جست‌وجوی استاد و پژوهشگر

Tom Fincham Hainesمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Computer Vision
• Computer Graphics
• +۱۵ مورد دیگر

Tom Fincham Haines is a Senior Lecturer in the Department of Computer Science at the University of Bath. He is affiliated with the UKRI Centre for Doctoral Training in Accountable, Responsible and Transparent AI, the Centre for Mathematics and Algorithms for Data (MAD), and multiple interdisciplinary institutes including Visual Computing, IAAPS, the Bath Institute for the Augmented Human, and the Institute for Digital Security and Behaviour (IDSB). He is actively accepting doctoral students and maintains a strong research presence in machine learning and its applications. Research Interests: Dr. Haines applies machine learning to computer vision and graphics, with a focus on graphical models, Bayesian non-parametric models, directional statistics, and active learning. His current interests include developing tools for artists, machine learning for education, online/real-time machine learning, causality, and scaling non-parametric methods for big data. His work contributes to several UN Sustainable Development Goals, particularly in health and security domains. Recent Research Trends: His recent publications (2024–2025) reflect a strong interdisciplinary focus, combining machine learning with applications in sonar imaging, drug detection, and creative media. He leverages deep learning and hybrid spectroscopic techniques for portable forensic devices and explores tangible interfaces for virtual film production, demonstrating a blend of technical innovation and societal impact. Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: Dr. Haines supervises doctoral students and has contributed to 8 supervised works. He is actively involved in multiple funded research projects, including as Principal Investigator (PI) on a Defence Science and Technology Laboratory project for synthetic opioid detection, and as Co-Investigator on EPSRC, MRC, Innovate UK, and National Police Chiefs' Council projects related to healthcare, chronic pain, and radicalization. His grant portfolio reflects strong collaboration across engineering, health, and security sectors. Labs and Teams: He is embedded in several research centers at the University of Bath, including the Centre for Mathematics and Algorithms for Data (MAD), the Institute for the Augmented Human, and the Institute for Digital Security and Behaviour (IDSB), where he contributes to AI, machine learning, and visual computing initiatives. His work spans collaborative teams in healthcare technology, digital forensics, and creative computing.

Amy K. Schmidمشاهده پروفایل

استاد

• Archaeal Molecular Biology
• Gene Regulatory Networks
• Microbial Stress Responses
• +۴ مورد دیگر

Amy K. Schmid is Professor of Biology at Duke University's Trinity College of Arts & Sciences, where she leads a research program focused on archaeal molecular biology and gene regulatory networks. She was promoted to full Professor in 2024 after serving as Associate Professor (2018-2024) and Assistant Professor (2009-2018). She is also Associate of the Duke Initiative for Science & Society since 2018. Research Interests: Dr. Schmid's laboratory investigates how microbial cells make decisions in response to environmental cues, with particular focus on molecular networks within free-living archaeal cells. Her work examines how these extremophiles regulate growth under optimal conditions and deploy damage repair systems during stress. She specializes in studying archaeal stress responses, particularly in hypersaline-adapted organisms that survive multiple extreme stressors. Her publication trends reveal a consistent focus on transcriptional regulation in archaea, with increasing emphasis on network evolution, cell decision-making, and the structural basis of regulatory mechanisms. Recent work has expanded into archaeal cytoskeleton, histone function, and metabolic regulation under extreme conditions. Awards & Recognition: NSF CAREER Award (2017-2022) Promoted to full Professor at Duke University (2024) Named to Endowed Bass Connections Professorship (2022) Grant Support: Dr. Schmid maintains an exceptionally strong funding record with multiple concurrent grants from NIH and NSF. Her current major projects include 'Structure, function, and evolution of gene regulatory networks in archaea' (NIH, 2025-2030), 'Transcription network evolution under extreme environmental selection' (NSF, 2024-2027), and 'Transitions: Modeling microbial community metabolic interactions under extreme conditions' (NSF, 2021-2026). She has served as mentor for multiple NIH training grants including the Cell and Molecular Biology Training Program. Research Environment: The Schmid Lab operates at the intersection of microbiology, systems biology, and extremophile research, utilizing cutting-edge genomic, proteomic, and computational approaches to understand fundamental principles of cellular decision-making in archaea.

