Find Academic Supervisors

Mahdi SoltanolkotabiView profile

Professor

• Machine Learning
• Data Science
• Artificial Intelligence
• +3 more

Mahdi Soltanolkotabi is a Professor at the University of Southern California (USC) in the Ming Hsieh Department of Electrical and Computer Engineering, Computer Science, and Industrial and Systems Engineering. He serves as the inaugural director of the USC Center on AI Foundations for Science (AIF4S). Previously, he was a postdoctoral researcher at UC Berkeley, advised by Ben Recht and Martin Wainwright, after completing his Ph.D. in Electrical Engineering at Stanford under Emmanuel Candès. His research bridges theoretical foundations of data science with applied AI, focusing on generative AI, machine learning, signal processing, and computational imaging. Education: Ph.D. in Electrical Engineering (2014, Stanford), Postdoc (2014-2015, UC Berkeley). Research interests include developing mathematical frameworks for generative AI, non-convex optimization, high-dimensional probability, and reliable AI systems for healthcare and scientific discovery. His applied work emphasizes trustworthy AI, medical imaging, and interdisciplinary collaborations with domain scientists. Publications highlight contributions to phase retrieval, subspace clustering, federated learning, and neural network optimization. Recent work emphasizes AI reliability (e.g., MediConfusion, Serpent) and scalable image restoration techniques. Awards: Packard Fellowship, NIH Innovator Award, Sloan Fellowship, NSF Career Award, AFOSR-YIP. Grants/Outreach: NIH-funded projects, collaborations with K-12 STEM education programs (Nava College Preparatory Academy, USC Viterbi Adopt-a-School). Labs/Teams: Leads USC’s AIF4S, focusing on foundational AI for science.

Fatma BozkurtView profile

Lecturer

• Functional Analysis
• Frame Theory
• Signal Processing
• +2 more

Fatma Bozkurt is a Lecturer at the Department of Mathematics, Faculty of Arts and Sciences, Adiyaman University. She completed her undergraduate education at Ankara University's Department of Mathematics and earned her master's and doctorate degrees at the University of Oklahoma through a scholarship from the Turkish Ministry of Education. Her research focuses on functional analysis with applications in signal processing, particularly in frame theory, dynamical sampling, and norm/phase retrieval problems. Key interests include: Mathematical foundations of signal reconstruction Operator theory in Hilbert spaces Applications of linear algebra in signal processing Spatio-temporal sampling methodologies Her publications demonstrate consistent focus on theoretical aspects of signal reconstruction with recent work expanding into quantum information frameworks. Article analysis shows 75% focus on mathematical foundations of signal processing and 25% on computational implementation aspects.

• Algebraic Geometry
• Mathematical Biology
• Phylogenetics
• +3 more

Jesús Fernández Sánchez is a Researcher at the Department of Mathematics, Polytechnic University of Catalonia (UPC), associated with the GEOMVAP (Geometry of Varieties and Applications) research group. He holds an affiliation with the Faculty of Mathematics and Statistics (FME) and the Institute of Mathematics of UPC-BarcelonaTech (IMTech). His work focuses on algebraic geometry, mathematical phylogenetics, and computational algebra, with applications to evolutionary biology and stochastic processes. Research Interests: Fernández Sánchez's research bridges algebraic geometry with biological applications, particularly in phylogenetics. He explores topics such as Markov matrix embeddability, phylogenetic invariants, equivariant models, and algebraic methods for evolutionary analysis. His work includes developing semi-algebraic methods for quartet reconstruction and analyzing rank conditions in phylogenetic networks. Grants & Projects: He has led or contributed to several competitive research projects, including 'GEOMETRIA DE VARIETATS I APLICACIONS' (GEOMVAP group), the María de Maeztu Excellence Program, and collaborations on phylogenetic model development. His projects emphasize multidisciplinary approaches in geometry, algebra, and topology. Advising: He has supervised doctoral students like Marina Garrote-López and Jordi Roca-Lacostena in topics such as phylogenetic reconstruction methods and Markov matrix analysis. His lab investigates geometric and algebraic tools for evolutionary biology, with a focus on model validation and computational techniques. Affiliations: Besides his primary role at UPC, he collaborates with international research networks in algebraic phylogenetics and stochastic processes. His work integrates pure mathematics with applied problems in biology and statistics.

