جست‌وجوی استاد و پژوهشگر

Daniel L. Hartlمشاهده پروفایل

استاد

• Evolutionary Biology
• Genomics
• Malaria Research
• +۳ مورد دیگر

Daniel L. Hartl is the Higgins Professor of Biology and Professor in the Department of Immunology and Infectious Diseases at Harvard University. His research focuses on the intersection of evolutionary biology and genomics, particularly studying organismal evolution, systems dynamics, and speciation. He employs model organisms like fruit flies, nematodes, yeast, and bacteria, alongside public health-relevant parasites such as the malaria parasite. His lab utilizes advanced molecular, statistical, and computational methods for genetic analysis. Affiliations: Harvard University, Department of Immunology and Infectious Diseases, Organismic and Evolutionary Biology (OEB). Key Projects: Malaria parasite genomics, drug resistance mechanisms, and evolutionary dynamics in microbial systems. Research interests emphasize genetic surveillance of malaria in Senegal, parasite transmission patterns, and the genetic basis of drug resistance. His work bridges basic science and public health, contributing to malaria elimination strategies globally. Hartl has been recognized with prestigious awards, including the 2019 Thomas Hunt Morgan Medal for his contributions to genetics. Lab Members: Includes researchers like Tulika Deb and Bridget Power, focusing on computational genomics and epidemiological modeling. His lab actively collaborates with institutions worldwide, publishing on topics ranging from transposable element evolution to the historical contributions of Mendel and Lewontin. Hartl also contributes to educational materials such as textbooks like Biology: How Life Works and Essential Genetics and Genomics .

• Industrial Organization
• Microeconomics
• Scientometrics
• +۶ مورد دیگر

Laurent Linnemer is a Professor of Economics at CREST (Centre de Recherche en Économie et Statistique) within the Institut Polytechnique de Paris. His research focuses on Industrial Organization, Microeconomics, and Scientometrics, with notable contributions on signaling in markets, vertical integration, and competition policy. He has co-authored studies on topics such as double marginalization, nonlinear pricing, and asymmetric information in real estate markets. His work also includes methodological advancements in scientometrics, particularly in ranking research output via RePEc. Linnemer has held editorial roles at Annals of Economics and Statistics and contributed biographical studies of prominent economists like Jean-Michel Grandmont and Jean-Jacques Laffont. His research spans experimental economics, merger analysis, and policy evaluation, with a strong empirical focus. Education details are not explicitly stated, though his academic positions imply doctoral training in economics. His research interests are structured around core themes: market structures, information asymmetries, and institutional impacts. Recent publications emphasize the interplay between market design and economic theory, with applications to real-world scenarios like parking prices and tournament dynamics. He has received ADRES Best Young Paper Awards in 2022, 2023, and 2024 for impactful contributions. Collaborations include co-authors like Philippe Choné and Michael Visser, and he has led teams analyzing experimental auctions, viager markets, and chess player behavior. Linnemer’s work bridges theoretical frameworks with empirical rigor, contributing to both academic discourse and policy recommendations.

Drew Crealمشاهده پروفایل

استاد

• Econometrics
• Macroeconomics
• Finance
• +۳ مورد دیگر

Drew Creal is a Professor of Economics at the University of Illinois Urbana-Champaign, affiliated with the Department of Economics within the College of Liberal Arts & Sciences. His research focuses on econometrics, macroeconomics, and finance, with an emphasis on time series analysis, stochastic volatility models, and asset pricing. He has published extensively in top journals such as the Journal of Econometrics, Journal of Financial Econometrics, and Journal of Business and Economic Statistics. His work often involves methodological advancements in econometric modeling, including Bayesian estimation, dynamic factor models, and generalized autoregressive score (GAS) models. His academic contributions span theoretical econometrics and applied finance, addressing topics like monetary policy uncertainty, international yield curves, and credit risk. He serves as an Associate Editor for several leading journals, including the Journal of Econometrics and the Journal of Applied Econometrics. His research has been supported by grants and collaborations with institutions such as the National Science Foundation and the Tinbergen Institute. Recent publications highlight innovations in cluster covariance estimation, observation-driven filtering techniques, and Bayesian regression trees for asset pricing. His work often bridges theory and practice, offering tools for analyzing financial markets and macroeconomic phenomena. Creal’s academic profile is further enriched by his role in organizing conferences and workshops, such as the Midwest Econometrics Group Conference, fostering interdisciplinary dialogue in econometrics and finance.

