Find Academic Supervisors

• Machine Learning
• Neural Networks
• Optimization Algorithms
• +7 more

Mehrdad Mahdavi is an Associate Professor in the field of Computer Science and Engineering. He has secured multiple National Science Foundation (NSF) grants, including EFRI BRAID: Neuroscience Inspired Visual Analytics, CAREER: Foundations of Collaborative Machine Learning, and CNS Core: Medium: When Next Generation Wireless Networks Meet Machine Learning. His research spans diverse areas of machine learning and optimization. His work focuses on Machine Learning , Optimization Algorithms , Quantum Computing , and Graph Neural Networks . He investigates memory-efficient training methods, generalization in unsupervised learning, quantum sampling for complex distributions, and distributed algorithms for collaborative learning. His research also intersects with healthcare applications, such as AI-driven lung ultrasound analysis for diseases like COVID-19. Recent publications highlight trends in Continual Learning , Temporal Graph Learning , Quantum Algorithms , and Federated Learning . His studies address theoretical frameworks for generalization, optimization challenges in non-convex and non-logconcave problems, and scalable solutions for graph-based machine learning tasks. He has contributed to energy consumption modeling, quantum sampling, and distributed risk minimization. As a Principal Investigator (PI) and Co-PI, he has led NSF-funded projects on collaborative machine learning , neuroscience-inspired visual analytics , and AI-enabled materials discovery . These grants underscore his focus on foundational research with applications in wireless networks, quantum computing, and interdisciplinary domains.

Quan ZhangView profile

Assistant Professor

• Machine Learning
• Statistics
• Interpretable Learning
• +7 more

Quan Zhang is an Assistant Professor in the Department of Accounting and Information Systems at the Broad College of Business, Michigan State University. With a Ph.D. in Information, Risk and Operations Management from the University of Texas at Austin (2020), an M.S. in Biostatistics from the University of Minnesota (2015), and a B.S. in Biological Science and Economics from Peking University (2012), his academic journey bridges diverse disciplines. Education Ph.D., Information, Risk and Operations Management - University of Texas at Austin (2020) M.S., Biostatistics (Ph.D. study) - University of Minnesota at Twin Cities (2015) B.S., Biological Science and Economics - Peking University (2012) His research focuses on interpretable machine learning , particularly balancing model interpretability and capacity using Bayesian and variational inference methods. Applications span telemedicine , quantitative marketing , medical data analysis , and online finance , with recent work on watermarking for copyright protection and robust representation learning. Selected publications include his 2025 paper on Beyond Invisibility: Learning Robust Visible Watermarks , 2023 work on Weibull Racing Survival Analysis , and foundational 2018 contributions to Nonparametric Bayesian Lomax Delegate Racing . His methodological innovations in Bayesian multinomial regression and survival modeling have been published in Advances in Neural Information Processing Systems and the Journal of Machine Learning Research . He contributes to software development with Python and R implementations for Bayesian models, available in his GitHub repository. His teaching includes graduate-level ITM885 Machine Learning and Optimization , emphasizing practical applications in business analytics.

• Physics
• Chemistry
• Machine Learning
• +5 more

Giuseppe De Luca is a Research Fellow at the Department of Physics and Chemistry - Emilio Segrè, University of Palermo. His work spans interdisciplinary domains including theoretical physics, biophysics, machine learning, and social dynamics. Research Themes : Model averaging techniques, liquid-liquid phase separation, quantum complexity, holography, and social exclusion effects in education. Methodologies : Bayesian inference, multiscale analysis, and machine learning optimization. Recent publications focus on weighted-average least squares estimation, structural degradation in biomaterials, and virtual reality applications in psychological research. He contributes to the SHARE project with statistical expertise in survey weighting and imputation strategies.