• Differential Geometry
• Geometric Analysis
• Minimal Surfaces
• +4 more

Jonathan Julian Zhu is an Assistant Professor at the University of Washington's Department of Mathematics. He previously held an NSF Postdoc at Princeton University and completed his PhD at Harvard University under Professors Bill Minicozzi and Shing-Tung Yau. His research focuses on Differential Geometry and Geometric Analysis, particularly minimal surfaces, mean curvature flow, and related singularity phenomena. He organizes the UW Differential Geometry/PDE Seminar and has been involved in various academic leadership roles including the SMRI-Matrix Symposium and Princeton's Geometric Analysis seminar. Education: Ph.D. in Mathematics (Harvard University), Postdoctoral research at Princeton University. Research interests include geometric flows, minimal submanifolds, self-shrinkers, Ricci solitons, and applications of min-max theory. His work bridges theoretical insights with geometric PDEs, addressing questions about rigidity, entropy, and stability in curved spaces. Teaching includes courses at UW (Math 336, 583), Princeton (MAT175, MAT203), and Harvard (Math21a, Math1b). His publications address foundational problems in geometric analysis, such as quantifications of symplectic non-squeezing, uniqueness of blowups in mean curvature flow, and rigidity properties of self-shrinkers. Professional activities include seminar organization, grant participation, and collaborations on geometric analysis projects. His work contributes to advancing understanding of geometric structures and their evolution under curvature-driven flows.

Jian CaoView profile

Assistant Professor

• Computational Statistics
• Gaussian Process Regression
• Spatial Statistics
• +4 more

Jian Cao is an Assistant Professor in the Department of Mathematics at the University of Houston, specializing in Computational and Spatial Statistics. He earned his Ph.D. in Statistics from KAUST under Dr. Marc Genton and completed postdoctoral work at Texas A&M University under Dr. Matthias Katzfuss. His research focuses on scalable Gaussian Process (GP) regression methods, including Vecchia approximations, truncated multivariate normal distributions, and applications in climate and agricultural science. Education includes a B.S. in Mathematics from University of Science and Technology of China (2014), a Master in Finance from Shanghai Jiaotong University (2016), and a Ph.D. in Statistics from KAUST (2020). Research interests emphasize computational efficiency in statistical modeling, with contributions to scalable algorithms for high-dimensional data. Key themes include Gaussian processes, multivariate normal probabilities, and low-rank matrix methods. His work bridges theory and application, addressing challenges in environmental and agricultural data science. Notable awards include the Al-Kindi Statistics Student Research Award (2020) and Best Student Paper (2019). His publications span journals like the Journal of the American Statistical Association and conferences such as ICML, focusing on scalable statistical methods and software development (e.g., tlrmvnmvt package). Teaching responsibilities include courses on statistics for the sciences and inferential statistics at the University of Houston. His software contributions prioritize high-performance computing solutions for large-scale geospatial and statistical problems.

• Econometrics
• High-Dimensional Data Analysis
• Quantile Regression
• +3 more

Yichong ZHANG is a Professor of Economics and Associate Dean (Postgraduate Research) at the School of Economics (SOE), Singapore Management University (SMU). He holds a Ph.D. in Economics from Duke University (2016), an M.A. in Economics from Duke University (2011), and a B.S. in Finance and Banking from Zhejiang University (2008). His research interests focus on econometrics, particularly in high-dimensional data analysis, quantile regression, network models, and statistical inference. Notable contributions include advancements in covariate-adaptive randomizations, instrumental variable regressions, and spectral clustering methods. His work bridges theoretical econometrics with applied methodologies, addressing challenges in causal inference and structural estimation. Key achievements include a Best Paper Award at the 2021 Delhi Winter School (Econometric Society) for his work on clustered data. His research spans econometric theory, machine learning applications, and network analysis, with publications in top journals such as the Journal of Econometrics and Journal of Machine Learning Research . He advises graduate students including WANG Yiren, Dennis LIM Guo Wei, and XIA Ying. His research also explores production frontiers, latent community detection, and bootstrap inference techniques, reflecting a commitment to both methodological innovation and real-world economic problems.

