Find Academic Supervisors

• Machine Learning
• Neural Networks
• Mathematical Analysis
• +3 more

Felix Voigtländer is a mathematics professor at Catholic University Eichstätt-Ingolstadt specializing in the theoretical foundations of machine learning and neural networks. His research focuses on Barron spaces and their applications in understanding neural network approximation capabilities. Voigtländer's primary research interests lie in mathematical analysis of neural networks, particularly examining how Barron spaces (functions with finite Fourier moments) relate to neural network approximation properties. His work demonstrates that functions in these spaces can be approximated by shallow neural networks with rates that don't severely deteriorate with dimension, providing mathematical justification for neural networks' effectiveness in high-dimensional problems. His recent publications analyze sampling numbers of Fourier-type Barron spaces and neural network performance for classification problems with Barron-class boundaries. These works establish important theoretical connections between function approximation theory and practical neural network performance, showing how dimensionality affects approximation and sampling rates in these specialized function spaces. Voïgtländer's research contributes significantly to the mathematical understanding of why neural networks can overcome the curse of dimensionality for certain function classes, with implications for both theoretical machine learning and practical neural network design.

Edward RinconView profile

Adjunct Professor

• Multicultural Research
• Survey Research Methods
• Hispanic Marketing
• +11 more

Edward Rincon, Ph.D., is an Adjunct Professor in the Marketing department at The University of Texas at Arlington, College of Business. He holds a Ph.D. in Educational Psychology from The University of Texas at Austin (1979) and has over four decades of professional experience in multicultural survey research and market analysis. Position: Adjunct Professor, Marketing Institution: The University of Texas at Arlington Email: edward.rincon@uta.edu His academic and professional work centers on advancing rigorous, inclusive methodologies in research involving multicultural populations, particularly Latinos and Asian Americans. Research Interests Dr. Rincon's research spans a broad range of interdisciplinary topics, including sources of bias in multicultural studies, healthcare and disease trends, political behavior, food insecurity, racial bias in ability testing, environmental contamination, and retail redlining. He emphasizes the importance of culturally and linguistically appropriate research design to ensure data validity and inclusivity. He is the developer of SERENITY® , a patent-pending, multi-modal, multilingual survey response system designed to overcome limitations in traditional survey practices such as English-only formats, single-mode data collection, and biased sampling frames. He also created Populus USA® , a mobile app providing demographic insights on U.S. race-ethnic groups. Professional Engagement and Impact Dr. Rincon is the founder of Rincon & Associates, LLC, a research firm that has served clients across banking, healthcare, government, education, media, and retail sectors. His expertise has been featured in numerous media outlets including the Wall Street Journal , Advertising Age , Dallas Morning News , and NPR . He has delivered presentations to professional organizations such as AAPOR, Texas Bar Association, and American Marketing Association. Teaching He has taught courses including Multicultural Research Methods (MARK 5350) and Marketing Research (MARK 4311) at UT Arlington, focusing on statistical methods, survey design, and Hispanic marketing strategies. Public Policy and Commentary Co-authored a public policy paper on Texas' economic prosperity and public-private infrastructure partnerships. Authored influential commentaries on immigration trends, Latino leadership, survey industry challenges, and food deserts in Dallas County. Advocated for improved multicultural research practices, warning against the pitfalls of generalist providers and promoting culturally competent methodologies. Notable Projects Launched a 2024 study on access to food providers in Dallas County, identifying over 90 food deserts, particularly in Southern Dallas. Conducted studies for major clients including UT Southwestern, Baylor Health Care, Texas Instruments, Coca-Cola, and American Airlines. Pioneered research on Latino consumer behavior, media usage, and political participation since the 1980s.

