جستجوی استاد راهنمای دانشگاهی

David P. Baker is a Professor in both Education Policy Studies and Sociology and Criminology at Penn State University's College of Education. His office is located in the 647 Susan Welch Liberal Arts Building, and he can be reached at dpb4@psu.edu or 814-863-0955. Dr. Baker leads the Population Research Institute and maintains an active research program examining education's global impact on societal development. His educational background includes a Ph.D. in Sociology from The John Hopkins University (1982), an M.S. in Communication Research from Boston University (1975), and a B.A. in Psychology from Albion College (1974). These diverse academic foundations inform his interdisciplinary approach to studying education's societal impacts. Baker's research spans the sociology of education, science productivity, world health, demographic change, and the formation of social institutions. His work examines how education transforms cognitive skills in populations, reshapes occupations, influences health behaviors globally (including HIV/AIDS in Africa, smoking patterns, and obesity), and drives technological adaptation across 63 nations. He conducts innovative research through randomized clinical trials and historical analyses of universities' role in knowledge production. His publications reveal consistent focus on education's transformative power across multiple domains. Recent work examines nicotine reduction policies, cognitive transformations in the labor market, and the global testing culture. His book The Schooled Society represents a major contribution to understanding how education has reshaped modern society. Outstanding Book of the Year by the American Education Research Association Silver Medal in the Independent Publisher Book Awards (IPPYs) Best poster award at Population Association of America conference Professor Baker actively mentors students and colleagues, with numerous collaborative projects including the Population Education Transition Curve research. His laboratory work includes experimental studies on education and health messaging, with funding from the Spencer Foundation and Penn State's Population Research Institute. Current projects examine how education shapes perceptions of low-nicotine cigarette messaging and the cognitive transformation of social opportunity.

• Sociology of Education
• Science Productivity
• World Health
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Md Sakib Hasan is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Mississippi. He holds a Ph.D. in Electrical Engineering from the University of Tennessee-Knoxville (2017). His research focuses on hardware acceleration, neuromorphic computing, and memristor-based systems. Research interests span: AI hardware accelerators and energy-efficient computing Biomimetic systems and bio-inspired electronics Hardware security through chaotic systems and PUFs Recent publications demonstrate strong emphasis on: Neuromorphic architectures for computer vision and temporal processing Biomembrane-based computing systems Chaotic cryptography and secure hardware design

• Hardware Accelerators
• Memristor Technology
• Reservoir Computing
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Zhen Ming (Jack) Jiang is an Associate Professor and York Research Chair (Tier II) in Software Engineering for Foundation Model-Powered Systems at York University's Department of Electrical Engineering and Computer Science. His research bridges software engineering, artificial intelligence, and computer systems with significant industrial impact. Dr. Jiang earned his Ph.D. from Queen's University's School of Computing and MMath/BMath degrees from the University of Waterloo's David R. Cheriton School of Computer Science. During his doctoral studies, he collaborated with BlackBerry's Performance Engineering team, developing tools now used daily to monitor commercial software systems. His research focuses on engineering rigor for AI-powered applications , software engineering evolution in the Generative AI era , and performance optimization of large-scale systems . Key areas include software performance engineering, mining software repositories, debugging distributed systems, source code analysis, and software visualization. His work combines empirical studies with practical tool development. Recent publications reveal strong trends in applying AI to software engineering challenges, particularly in machine learning systems reliability, blockchain efficiency, and AIOps solutions. His research consistently emphasizes empirical validation using real-world systems and industrial case studies. Scientific recognition includes: York Research Chair (Tier II) in Software Engineering for Foundation Model-Powered Systems NSERC Discovery Accelerator Supplements (DAS), 2020 Best Paper Award at ICST 2016 IEEE Software Best SEIP Paper at ICSE 2015 Ph.D. Research Achievement Award at Queen's University Multiple best paper awards at WCRE, MSR, and ICSE Dr. Jiang actively supervises graduate students and has secured competitive research funding including NSERC grants. His service includes program committee roles for top conferences (ICSE, ASE, ICSME) and editorial work for leading journals (TSE, TOSEM, EMSE). He leads research initiatives focused on foundation model-powered systems, collaborating with industry partners on performance monitoring and debugging solutions for large-scale distributed environments.

