جست‌وجوی استاد و پژوهشگر

• Marine and Estuarine Ecology
• Estuaries
• Fisheries Management
• +۸ مورد دیگر

James Tweedley is a Senior Lecturer at Murdoch University, affiliated with the School of Environmental and Conservation Sciences within the College of Environmental and Life Sciences. He is also part of the Centre for Sustainable Aquatic Ecosystems and Harry Butler Institute. His research focuses on aquatic ecosystem health, fisheries management, and restoration strategies, with particular expertise in estuarine ecology and benthic macroinvertebrates. Education: Tweedley holds a BSc (Marine Biology) from the University of Portsmouth (2005), an MRes in Marine Biology from the University of Plymouth (2006), and a PhD in Marine Science from Murdoch University (2011). Research Interests His work spans two core areas: (1) understanding ecological linkages between fish/invertebrate communities and environmental drivers to develop estuarine health indices, and (2) fisheries management including aquaculture-based enhancement, artificial reefs, and socio-economic dimensions of fishing. Recent projects address eutrophication mitigation via mussel habitat restoration, barrier impacts on fish migration, and invasive species effects on ecosystems. Key Contributions Tweedley has pioneered methods for assessing fish communities in hypersaline environments and evaluated artificial reef effectiveness in temperate waters. His work on mass mortality events and tidal exclusion barriers provides critical insights for conservation planning. Awards - Young Tall Poppy and West Australian Young Tall Poppy of the Year (2021) - Recognizing outstanding research and science communication. Grants & Collaboration Funded projects include shellfish reef restoration studies and small-scale fisheries management in Indonesia. Collaborates with institutions like the Australian Institute of Policy and Science and local Indigenous communities for culturally significant estuary monitoring. Labs/Teams Leads the Sustainable Aquatic Ecosystems research group and contributes to the Harry Butler Institute’s coastal resilience initiatives.

Osama Mahmoudمشاهده پروفایل

مدرس

• Predictive Modelling
• Health Data Science
• Machine Learning
• +۶ مورد دیگر

Dr. Osama Mahmoud is a Lecturer (Assistant Professor) in Data Science and Statistics at the University of Essex, affiliated with the School of Mathematics, Statistics and Actuarial Science (SMSAS) and the Department of Mathematical Sciences. He holds dual roles as the Director of the BSc Data Science and Analytics programme and Deputy Director of Research at SMSAS. His academic journey includes a PhD in Statistics from the University of Essex and an Honorary Senior Researcher position at the University of Bristol Medical School (2020–2024). Dr. Mahmoud's research focuses on Predictive Modelling, Health Data Science, Machine Learning, Bio and Medical Statistics, and Explainable AI. He pioneered the Slope-Hunter method (Nature Communications, 2022) for bias correction in genome-wide studies and developed tools like the Proportion Overlapping Score (POS) and open-source packages on CRAN/GitHub. His work bridges statistical methodology with applications in healthcare, environmental epidemiology, and industrial collaboration. Key contributions include studies on sleep-breast cancer mortality linkages (2025), lung function-cardiovascular risk relationships (2024), and smoking's role in depression recovery (2022). He has secured grants supporting collaborations with UNDP, Rolls Royce, and Recruitment Smart. Teaching innovations include pioneering programming and text analytics modules. Education: PhD in Statistics, University of Essex Affiliations: UK Reproducibility Network (UKRN) Institutional Lead, British Data Science Society Member Grants: Projects in Data Science, theoretical/applied Statistics, and industrial partnerships Tools: ESKNN, OTE, propOverlap packages on CRAN His work emphasizes reproducibility, with over 50 peer-reviewed publications and international teaching engagements at ICTP (Italy), EuADS (Luxembourg), and UK institutions.