• Cognitive Neuroscience
• Neurodegenerative Diseases
• Posterior Cortical Atrophy
• +5 more

Dr. Ahmed Samrah is a researcher specializing in cognitive neuroscience and neurodegenerative disorders, with a particular focus on posterior cortical atrophy (PCA) and Alzheimer's disease. His work explores language impairments, connected speech analysis, and diagnostic strategies across diverse linguistic and ethnic populations. His research interests include: Cognitive and linguistic deficits in PCA Connected speech as a diagnostic tool in dementia Language-specific biomarkers in non-English speakers Memory aids and rehabilitation strategies for PCA patients Neuroimaging correlates of cognitive decline Cross-cultural and multilingual neuropsychology The recent publications highlight a strong trend in using speech and language features for early and culturally sensitive diagnosis of neurodegenerative diseases. His collaborative work spans systematic reviews, clinical studies, and neuroimaging research, emphasizing the importance of task design and linguistic diversity in cognitive assessment. Scientific contributions include: Investigating sentence repetition in PCA Developing protocols for scoping reviews on memory aids and non-English speech markers Contributing to clinical guidelines for PCA diagnosis and management Identifying white matter correlates of memory impairment Advancing understanding of connected speech in Bengali speakers with Alzheimer’s Dr. Samrah actively collaborates with leading researchers in neurology and neuropsychology. While no formal advising or grant information is available in the provided text, his publication record suggests involvement in multidisciplinary research teams focused on dementia and cognitive aging. There is no indication of teaching roles or lab leadership in the current data.

Daniel CornelView profile

Researcher

• Flood Modeling
• Hydrology
• Computational Fluid Dynamics
• +3 more

Daniel Cornel is a researcher affiliated with TU Wien's Department of Engineering Hydrology, part of the E222-02 research group. He holds a Dipl.-Ing. (engineering diploma) and a Dr.techn. (technical doctorate). His work focuses on integrating computational methods with environmental hydrology, emphasizing flood modeling, high-performance computing, and geospatial visualization. Cornel collaborates extensively with experts like Prof. Günter Blöschl and Prof. Jürgen Waser on projects such as the HORA 3.0 flood risk zoning initiative. His research interests span GPU-accelerated flood simulations, hydrodynamic modeling at regional scales, and decision-support systems for disaster management. Notable contributions include developing interactive visualization tools for real-time flood data and VR-based training modules for emergency response. Cornel also explores algorithm optimization for terrain modeling and subsurface network visualization. Key projects include the 'Integrated Simulation and Visualization for Flood Management' framework and the 'Master of Disaster' VR training system. His work bridges computational science with environmental engineering, addressing both technical challenges and practical applications in flood risk mitigation.

Becky DawsonView profile

Associate Professor

• Epidemiology
• Public Health Policy
• Global Health
• +3 more

Dr. Becky Dawson is a tenured Associate Professor in the Department of Biology at Allegheny College, specializing in epidemiology with affiliations to Global Health Studies and Healthcare Management programs. She holds a PhD from the University of Maryland, Baltimore, an MPH from Emory University, and a BS from Allegheny College. Her work focuses on translating epidemiological research into actionable public health strategies through her educational platform Epi(demiology) Matters , emphasizing interdisciplinary collaboration and critical analysis of scientific literature. Educational Background PhD - University of Maryland, Baltimore MPH - Emory University BS - Allegheny College Dr. Dawson's research examines complex interactions between epidemiological data, clinical medicine, and environmental factors in both infectious and non-infectious diseases. Her work on toxic shock syndrome and cancer etiology demonstrates how epidemiological evidence must be combined with laboratory results and policy considerations to create effective public health interventions. She actively addresses challenges in moving from research to implementation, including legal and political hurdles. Her recent publications in Epi(demiology) Matters explore epidemiological methodology, statistical literacy, and ethical considerations in public health. The articles emphasize the importance of holistic literature analysis beyond abstracts, while addressing contemporary issues like abortion policy, vaccine hesitancy, and health misinformation through epidemiological frameworks. As an educator, Dr. Dawson develops curricula and outreach programs to improve epidemiological understanding among students and the general public. She serves as Research Director at a community hospital and contributes health-related content to Erie News Now (NBC/CBS). Her approach combines technical expertise with accessible communication strategies to build healthier communities through informed decision-making.