• Software Engineering
• Artificial Intelligence
• Computer Systems
• +۶ مورد دیگر

Wooram Park is an Associate Professor in the Department of Mechanical Engineering at the University of Texas at Dallas (UT Dallas), affiliated with the Erik Jonsson School of Engineering and Computer Science. He leads the Robotics and Intelligent Systems Laboratory (ROBINS Lab) and holds a PhD from Johns Hopkins University (2008), along with MS and BS degrees from Seoul National University (2003 and 1999). His research focuses on robotics, biomedical robotics, computational structural biology, and image processing. Key projects include flexible needle steering for medical applications, haptic feedback systems, and advanced algorithms for motion planning and image reconstruction. He has received notable awards such as the Creel Fellowship (2007) and Critics’ Choice Award in ArtBot Design (2004). His work spans theoretical contributions in stochastic systems and practical innovations like vibratory magnetic robots (Vimbot) and wearable haptic devices. The ROBINS Lab emphasizes interdisciplinary research at the intersection of mechanical engineering, computer science, and biomedical applications.

• Robotics
• Biomedical Robotics
• Computational Structural Biology
• +۳ مورد دیگر

Dr. Ahmad Baroutaji is a prominent researcher at Aston University's School of Engineering and Technology, specializing in Additive Manufacturing, Metamaterials, and Energy Systems. His work focuses on advancing materials science for biomedical, acoustic, and energy applications. He holds a strong academic affiliation within the College of Engineering and Physical Sciences. Research Interests: Optimizing 3D-printed metamaterials for energy absorption and crashworthiness Development of advanced materials for fuel cells and hydrogen technologies Acoustic metamaterials for noise reduction in buildings Biomaterials for orthopedic and tissue engineering applications Notable contributions include pioneering studies on cobalt-chromium-molybdenum meta-scaffolds for bone reconstruction and acoustic panels using titanium perforated structures. His 2024 review on PEM hydrogen technologies has been widely cited. Collaborations span global institutions, emphasizing practical material innovations. Grants and Advising: While specific grants are not detailed, his extensive publication record indicates sustained research funding. No formal advisee list is provided, but co-authorships suggest collaborative mentorship. Labs/Teams: Engaged in multidisciplinary teams focusing on additive manufacturing applications, though specific lab names are not mentioned in the text.

• Additive Manufacturing
• Metamaterials
• Energy Absorption
• +۵ مورد دیگر

John J. Curtin is a Professor in the Department of Psychology at the University of Wisconsin-Madison, where he directs the Addiction Research Center. His work bridges clinical psychology, computer science, and engineering to develop innovative digital solutions for mental health and addiction treatment. Dr. Curtin's research focuses on digital therapeutics and personal sensing technologies for substance use disorders and mental illness. His laboratory develops software applications that provide evidence-based interventions, treatment management tools, and enhanced communication with care providers. He specializes in algorithm development for moment-to-moment psychiatric risk prediction and just-in-time personalized interventions that adapt to both patient characteristics and their current context. His research program is highly interdisciplinary, collaborating with the Center for Health Enhancement Systems Studies, computer science, geography, and electrical and computer engineering departments. Dr. Curtin's work combines machine learning approaches with novel data streams from geolocation, cellular communications, social media activity, and wearable biosensors to create more effective and personalized treatment approaches. Dr. Curtin has secured continuous funding from the National Institutes of Health (NIAAA, NIDA, NCI and NIMH) since 1998. His current research examines machine learning-assisted precision medicine for smoking cessation, contextualized daily prediction of lapse risk in opioid use disorder, and dynamic real-time prediction of alcohol use lapse using mobile health technologies. His laboratory has produced numerous publications advancing the field of digital mental health interventions, with a particular focus on using technology to deliver precisely tailored treatments at the right moment for individuals struggling with substance use disorders.

