جست‌وجوی استاد و پژوهشگر

• Muscle Physiology
• Muscular Dystrophy
• Exercise Physiology
• +۳ مورد دیگر

Professor Alan Hayes serves as Acting Director of the Australian Institute for Musculoskeletal Sciences (AIMSS) within Victoria University's College of Sport, Health & Engineering. With over 120 publications and an h-index of 44, his academic career spans more than 25 years since joining Victoria University in 1997. His research has significantly advanced understanding in muscle physiology, particularly regarding muscular dystrophy and age-related muscle wasting conditions. Hayes completed his academic foundation at The University of Melbourne, earning both his BSc(Hons) in 1991 and PhD in Physiology in 1995. His doctoral work pioneered research on exercise in models of Duchenne muscular dystrophy, demonstrating beneficial effects even in older animals. In 2021, he furthered his educational credentials with a Graduate Certificate in Tertiary Education from Victoria University. Professor Hayes' research program centers on the principle that 'Muscle Mass Matters,' investigating mechanisms of muscle wasting and potential therapeutic interventions. His early work established foundational knowledge on beta-agonists like clenbuterol for muscular dystrophy treatment. He later published seminal papers on protein supplementation timing for resistance training outcomes, winning a Victorian State Government award. His discovery that mitochondria contribute significantly to muscular dystrophy phenotype has driven numerous clinical trials. Current research focuses on drug repurposing for muscular dystrophy, age-related loss of bone and muscle mass, and developing physiological models for muscle wasting conditions. Analysis of his recent publications reveals a sophisticated research trajectory spanning basic science to clinical applications. His work demonstrates strong translational focus, from laboratory models of muscular dystrophy to human studies on sarcopenia and osteosarcopenia. The publications reflect interdisciplinary collaboration across physiology, endocrinology, geriatrics, and pharmacology, with consistent emphasis on therapeutic interventions including exercise, nutrition, and pharmacological approaches. Victorian State Government award for Applied Research Vice Chancellor's Citation for Research Excellence in Research Supervision (2011) Hayes actively supervises Masters Research and PhD students across Biomedical and Clinical Sciences (FOR 32), Health Sciences (FOR 42), Biological Sciences (FOR 31), and Education (FOR 39). He designed a unique three-way research partnership between The University of Melbourne, Victoria University, and Western Health, co-locating basic science with clinical trials at Sunshine Hospital. As a regular reviewer for Australian and International grants and over 30 journals, he contributes significantly to research evaluation. He also serves as Victoria and Tasmania council representative for the Australian and New Zealand Society for Sarcopenia and Frailty Research. Professor Hayes leads the Biomedical and Life Sciences program at Victoria University and previously served as Head of the Exercise Metabolism Unit. He played a pivotal role in establishing the Western Centre for Health Research and Education and currently serves as Acting Director of AIMSS. His work extends to curriculum development, having contributed to implementing core concepts of physiology across Australian educational institutions.

Zihao Fuمشاهده پروفایل

مدرس

• Natural Language Processing
• Large Language Models
• Text Generation
• +۵ مورد دیگر

Zihao Fu is an Affiliated Lecturer at the University of Cambridge within the Faculty of Modern and Medieval Languages and Linguistics, specifically at the Language Technology Lab. He also serves as a Research Assistant Professor at The Chinese University of Hong Kong (CUHK). His research spans Natural Language Processing , Large Language Models , and Biomedical Applications with a focus on Parameter-Efficient Fine-Tuning and Algorithmic Fairness . Previously, he was a Postdoctoral Researcher at both University of Cambridge (2021-2024) and University of Oxford (2024-present). Dr. Fu's academic background includes a Ph.D. in Systems Engineering from The Chinese University of Hong Kong (2017-2021), followed by postdoctoral training at Cambridge and Oxford. His technical expertise integrates Computational Linguistics , Machine Learning , and Algorithmic Fairness to address challenges in Biomedical Named Entity Recognition and Knowledge Base-to-Text Generation . His recent publications (2020-2025) demonstrate a trajectory from foundational work in Text Generation and KB-to-Text Systems to cutting-edge research in Biomedical LLMs and Algorithmic Fairness Toolkits . Notable contributions include the BAND Biomedical Alert Dataset (AAAI 2024) and theoretical studies on LLM Watermarking and Parameter Stability . While no scientific awards are explicitly listed, his service as a Reviewer for top conferences (AAAI, ACL, NeurIPS) indicates field recognition. As an educator, he has taught courses like Computational Linguistics at Cambridge (2022-2023) and Advanced Financial Infrastructure at CUHK. His technical projects include open-source tools like StreamTask (parallel processing framework) and CSTL (C++ STL wrapper for Python), reflecting practical implementation skills alongside theoretical contributions.