• Aquamedicine
• Fish Viral Diseases
• Fish Immune Defense
• +3 more

Maria Krudtå Dahle serves as a 20% Professor II in Aquamedicine at the Norwegian Fisheries College, part of UiT The Arctic University of Norway in Tromsø. Her academic position focuses on fish health research within the Norwegian aquaculture sector, with particular emphasis on viral pathogens affecting salmonids. Dr. Dahle's research interests center on aquamedicine, fish viral diseases, fish immune defense, and vaccinology. Her work primarily investigates the immune responses of salmon to viral pathogens including Piscine orthoreovirus (PRV), Infectious Salmon Anemia Virus (ISAV), and Salmon Gill Poxvirus. She employs advanced molecular techniques such as transcriptomics, metabolomics, and cell culture models to understand host-pathogen interactions in fish. Her research group has established important cellular models including the ASG-10 salmon gill epithelium cell line and LG-1 lumpfish gill cell line. Analysis of Dr. Dahle's publication record reveals a strong focus on molecular mechanisms of viral infections in salmon, particularly examining how red blood cells function as sensors of infection and stress. Her research shows increasing integration of multi-omics approaches to identify biomarkers for early disease detection. A significant portion of her work investigates PRV variants and their effects on salmon health, with practical implications for aquaculture disease management and monitoring. Dr. Dahle is an active member of the Fish Immunology and Vaccinology research group at UiT, collaborating extensively with Norwegian research institutions including the Norwegian Veterinary Institute and the Norwegian Institute of Food, Fisheries and Aquaculture Research (Nofima). Her work bridges fundamental immunological research with practical applications for the aquaculture industry.

• Applied Mathematics
• Theoretical Mechanics
• Computational Mechanics
• +7 more

Dr. Luis Espath is an Assistant Professor of Applied Mathematics at the University of Nottingham, affiliated with the School of Mathematical Sciences. His research spans theoretical and computational mechanics, uncertainty quantification, and machine learning. He has held postdoctoral and research scientist roles at KAUST and RWTH Aachen, respectively, and authored the Springer Nature book Mechanics & Geometry of Enriched Continua . Education : Engineering Diploma (2007) at PUCRS, Brazil; Master’s and Ph.D. (2013) at UFRGS, Brazil; Habilitation in Mathematics (2021) at RWTH Aachen, Germany. Dr. Espath’s research focuses on the application of phase-field and continuum mechanics to model complex systems, with an emphasis on thermodynamic consistency and computational efficiency. He integrates stochastic optimization and machine learning into fluid dynamics and structural modeling. His recent publications highlight expertise in phase-field gradient theory, fluid-structure interaction, and Bayesian experimental design. The 2021 articles reflect his work on Navier-Stokes equations, reactive Cahn-Hilliard systems, and divergence-free fluid flow approximations.

Alexandru PopaView profile

Adjunct Professor

• Roman Frontier Studies
• Romanization Processes
• Protohistoric Archaeology
• +4 more

Dr. Alexandru Popa serves as an Adjunct Professor at the Institute for History within the Faculty of Philosophy, Arts, History, and Social Sciences at the University of Regensburg . His academic journey includes a 2013 habilitation at the University of Bonn, a 1998 PhD from Iaşi, and a 1991 diploma from Chișinău. Academic Appointments: Adjunct Professor at University of Regensburg Research Focus: Roman frontier studies, Dacian archaeology, and cultural interactions between the Roman Empire and Barbarian territories Methodological Expertise: Geomagnetic surveys, phosphate mapping, and interdisciplinary approaches to settlement analysis His recent publications (2013-2020) examine Roman military structures in Eastern Dacia, cultural exchange dynamics along Danubian frontiers, and archaeological heritage preservation techniques. Notable projects include contributions to the Romanian National Limes Program and the development of mobile applications for field archaeology. Email: alexandru.popa@ur.de