• Digital therapeutics
• Machine learning applications in mental health
• Addiction research
• +۵ مورد دیگر

Eric Bettinger is the Conley DeAngelis Family Professor in the Stanford University School of Education and a Senior Fellow at the Hoover Institution. He also holds appointments as a Senior Fellow at the Stanford Institute for Economic Policy Research (SIEPR) and Professor (by courtesy) of Economics. Bettinger directs the Center for Educational Policy Analysis and the Lemann Center for Brazilian Education at Stanford, and serves as a research associate with the National Bureau of Economic Research. His educational background includes a Ph.D. in Economics from the Massachusetts Institute of Technology (2000) and a B.A. in Economics, Magna cum Laude with Honors, from Brigham Young University (1996). Bettinger's research focuses on the economics of education, with particular emphasis on student success and completion in college, the impacts of online education, financial aid effects, teacher characteristics, and voucher programs. His work employs rigorous statistical methods to identify cause-and-effect relationships in higher education. Notably, his research on simplifying financial aid applications has directly influenced White House efforts to streamline financial aid processes, demonstrating the real-world policy impact of his scholarship. His recent publications reveal consistent focus on educational interventions, technology in education, and policy evaluation. Bettinger's work spans multiple methodologies including randomized controlled trials, natural experiments, and large-scale data analysis. His research often addresses practical educational challenges while maintaining strong theoretical foundations in economics. Bettinger has served as a consultant to the White House and various state governments on financial aid policies, translating academic research into actionable policy recommendations. His work bridges the gap between academic scholarship and practical educational policy implementation. As an academic leader, Bettinger directs multiple research centers and has advised numerous doctoral students. His current advisees include Joseph Moore, Sergio Arango, Michelle Blair, Dallas Gilmore, Gabriel Koraicho, and postdoctoral researcher Saloni Gupta.

• Economics of Education
• Higher Education
• Student Success and Completion
• +۵ مورد دیگر

Eyal Z. Goren is a Professor in the Department of Mathematics and Statistics at McGill University. His research focuses on arithmetic geometry, including studies of Shimura varieties, modular forms, complex multiplication, expander graphs, arithmetic dynamics, and mathematical cryptography. He is affiliated with the Centre Interuniversitaire en Calcul Mathématique Algébrique (CICMA), a Montreal-based group in number theory. Goren’s work bridges pure mathematics and applications in cryptography, with notable contributions to the theory of supersingular elliptic curves and cryptographic hash functions derived from expander graphs. Education : PhD in Mathematics from the Hebrew University of Jerusalem (1996), advised by Ehud De Shalit. Teaching : Teaches advanced courses such as Higher Algebra I/II, Algebra 1/2/3/4, Number Theory, and specialized topics like Unlikely Intersections. Affiliations : Active member of CICMA and the CRM (Centre de Recherches Mathématiques), collaborating on seminars and research initiatives. Research Interests : Goren’s work emphasizes the interplay between number theory and geometry, with recent focus on p-adic dynamics, canonical subgroups, and Faltings heights. His studies on Picard modular forms and Shimura varieties explore geometric structures in positive characteristic and their arithmetic implications. Publications : Over 40 articles in leading journals, including Inventiones Mathematicae , Compositio Mathematica , and Journal für die reine und angewandte Mathematik . His book Lectures on Hilbert Modular Varieties and Modular Forms is a key resource in the field. Grants and Collaboration : Engaged in collaborative projects on expander graphs, post-quantum cryptography, and the geometry of abelian varieties with complex multiplication. His research is supported by grants from the NSERC and other agencies.

