جستجوی استاد راهنمای دانشگاهی

Eylem Asmatuluمشاهده پروفایل

دانشیار

• Recycling and Reuse of Materials
• Composite Materials Engineering
• Nanotechnology Safety
• +۷ مورد دیگر

Dr. Eylem Asmatulu is an Associate Professor in the Department of Mechanical Engineering at Wichita State University (WSU), part of the College of Engineering. She joined WSU as an educator in 2015, became an Assistant Professor in 2017, and was promoted to her current rank in 2023. Previously, she worked in Environmental Health and Safety at WSU. She currently advises three PhD, three MS, and two BS students, having graduated 13 students since 2015. Her research focuses on recycling and reuse of materials, composite materials engineering, nanotechnology safety, and sustainable manufacturing. She has secured over $1.7 million in grants, published 131+ articles (h-index 26), and contributed to fields like thermal energy storage, biomedical nanomaterials, and aerospace material science. Her work emphasizes interdisciplinary approaches, combining machine learning for material property prediction and addressing safety concerns in nanotechnology across industries. Key projects include flame-retardant composites, biogas production optimization, and superhydrophobic nanofiber applications for water treatment. She actively promotes sustainability through recycling innovation and lean manufacturing practices in aerospace. Dr. Asmatulu’s research has been cited over 2,870 times. Her contributions span academia and industry, with a focus on translating innovations into practical solutions for environmental and industrial challenges.

Laurie Wilcoxمشاهده پروفایل

استاد

• Vision Science
• Neuroscience
• Binocular Vision
• +۴ مورد دیگر

Laurie Wilcox is a Full Professor in the Department of Biology at York University, affiliated with the Faculty of Science. Her research focuses on stereopsis, binocular vision, and depth perception, particularly exploring how the visual system processes binocular disparity signals. She leads a laboratory investigating cortical systems for fine and coarse disparities, with studies on amblyopia and applied collaborations with companies like Christie Digital and IMAX. Her work bridges basic neuroscience and applied research, addressing depth perception in 2D/3D displays and VR environments. Key interests include stereoscopic volume representation, perceptual grouping, and the impact of monovision on depth judgments. Recent studies examine lightness constancy in virtual reality, depth magnitude errors in 3D displays, and neural activation patterns in object-selective visual cortex. Wilcox has published extensively on binocular vision mechanisms, including coarse stereopsis in strabismus patients and the role of motion parallax in depth perception. Her applied projects evaluate visual fidelity in stereoscopic content, compression algorithms, and ergonomic considerations for XR devices. She also investigates how environmental context (e.g., familiar size, natural scenes) modulates depth perception accuracy across real and virtual environments. Her research emphasizes translational applications, aiming to optimize display technologies through insights from human visual processing. Ongoing work explores perceptual integration of binocular and monocular cues, attention modulation by depth, and the neurophysiological underpinnings of stereoscopic vision.

Emily Cookمشاهده پروفایل

استادیار

• Higher Education Policy
• Economics of Education
• STEM Education
• +۳ مورد دیگر

Emily Cook is an Assistant Professor at Texas A&M University, focusing on higher education economics, policy analysis, and institutional decision-making. Her research examines topics such as college pricing structures, STEM education funding, vaccine mandate impacts, and market dynamics in education and aviation sectors. She employs rigorous empirical methodologies to evaluate policy outcomes and institutional behaviors. Her work highlights how federal funding influences STEM curricula, how tuition policies affect student major choices, and the role of peer institutions in shaping reopening strategies during crises. Cook also investigates the equity implications of college pricing models and the structural evolution of industries such as airlines and higher education markets. Notable areas of focus include the interplay between public health policies and educational continuity, the competitive landscape of US universities, and the socioeconomic factors influencing student transitions from high school to college.