• Financial Engineering
• Risk Management
• Quantitative Finance
• +9 more

Professor Mi-Hsiu Chiang is a faculty member at the Department of Money and Banking, National Chengchi University (NCCU), College of Commerce , with a career spanning over two decades in quantitative finance, credit risk modeling, and financial engineering. He holds a PhD in Quantitative Finance from Imperial College London and has published extensively in top-tier journals like Pacific-Basin Finance Journal , Financial Management , and Review of Quantitative Finance and Accounting . Education: PhD (Quantitative Finance), MSc (Mathematics), BSc (Mathematics) from Imperial College London. Research Focus: Credit risk modeling, options pricing, machine learning applications in finance, ESG investments, and climate risk hedging. Awards: Recipient of MOST Research Awards, Senior Outstanding Teacher (20 years) at NCCU, and several academic prizes including UMC Management Paper Award and Fubon Paper Award. Projects: Led numerous research initiatives funded by the National Science and Technology Council, including studies on climate change risk replication using GPT and machine learning, ESG sentiment in credit default prediction, and behavioral biases in derivatives markets. His recent publications emphasize stochastic dominance in stock markets, liquidity-driven strategies, and empirical analyses of credit and information risks. He has also contributed to behavioral finance by examining the disposition effect in options and convertible bonds. His work bridges quantitative methods with practical financial applications, including synthetic CDO valuation and TIPS pricing under inflation jumps. Scientific Awards 科技部研究獎勵 資深優良教師 (20年), 國立政治大學 第十屆聯電經營管理論文獎優等獎, 中華民國管理科學學會 台灣財務金融學會富邦論文獎 台大管理論叢宋作楠基金會論文獎 資深優良教師 (10年), 國立政治大學 學術研究成果國際化優等研究獎, 政治大學 教學特優教師, 政治大學商學院

Jiaming XuView profile

Associate Professor

• Machine Learning
• Networks
• Data Science
• +8 more

Jiaming Xu is an Associate Professor of Business Administration in the Decision Sciences area at Duke University's Fuqua School of Business, where he has been a faculty member since July 2018. He is also a Faculty Network Member of the Duke Institute for Brain Sciences. His academic journey includes positions as an Assistant Professor at Purdue University's Krannert School of Management (2016-2018), a Research Fellow at the Simons Institute for the Theory of Computing at UC Berkeley (2016), and a Postdoctoral Fellow at the Statistics Department of the Wharton School at the University of Pennsylvania (2015). Ph.D. in Electrical and Computer Engineering from University of Illinois, Urbana-Champaign (2014) M.S. in Electrical and Computer Engineering from University of Texas, Austin (2011) B.S.E. in Electrical and Computer Engineering from Tsinghua University, China (2009) Professor Xu's research focuses on developing fundamental methodologies for inferring information from data to enable downstream data-driven decision-making at scale. His work spans machine learning, networks, high-dimensional statistics, and information theory. He develops algorithms for improving decision-making efficiency under uncertainties and resource constraints while addressing emerging privacy and security issues. His research has significant implications for network data privacy, where he has demonstrated how anonymized data can still be used to re-identify individuals through unique behavioral patterns and network connections. His recent publications reveal a strong focus on graph matching problems, community detection in networks, and privacy-preserving machine learning. Xu has made significant contributions to understanding information-theoretic thresholds in random graph matching, developing efficient algorithms for network alignment, and establishing fundamental limits for community detection. His work increasingly addresses the challenges of federated learning and privacy-preserving data analysis, reflecting the growing importance of these areas in both theoretical and practical contexts. Scientific Awards and Recognition: NSF CAREER Award (2022) for Federated Learning: Statistical Optimality and Provable Security Simons-Berkeley Fellowship (2016) Excellence in Teaching Award in the MQM program (awarded twice) Professor Xu has successfully mentored several students who have gone on to prestigious positions, including Sophie H. Yu (Assistant Professor at the Wharton School), Hanjing Zhu (Researcher at Amazon), Liren Yu (Researcher at Huawei), and Zhiyi Tian (Data Scientist at IQVIA). His research has been supported by multiple significant grants including an NSF CAREER award (2022-2027), a CIF Medium grant for Learning in Networks (2019-2023), and BIGDATA and CRII grants focused on network analysis and high-dimensional data (2018-2021). At Duke, Professor Xu teaches Modern Analytics (Deep Learning), Decision Analytics & Modeling in the MQM program, and Decision Models in the MBA and WEMBA programs. His work bridges theoretical foundations with practical applications, particularly in the areas of network privacy and data security, where he has demonstrated how seemingly anonymized data can still be used to identify individuals through sophisticated matching algorithms.