Tracy Campمشاهده پروفایل

استاد

• Ad Hoc Networks
• Wireless Sensor Networks
• Machine Learning
• +۳ مورد دیگر

Tracy Camp is a Professor and Founding Department Head of Computer Science at the Colorado School of Mines. She leads the Toilers research group, focusing on ad hoc networks and wireless sensor systems for geosystems. With over 20 NSF grants and $20M in funding, her work has produced 12 software tools used globally. She holds ACM and IEEE Fellowships, a Fulbright Scholarship, and the Mines Outstanding Faculty Award. Education: B.S. Mathematics, Kalamazoo College (1987) M.S. Computer Science, Michigan State University (1989) Ph.D. Computer Science, The College of William & Mary (1993) Research Interests: Her work bridges machine learning and geosystems, including dam integrity monitoring via seismic data, UAV communication protocols, and secure encrypted traffic classification. She emphasizes interdisciplinary approaches for real-world challenges like disaster response and environmental safety. Awards: ACM Fellow (2017) IEEE Fellow (2015) NSF CAREER Award (2007) Fulbright Scholar (2006) Grants & Impact: Over 80 refereed publications and 12 invited articles, cited ~7,000 times. Her grants include initiatives to broaden participation in computing, such as the S-STEM scholarship program. Software tools developed under her grants have been adopted by 3,000+ researchers in 86 countries. Labs & Teams: Directs the Toilers group, advancing ad hoc network evaluation and geophysical monitoring. Collaborates on projects like the ADMIRE dam monitoring system and the DREAM master’s program for underrepresented students.

Jeremy Gaskinsمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Longitudinal Data Analysis
• Missing Data Methods
• +۳ مورد دیگر

Jeremy Gaskins, PhD, is an Associate Professor in the Department of Bioinformatics & Biostatistics at the University of Louisville's School of Public Health and Information Sciences (SPHIS). He joined UofL in 2013 as an Assistant Professor, earning tenure and promotion to his current rank in 2019. His expertise lies in Bayesian statistical methods for complex data structures, including longitudinal analysis, missing data imputation, and joint modeling of mixed data types. He collaborates with researchers across multiple departments, including OB/GYN, Radiation Oncology, and Surgery. Education: Ph.D. (2013) in Statistics, University of Florida B.S. (2007) in Mathematics and Applied Mathematics, Auburn University Research Interests: Development of Bayesian methods for longitudinal and clustered data Computational strategies for complex model inference Applications in medical and public health research Missing data mechanisms and imputation techniques Teaching: PHST 661: Probability PHST 662: Mathematical Statistics Collaborations & Labs: Active collaborations with UofL medical departments on applied health research Focus on translational statistics for biomedical and public health problems

Yingyan Zengمشاهده پروفایل

استادیار

• Data Quality
• Data Valuation
• Data Sharing
• +۴ مورد دیگر

Dr. Yingyan Zeng is an Assistant Professor at the Department of Mechanical and Materials Engineering, University of Cincinnati, leading the Data-Centric Intelligence Lab. Her research focuses on creating data quality assurance paradigms to enhance AI modeling performance in Manufacturing Industrial Internet and Healthcare Systems. Ph.D. in Industrial and Systems Engineering, Virginia Tech (2024) M.S. in Computer Engineering, Virginia Tech (2024) B.Eng. in Industrial and Systems Engineering, Shanghai Jiao Tong University (2019) Her work spans data generation, acquisition, valuation, and sharing, with applications in additive manufacturing, bio-manufacturing, semiconducting, and IoT infrastructure design. She employs statistical and machine learning models, optimization methods, and interpretable AI techniques. Key trends in her publications include data-centric AI, privacy-preserving frameworks, synthetic data generation, and reinforcement learning in manufacturing. Her research often integrates domain knowledge and addresses challenges in industrial cyber-physical systems. 2022 Finalist, QSR Best Paper Competition (INFORMS Annual Meeting) She has secured industry grants, such as the MME Industry 4.0/5.0 Institute project on AI-driven reinforcement learning for factory operations. Her service roles include VP Marketing and Finance at the INFORMS VT Student Chapter and membership in IEEE Technical Committees.

