , and health economics . Publication Trends: Her articles span orthopaedic interventions , diabetes-depression comorbidity , tobacco cessation , and public health strategies . Recurring themes include comparative effectiveness, economic evaluations, and statistical methodology for pragmatic trials.

• Evolutionary Algorithms
• Optimization
• Multi-Objective Optimization
• +3 more

Ofer M. Shir is a faculty member affiliated with Tel-Hai College and MIGAL - Galilee Research Institute in Israel. His primary research focuses on evolutionary algorithms, multi-objective optimization, and their applications in quantum control, machine learning, and computational science. He has collaborated extensively with institutions and researchers globally, contributing to advancements in optimization theory and practical problem-solving through evolutionary computation. Shir's work spans theoretical foundations, such as covariance-Hessian relations in evolution strategies, to applied domains like quantum control experiments and algorithmic-guided discovery of viral epitopes. He has developed and benchmarked algorithms like the CMA-ES and SMS-EMOA, emphasizing their performance in complex, real-world scenarios. His contributions also include methodologies for sequential experimentation and improving model accuracy through techniques like batch normalization. Shir has published extensively in top-tier venues such as IEEE Transactions on Evolutionary Computation, Genetic Programming and Evolvable Machines, and the GECCO conference series. His research bridges theoretical computer science with practical applications, impacting fields from bioinformatics to engineering.

• Cognitive Psychology
• Human Memory
• Reasoning
• +4 more

John Dunn is an Adjunct Professor at the School of Psychological Science, The University of Western Australia (UWA). He holds a PhD and BA (Hons) in Psychology from UWA (1985 and 1976, respectively). Previously, he served as a Professor at the University of Adelaide (2005–2016) and held roles including Interim Dean at Murdoch University (2017–2018) and a part-time Professorship at Edith Cowan University (2018–2021). His research focuses on cognitive psychology, human memory, reasoning, decision making, and methodological issues in experimental psychology. He has led or co-led nine grants, including projects on reasoning models and eyewitness identification. His recent work emphasizes statistical methodologies and interdisciplinary applications. **Research Interests**: His expertise spans cognitive processes such as recognition memory, decision-making under uncertainty, and statistical modeling. He has contributed to debates on measurement, Bayesian analysis, and experimental design. His studies often bridge theoretical frameworks with practical applications, such as improving forensic procedures. **Grants & Collaborations**: Dunn has secured funding for projects like 'State-trace analysis: Theory and application' (2013–2019) and 'Uncovering the processes underlying human reasoning' (2017–2018). His collaborations span institutions globally, addressing topics like eyewitness identification and dual-process theories. **Recognition**: While no explicit awards are listed, his sustained contributions to cognitive psychology and interdisciplinary research reflect significant scholarly impact.

Fredrik Lundvall WollbraatenView profile

Research Fellow

• Bayesian Statistics
• Computational Statistics
• Data Science
• +3 more

Fredrik Lundvall Wollbraaten is a Research Fellow at the University of Oslo, affiliated with the Department of Mathematics within the Faculty of Mathematics and Natural Sciences. His PhD research, supervised by Professor Geir Olve Storvik and in collaboration with Professor Ingrid Glad and Associate Professor Morten Stakkeland (ABB), focuses on sequential learning and decision-making from maritime sensor data, including failure detection and maintenance rule optimization. His work combines methodological development in model building, inferential tools, and computational methods. Education: Master's in Statistics from the University of Oslo (2020), Bachelor's in Mathematics from NTNU (2018). Prior to his PhD, he worked at RagnaRock Geo (AI startup), SpareBank 1 Markets (equity research analyst), and Nordea Markets (Data Scientist). His academic interests span applied Bayesian methods, parallelized MCMC simulations, and statistical learning techniques. He is part of the Big Insight group and contributes to projects on statistical and machine learning methods for sensor data analysis. Fredrik is also actively involved in the Oslo Sports Student Association (OSI).