• Bayesian Statistics
• Parametric Inference
• Information Theory
• +۷ مورد دیگر

Professor Camila Caiado is affiliated with Durham University as a Professor in the Faculty of Science, Department of Mathematical Sciences . Her research spans Bayesian Statistics, Parametric Inference, Information Theory , and Stochastic Processes , with interdisciplinary applications in Health Informatics and Geophysics . Key Research Themes : Uncertainty Quantification, Computational Statistics, Decision Theory, and Applications of Bayesian Methods in Complex Systems Notable Collaborations : Atom Bank, Healthcare Institutions, Petroleum Engineering Projects Recent Publications focus on digital twin banking models , healthcare utilization during pandemics , and geochronological uncertainty analysis . She employs agent-based modeling and machine learning for pandemic planning and medical diagnostics. Academic Leadership : Active in Durham Research Methods Centre and Institute for Data Science. No formal awards or supervisees are explicitly mentioned in available data.

Massimiliano Tamborrinoمشاهده پروفایل

دانشیار

• Stochastic Processes
• Approximate Bayesian Computation (ABC)
• Parallel-in-Time Numerical Methods
• +۴ مورد دیگر

Massimiliano Tamborrino is an Associate Professor in the Department of Statistics at the University of Warwick since August 2024. He holds a WIHEA Fellowship (2023-2026) and has organized the One World ABC Seminar (2020–present), focusing on approximate Bayesian computation (ABC) and simulation-based inference. He co-organizes the BioInference conference series, which explores mathematical modeling in biological systems. His research integrates stochastic processes, numerical methods, and statistical inference, with applications in neuroscience, biology, and parallel-in-time algorithms. Education: PhD in Probability Theory and Statistics from the University of Copenhagen (2013), supervised by Prof. Susanne Ditlevsen. Previously held roles include University Research Assistant at JKU Linz (2014–2019) and Postdoc at the University of Copenhagen (2012–2014). Research interests include stochastic processes (diffusions, point processes), parallel-in-time numerical schemes (PinT), and ABC methods. His work bridges stochastic numerics with computational statistics, particularly in neuroscience and biological systems. He has developed R packages for exact simulation of non-Gaussian processes (e.g., shot noise, OU processes). Awards: WIHEA Fellow (2023–2026). Active in grants, including leadership of the EPSRC-funded project on AI-informed decision-making using Decision Field Theory. Teaching responsibilities include ST232/ST233: Introduction to Mathematical Statistics for undergraduate students.

• Signal Processing
• Communications Engineering
• Radar Systems
• +۵ مورد دیگر

Assoc. Professor Qinghua Guo holds a position at the University of Wollongong's School of Electrical, Computer and Telecommunications Engineering. He earned his PhD from City University of Hong Kong (2008) and has been affiliated with the University of Wollongong since 2019. His research focuses on signal processing, communications, radar systems, machine learning, and optical sensing, with notable contributions to graphical models and Bayesian inference. Prof. Guo is recognized as a top 2% influential scientist globally (Stanford University) and a leader in Electronics and Computer Science (Research.com). He serves as an Associate Editor for IEEE Transactions on Signal Processing and IEEE Wireless Communications Letters, among other roles. His teaching spans undergraduate to postgraduate levels, including specialized subjects in engineering and information sciences. His recent research emphasizes integrated sensing and communication systems, federated learning, and secure beamforming in 5G/6G networks. He has supervised over 30 PhD and Master’s students in areas like MIMO systems, signal processing for IoT, and quantum sensing. Key funded projects include AFDM-SCMA (2025–2028) for IoT connectivity and Grant-Free Multiple Access for LEO satellite networks (2023–2027). His work bridges theoretical advancements with practical applications in robotics, energy systems, and defense technologies.