• Cyber Physical Systems
• Risk/Reliability Analysis
• Deep Learning
• +۳ مورد دیگر

Dr. Saptarshi Sengupta is an Assistant Professor in the Department of Computer Science at San José State University (SJSU), leading the Machine Intelligence and Complex Systems (MICoSys) Lab. He advises the ACM student club at SJSU and holds a 'Alien of Extraordinary Ability' visa (Einstein Visa) from USCIS. His work focuses on resilient cyber-physical systems, risk analysis, and deep learning applications in healthcare and industrial systems. Education: Ph.D. in Electrical Engineering, Vanderbilt University M.S. in Electrical Engineering, Vanderbilt University B.Tech. in Electronics & Communication Engineering, West Bengal University of Technology Research Interests: Cyber-Physical Systems Security Healthcare AI for Cancer and Chronic Disease Prediction Battery Prognostics and Energy Systems Machine Learning for Complex Systems Analysis Key Achievements: Dr. T.M.A. Pai Gold Medal Award for Healthcare AI contributions Recipient of multiple best paper awards at international conferences Author of over 30 peer-reviewed publications Labs & Teams: Leads the MICoSys Lab, developing AI solutions for healthcare diagnostics, industrial prognostics, and smart infrastructure systems. Collaborations include interdisciplinary projects with biomedical and engineering domains.

Jiahua Chenمشاهده پروفایل

استاد

• Finite Mixture Models
• Density Ratio Model
• Variable Selection
• +۴ مورد دیگر

Professor Jiahua Chen is a faculty member in the Department of Statistics at the University of British Columbia (Vancouver Campus), holding the rank of Professor since 2001. He is a member of the Royal Society of Canada (2022) and has held a Canada Research Chair (Tier I) from 2007-2020. His research focuses on finite mixture models, density ratio modeling, variable selection, empirical likelihood, survey sampling, asymptotic theory, and experimental design. Education : - Ph.D. in Statistics, University of Wisconsin-Madison (1990) - M.Sc. in Systems Science, Academia Sinica, China (1985) - B.Sc. in Mathematics/Physics, University of Science and Technology of China (1982) Key Awards : - Gold Medal, Statistical Society of Canada (2014) - CRM-SSC Prize (2005) - Fellowships: ASA (2009), IMS (2005) Professional Contributions : - Editorships in journals like Canadian Journal of Statistics and Statistical Science - Leadership roles in statistical societies (e.g., President, International Chinese Statistical Association, 2005-2006) Software : - Developer of the MixtureInf2.0 R package for finite mixture model analysis.

Gennady Samorodnitskyمشاهده پروفایل

استاد

• Stochastic Modeling
• Heavy Tailed Distributions
• Long-Range Dependence
• +۶ مورد دیگر

Gennady Samorodnitsky is a Professor in the School of Operations Research and Information Engineering (ORIE) at Cornell University. He holds a B.S. from the Moscow Steel and Alloys Institute (1978), M.S. from Technion – Israel Institute of Technology (1983), and a D.Sc. from Technion (1986). He joined Cornell in 1988 and has held visiting positions at the University of North Carolina at Chapel Hill and Boston University. His research focuses on stochastic processes, particularly heavy-tailed distributions, long-range dependence, and extreme value theory, with applications in finance, teletraffic, and climate modeling. Education: B.S., Moscow Steel and Alloys Institute, USSR, 1978 M.S., Technion – Israel Institute of Technology, 1983 D.Sc., Technion – Israel Institute of Technology, 1986 Samorodnitsky’s research interests span stochastic modeling, including heavy-tailed processes, self-similar processes, and extreme value analysis. He examines the behavior of financial and telecommunication systems under long memory and non-Gaussian conditions. Key areas include the statistical analysis of extremes in climate data and the theoretical foundations of stable and infinitely divisible processes. His work bridges probability theory with applications in risk management, network traffic analysis, and climate science. His publications explore topics such as high-level excursion sets in random fields, tail inference, and the interplay between ergodic theory and stochastic processes. He has contributed to books like Stochastic Processes and Long Range Dependence and authored numerous technical reports on topics like ruin probabilities and multivariate extremes. Samorodnitsky teaches advanced courses, including ORIE 7590: Martingales in Discrete and Continuous Time , and maintains an active role in academic conferences and collaborations. His research group investigates cutting-edge problems in high-dimensional extremes, privacy-aware learning, and topological data analysis.