• Computer Vision
• Machine Learning
• 3D Reconstruction
• +۵ مورد دیگر

Andreas Geiger is Professor and Head of the Department of Computer Science at the University of Tübingen, Germany, where he leads the Autonomous Vision Group (AVG) within CyberValley. He serves as core faculty at the Tübingen AI Center, Principal Investigator in the Excellence Cluster ML in Science and CRC Robust Vision, and ELLIS fellow coordinating the ELLIS PhD program. Prof. Geiger's research centers on machine learning for computer vision, natural language processing, and robotics, with emphasis on 2D/3D scene representations, geometry reconstruction, and robust model development. His group pioneers neural rendering techniques like Gaussian splatting while addressing real-world challenges in autonomous driving, VR/AR, and scientific document analysis through discriminative and generative approaches. Recent publications (2025) demonstrate strong focus on 3D scene understanding using Gaussian splatting, autonomous driving simulation with world models like ReSim and Vista, and generative methods for controllable 3D scenes. The group maintains Scholar Inbox for personalized academic recommendations while advancing reinforcement learning for self-driving (CaRL) and robust perception evaluation (EMPERROR). Major recognitions include: Best Paper Awards at CVPR 2024/2021, 3DV 2017/2015, GCPR 2015 Sage 10-Year Impact Award 2024 and Longuet-Higgins Prize 2022 ERC Starting Grant 2019 and IEEE PAMI Young Researcher Award 2018 nuPlan Challenge Winner 2023 and German Pattern Recognition Prize 2017 Prof. Geiger actively supervises PhD students including Reiser (3D view synthesis), Baur (Lidar scene flow), Jaeger (reinforcement learning), and Liao (urban scene transfer). His group secures substantial funding through the ERC Starting Grant, Vector Stiftung's MINT Innovation Program, ML in Science Cluster, and CRC Robust Vision, while collaborating with industry via Waymo and nuPlan challenges. The Autonomous Vision Group operates from CyberValley's new campus building with a diverse international team (12+ nationalities). They maintain Scholar Inbox for academic discovery and organize regular technical retreats and unique team-building activities like deep cave expeditions, fostering innovation in 3D vision and autonomous systems.

Felix Abramovichمشاهده پروفایل

استاد

• Statistical Learning
• High-dimensional Inference
• Sparsity
• +۵ مورد دیگر

Felix Abramovich is a Professor in the Department of Statistics and Operations Research at Tel Aviv University. His research focuses on theoretical foundations of statistical and machine learning, specializing in high-dimensional inference, sparsity, model selection, and nonparametric estimation techniques. Professor Abramovich's work spans statistical learning theory, classification methods, wavelet-based estimation, and inverse problems. His research develops minimax-optimal procedures for sparse high-dimensional data and explores the interplay between Bayesian methods and frequentist optimality. Key themes include adaptive estimation under sparsity constraints, theoretical analysis of classification algorithms, and wavelet applications in nonparametric statistics. Analysis of Abramovich's recent publications reveals a strong emphasis on classification methodologies, high-dimensional regression, and deep learning theory. His work consistently develops minimax-optimal procedures, examines sparsity patterns in complex data structures, and bridges Bayesian and frequentist frameworks. The research demonstrates increasing focus on modern machine learning challenges including neural network theory and multiclass classification. Professor Abramovich actively recruits students and post-doctoral researchers with strong mathematical statistics backgrounds to work on fundamental problems in statistical learning theory and high-dimensional inference.

Gerald Grabherrمشاهده پروفایل

دانشیار

• Roman Archaeology
• Provincial Roman Archaeology
• Roman Road Systems
• +۷ مورد دیگر