• Statistical Science
• Mathematical Statistics
• Multivariate Analysis
• +4 more

Kiyoshi Inoue is a Professor at the School of Political Science and Economics, Waseda University. He specializes in statistical science, mathematical statistics, and multivariate analysis, with a focus on heteroscedastic regression models and inverse regression problems. His research includes asymptotic improvements of estimators like the Graybill-Deal estimator and iterative weighted least squares methods. Professional Memberships: Japan Statistical Society, Mathematical Society of Japan His teaching portfolio spans advanced linear algebra, mathematical statistics, and calculus courses at both undergraduate and graduate levels. Research projects funded by the Japan Society for the Promotion of Science (2001-2003) explored high-accuracy estimators for inverse regression, with applications in econometrics and statistical modeling. Key trends in his publications include asymptotic theory development for heteroscedastic models, iterative estimation techniques, and robust methods under non-normality. His work bridges theoretical statistics with practical applications in economics and education.

Jeffrey GrahamView profile

Associate Professor

• Applied Mathematics
• Computational Mathematics
• Numerical Analysis
• +6 more

Jeffrey Graham is an Associate Professor in the School of Math and Computer Sciences at Susquehanna University, where he has served as Head of Mathematical Sciences since 2007. He previously held roles as Acting Head of Physics (2009–2014) and Assistant Professor (2001–2007), with earlier experience at Western Carolina University (1992–2001). PhD in Mathematics (1992), Rensselaer Polytechnic Institute MS in Mathematics (1986), Texas Tech University BS in Computer Science and Mathematics (1984), Iowa State University His research bridges mathematics and computer science, focusing on inverse problems, differential equations, and computational methods. He has pioneered educational technologies, including MATLAB GUIs and Excel-based tools, and explored intersections with music theory via group theory applications. Jeffrey’s recent publications span quantum computing (2010), financial mathematics (2014), and leadership strategies (2011), reflecting his interdisciplinary approach. His work emphasizes pedagogical innovation and computational modeling across fields. Jeffrey has secured multiple grants, including NSF travel funding (1993), Microsoft instructional lab support (1997), and NSF REU grants (2008–2010). He has served on editorial boards, curriculum committees, and search committees, demonstrating institutional leadership.

Ami WieselView profile

Professor

• Signal Processing
• Machine Learning
• Covariance Estimation
• +3 more

Ami Wiesel is a Professor at The Rachel and Selim Benin School of Computer Science and Engineering at The Hebrew University of Jerusalem. His research focuses on statistical signal processing, machine learning, and covariance estimation. Previously, he completed his postdoctoral studies at the University of Michigan with Professor Alfred Hero, earned his PhD in Electrical Engineering from Technion under Professors Yonina Eldar and Shlomo Shamai, and obtained his MSc and BSc in Electrical Engineering from Tel Aviv University. His research interests include robust covariance estimation , statistical learning , signal detection , and MIMO communications . Wiesel has made significant contributions to the field of structured covariance estimation, particularly in elliptical distributions and Tyler's estimator. His work bridges theoretical statistics with practical applications in radar systems, wireless communications, and hyperspectral imaging. Wiesel's publications show a clear trend toward integrating deep learning with traditional statistical signal processing methods. His recent work explores unbiased estimation using neural networks, fair principal component analysis, and deep learning applications for target detection with constant false alarm rate. His research spans theoretical foundations in covariance estimation to practical implementations in radar and communications systems. Among his notable scientific achievements are: Young Author Best Paper Award (2019) for 'Learning to Detect' Young Author Best Paper Award (2006) for 'Linear precoding via conic optimization for fixed MIMO receivers' Student Paper Award (2017) for 'Deep MIMO detection' Wiesel has advised numerous graduate students who have gone on to publish significant work in the field. His research has been supported by various grants focusing on statistical signal processing, machine learning applications, and radar systems. His monograph 'Structured Robust Covariance Estimation' (2015) has become a reference in the field. He maintains an active research group focusing on the intersection of statistical learning and signal processing, with applications in communications, radar, and medical imaging.