Elena Pesaventoمشاهده پروفایل

استاد

• Time Series Analysis
• Econometrics
• Applied Macroeconomics
• +۵ مورد دیگر

Elena Pesavento is a Full Professor in the Department of Economics at Emory University, where she has held leadership roles including Department Chair (2010–2013) and President of the Emory College Senate (2019–2020). She specializes in Time Series Analysis, Econometrics, and Applied Macroeconomics, with a focus on structural breaks, cointegration, and impulse response functions in VAR models. Education: PhD in Economics, University of California, San Diego (2000) BA in Statistics and Economics, Università di Padova, Italy (1993) Visiting Scholar, University of California, Berkeley (1994) Research Interests: Her work addresses challenges in econometric inference under non-stationarity and high persistence. Recent projects include nonlinear VAR models, oil price volatility, and demographic-driven forecasts. She collaborates extensively on topics like inflation anchoring and monetary policy effects. Awards & Grants: NSF Grant: $188,548 for impulse response analysis in nonlinear VARs (2023) Marco Fanno Fellowship (2008), Jean Monnet Fellowship (2006) Emory Women of Impact Circle Grant ($23,000 for leadership programs) Service & Leadership: Director of Emory's Women in Leadership Program, mentor for junior economists, and contributor to academic governance. She has organized conferences, served on editorial boards (Empirical Economics, Journal of Applied Econometrics), and reviewed NSF grants. Labs/Teams: Leads research groups on econometric methods and macroeconomic policy analysis. Collaborations include projects on connectedness of credit risk and state-dependent local projections.

Wei Aiمشاهده پروفایل

استادیار

• Data Science for Social Good
• Machine Learning
• Causal Inference
• +۴ مورد دیگر

Wei Ai is an Assistant Professor at the University of Maryland, affiliated with the College of Information (INFO) and the Institute for Advanced Computer Studies (UMIACS). His research focuses on data science for social good (DSSG), integrating machine learning, causal inference, and experimental design to address societal challenges in education, virtual collaboration, and quantum computing. He leads the Center for Educational Data Science and Innovation (EDSI) and has secured grants from the NSF, Gates Foundation, and Walton Family Foundation for projects like M-Powering Teachers and classroom quality assessment tools. Education: PhD in Information from the University of Michigan (advised by Qiaozhu Mei). Previous academic roles include teaching at the University of Michigan and Peking University in courses like Data Mining and Information Retrieval. Research Interests: Machine Learning and Causal Inference, AI for Education, Virtual Teams and Social Identity, Large Language Models for Social Applications. He has published in venues such as PNAS, Management Science, ACL, and the Web Conference. Grants: Major awards include a NSF grant on middle-grade math instruction analysis (with Min Sun) and a Gates Foundation grant for classroom dataset development (with Jing Liu). Labs/Teams: CLIP Lab member, collaborating on interdisciplinary projects with UMIACS and the Joint Quantum Institute. Prospective students: Open to mentoring PhD students through INFO and Computer Science programs. Actively supervises current students in education technology and quantum computing domains.

• High-Performance Computing
• Energy-Efficient Computing
• Parallel and Distributed Systems
• +۵ مورد دیگر

Ravi Reddy Manumachu is an Assistant Professor in the School of Computer Science at University College Dublin (UCD), Ireland. He holds a B.Tech from IIT Madras (1997) and a PhD in Computer Science from UCD (2005), specializing in high-performance heterogeneous computing and energy-efficient systems. His research focuses on optimizing performance and energy efficiency in modern heterogeneous platforms like clouds, grids, and supercomputers through novel models and algorithms. Key contributions include functional performance/energy models, energy-prediction frameworks, and extensions like Heterogeneous MPI and ScaLAPACK for heterogeneous clusters. He has published over 69 articles in top journals/conferences, with recent works addressing data transfer energy measurement, scalable allreduce algorithms (SUARA), and portable programming models (OpenH). Professional roles include Assistant Professor at UCD (2023–present), SEAI Research Fellow (2022–2023), and prior industrial experience at Ansys, Siemens, and IONA Technologies. He has certifications in university teaching, GDPR, and research integrity. Languages include English (fluent), Telugu, and Hindi. Research trends emphasize bi-objective optimization (performance-energy), hardware heterogeneity challenges, and scalable communication algorithms for deep learning. His work addresses energy non-proportionality in CPUs and GPU-CPU interactions, with practical solutions for real-world applications like matrix operations and gene sequencing.