Associate Professor Gerald Grabherr is a distinguished scholar of Roman Provincial Archaeology at the Institute of Archaeology, Leopold-Franzens University of Innsbruck. With over two decades of academic experience, he has established himself as a leading expert in Roman road systems, particularly the Via Claudia Augusta, and Alpine archaeology. His educational background includes: 1986-1994: Regular studies in Classical Archaeology and Art History 1994-2001: Doctoral studies in Classical Archaeology 2007: Habilitation Professor Grabherr's research focuses on Roman settlement structures in Alpine regions, military-civilian transitions in Roman frontier zones, and provincial Roman material culture. His work combines traditional archaeological methods with interdisciplinary approaches, examining how Roman infrastructure shaped regional development and cultural exchange across the Alpine territories. His recent publications demonstrate a growing focus on late antique transformations of Roman settlements, particularly fortified hilltop sites like Burgbichl in Irschen. His work bridges military history, religious studies, and material culture analysis, revealing complex patterns of continuity and change during the transition from Roman to post-Roman periods in the Alpine region. Among his notable recognitions: Prize of the City of Innsbruck for scientific research at the University of Innsbruck (2008) Professor Grabherr has secured significant research funding, including FWF projects, Autonomous Province of Bolzano grants, and an Erasmus+ Strategic Partnership. He has supervised numerous excavation projects across the Alpine region, from Tyrol to Carinthia, and collaborates extensively with international institutions in Portugal, Slovenia, and Italy. His current research team focuses on the IRSCHEN project (Interdisciplinary Resilience through Science and Cultural Heritage Education Network), investigating the late antique settlement on Burgbichl in Irschen, and conducting ongoing fieldwork at multiple Roman sites in the Eastern Alps.

Juan Antonio Fernández del Pozoمشاهده پروفایل

دانشیار

• Decision Analysis
• Intelligent Decision Support Systems
• Probabilistic Graphical Models
• +۱۰ مورد دیگر

Juan Antonio Fernández del Pozo is an Associate Professor of Statistics and Operations Research at the School of Computer Science, Technical University of Madrid (UPM), and a member of the Computational Intelligence Group. With over 60 publications and participation in more than 30 national and international research projects, he is a leading researcher in decision analysis and intelligent systems. His educational background includes: Doctorate in Computer Science from the School of Computer Science, UPM (Thesis: "Listas KBM2L para la síntesis de conocimiento en sistemas de ayuda a la decisión") His research focuses on probabilistic graphical models (Bayesian networks/influence diagrams) for decision support, evolutionary algorithm optimization, and machine learning for data streams with concept drift. Key application domains span Social Network Analysis, Air Traffic Management, structural health monitoring, Industry 4.0, and social service quality modeling in collaboration with Spanish Foundations. His work emphasizes knowledge acquisition in large decision tables, explanation synthesis, and sensitivity analysis. Publication trends reveal two phases: foundational work (2000-2013) on Bayesian networks and medical decision systems (e.g., neonatal jaundice modeling), and recent applied research (2016-2018) addressing environmental restoration, transportation scheduling, and social innovation networks. Scientific awards: No scientific awards were mentioned in the provided text. Advising and grants: Supervised 12+ Master's Degree projects Supervised 50+ final degree projects Participated in 30+ research projects Served on 18+ doctoral thesis evaluation panels Labs and teams: Active member of the Computational Intelligence Group at UPM

• Machine Learning
• Bioinformatics
• Pattern Recognition
• +۵ مورد دیگر

Riikka Huusari is a Visiting Professor at the Department of Computer Science at Aalto University. Her research focuses on advanced machine learning techniques, particularly in the domains of bioinformatics, pattern recognition, and computational biology. She has collaborated with Professor Juho Rousu and other researchers on various projects. Her recent work includes scalable methods for two-view learning, cross-view kernel transfer, and entangled kernel approaches that extend beyond traditional separability. Publications in venues such as Briefings in Bioinformatics, Machine Learning, and the Journal of Machine Learning Research highlight her contributions to these fields. Riikka's research applies mathematical and computational techniques to solve complex problems in drug discovery and multiview data analysis. She utilizes projection operators, sparse kernel methods, and low-rank decompositions to improve predictive modeling and algorithmic efficiency. Her work bridges theoretical machine learning with practical biomedical applications.

Tuan Leمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Text Mining
• +۳ مورد دیگر

Tuan Le is an Assistant Professor in the Department of Computer Science at New Mexico State University (NMSU), serving as Coordinator of the Master of Data Analytics Program. He holds a Ph.D. from the School of Information Systems at Singapore Management University (2017). His research focuses on data mining, machine learning, text mining, visualization, and graph mining, with applications in semantic analysis, anomaly detection, and interactive visualization techniques. Education: Ph.D., School of Information Systems, Singapore Management University (2017). Research Interests include advanced topic modeling, visualization of complex datasets, graph-based data analysis, and leveraging pre-trained language models for document structure learning. His work emphasizes practical applications in user preference modeling, multi-view anomaly detection, and semantic information retrieval. Notable awards include the 2024 Patricia Christmore Faculty Teaching Award nomination and the AAAI 2014 Honorable Mention for Outstanding Paper. He has also held postdoctoral roles at Carnegie Mellon University and visiting positions at Oakland University. Academic contributions include pioneering projects such as ContraVis (contrastive visual topic modeling) and SEMAFORE (semantic visualization with manifold regularization), reflecting his expertise in developing innovative tools for data analysis and visualization.