Xuefei LUمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Uncertainty Quantification
• Big Data Problems
• +۹ مورد دیگر

Xuefei LU is an Associate Professor at SKEMA Business School (France), affiliated with the SKEMA Center for Analytics and Management Science and the Digitalization Research Center. Her academic journey includes a Ph.D. in Statistics from Bocconi University (Italy) and an MSc in Analytics from the University of Manchester (UK). Prior roles include Assistant Professorships at SKEMA Business School and the University of Edinburgh Business School, along with a postdoctoral position at Politecnico di Milano (Italy). Her research focuses on Statistical Machine Learning , Uncertainty Quantification , and Big Data Problems , with applications in epidemiological modeling, decision analysis, and industrial systems. She has developed frameworks for identifying critical components in complex systems and pioneered methods like the Cohort Shapley value for fairness in SME financing. Her work has been recognized with prestigious awards, including the 2024 INFORMS Data Mining Best Paper Award (Runner-Up) and multiple Excellence in Reviewing Awards from the European Journal of Operational Research. She actively supervises doctoral students in areas like anomaly detection and pandemic modeling. Xuefei LU collaborates across disciplines, contributing to labs focused on Reliability Engineering , Molten Salt Reactors , and Transportation Systems . Her research bridges theoretical advancements with practical applications in public health, infrastructure resilience, and sustainable energy systems.

Sudharrman Jayaweeraمشاهده پروفایل

استاد

• Cognitive Radios
• Cooperative Communications
• Machine Learning
• +۶ مورد دیگر

Sudharman K. Jayaweera is a Professor in the Department of Electrical and Computer Engineering at the University of New Mexico , where he directs the Communications and Information Sciences Lab (CISL). His career spans roles as Associate Chair and Graduate Program Director (2014-2015), Faculty Fellow at Kirtland Air Force Research Lab (2009-2011), and NRC Senior Fellow at the Naval Postgraduate School (2013). He founded Bluecom Systems and Consulting, focusing on telecom/satcom, machine learning, and autonomous systems. Research Interests include cognitive radios, machine learning in wireless communications, satellite communications, and smart grid security. His work emphasizes statistical signal processing, information theory, and autonomous systems. Recent publications highlight reinforcement learning for spectrum sensing, wideband cognitive radio optimization, and smart grid stochastic control. Scientific Awards include IEEE best paper awards, Fellow of EAI, and prestigious scholarships like the John Crowford Scholarship (Australia). He serves on editorial boards of IEEE Transactions in Vehicular Technology and EURASIP Journal on Advances in Signal Processing. Education features a PhD in Electrical Engineering from Princeton University (2003) and a BE (First Class Honors) from the University of Melbourne (1997). His lab, CISL, and collaborations with institutions like AFRL and NPS underscore his interdisciplinary impact.

• Machine Learning
• Optimization Algorithms
• Deep Learning
• +۷ مورد دیگر

Bin Gu is a professor at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), specializing in machine learning and artificial intelligence. Previously affiliated with institutions including Nanjing University of Information Science and Technology (former position) and Nanjing University of Aeronautics and Astronautics (PhD 2011). His research focuses on optimization algorithms, spiking neural networks, federated learning, kernel methods, adversarial robustness, and neuromorphic computing. Education: PhD in Computer Science (2011) from Nanjing University of Aeronautics and Astronautics. Prior affiliations include Tianjin University, Boston University, University of Science and Technology of China, and Southeast University. Research Interests: Extensive work on machine learning theory and applications, including robust learning, federated systems, neural architecture design, and privacy-preserving techniques. Over 200 publications in top venues such as AAAI, NeurIPS, ICLR, ICML, KDD, and IEEE journals. Publications Trends: Recent focus on spiking neural networks (SNNs), federated learning frameworks, and optimization methods for handling adversarial attacks and privacy constraints. Notable contributions include scalable algorithms for kernel-based learning, robust SVM formulations, and neuromorphic computing architectures. Labs/Teams: Active in AI research groups focused on neural networks, optimization, and distributed learning systems. Collaborates with industry and academic partners on applied AI solutions.

Gabriele SANTINمشاهده پروفایل

پژوهشگر

• Numerical Analysis
• Kernel Methods
• Approximation Theory
• +۵ مورد دیگر

Gabriele Santin is a Researcher at Ca' Foscari University of Venice's Department of Environmental Sciences, Computer Science and Statistics. He holds a PhD in Computational Mathematics from the University of Padua and has held postdoctoral positions at the University of Stuttgart and the Bruno Kessler Foundation. His research focuses on kernel-based approximation methods, numerical analysis, and applications in scientific computing, including partial differential equations and biomedical engineering. Research interests include kernel interpolation, greedy algorithms, convergence analysis, and data-driven modeling. He has contributed to advancing numerical techniques for solving PDEs, optimizing kernel methods, and analyzing stability in non-Lipschitz domains. His work bridges theoretical foundations with practical applications in fields like medical imaging, transportation systems, and epidemic modeling. Publications highlight contributions to kernel-based greedy algorithms, image interpolation, and surrogate modeling. He is affiliated with the Research Institute for Complexity and actively collaborates with institutions like SimTech (University of Stuttgart). His expertise spans numerical methods, machine learning, and interdisciplinary problem-solving.