• Computer Vision
• Optimization
• Machine Learning
• +3 more

Sameer Agarwal serves as an Affiliate Professor in the Department of Computer Science & Engineering at the University of Washington, Seattle, while concurrently working as an engineer at Google. His dual affiliation bridges academic research with industrial application in computer vision. His primary research domains include: Computer Vision Optimization Machine Learning Image Processing 3D Reconstruction Nonlinear Least Squares Agarwal specializes in developing algorithmic solutions for complex vision problems, particularly through algebraic methods and optimization frameworks. His work has produced influential open-source tools like Ceres Solver for bundle adjustment and contributed to practical global optimization techniques in multiview geometry. Publication analysis reveals consistent focus on efficient computational methods across vision and graphics, with landmark contributions in multiview geometry (2008), multidimensional scaling (2007), and environment map sampling (2003). His research trajectory emphasizes translating theoretical optimization into robust, deployable systems. No information regarding student advisement, grant funding, or laboratory teams was provided in the source materials.

Eunshin ByonView profile

Professor

• Reliability Engineering
• Fault Diagnosis
• Condition Monitoring
• +8 more

Eunshin Byon is a Professor and Richard Wilson Faculty Scholar in Industrial and Operations Engineering at the University of Michigan, serving as Director of Masters Programs. Her expertise spans reliability engineering, data-driven decision-making, and stochastic systems optimization for critical infrastructure including renewable energy grids and manufacturing processes. Education: Ph.D. in Industrial and Systems Engineering, Texas A&M University M.S. in Industrial and Systems Engineering, KAIST B.S. in Industrial and Systems Engineering, KAIST Dr. Byon's research pioneers digital twin calibration and uncertainty quantification methodologies, integrating stochastic simulation with big data analytics to solve complex reliability challenges. Her work bridges theoretical advances in sequential computer experiments with practical applications in energy sustainability and predictive maintenance, emphasizing variance reduction techniques and operational decision frameworks for interconnected systems. Recent publications reveal a concentrated focus on parameter calibration innovations and computational efficiency in stochastic modeling, demonstrating consistent advancement in digital twin technologies for energy systems. These works increasingly incorporate machine learning elements while maintaining rigorous statistical foundations for uncertainty propagation. Scientific Awards: teaching award from the QCRE division of IISE (2024) Richard Wilson Faculty Scholar (2023) 2022 Best Associate Editor award, INFORMS Journal on Data Science 2021 Department Faculty Award Dr. Byon has mentored multiple doctoral candidates to successful completion, with students placing at Clemson University and Zhejiang University. Her research is sustained by significant external funding including an NSF grant for digital twin calibration (2023-2025) and Ford Motor Company research support (2023-2025), focusing on real-world implementation of stochastic optimization frameworks. She leads a specialized research group developing next-generation reliability assessment tools, collaborating with industry partners on renewable energy integration challenges while advancing fundamental methodologies in stochastic simulation and data analytics for complex engineered systems.