Eve S. Pufferمشاهده پروفایل

دانشیار

• Global Mental Health
• Child Development
• Family Therapy
• +۴ مورد دیگر

Eve S. Puffer is the Pamela and Jack Egan Associate Professor of Psychology & Neuroscience at Duke University, affiliated with Trinity College of Arts & Sciences. She holds additional roles as Associate Research Professor of Global Health (Duke Global Health Institute) and affiliate of the Center for Child and Family Policy. Her work focuses on global mental health interventions, particularly in sub-Saharan Africa, addressing child mental health, family functioning, and HIV prevention. She has led studies in Kenya, Liberia, Thailand, and Guatemala, emphasizing culturally adapted, community-based approaches. Education: PhD (2008), MA (2005) in Psychology from the University of South Carolina; BS (2002) from Furman University. Research Interests: Dr. Puffer develops interventions to reduce violence against children, promote positive parenting, and address HIV risk behaviors. Her work integrates family systems and community engagement, often collaborating with religious institutions and local leaders. Recent projects include adapting interventions for refugee families in Jordan and virtual family support during the pandemic. Grants & Recognition: Principal investigator on NIH-funded projects (e.g., ESTEEM intervention in Guatemala) and grants from the International Rescue Committee and Oxford University. Notably selected for the Thomas Langford Award (2023) and featured in Duke’s global health initiatives. Labs/Teams: Active in Duke Initiative for Science & Society and Center for Child and Family Policy, focusing on scalable mental health solutions for marginalized populations.

Patricia K. Tongمشاهده پروفایل

دانشگاهی

• Economic Policy
• Public Finance
• Labor Economics
• +۱۲ مورد دیگر

Patricia K. Tong is an economist at RAND and a Professor of Policy Analysis at the RAND School of Public Policy. She specializes in public finance and labor economics, with a strong focus on military and civilian personnel policy, compensation, and health economics. Her research addresses critical issues in military personnel retention, family caregiving, retirement benefits, and access to mental and behavioral health services. Research Interests: Tong’s work combines qualitative and quantitative methods to evaluate monetary and nonmonetary compensation policies, labor market outcomes, and social welfare impacts. Her expertise spans economic policy, health economics, military personnel systems, and family support structures. She has led studies on Social Security survivor benefits, tax policy anti-poverty effects, and nurse staffing standards. Publication Trends: Her recent research centers on military compensation, health care access for service members and families, and financial inclusion. Articles reflect a consistent policy-oriented focus, often supporting governmental reviews such as the Quadrennial Review of Military Compensation, and addressing timely issues like pandemic caregiving and unbanked populations. Scientific Contributions: Published in top journals including American Economic Review and Health Economics Key contributor to national policy discussions on military pay, retention, and health care Research used to inform federal and state-level policy decisions Advising and Grants: While specific students are not listed, her leadership in major research projects indicates a supervisory and mentoring role. She has led federally funded studies on military compensation, health care access, and financial inclusion, demonstrating sustained grant support from defense and public policy agencies. Labs and Teams: Tong conducts research within RAND’s interdisciplinary policy teams, particularly those focused on defense, health, and economic security. Her work often involves collaboration with military stakeholders, federal agencies, and public health experts to develop actionable policy solutions.

Jana Shenمشاهده پروفایل

استاد

• Molecular Simulation
• Data Science
• Kinases
• +۱۲ مورد دیگر

Jana Shen is a Professor in the Department of Pharmaceutical Sciences at the University of Maryland School of Pharmacy, where she leads an interdisciplinary research group at the intersection of chemistry, biology, physics, and computer science. Her lab develops and applies advanced simulation and data science tools to understand biomolecular mechanisms and accelerate drug discovery. Education: Postdoc, The Scripps Research Institute (2003–2007) PhD, University of Minnesota at Twin Cities (1999–2003) MS, University of Calgary, Canada (1996–1999) Diplom-Chemie, Bergische Universität Wuppertal, Germany (1991–1995) Her research focuses on molecular simulation , data science , and computational biophysics , with applications in kinases , GPCRs , transmembrane transporters , and pH-responsive materials . She has pioneered the development of continuous constant pH molecular dynamics (CpHMD) methods and their applications in drug design and biomolecular mechanisms. The recent publications highlight a strong trend in computational drug discovery , particularly in covalent inhibitors , opioid receptor mechanisms , antiviral design , and the integration of machine learning with molecular dynamics . These works span high-impact journals such as eLife , JACS , Nature Communications , and ACS journals. Scientific Awards: National Science Foundation CAREER Award American Chemical Society HP Outstanding Junior Faculty Award Junior Faculty Research Award (University of Oklahoma, 2008, 2009) Phi Kappa Phi, University of Minnesota Louise T. Dosdall Graduate Fellowship Nova Graduate Fellowship Dr. Shen has mentored numerous PhD students and postdoctoral fellows, many of whom have gone on to successful careers in academia and industry. Her research is supported by major agencies including the National Institutes of Health , National Science Foundation , and FDA . She leads the Shen Lab, which actively develops open-source tools such as DeepCys , CpHMD , and PKAD-3 , and maintains databases for covalent ligandability and pKa predictions.