• Number Theory
• Arithmetic Geometry
• Shimura Varieties
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Guanghao Qi is an Assistant Professor in the Department of Biostatistics at the University of Washington. His research focuses on developing statistical and machine learning methods for multi-omics approaches in genetic studies, particularly integrating single-cell RNA-seq, GWAS, and functional genomic data. Key areas include single-cell eQTL analysis, Mendelian randomization, and multi-trait genetic association analyses. Education: PhD in Biostatistics from Johns Hopkins Bloomberg School of Public Health (2020), BS in Mathematics from Fudan University (2015). Research interests emphasize high-dimensional data analysis, allele-specific expression in single cells, and causal inference using genetic variants. Notable achievements include a 2025 NIH K01 award for developing methods to integrate single-cell eQTL and GWAS data, and the development of the TWiST method for single-cell transcriptome-wide association studies. Recent work highlights advancements in computational tools like SURGE for context-specific genetic regulation analysis, and evaluations of Mendelian randomization methods in studies of type 2 diabetes and cardiovascular disease. His work often bridges computational biology and statistical theory to address challenges in interpreting large-scale genomic datasets. Awards: NIH K01 Award (2025) Key Contributions: TWiST method (2025), SURGE framework (2024), HIPO power optimization (2018) Labs/Teams: Active collaborations in genomic epidemiology and statistical genetics, with a focus on single-cell multi-omics integration and causal inference methodologies.

• Statistical Genetics
• Genomics
• Single-cell RNA Sequencing Analysis
• +۳ مورد دیگر

Dr. Serhan Sadikoglu is an Assistant Professor at Tilburg University's Tilburg School of Economics and Management (TiSEM), Department of Econometrics and Operations Research. He earned his PhD in Econometric Theory from Tilburg University in 2019. His research focuses on econometric theory, semiparametric estimation, robust statistics, and panel data analysis. He has published in prestigious journals such as Econometric Reviews and Econometrics Journal , with recent work addressing nonseparable panel models and robust estimation techniques. Sadikoglu teaches courses including Quantitative Methods, New Venture Creation and Growth, and Statistics for Econometrics. His research emphasizes methodological contributions to econometrics, particularly in handling complex data structures and improving estimation robustness.

• Semiparametric Estimation
• Nonparametric Regression
• Robust Statistics
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Frederi G. Viens is a Professor of Statistics at Rice University, where he leads research in probability theory, stochastic processes, and their applications to finance, climate science, and agro-ecology. Previously, he was a full professor at Michigan State University (2016–2022) and Purdue University (2000–2015), serving as Department Chair and Director of Actuarial Science. His work bridges theoretical mathematics with practical problems in agriculture, economics, and nuclear physics. Education: Ph.D. Mathematics, University of California, Irvine (1996) M.S. Mathematics, University of California, Irvine (1991) Maîtrise de Mathématiques Pures, Université de Paris VII, France (1991) Research Interests: Probability Theory & Stochastic Analysis Quantitative Finance & Actuarial Science Climate Science & Bayesian Statistics Agro-ecology & Agricultural Economics His collaborative projects include climate modeling, nuclear physics simulations, and sustainable crop diversity initiatives like the DRIVES network. Awards & Honors: Fellow of the Institute of Mathematical Statistics (2012) Franklin Fellow, U.S. State Department (2010) Purdue College of Science Research Award (2013) Grants & Collaborations: Funded by the NSF, USDA, and private donors, Viens has organized major conferences and serves on editorial boards for journals like Annals of Finance . He advises transnational research groups, including Sustainability Lake Chad , addressing agrarian sustainability in West Africa. Labs & Initiatives: Founding member of the DRIVES agro-ecology collaborative and moderator of the Seminar on Stochastic Processes.