Andrzej T GaleckiView profile

Research Professor

• Biostatistics
• Geriatrics
• Gerontology
• +6 more

Andrzej T Galecki is a Research Professor in both the Department of Internal Medicine, Geriatric and Palliative Medicine and the Department of Biostatistics at the University of Michigan. He serves as the Director of the Design, Data and Biostatistics Core of the Older Americans Independence Center at the University of Michigan. His academic credentials include an MD from the Medical Academy of Warsaw (1981), a PhD from the Institute of Mother and Child Care in Warsaw (1987), and an MS in Applied Mathematics from the Technical University of Warsaw (1977). Dr. Galecki's research focuses on the application of modern statistical methods to studies in geriatrics and gerontology. His expertise spans nonlinear mixed effects models, population pharmacokinetics and pharmacodynamics analysis, modeling of covariance structure in longitudinal data analysis, and generalized linear models for categorical data. He has developed computational methods for analyzing correlated and overdispersed data, with applications across pharmacokinetic and pharmacodynamic studies, longitudinal research, survey sampling, and genetic studies. His work with mixed-effects models has led to significant methodological advancements, including extensions that allow between-subject variation to be modeled as mixtures of underlying distributions. He developed the SAS/IML NLMEM for computational methods in PK/PD population studies and contributed to the implementation of covariance structure modeling in PROC MIXED. His recent publications demonstrate a strong focus on aging research, particularly examining cognitive impairment in older adults, kidney function in diabetes, and interventions in nursing home settings. His work often involves complex statistical modeling of longitudinal data and has been applied to important clinical questions in geriatrics and chronic disease management. Dr. Galecki has published influential methodological books including 'Linear Mixed-Effects Models Using R. A Step-by-Step Approach' (2013) and the third edition of 'Linear Mixed Models: A Practical Guide Using Statistical Software' (2022), which have become standard references in the field. As Director of the Design, Data and Biostatistics Core of the Older Americans Independence Center, Dr. Galecki leads a team providing statistical expertise for aging research. His work involves both methodological development and collaborative research across multiple disciplines, particularly in geriatrics, diabetes, and kidney disease. Through his leadership, he has secured NIH funding for multiple projects focused on aging and independence in older adults. His research has been widely cited and has influenced statistical practice in biomedical research, particularly in the analysis of longitudinal data in geriatric and clinical studies.

• Machine Learning
• Software Testing
• Safety Critical Systems
• +7 more

Karl Meinke is a Professor at KTH Royal Institute of Technology, where he serves as Head of the Computer Science Department and Head of the Division of Theoretical Computer Science within the School of Electrical Engineering and Computer Science. His research focuses on applying machine learning techniques to software testing, particularly for safety-critical systems like autonomous vehicles and embedded systems. His research interests span machine learning, software testing, safety critical systems, embedded systems, autonomous driving, digital pathology, and graph neural networks. Meinke has developed innovative approaches like Learning-Based Testing that combine machine learning with formal methods for system validation. His work bridges theoretical computer science with practical applications in automotive systems and medical diagnostics. His recent publications show a strong trend toward applying graph neural networks to diverse domains including program analysis, digital pathology, and autonomous vehicle testing. His research demonstrates a consistent focus on solving the test oracle problem and generating meaningful test cases for complex systems where traditional testing approaches fall short. Meinke actively collaborates with Karolinska Institutet (KI), indicating interdisciplinary work between computer science and medical research. He is responsible for Masters level education in software testing at KTH and serves as examiner for several advanced courses including Degree Projects in Computer Science and Software Reliability. His research group has developed tools like LBTest for learning-based testing of reactive systems, and he has secured funding for projects such as the ITEA3 Testomat Project focused on next-level test automation. His work has significant implications for validating autonomous systems where safety is paramount. Meinke leads research in using machine learning to address fundamental challenges in software testing, particularly for systems where traditional test oracles are unavailable or impractical. His approach of combining active learning with formal specifications has created new pathways for validating complex cyber-physical systems.

• Computational Mathematics
• Applied Mathematics
• Monte Carlo Methods
• +4 more

Jonathan B. Goodman is a Professor of Mathematics at the Courant Institute of Mathematical Sciences , New York University , where he has been an instructor since 2000. His research spans computational mathematics , applied mathematics , and stochastic methods in finance . He earned his Ph.D. from Stanford University in 1982, specializing in computational and applied mathematics. Research Interests: Mathematical theory of shock waves, Monte Carlo methods in quantum chemistry, anisotropic finite element refinement, stochastic processes, Bayesian graduation methods, and computational finance. Teaching: Courses include Scientific Computing (Fall 2024), Numerical Methods II (Spring 2024), Stochastic Calculus (Fall 2022), and Mathematics of Finance (Spring 2019). Teaching materials span over two decades. Students: Advisees include PhD and Masters students working on topics such as adaptive refinement algorithms, dynamic hedging with transaction costs, importance sampling for Value at Risk, and Bayesian mortality rate graduation. Software: Developed Acor , a program for estimating autocorrelation time and statistical error bars in Markov chain Monte Carlo simulations, in collaboration with Alan Sokal.