Andrew Rothمشاهده پروفایل

استادیار

• Bioinformatics
• Cancer Evolution
• Computational Biology
• +۶ مورد دیگر

Dr. Andrew Roth is an Assistant Professor in the Departments of Pathology & Laboratory Medicine and Computer Science at the University of British Columbia, with additional appointments at the BC Cancer Research Centre and BC Cancer Agency's Department of Molecular Oncology. His interdisciplinary work bridges computational science and cancer biology, focusing on developing novel methods to understand tumor evolution and heterogeneity. Dr. Roth's research centers on applying statistical machine learning to high-dimensional cancer biology, with expertise in probabilistic graphical models, non-parametric Bayesian methods, and computational statistics. His primary focus is developing computational methods for studying clonal population structures and tumor evolution. His work leverages variational and sequential Monte Carlo methods to extract biologically interpretable insights from complex genomic datasets, with significant implications for understanding cancer progression, treatment resistance, and metastasis. Dr. Roth's publication record demonstrates consistent innovation in computational cancer genomics, particularly in clonal evolution, tumor heterogeneity, and single-cell analysis. His methodological contributions (including PyClone, ReMixT, and TITAN) have advanced our ability to reconstruct evolutionary histories of tumors and identify critical transitions in cancer progression across multiple cancer types including breast, ovarian, and medulloblastoma. Scientific Recognition Publications in high-impact journals including Nature, Nature Methods, Genome Biology, and Genome Research Work applied in high-profile studies of breast and ovarian cancer published in Nature and Nature Genetics Academic Supervision Dr. Roth actively mentors graduate students across Bioinformatics and Computer Science programs. His lab currently includes PhD student Eric Lee working on 'Computational approaches for exploring the spatial dynamics of the cellular ecosystem,' along with numerous other graduate students and trainees. He has supervised Master's theses on liquid biopsies for cancer monitoring, differential RNA expression analysis using Bayesian phylogenetic modeling, and spatial characterization of tumor microenvironments with spatially aware clustering methods. Research Environment Dr. Roth leads a research group at the BC Cancer Research Centre within the Genome Sciences Centre, a high-throughput genome sequencing facility recognized as a leader in genomics and bioinformatics. His lab collaborates extensively with clinicians, molecular biologists, and bioinformatic scientists to develop and apply single-cell multiomic methods for studying cancer evolution, particularly in follicular lymphoma. The lab operates within one of Canada's premier cancer research environments with access to robust computational and sequencing technology facilities.

• High-dimensional statistics
• Statistical learning theory
• Online learning
• +۳ مورد دیگر

Jaouad Mourtada is an Assistant Professor in the Department of Statistics at ENSAE Paris, a founding member of the Institut Polytechnique de Paris, and a permanent member of CREST (Center for Research in Economics and Statistics). Since September 2020 he has held this faculty position, after completing a post-doctoral fellowship at the University of Genoa and earning his PhD from École Polytechnique. Education PhD in Statistics, École Polytechnique (2016–2019) MSc in Mathematics, "Probability and Random Models", Université Pierre et Marie Curie (2016) MSc in Mathematics, "Fundamental Mathematics", Université Pierre et Marie Curie (2015) BSc in Mathematics, Université Pierre et Marie Curie & École Normale Supérieure (2013) Student at École Normale Supérieure (2012–2016) Research Interests Mourtada’s work lies at the intersection of statistics and learning theory, with a focus on understanding the fundamental complexity of prediction and estimation tasks. His interests span: High-dimensional statistics and minimax theory Statistical learning theory and generalization bounds Online learning, regret minimization, and expert aggregation Density estimation and robust statistics Random forests, kernel methods, and convex optimization Research Output Trends Across more than fifteen recent publications, Mourtada has systematically advanced the understanding of statistical and computational limits in learning. His contributions range from exact minimax analyses of linear least squares and novel robust regression guarantees to refined PAC-Bayesian bounds for aggregation and sharp asymptotics for ridge regression. A recurrent theme is the development of estimators that achieve optimal or near-optimal rates while remaining computationally tractable and adaptive to unknown parameters. Scientific Awards & Recognition While the provided materials do not list specific awards, his sustained publication record in top venues such as Annals of Statistics , Journal of Machine Learning Research , Journal of the European Mathematical Society , and leading ML conferences (NeurIPS, COLT, AISTATS) attests to significant peer recognition. Teaching & Mentoring Mourtada has extensive teaching experience at both undergraduate and graduate levels, covering probability, statistics, and machine learning. Courses delivered include: Statistical Learning Theory (M2 Data Science, École polytechnique & ENSAE) Probability Theory (ENSAE) Python for Probability, Statistics, and Machine Learning (École polytechnique) Optimization for Data Science (M2 Data Science, École polytechnique) Laboratories & Collaborations He is affiliated with CREST/ENSAE and has previously collaborated with the Laboratory for Computational and Statistical Learning at the University of Genoa, the Center for Applied Mathematics (CMAP) at École Polytechnique, and maintains ongoing research ties with international scholars in statistical learning and optimization.