Sajjad Bighamمشاهده پروفایل

دانشیار

• Heat Transfer
• Thermal Management
• Multiphase Flows
• +۱۴ مورد دیگر

Dr. Sajjad Bigham is an Associate Professor in the Department of Mechanical and Aerospace Engineering at North Carolina State University and serves as an Adjunct Associate Professor at Michigan Technological University. He holds a PhD in Mechanical Engineering from the University of Florida and directs the Energy-X Lab (Energy eXploration Laboratory), which focuses on high-impact research in energy science and technology. His research interests encompass: Advanced thermal management solutions including microscale heat transfer, boiling/condensation phenomena, and interfacial transport Energy-efficient systems for HVAC&R, desalination, and clean water production Development of micro/nano-engineered materials and devices for energy conversion/storage Sorption-based gas management and multiphase systems under extreme conditions Recent publications demonstrate strong focus on thermal management innovations (45%), sustainable energy systems (30%), and advanced materials applications (25%). Dominant themes include heat transfer enhancement techniques, energy-efficient appliance design, desalination technologies, and microscale phase-change phenomena, with increasing emphasis on additive manufacturing approaches. Dr. Bigham leads the Energy-X Lab research group, which tackles high-risk, high-reward problems across four thrust areas: Terrestrial and space life support systems Advanced thermal management Clean energy production Clean water supply The lab's mission is to improve energy efficiency, reliability, and economy across defense, environmental, and energy sectors.

• Observational cosmology
• Galactic astronomy
• Exoplanet science
• +۵ مورد دیگر

David W. Hogg is Professor of Physics and Data Science in the Center for Cosmology and Particle Physics in the Department of Physics at New York University. He serves as Senior Research Scientist in the Astronomical Data Group in the Center for Computational Astrophysics of the Flatiron Institute and maintains an affiliation with the Max-Planck-Institut für Astronomie in Heidelberg. His primary research focuses on observational cosmology, particularly approaches that use galaxies to infer physical properties of the Universe. He also conducts significant research on stellar kinematics in the Milky Way and the measurement and discovery of exoplanets. Across all domains, Hogg develops engineering systems and statistical methodologies that enable large-scale astrophysical projects for both his research group and the broader community. Recent work demonstrates expertise in robust statistical methods, particularly dimensionality reduction techniques like Robust-HMF. His research bridges theoretical statistics with practical applications in major astronomical surveys including Gaia, SDSS-V, and SPHEREx. He frequently explores connections between Bayesian and frequentist approaches to astronomical data analysis, with recent work on nuisance parameter integration, anomaly detection, and robust matrix factorization. Research supported by NYU, NASA, NSF, Moore Foundation, Sloan Foundation Additional support from Max Planck Society, Humboldt Foundation, ERC, Simons Foundation Hogg is actively involved in major astronomical projects including Astrometry.net, Gaia, and SDSS, with long-term comprehensive goals of analyzing all galaxies, stars, and astronomical images. His work emphasizes open science principles, reproducible research practices, and the development of publicly accessible tools for the astronomical community.

Judy Hartمشاهده پروفایل

دانشیار

• Renewable Energy Materials
• Photocatalysis
• Solar Cells
• +۵ مورد دیگر

Associate Professor Judy Hart is a materials scientist at the School of Materials Science & Engineering, UNSW Sydney , specializing in the development of semiconducting materials for renewable energy applications. Her work integrates computational (DFT) and experimental approaches to understand composition-property relationships in systems like solid solutions , heterostructures , and doped materials for photocatalysis and solar cells . She leads projects funded by ARC Discovery and Linkage grants , including work on photo-electro-catalysis systems and stabilizing ceramic materials . Education: PhD in Materials Engineering (Monash University, 2007), BEng (Materials) (Monash, 2002) Professional Experience: Senior Lecturer (UNSW, 2017–), Lecturer (UNSW, 2013–2017), University of Bristol (2007–2012) Research Interests Her research focuses on designing materials for renewable energy , particularly photoelectrochemical water splitting and organic oxidation reactions . Key areas include Density Functional Theory (DFT) , defect engineering , band gap tuning , and nanostructured materials . She investigates ferroelectric polarization effects , metal oxide heterostructures , and stability of battery components , with applications in hydrogen production , CO2 conversion , and advanced battery materials . Scientific Awards Ramsay Memorial Fellowship (University of Bristol, 2007–2009) Teaching Contributions She is co-author of the 1st Australian & New Zealand edition of "Materials Science and Engineering: An Introduction" , and teaches courses on computational materials science , corrosion-resistant surfaces , mechanical behavior of metals , and materials design .