Brian Kellerمشاهده پروفایل

استادیار

• Missing Data Analysis
• Bayesian Statistics
• Statistical Computing
• +۳ مورد دیگر

Brian Keller is an Assistant Professor in the Department of Educational, School & Counseling Psychology at the University of Missouri's College of Education & Human Development. His research focuses on statistical methodologies, particularly missing data analysis, Bayesian statistics, structural equation modeling, and multilevel modeling. He is the developer of Blimp statistical software, which supports advanced statistical computing methods. Key areas of expertise include missing data imputation techniques, Bayesian computational methods, and the application of statistical models to educational and psychological data. His work emphasizes methodological innovation, software development, and the rigorous analysis of complex datasets. Recent research trends in his articles highlight advancements in latent variable modeling, nonlinear effects, and the integration of computational tools like Blimp. His contributions address challenges in multilevel models, missing data mechanisms, and model comparison criteria such as DIC and WAIC. No specific scientific awards are listed for Brian Keller in the provided information. His professional affiliations include the College of Education & Human Development and the Blimp Statistical Software initiative. He advises students in the department but no specific advisee names are provided. Research grants and funding details are not explicitly mentioned in the text. Blimp Statistical Software is a key collaborative project, reflecting his commitment to advancing statistical methods and software solutions for researchers.

Fahad Ahmadمشاهده پروفایل

مدرس

• Machine Learning
• Cybersecurity
• Medical Informatics
• +۶ مورد دیگر

Fahad Ahmad is a Lecturer in the School of Computing within the Faculty of Technology at the University of Portsmouth. He holds affiliations with the Portsmouth AI and Data Science Centre, Centre for Cybercrime and Economic Crime, and Portsmouth Centre for Advanced Materials and Manufacturing. His research focuses on machine learning applications in healthcare, cybersecurity, and quantum computing. He supervises PhD students in topics like quantum machine learning for securing IoT medical devices. Key research areas include: Medical imaging diagnostics using deep learning (e.g., echocardiograms, X-rays) Cybersecurity for financial systems and SDN-NFV networks Quantum key distribution for post-quantum security AI-driven health management systems Recent work emphasizes: Human activity recognition through machine learning Cancer subtype classification using RNA expression data Emotional empathy modeling in intelligent agents His articles span healthcare technology, cybersecurity frameworks, and hybrid AI architectures. He actively contributes to international conferences and journals, with over 60 peer-reviewed publications. Research collaborations include institutions in Pakistan and the UK.

Armin Zareمشاهده پروفایل

استادیار

• Control Theory
• Fluid Mechanics
• Stochastic Dynamical Modeling
• +۳ مورد دیگر

Armin Zare is an Assistant Professor at the University of Texas at Dallas in the Department of Mechanical Engineering , affiliated with the Center for Wind Energy . He obtained his PhD in Electrical Engineering from the University of Minnesota (2016) and previously held a postdoctoral position at the University of Southern California. Education : PhD (2016), MS (2016) in Electrical Engineering from University of Minnesota; BS (2010) in Electrical Engineering from Sharif University of Technology Research Interests : Modeling, dynamics, and control of large-scale and distributed systems; optimization; dynamics and control of complex fluid flows; renewable energy generation. His work focuses on stochastic modeling of turbulent flows and wind farm dynamics using control theory. Recent Publications highlight advancements in wind farm turbulence modeling, stochastic receptivity analysis, and covariance completion algorithms, reflecting his expertise at the intersection of control systems and fluid dynamics. Scientific Awards Young Investigator Program Award, Air Force Office of Scientific Research (2023) Doctoral Dissertation Fellowship, University of Minnesota (2015) Best Student Paper Award Finalist, American Control Conference (2014) Advisees include PhD students working on topics like wall turbulence modeling, wind farm forecasting, and stochastic dynamical systems, with several former students having completed theses on wind turbine wake modeling and fluid flow uncertainties.