Kalimuthu Krishnamoorthyمشاهده پروفایل

استاد

• Statistical Methodology
• Occupational Exposure Analysis
• Missing Data
• +۶ مورد دیگر

Professor Kalimuthu Krishnamoorthy holds the Philip and Jean Piccione Endowed Chair in Statistics at the University of Louisiana at Lafayette. His research focuses on statistical methodologies for occupational exposure analysis, missing data, and tolerance regions, with contributions to censored data analysis and calibration techniques. He has advised over 30 Ph.D. students and led NIOSH-funded projects on exposure assessment. His work includes developing statistical software tools like StatCalc and over 200 peer-reviewed articles. Education: Ph.D. (Statistics, 1985) Indian Institute of Technology-Kanpur; M.Sc. & B.Sc. (Statistics) Madras University. Grants: Multiple NIOSH grants (R01-OH series) totaling $2.6M, focusing on exposure data analysis methodologies. Labs/Teams: Leads statistical research groups at the University of Louisiana, collaborating on occupational health and environmental statistics.

• Financial Econometrics
• Time Series Modelling
• Volatility Models
• +۶ مورد دیگر

Dr. Chao Wang is a Senior Lecturer at the University of Sydney's Sydney Business School. He holds a PhD in Econometrics from the same university, along with master's degrees in Machine Learning & Data Mining (Helsinki University of Technology) and Mechatronic Engineering (Beijing Institute of Technology). His research focuses on financial econometrics, time series modeling, and volatility analysis, particularly using Bayesian methods and high-frequency data. He teaches courses like Quantitative Business Analysis (BUSS1020) and Machine Learning for Business (QBUS6840). Dr. Wang's research interests include parametric/non-parametric volatility models, Bayesian MCMC estimation, and applications of machine learning in finance. His work addresses microstructure noise in high-frequency data and integrates realized measures like variance and range. He has supervised students on topics such as spatiotemporal volatility forecasting and financial technology-based risk management. Grants: Machine learning and high-frequency data-based risk forecasting (2022), financial tail risk forecasting with deep learning (2021), and parametric tail risk forecasting (2020). Key collaborations: With Prof. Robert Gerlach and Minh-Ngoc Tran on Bayesian frameworks and realized measures. Publications: Over 15 peer-reviewed articles in top journals like Quantitative Finance and Journal of Financial Econometrics , focusing on risk forecasting methodologies. His work bridges econometric theory and practical financial applications, emphasizing robust risk prediction under volatile market conditions.