Chuanhai Liuمشاهده پروفایل

استاد

• Statistical Inference
• Computational Methods
• Bayesian Analysis
• +۴ مورد دیگر

Chuanhai Liu is an Associate Head and Professor of Statistics at Purdue University’s Department of Statistics. He specializes in computational methods for statistical inference, Bayesian analysis, and big data computing systems. His research emphasizes robust algorithms, inferential models, and probabilistic methods. Education: M.S., Wuhan University, Probability and Statistics (1987) M.A., Harvard University, Statistics (1990) Ph.D., Harvard University, Statistics (1994) Research Interests: Computational methods (EM algorithms, MCMC), statistical software systems for big data, theoretical foundations of inference, and prior-free probabilistic modeling. His work bridges computational efficiency and statistical validity, with applications in network analysis, environmental data, and machine learning. Awards: Fellow of the American Statistical Association (2007) Elected Member of the International Statistical Institute (2006) Frank Wilcoxon Prize (2000) Advising & Grants: Supervised 9 Ph.D. students, including interdisciplinary collaborations. His grants focus on distributed statistical computing and inferential frameworks. Active in software development for large-scale data analysis. Labs/Teams: Leads statistical computing initiatives at Purdue, contributing to scalable algorithms and probabilistic inference systems.

Margaret Drewمشاهده پروفایل

دانشیار

• Gender Violence
• Trauma and Healing
• Feminist Jurisprudence
• +۴ مورد دیگر

Margaret Drew is an Associate Professor at UMass School of Law , focusing on Gender Violence , Human Rights , and Legal Ethics . With decades of practical legal experience in Massachusetts courts, she transitioned to academia in 2005 after a career in family law, probate, and residential real estate litigation. Education: BA from University of Massachusetts Boston, JD from Northeastern University School of Law, LLM in Taxation from Boston University School of Law Her research explores the intersection of law and gender-based violence , including trauma healing frameworks and feminist jurisprudence. She co-founded the Human Rights at Home Blog and has designed clinics enabling students to work directly with survivors of domestic abuse. Recent publications address complex social determinants in HIV care, feminist perspectives on pandemic response, and human rights remedies in domestic violence cases. In 2025, she received the Lifetime Achievement Award for her contributions. Scientific Awards: 20/20 Vision Award (ABA Commission on Domestic and Sexual Violence) Lifetime Achievement Award (May 1, 2025) As a former Director of Clinics at multiple law schools and current member of the American Bar Association and National Association of Women Lawyers , she continues to advocate for survivors while teaching courses like Intimate Partner Violence and State Attorney General Clinic .

Cosmin Ioan Romanمشاهده پروفایل

مدرس

• Microsystem Technology
• Nanotechnology
• Solid-State Sensors
• +۴ مورد دیگر

Dr. Cosmin Ioan Roman is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich, affiliated with the Chair in Micro and Nanosystems since 2006. His research focuses on solid-state micro and nanotransducers, spanning from traditional Silicon micromachining to carbon nanotube-based (CNT) devices for bio-sensing applications, with an emphasis on energy-efficient transducer concepts. Doctoral Degree: National Polytechnic Institute of Grenoble (INPG) Roman's expertise lies in multi-physics and compact modeling of transducers. His work bridges materials science, semiconductor device physics, and biomedical sensing, utilizing advanced fabrication techniques for scalable sensor arrays on flexible substrates. The selected publications highlight his contributions to tactile sensing and cell rheology. His co-supervised doctoral thesis on carbon nanotube resonators demonstrates his interdisciplinary approach to nanoscale and biomedical systems.