Amin Darvazehbanمشاهده پروفایل

پژوهشگر ارشد

• Electromagnetic Antenna Design
• Medical Imaging
• Microwave Engineering
• +۵ مورد دیگر

Dr. Amin Darvazehban is an Adjunct Research Fellow at the School of Electrical Engineering and Computer Science, The University of Queensland. His research focuses on electromagnetic antenna design for biomedical imaging systems, particularly targeting torso and liver diagnostics. Key Research Areas: Medical microwave imaging systems Metasurface and reconfigurable antenna technologies Dielectric property analysis for diagnostics Biomedical electromagnetic sensors Quantum signal processing applications Publication Trends: Recent work highlights electromagnetic solutions for non-invasive steatotic liver detection, torso imaging optimization, and metasurface-based antenna systems. His articles span IEEE journals in Antennas, Microwaves in Medicine, and Biosensors. Education: Completed his PhD thesis in 2021 on 'Reconfigurable antennas for electromagnetic torso imaging' at the School of Information Technology and Electrical Engineering, The University of Queensland. Patent: Co-inventor of an apparatus for electromagnetic characterization of internal features (US20230228917A1, 2023).

Natesh Pillaiمشاهده پروفایل

استاد

• Applied Probability
• Computational Methods
• Markov Chain Monte Carlo (MCMC)
• +۵ مورد دیگر

Natesh Pillai is a Professor in the Department of Statistics at Harvard University and a Distinguished Engineer at LinkedIn, focusing on Responsible AI. He holds a Bachelors from IIT Madras, a PhD from Duke University's Department of Statistical Science (2008), and completed a postdoc at the University of Warwick's CRiSM (2008-2010). His research spans applied probability, computational methods, MCMC theory, algorithmic fairness, and climate science. He serves on editorial boards for journals like SIAM Journal on Mathematics of Data Science and Harvard Data Science Review . Key awards include the 2018 Young Statistical Scientist Award and 2021 Fellowship in the Institute of Mathematical Statistics. His work emphasizes bridging theory and practice, with contributions to statistical methodology, causal inference, and scalable computational techniques. Recent collaborations include industry roles at Amazon (2021-2023) and interdisciplinary climate science projects analyzing agricultural yield predictability. Education: Bachelor's: Indian Institute of Technology (IIT) Madras PhD: Duke University, Department of Statistical Science Research Focus: MCMC mixing times, Bayesian methodology, reinforcement learning, climate data modeling Publications emphasize algorithmic efficiency, fairness in AI, and probabilistic frameworks for complex systems. His lab integrates theoretical rigor with real-world applications, including climate modeling and healthcare analytics.

Daniela Castro Camiloمشاهده پروفایل

مدرس ارشد

• Extreme Value Theory
• Environmental Risk Analysis
• Spatial Statistics
• +۳ مورد دیگر

Dr. Daniela Castro Camilo is a Senior Lecturer in Statistics at the University of Glasgow's School of Mathematics & Statistics. Her research focuses on extreme value theory applied to environmental hazards, including landslides, climate extremes, and risk assessment. She leads projects like the Mitigating Landslides Impacts in Scotland (MLIS) and Geostatistical Binary Models for Extremes (GEOBEx) , funded by the Scottish Government and EPSRC. She collaborates with institutions such as the British Geological Survey and the Met Office. Her work bridges statistical methodology and environmental applications, with notable contributions to landslide hazard modeling and spatial extremes. She actively participates in international conferences and organizes events like the ESS-sponsored session at the 2024 RSS Conference. She is a core member of GLE²N (Glasgow-Edinburgh Extremes Network), fostering interdisciplinary research in statistical risk analysis. Recent projects include developing probabilistic forecasting tools for weather-driven faults in electricity networks and advancing Bayesian methods for extreme event prediction. Her research emphasizes practical solutions for resilience to environmental disasters, combining cutting-edge statistical techniques with real-world data challenges.

Danhong Songمشاهده پروفایل

مدرس

• Statistics
• Data Analysis
• Multivariate Analysis
• +۲ مورد دیگر

Danhong Song is a Full-time Lecturer in the Department of Mathematics, Applied Mathematics, and Statistics (MAMS) at Case Western Reserve University. She teaches STAT 244 (Statistical Theory with Application II) and STAT 326/426 (Multivariate Analysis and Data Mining). Her research interests align with statistical methodologies and data-driven analytical approaches, reflected in her course offerings. She can be reached at danhong.song@case.edu and is located in Sears 572. Her academic responsibilities include undergraduate and graduate instruction in statistical theory and applications. She contributes to the department's teaching mission through courses focused on multivariate analysis and data mining techniques. No specific grants or awards are mentioned in the current profile.