Alwell Julius Oyetمشاهده پروفایل

استاد

• Longitudinal Data Analysis
• Spatial Data Analysis
• Time Series Analysis
• +۳ مورد دیگر

Alwell Julius Oyet is a Professor of Statistics in the Department of Mathematics and Statistics at Memorial University of Newfoundland. He earned his B.Sc. (1986) and M.Sc. (1992) from the Federal University of Technology, Owerri, Nigeria, and his Ph.D. (1997) from the University of Alberta, Canada. His research focuses on longitudinal data analysis, spatial statistics, time series, statistical computing, multinomial data, and robust methods. He maintains active research collaborations in these areas. Oyet teaches undergraduate and graduate courses including Design of Experiments, Time Series Analysis, Multivariate Analysis, and Computational Statistics. He has extensive experience developing course materials and statistical computing resources.

Jennifer Wong-Maمشاهده پروفایل

استادیار

• Wireless Distributed Embedded Systems
• Sensor Prediction for Energy Efficiency
• Statistical Techniques for Optimizing Designs
• +۱ مورد دیگر

Jennifer Wong-Ma is a Research Assistant Professor in the Department of Computer Science at Stony Brook University. She holds a Ph.D., M.S., and B.S. in Computer Science and Engineering from UCLA (2006). Her research focuses on low-power wireless systems, sensor optimization, and statistical design methodologies. Current projects include wireless radio simulation, force-directed ad-hoc network models, and statistical link generators. Education: Ph.D. in Computer Science, UCLA, 2006 M.S. in Computer Science and Engineering, UCLA B.S. in Computer Science and Engineering, UCLA Research Interests: She explores energy-efficient sensor networks, statistical optimization for manufacturing variability, and wireless distributed systems. Her work emphasizes practical applications of theoretical models in embedded computing environments. Awards: 2012 Department Award for Undergraduate Education Teaching: Instructs courses such as CSE 220, CSE 300, CSE 651, and others across undergraduate and graduate levels, reflecting expertise in both foundational and advanced computer science topics.

Ammar Al-Baziمشاهده پروفایل

دانشیار

• Operations Research
• Supply Chain Management
• Simulation and Optimization
• +۳ مورد دیگر

Dr. Ammar Al-Bazi is an Associate Professor in Operations and Supply Chain Simulation at Aston Business School, part of the College of Business and Social Sciences at Aston University. He holds a PhD in Simulation and Optimization from Teesside University and an MSc in Statistics and Operational Research from the University of Baghdad. He is a Senior Fellow of the Higher Education Academy (SFHEA) and has led multiple funded projects, including those supported by Innovate UK and the European Regional Development Fund (ERDF). His research focuses on applying simulation, optimization, AI, and digital technologies to solve real-world challenges in logistics, energy, and manufacturing. Education: PhD in Simulation and Optimization, Teesside University (UK) MSc in Statistics and Operational Research, University of Baghdad (Iraq) BSc (Honors) in Operational Research, Al-Mansour College University (Iraq) Research Interests: Simulation and optimization techniques for supply chain management Energy efficiency and demand-side management Transportation and logistics optimization (including drones) Artificial Intelligence applications in manufacturing and healthcare Risk mitigation and disruption management in operations Recent Article Trends: Dr. Al-Bazi's recent work emphasizes digital twin technology, IoT integration in energy systems, and resilience in supply chains. His studies often combine machine learning with traditional optimization methods to address challenges like emissions prediction, consumer behavior modeling, and pandemic logistics. Awards and Roles: Senior Fellow of the Higher Education Academy (SFHEA) External Examiner for PhD programs at multiple universities Editorial board member of journals like Supply Chain Analytics and Journal of Information and Communication Technology Grants and Funding: £39,751 (Innovate UK) for dynamic railway contaminant control barriers £202,308 (KTP, Innovate UK) for collaboration with The Birmingham Assay Office Multiple KEEN and KTP projects focusing on SME performance improvement Advisory and Teaching: He supervises PhD students on topics like behavioral decision-making in healthcare logistics and renewable energy integration. He teaches courses in operations management, simulation for project management, and business analytics.