Mi-Ok Kimمشاهده پروفایل

استاد

• Biostatistics
• Causal Inference
• Clinical Trial Design
• +۱۲ مورد دیگر

Dr. Mi-Ok Kim is a Professor in the Department of Epidemiology and Biostatistics at the University of California San Francisco (UCSF) and serves as the Director of the Biostatistics Core at the Helen Diller Family Comprehensive Cancer Center (HDFCCC). She joined UCSF from Cincinnati Children’s Hospital Medical Center, where she led the Biostatistics Unit for the Cancer and Blood Diseases Institute. Her role involves leading strategic development of shared biostatistical resources and providing expert statistical support across basic, clinical, and population sciences research. Dr. Kim earned her MS (2000) and PhD (2003) in Statistics from the University of Illinois at Urbana-Champaign. Her research program centers on methodological advancements in biostatistics, including non- and semi-parametric inference, longitudinal and survival data analysis, and causal inference using structured data from registries, network databases, and electronic health records. She has a strong focus on comparative effectiveness research (CER) and patient-centered outcomes research (PCOR), particularly in the context of hierarchical data structures. Her recent publications reflect a broad interdisciplinary reach, spanning oncology, pediatrics, public health, and machine learning applications in healthcare. Themes include health disparities, clinical trial design, biomarker analysis, and statistical methods for integrating aggregate and individual-level data. She has published in high-impact journals such as JAMA Dermatology , Annals of Applied Statistics , and Pediatrics . Dr. Kim has received multiple honors and awards, including: Fellowship, University of Illinois at Urbana-Champaign (1997) First Prize, Capital One Financial Data Challenge Competition (2001) Norton Prize, Robert Bohrer Workshop for Student Papers in Statistics (2002) Junior Faculty Travel Award, International Conference on Robust Statistics (2004) Travel Award, AACR Cancer Biostatistics Workshop (2008) She has served as Principal Investigator on numerous grants from NIH, NSF, and PCORI, supporting research in areas such as pediatric Crohn’s disease, kidney transplantation, and meta-analysis methods. Her work emphasizes rigorous statistical methodology to reduce bias and improve efficiency in real-world data analysis. She collaborates extensively with multidisciplinary teams across UCSF and beyond, contributing to clinical studies, protocol development, and outcomes research. Dr. Kim leads a research team focused on optimal handling of complex data structures in treatment selection and outcome analysis, with applications in chronic disease management and cancer care. She is also engaged in developing frameworks for post-market monitoring of machine learning-based medical devices, reflecting her forward-looking approach to biostatistical innovation.

• Time Series Analysis
• Financial Econometrics
• Volatility Modeling
• +۵ مورد دیگر

Professor Shelton Peiris is an Associate Professor at the School of Mathematics and Statistics , University of Sydney , where he has been since 1990. He holds visiting appointments at institutions worldwide, including University of Waterloo , University of Manitoba , and University of Malaya . Currently, he serves as Sub Dean (Student Affairs) in the Faculty of Science and coordinates interdisciplinary teaching with the School of IT for MIT/MDS degrees. His research focuses on time series analysis, financial econometrics, and technology integration in statistics education. Education: PhD in Statistics, Monash University , 1987 Shelton's research interests include statistical analysis of stationary and non-stationary time series, theory and applications of estimating functions, financial time series modeling, saddlepoint and Edgeworth approximations, and exploring technology's role in statistics education. He is a member of the Statistics Research Group at the University of Sydney and leads projects in financial econometrics, generalized autoregressive models, and nonlinear time series analysis. Recent publication trends highlight his work in financial econometrics, particularly volatility and duration modeling, stochastic processes, and hybrid forecasting methods combining traditional statistical techniques with machine learning advancements like GANs and neural networks. His collaborations span Australia, Canada, Malaysia, and Indonesia, with notable grants including ARC Linkage and University of Malaya Research Grants . Scientific Awards: 2012: Faculty of Science Teaching Citation 2011: Bronze Medal, University Putra Malaysia 2007: Bronze Medal, University Putra Malaysia 1983: Monash University Graduate Scholarship Elected Member, International Statistical Institute (ISI) Fellow, Royal Statistical Society (FRSS) Honorary Fellow, Institute of Applied Statistics, Sri Lanka (FIASSL) Current Research Students: Leonard Mushunje - High-Dimensional Financial Functional Time Series Data Grants: ARC Linkage Grant (2005-2007): Modelling Stock Market Liquidity ARC Bridging Grant (2012-2014): Financial Duration Modeling University of Malaya Research Grants (2011-2015): Volatility Models, GARCH MOHE Malaysia Grant (2013-2015): Robust Control Charts Professor Peiris contributes to editorial boards such as the Journal of Statistical Computation & Simulation and Sri Lankan Journal of Applied Statistics . His teaching roles include MATH1015 (Statistics for Life Science) , MATH1905 (Statistics Advanced) , and advanced honors courses in time series analysis.