Timothy A. Brownمشاهده پروفایل

استاد

• Classification of Anxiety and Mood Disorders
• Psychometric Methods
• Structural Equation Modeling
• +۳ مورد دیگر

Dr. Timothy A. Brown is Professor Emeritus in Psychological & Brain Sciences at Boston University, where he directs research at the Center for Anxiety & Related Disorders. His expertise spans anxiety/mood disorders classification, temperament-psychopathology relationships, and advanced statistical methodologies. His research focuses on dimensional models of emotional disorders, psychometric evaluation of diagnostic instruments, and structural equation modeling applications. He developed influential frameworks for understanding comorbidity patterns through latent variable modeling. Publications demonstrate consistent themes in psychometric validation, transdiagnostic approaches, and longitudinal modeling of psychopathology. Recent work emphasizes COVID-19 mental health impacts and clinical assessment optimization. Dr. Brown teaches graduate courses in multivariate statistics, including confirmatory factor analysis and structural equation modeling. He authored the authoritative textbook Confirmatory Factor Analysis for Applied Research . He serves as Associate Editor of the Journal of Abnormal Psychology and collaborates internationally on classification systems for emotional disorders.

Nathaniel Rosiمشاهده پروفایل

استاد

• Inorganic Chemistry
• Materials Chemistry
• Metal-Organic Frameworks
• +۲ مورد دیگر

Nathaniel Rosi is the Covestro Professor of Chemistry at the University of Pittsburgh's Dietrich School of Arts and Sciences, Department of Chemistry. His research focuses on designing and synthesizing new materials using chemical building blocks like metal clusters and biomolecules to create hierarchical structures with tailored properties for energy, medicine, and other applications. Research interests span inorganic and materials chemistry, metal-organic frameworks (MOFs), nanoparticle assembly, and sustainable energy materials. The Rosi Lab develops unifying design principles for organizing building blocks across multiple length scales, employing diverse synthetic methods and characterization techniques including NMR, TEM, X-ray diffraction, and sorption analysis. Scientific awards include: Chancellor's Distinguished Research Award (2014) Kavli Fellow (2012) ChemComm Emerging Investigator (2011) NSF CAREER Award (2010-2015) US Delegate for Transatlantic Frontiers of Chemistry Conference (2008) The Rosi Research Laboratory trains students in interdisciplinary approaches at the chemistry-biophysics interface and welcomes graduate students to explore its multi-disciplinary research environment.

Corlett Woodمشاهده پروفایل

استادیار

• Evolutionary Ecology
• Plant-Microbe Interactions
• Genetic Pleiotropy
• +۱ مورد دیگر

Corlett Wood is Assistant Professor of Biology at the University of Pennsylvania's School of Arts & Sciences. She holds a PhD in Biology from University of Virginia (2015) and BA in English Literature from Swarthmore College (2008), with postdoctoral training at University of Toronto. Her research investigates the evolution, ecology, and genetics of species interactions in plant root microbiomes. The Wood Lab studies how genetic pleiotropy influences mutualistic and parasitic interactions using Medicago plants and their microbial symbionts. Research combines fieldwork, greenhouse experiments, and genomics to examine how shared genetic pathways impact host adaptation. Current projects investigate genetic tradeoffs between mutualism and parasitism, context-dependent trait expression, and community assembly processes. Field research occurs at University of Pittsburgh's Pymatuning Laboratory of Ecology, University of Virginia's Mountain Lake Biological Station, and University of Toronto's Koffler Scientific Reserve. Professor Wood received the Banting Postdoctoral Fellowship (2018) and NSF Graduate Research Fellowship (2010-2013), and directs the International Area Studies/Global Studies program. Honors and Awards: Banting Postdoctoral Fellowship (2018) Ecology & Evolutionary Biology Postdoctoral Fellow (2016-2018) Andrew Fleming Prize in Biology (2016) NSF Graduate Research Fellowship (2010-2013)