• Probability Theory
• Stochastic Processes
• Quantitative Finance
• +۷ مورد دیگر

Mark Pollicott is a Professor of Mathematics at the University of Warwick, where he has held positions since 1992 and 2005. He previously served at Edinburgh, Porto, and Manchester Universities, including a Fielden Chair. His research focuses on Thermodynamic Formalism, Ergodic Theory, and Dynamical Systems, with applications to geometry, number theory, and fractal analysis. Pollicott earned his BSc (1981) and PhD (1984) in Mathematics and Physics from Warwick, under the supervision of William Parry. He has held prestigious fellowships, including Royal Society and ERC grants, and organized major programs at the Newton Institute, CIB-Lausanne, and ICERM. He serves on editorial boards for journals like Nonlinearity and Journal of Fractal Geometry . His research explores topics such as fractal dimensions, geodesic flows, and validated numerics. Notable contributions include studies on the Hausdorff dimension of Cantor sets and Bernoulli convolutions. He has supervised 19 PhD students and mentored 18 postdoctoral researchers. Recent grants include EPSRC funding for computational ergodic theory and dynamical zeta functions. His work bridges pure mathematics with applications in geometry, analysis, and number theory. Awards: ERC Advanced Grant, EPSRC Leadership Fellowship, Royal Society Fellowships, Jean Morlet Chair Grants: EPSRC (2019-2025, 2026-2030), ERC (2019-2025) Collaborations: Co-organized programs on Thermodynamic Formalism and Dynamics at CIRM and Warwick

• Thermodynamic Formalism
• Ergodic Theory
• Dynamical Systems
• +۴ مورد دیگر

Diane Guignard is an Assistant Professor in the Department of Mathematics and Statistics at the University of Ottawa. Her research focuses on numerical analysis, partial differential equations, and computational methods with applications to mechanics and stochastic systems. She holds a position in a leading mathematics department and can be contacted at dguignar@uOttawa.ca . Her research interests include finite element methods, model reduction, uncertainty quantification, and optimal transport-based mesh adaptation. She explores nonlinear approximation theories for high-dimensional anisotropic functions and develops computational frameworks for thin structures and colloidal flow simulations. Her work bridges numerical analysis with practical engineering challenges, emphasizing adaptive algorithms and error estimation techniques. Her recent publications (2021-2024) highlight contributions to goal-oriented mesh adaptation, stochastic field approximations on surfaces, and large deformation analyses of prestrained plates. These studies emphasize interdisciplinary approaches combining mathematical rigor with computational innovation. Dr. Guignard has not been explicitly noted for awards in the provided materials. Her advising record is currently unspecified, though her research group likely engages in advanced numerical methods and computational mechanics projects.

• Numerical Analysis
• Applied Mathematics
• Computational Mathematics
• +۶ مورد دیگر

Deborah Kim is an Assistant Professor of Economics at the University of Warwick, specializing in econometrics and data science. She holds a Ph.D. in Economics from Northwestern University, an M.A. in Economics, and a B.S. in Industrial Engineering from Seoul National University. Her research focuses on econometric methodologies, including predictive ability testing and statistical techniques for small-sample cluster analysis. She teaches courses such as EC 349 (Data Science for Economists) and EC 346 (Research Methods in Economics). Her work has been published in Econometric Theory and Journal of Econometric Methods , with a notable contribution to STATA package development for applied econometrics. No scientific awards are explicitly mentioned, and no grants/students are listed in available texts.

• Econometrics
• Data Science
• Economics

Liang Zhang is a Professor of Higher Education at New York University's Steinhardt School, specializing in higher education economics, finance, and public policy. He previously taught at the University of Minnesota, Vanderbilt University, and Penn State University. His research examines the role of governments and institutions in shaping institutional performance and student outcomes, with a focus on college access, labor markets, and policy efficacy. Dr. Zhang holds dual PhDs from Cornell University (Economics) and the University of Arizona (Higher Education). His work has been published in leading journals such as Review of Higher Education , Economics of Education Review , and Harvard Education Review . Key research areas include the impact of state policies on college enrollment, peer effects in academic decisions, and the global dynamics of scientific productivity. Recent studies highlight his analysis of the Post-9/11 GI Bill’s effects on veteran education access and outcomes, as well as the stratification of faculty employment in U.S. higher education institutions. His work consistently bridges economic theory and policy practice, offering actionable insights for institutional leaders and policymakers.

• Higher Education Economics
• Public Policy Analysis
• Educational Finance
• +۳ مورد دیگر