• structural optimisation
• computational mechanics
• high performance computing
• +۷ مورد دیگر

Associate Professor Vivien Challis is a leading academic in Applied and Computational Mathematics at Queensland University of Technology (QUT), affiliated with the Faculty of Science and the School of Mathematical Sciences . She earned her PhD in Mathematics from The University of Queensland in 2009 and has since built an international reputation in structural optimisation, computational mechanics, and high-performance computing. Her research focuses on piezoelectric metamaterials , topology optimization , and GPU-accelerated computational methods , with recent work advancing robotics applications and biomedical prosthetics. She leads the 2022 Australian Research Council Discovery Project DP220102759 on optimising piezoelectric materials for robotics and contributed to ARC projects DP240102104 and DP170104307. 2025: GridapTopOpt.jl (Julia toolbox for level set optimization) 2025: Wave power absorption by floating plates (piezoelectric energy harvesting) 2025: FF-PINNTO framework (neural network-based optimization) In 2024, she received the JH Michell Medal from ANZIAM, recognizing her contributions to industrial and applied mathematics. Her teaching includes first-year calculus (MXB105) and second-year computational mathematics (MXB226), and she directed QUT's Mathematics Summer School (2022-2024).

Gemma Moranمشاهده پروفایل

استادیار

• Bayesian Statistics
• Machine Learning
• Deep Generative Models
• +۴ مورد دیگر

Gemma Moran is an Assistant Professor in the Statistics Department at Rutgers University. Her office is located at Hill Center 475, 110 Frelinghuysen Road, Piscataway, NJ 08854. She teaches courses including MSDS597 Data Wrangling with Python, MSDS534 Statistical Learning for Data Science, and STAT588 Data Mining. Dr. Moran received her PhD in Statistics from the University of Pennsylvania, where she was advised by Edward George and Veronika Rockova. Prior to joining Rutgers, she was a postdoctoral researcher at the Columbia Data Science Institute working with David Blei. Dr. Moran's research focuses on developing flexible Bayesian models for analyzing high-dimensional data. Her recent work includes developing identifiable and interpretable deep generative models, particularly variational autoencoders, and creating improved tools for Bayesian model criticism. Her methodological contributions bridge statistics, machine learning, and data science, with applications spanning computer vision, biomedical imaging, and materials science. Analysis of Dr. Moran's publication record reveals a strong emphasis on Bayesian methodology, particularly in variable selection (spike-and-slab approaches), deep generative modeling, and model criticism. Her work consistently addresses the challenge of maintaining statistical rigor while developing practical machine learning solutions, with a growing focus on model interpretability and identifiability in complex models. Dr. Moran actively mentors students at multiple levels. Prospective PhD students are encouraged to apply to the Rutgers Statistics PhD program and mention her name. For Masters of Data Science students, she supervises capstone projects requiring independent revisions of group work using different statistical methods or datasets. She also encourages students interested in research to take her MSDS-534 Statistical Learning for Data Science course as preparation for research projects.

Anders Miltnerمشاهده پروفایل

استادیار

• Program Synthesis
• Automated Refactoring
• Automated Verification
• +۱ مورد دیگر

Anders Miltner is an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU), specializing in programming languages and automated reasoning. He holds a PhD in Computer Science from Princeton University (2020), an MA in Mathematics from the University of Pennsylvania (2013), and a BSE in Computer Science and Mathematics from the same institution (2013). His research focuses on program synthesis, automated refactoring, and functional programming, aiming to automate tedious aspects of programming to enhance developer productivity. Education: PhD, Computer Science, Princeton University, 2020 MA, Mathematics, University of Pennsylvania, 2013 BSE, Computer Science and Mathematics, University of Pennsylvania, 2013 Research Interests: Program synthesis, automated refactoring, automated verification, and functional programming. He explores how tools can automate programming tasks to allow developers to focus on core algorithms. Projects include Burst (program synthesis), Hanoi (data structure verification), and Type-Directed Refactorings (syntax-independent refactorings). Scientific Awards: NSERC Discovery (2023) NSERC Discovery Launch Supplement (2023) POPL Distinguished Paper Award (2022) PLDI Distinguished Paper Award (2020) Advising & Grants: Mentors PhD students Clement Wan and Ellenor Taghayor, and MSc/undergraduate students. Leads projects funded by NSERC grants and a Microsoft Research fellowship. Serves on conference committees for PLDI, POPL, and others. Labs & Teams: Collaborates on the Hanoi project (representation invariant generation) within the Tangent Lab, involving institutions like UCLA and UT Austin.