Karina Gibertمشاهده پروفایل

استاد

• Data Science
• Artificial Intelligence
• Ethics in AI
• +۷ مورد دیگر

Karina Gibert is a Full Professor at Universitat Politècnica de Catalunya (UPC), specifically at the Faculty of Computer Science of Barcelona (FIB) in the Department of Statistics and Operations Research. With a permanent teaching position since 1990, she contributes to research and education with a focus on Data Science, Artificial Intelligence, Explainable AI, and Ethics in AI. Full Professor, UPC (since 1990) PhD in Informatics Engineering Postgraduate in Higher Education Teaching Director and co-founder of IDEAI research center Dean of the Official Professional College on Informatics Engineering of Catalonia Active in bridging the gender gap in STEAM through multiple initiatives Her research focuses on extracting strategic knowledge from data and intelligent systems with ethical and explainable perspectives. She has led various projects including Diet4You for personalized diets, INSESS-COVID19 for social vulnerability analysis, Top Rosies Talent for female AI development, and ciutadanIA for AI culture promotion. Her work spans health, environment, sustainability, tourism, and social technology applications. As an editor of Environmental Modeling and Software journal and active academic, she contributes to international conferences, working groups, and research collaborations. Her service includes membership on various ethics and AI strategy committees and advisory boards. Women Tech Award 2023 National Informatics Engineering Award in Digital Dissemination 2022 Ada Byron Award 2022 Honorific Mention of Creu Casas award 2021 donaTIC 2018 Award Finalist at AMETIC Awards 2021 Finalist of European Social Services Awards 2021

Radu Craiuمشاهده پروفایل

استاد

• Data Science
• Statistical Computing
• Efficiency of Monte Carlo methods
• +۸ مورد دیگر

Radu Craiu is a Professor in the Department of Statistical Sciences at the University of Toronto. Born in Bucharest, Romania, he earned his PhD in Statistics from the University of Chicago in 2001 under the supervision of Xiao-Li Meng. He joined the University of Toronto as an Assistant Professor, was promoted to Associate Professor in 2006, and to Full Professor in 2013. He served as Department Chair from 2018–2022 and 2023–2024. Education: PhD in Statistics (2001), University of Chicago MS in Mathematics (1996), University of Bucharest BS in Mathematics (1995), University of Bucharest His research focuses on computational methods in statistics, particularly Markov chain Monte Carlo (MCMC) algorithms, Bayesian inference, copula models, model selection, and statistical genetics. He has collaborated on interdisciplinary projects in genetics, ecology, astronomy, and demography, emphasizing flexible statistical modeling and efficient computational techniques. Radu Craiu has received prestigious awards, including the CRM-SSC Prize (2016), CJS Best Paper Award (2019), and fellowships from the American Statistical Association (2022), Institute of Mathematical Statistics (2020), and International Statistical Institute (2015). He actively contributes to editorial roles for journals like the IMS Bulletin and Harvard Data Science Review .

Keming Yuمشاهده پروفایل

استاد

• Quantile Regression
• Bayesian Statistics
• Survival Analysis
• +۱۲ مورد دیگر

Keming Yu is a Professor and Chair in Statistics at the Department of Mathematics, Brunel University London, within the College of Engineering, Design and Physical Sciences. He is also the Impact Champion for REF in Mathematical Sciences. He joined Brunel in 2005 after holding positions at the University of Plymouth, Lancaster University, and The Open University. He earned his PhD from The Open University and earlier degrees in Mathematics and Statistics from Chinese institutions. PhD in Statistics – The Open University, UK MSc in Statistics – China BSc in Mathematics – China His research centers on quantile regression, Bayesian modeling, survival analysis, and statistical methods for big data . His work spans applications in health, finance, environment, and social sciences. He has made significant contributions to robust and flexible regression methods, including expectile, mode, and censored quantile regression. His recent publications (2023–2025) show a strong focus on streaming data, spatiotemporal modeling, high-dimensional data, and Bayesian methods . He frequently publishes in top-tier journals such as the Journal of the Royal Statistical Society Series A, B, and C , Statistica Sinica , and Computational Statistics and Data Analysis . His work often involves collaboration with international researchers, especially in China and Europe. He has contributed to methodological discussions in leading statistical journals, demonstrating active engagement with the academic community. His work on financial risk, environmental statistics, and health data analysis reflects interdisciplinary impact. Reviewed and contributed to discussions on safe testing, confidence sequences, and betting-based inference. Active in developing methods for nonignorable missing data, censored models, and functional covariates. He supervises PhD students and is involved in teaching and curriculum development, including as Course Director for the MSc Statistics with Data Analytics. His research is supported by extensive publication output and academic service. He leads or contributes to research on Bayesian models, robust regression, and scalable methods for big data , often involving collaborations in interdisciplinary teams. His lab or research group focuses on statistical methodology development with real-world applications.