Keith Freelandمشاهده پروفایل

دانشیار

• Mathematical Finance
• Financial Risk Management
• Equity-Linked Insurance Products
• +۳ مورد دیگر

Keith Freeland is an Associate Professor (Teaching Stream) at the University of Waterloo's Department of Statistics and Actuarial Science, and Director of the Business Administration and Mathematics Double Degree Program. He holds a PhD in Business Administration from the University of British Columbia and a BSc in Actuarial Science from the University of Calgary. His career includes roles as an assistant professor at Waterloo from 2000–2010. Research interests historically focused on equity-linked insurance products and discrete-valued time series analysis, though he is no longer actively researching. He teaches courses like MATBUS 470 (Derivatives) and MATBUS 472 (Risk Management), emphasizing mathematical finance and financial risk management. Notable publications include work on categorical ARMA models, quasi-locally powerful tests for conditional variance, and integer-valued time series analysis. He holds the ASA designation from the Society of Actuaries (1991). Freeland's contributions bridge actuarial science, statistics, and financial mathematics, with teaching and past research centered on practical applications in risk management and insurance product modeling.

Elli Papangelakisمشاهده پروفایل

استادیار

• Fluvial Geomorphology
• River Restoration
• Urbanization Impact on Rivers
• +۳ مورد دیگر

Elli Papangelakis is an Assistant Professor and the Fairley Gadsby Research Chair in Fluvial Geomorphology at McMaster University's School of Earth, Environment and Society. Her research focuses on fluvial geomorphology, particularly the impacts of urbanization on river environments and the development of river restoration techniques. She holds a Ph.D. from the University of Waterloo (2019), an M.Sc. from the University of British Columbia (2015), and a B.Sc. from the University of Toronto (2013). Her research interests include ecological impacts of climate change, geomorphological landscape evolution, land-use planning, and hydrological processes. Key projects include studying Spencer Creek watershed management in Hamilton and developing tools like the Stream Power Index (SPIN) for erosion risk assessment in urban rivers. She actively promotes geoscience education through initiatives like GeoTrails, which uses digital platforms to enhance public engagement with geology. Elli has authored over 20 peer-reviewed articles since 2015, with recent work focusing on bedload transport dynamics, sediment augmentation strategies, and river restoration success metrics. Her teaching includes courses on Earth Surface Processes and Sediment Transport in Fluvial Environments. She maintains an active research lab on Spencer Creek, collaborating with students to advance fluvial geomorphology practices. Awards: Fairley Gadsby Research Chair (McMaster University) Grants: Chair-funded research projects on fluvial geomorphology and urban river management Labs/Teams: Outdoor research lab on Spencer Creek; collaboration with McMaster's Earth Science department

• Planet Formation
• Extrasolar Planets
• Accretion Disk Physics
• +۳ مورد دیگر

James Owen is a Reader in Astrophysics at the Department of Physics within the Faculty of Natural Sciences at Imperial College London. He holds a Senior Royal Society University Research Fellowship and previously served as a Hubble Fellow at the Institute for Advanced Study in Princeton and a CITA Fellow in Toronto. His research focuses on planet formation, extrasolar planets, and accretion disc physics, with particular emphasis on atmospheric escape mechanisms and protoplanetary disc structures. He is affiliated with the Astrophysics Group and the Physics Permanent Researchers at Imperial College. His key research interests include understanding how close-in super-Earths and mini-Neptunes form and evolve under intense stellar irradiation, driving atmospheric evaporation. He also investigates structures in protoplanetary discs observed via high-resolution imaging, linking these to planet formation processes. His work combines analytic theory and simulations to study hydrodynamic instabilities and disc dynamics. Notable awards include the Hubble Fellowship and CITA Fellowship. His recent work highlights the role of disc photoevaporation on giant planet migration and the observational signatures of embedded planets. He actively contributes to missions like JWST and UV-SCOPE for exoplanet characterization. Key affiliations: Astrophysics Group, Physics Permanent Researchers, Physics of Universe Labs: Blackett Laboratory at South Kensington Campus Future research directions include analyzing the growing dataset of protoplanetary discs and advancing models of exoplanet atmospheric evolution through neural network emulations.