Sharanne Raidalمشاهده پروفایل

استاد

• Veterinary Medicine
• Equine Health
• Pharmacokinetics
• +۷ مورد دیگر

Sharanne Raidal is a Professor at Charles Sturt University, affiliated with the Faculty of Agricultural, Environmental and Veterinary Sciences and based at the Gulbali Research Institute. She is actively involved in equine veterinary research and serves as a Registered Supervisor, mentoring graduate research students. Externally, she holds the position of Assistant Chief Examiner at the Australian New Zealand College of Veterinary Sciences since July 2018, contributing to professional standards in veterinary education and assessment. Research Interests: Her work spans several critical areas in veterinary science, particularly focused on equine health. Key interests include pharmacokinetics of drugs such as meloxicam and salbutamol, gastrointestinal diseases (especially gastric ulcers in performance horses), neonatal foal care (including sepsis and diarrhea), respiratory physiology, and the impact of transport on horse welfare. Her recent studies leverage advanced techniques like metagenomics and fecal microbiota transplantation, reflecting a modern, interdisciplinary approach to animal health. The analysis of her recent publications reveals a consistent focus on improving clinical outcomes in horses through evidence-based interventions. Trends include biomarker discovery (e.g., nucleosomes in sepsis), microbiome modulation, non-invasive respiratory support, and stress reduction during transport. Her research bridges laboratory science with practical applications in equine management and veterinary practice. Scientific Engagement: Regular speaker at industry events such as the Southern Breeders Seminar and AgriFutures symposium. Contributor to media discussions on equine drug studies (e.g., CSU’s research on performance horse medications). Active participant in academic and teaching activities, including CSU Ed 2015. Advising and Grants: As a Registered Supervisor, Dr. Raidal mentors postgraduate research students. While specific grants are not listed, her sustained research output and leadership in collaborative studies suggest successful grant acquisition and project leadership in equine health and pharmacology. Laboratories and Research Teams: She is embedded in the Gulbali Research Institute, a hub for agricultural, environmental, and veterinary research at CSU. Her collaborations span national and international networks, particularly in studies involving equine transport, microbiota, and critical care, often working with multidisciplinary teams across institutions.

Brian Theyelمشاهده پروفایل

استادیار

• cellular neuroscience
• Neural circuits
• neuromodulation
• +۵ مورد دیگر

Brian Theyel, M.D., Ph.D. is an Assistant Professor of Psychiatry and Human Behavior at Brown University's Warren Alpert Medical School. His primary academic appointment is in the Department of Psychiatry and Human Behavior, where he conducts research on the cellular mechanisms underlying neuropsychiatric disorders and provides clinical services in the psychiatric emergency room at Butler Hospital. Dr. Theyel earned both his MD and PhD from the University of Chicago in 2012, completing a rigorous dual-degree program that prepared him for his career as a physician-scientist. His educational background reflects a strong foundation in both clinical medicine and basic neuroscience research. Dr. Theyel's research centers on understanding how unique properties of interneurons relate to neuropsychiatric disease, with a particular focus on how inhibitory interneurons regulate brain activity. His laboratory employs advanced techniques including whole-cell patch-clamp recording, local field potential measurements, in vitro neuroimaging, cell-specific gene knockout strategies, and sophisticated neuroanatomical methods. His most significant discovery involves how a specific inhibitory interneuron puts 'the brakes' on brain activity by firing repeatedly in the 'backward' direction during periods of elevated neural activity. Analysis of Dr. Theyel's publication record reveals a consistent focus on neural circuit mechanisms, particularly examining ectopic action potentials in interneurons and their implications for neuropsychiatric disorders. His work bridges basic neuroscience with clinical psychiatry, exploring how cellular and microcircuit dysfunctions contribute to conditions like epilepsy and other neurological disorders. Recent publications show an increasing emphasis on neurodevelopmental disorders and the application of circuit-based approaches to understanding psychiatric disease. Brown University General Psychiatry Residency Education Committee Award (2021) Excellence in Triple Board Training Award - Brown University (2021) Psychiatry Research Mentoring Award – Brown University (2019) Martin Keller Outstanding Resident Award (2016) Chief Resident, Brown University Psychiatry Residency Training Program (2015-2016) Brown Mind-Brain Research day Poster award – 2nd place among residents/fellows (2015) NIMH Outstanding Resident Award (ORAP) (2014) Dr. Theyel is actively involved in teaching and mentoring, supervising residents in the Residency Continuity Clinic and teaching courses including a longitudinal seminar series on 'Evidence-Based Medicine' for psychiatry residents. He co-directs the Brain & Behavior course for first-year medical students at Brown. His research is supported by multiple prestigious funding sources including the Brown Department of Psychiatry and Human Behavior, NINDS, the Norman Prince Neurosciences Institute, the Carney Foundation, and the Simons Foundation. His collaborative network includes prominent researchers in neuroscience, particularly Barry Connors with whom he has published multiple papers. Dr. Theyel maintains an active laboratory dedicated to determining the underpinnings of neuropsychiatric disease at the cellular and microcircuit levels, with the goal of uncovering potential novel treatment targets. His work bridges the gap between basic neuroscience and clinical psychiatry, making significant contributions to understanding how neural circuit dysfunctions contribute to psychiatric disorders.

Michael Miorمشاهده پروفایل

استادیار

• NoSQL database design
• Data integration
• Schema management
• +۲ مورد دیگر

Michael Mior is an Assistant Professor in the Department of Computer Science at Rochester Institute of Technology's (RIT) Golisano College of Computing and Information Sciences, where he has served since 2018. He directs the Data Unity Lab and teaches core courses including Principles of Data Mining, Introduction to Big Data, and Advanced Database Skills for NoSQL/NewSQL systems. His educational background includes: BS in Computing Science, University of Ontario Institute of Technology (Canada) MS in Computer Science, University of Toronto (Canada) PhD in Computer Science, University of Waterloo (Canada) Dr. Mior's research centers on schema design and management and data integration for non-relational data , with expertise in NoSQL database optimization. He develops open-source tools like NoSE (automated schema design), JSONoid (schema discovery for JSON), and ESON (denormalized database normalization), addressing challenges in database evolution and semantic data understanding. His recent publications (2020-2023) reveal a strong focus on practical database tooling, with trends including schema evolution for NoSQL systems (CoDEvo), educational applications for relational theory (Relational Playground), and machine learning techniques for semantic data analysis. This work bridges theoretical database concepts with real-world big data scalability requirements. As director of the Data Unity Lab, Dr. Mior leads research on data management tooling while mentoring student projects. His GitHub activity (247 repositories, 5k+ stars) demonstrates active contributions to open-source database projects including Apache Calcite and JSON Schema tools. The Data Unity Lab develops key projects such as Locomotor (Redis script automation) and contributes to industry standards through affiliations with Sourcemeta Research and JSON Schema organizations.

Nathaniel Johnstonمشاهده پروفایل

پژوهشگر

• Quantum Information Theory
• Quantum Entanglement
• Matrix Analysis
• +۱۶ مورد دیگر

Nathaniel Johnston is a researcher specializing in quantum information theory, combinatorics, and computational mathematics. He is the creator of QETLAB , a MATLAB toolbox for quantum entanglement, and the founder of ConwayLife.com , a major resource on cellular automata including the comprehensive LifeWiki. He also maintains StatDistributions.com and PlatPrices.com. His research spans: Quantum entanglement and quantum error correction Superpermutations and combinatorics on words Conway’s Game of Life and cellular automata Matrix norms and linear preserver problems Applications of linear algebra to puzzles like Lights Out Geometric optimization and calculus problems He produces educational math videos using Manim, covering advanced topics in an accessible format. His scholarly output includes significant contributions to open problems in mathematics and software tools widely used in the research community. He actively engages with users via GitHub and social media (Bluesky). Notable Projects: QETLAB : A MATLAB toolbox for quantum entanglement, actively developed and cited in research. ConwayLife.com / LifeWiki : The largest encyclopedia of Conway’s Game of Life patterns. StatDistributions.com : A tool for calculating p-values and test statistics. PlatPrices.com : A non-academic site tracking PlayStation game prices and trophies. Scientific Contributions: Disproved the minimal superpermutation conjecture. Developed algorithms for computing hard-to-compute matrix norms. Created accessible explanations of complex topics via video and interactive tools. While his current institutional affiliation is not stated, his body of work, software development, and scholarly communication confirm his status as an active academic researcher in applied and computational mathematics.