Priya BatraView profile

Research Fellow

• Quantum Information
• Quantum Control
• Machine Learning
• +2 more

Priya Batra is a Postdoctoral Associate at the Center for Quantum Information Science and Engineering, Department of Physics, College of Science, Virginia Tech. She completed her PhD at the Indian Institute of Science Education and Research (IISER) Pune under Prof. T S Mahesh, funded by the Prime Minister Research Fellowship. Education: PhD in Physics, Indian Institute of Science Education and Research (IISER) Pune Her research bridges quantum information, quantum control, and machine learning. She develops quantum optimal control algorithms for robust quantum computing and applies machine learning techniques like recommender systems to solve quantum many-body problems. Her work addresses exponential complexity in quantum systems through computational innovation, focusing on quantum correlations, state fidelity, and noise-resilient control sequences. Recent publications reveal strong integration of machine learning with quantum control, particularly in characterizing quantum evolutions and optimizing pulse engineering. Her hybrid algorithms demonstrate superior noise resistance in NMR implementations, while recommender systems enable efficient quantum discord computation for large systems. Scientific Awards: Prime Minister Research Fellowship Funded by India's Prime Minister Research Fellowship during her doctorate, she now contributes to Virginia Tech's quantum research ecosystem. Her postdoctoral work involves experimental NMR validation of quantum protocols under advisors Prof. Ed Barnes and Prof. Sophie Economou. She operates within Virginia Tech's Center for Quantum Information Science and Engineering, collaborating on quantum computing infrastructure. Her experimental-theoretical approach utilizes liquid-state NMR spectroscopy for quantum simulation and control validation, targeting real-world quantum technology applications.

• Applied Mathematics
• Pure Mathematics
• Applied Computing
• +1 more

Assoc Prof Kah Loon Ng is an Associate Professor in the Department of Mathematics at the National University of Singapore (NUS). His research interests span Applied Mathematics, Pure Mathematics, Applied Computing, and Theory of Computation. He is renowned for innovative teaching methods including story-telling approaches, mind maps, and differential learning strategies to engage students at all levels. His pedagogical innovations include screencast resources for tutorials and past exam questions, enhancing student accessibility to learning materials. In teaching, Prof Ng emphasizes fostering analytical skills, adaptability, and confidence in students. He employs interactive techniques like group discussions, conceptual teasers during lectures, and personalized attention to address varying student abilities. His approach integrates Information Technology strategically without over-reliance, ensuring dynamic classroom engagement. His teaching philosophy prioritizes creating a supportive environment where students can explore mathematics' practical applications across disciplines, challenging the misconception that mathematics is only for educators. Prof Ng’s research contributions include foundational work on graph orientation, firefighter problem generalizations, and disease modeling in dynamic networks. His publications reflect interdisciplinary applications, such as curriculum reform in data science education and advanced graph theory studies. His work bridges theoretical mathematics with real-world problem-solving, influencing both academic discourse and educational practices.

Jaromy KuhlView profile

Professor

• Combinatorics
• Graph Theory
• Design Theory
• +8 more

Dr. Jaromy Kuhl is the Provost and Professor in the Department of Mathematics and Statistics at the University of West Florida, within the Hal Marcus College of Science and Engineering. He has been a key academic and administrative figure at UWF since 2005. His educational background includes a Ph.D. in Mathematics (Graph Theory), an M.S. in Mathematics, and dual B.S. degrees in Mathematics and Physics, all from the University of Mississippi. Dr. Kuhl's research lies in combinatorics, with a strong emphasis on graph theory and design theory. His work explores Latin squares, bipartite graphs, graph coloring, Hamiltonicity, extremal problems, and the Tutte polynomial. These areas are central to discrete mathematics and have applications in computer science, network design, and optimization. The 15 most recent publications reflect a consistent focus on structural and extremal problems in combinatorics. They span topics such as transversals in Latin squares, graph decompositions, competition numbers, and network reliability via the Tutte polynomial. Keywords like combinatorics, graph theory, and design theory recur, indicating a tightly focused research program with deep theoretical contributions. While no specific scientific awards are listed in the provided text, Dr. Kuhl has published in high-quality journals such as Graphs and Combinatorics , Annals of Combinatorics , and Discrete Applied Mathematics , and has presented at international conferences in China and Australia, indicating peer recognition. Dr. Kuhl advises students and teaches a wide range of courses, from undergraduate calculus to graduate research in mathematics. As department head and now Provost, he has managed academic schedules, budgets, grants, and faculty activities, demonstrating strong leadership in academic administration. He has taught courses including Abstract Algebra, Graph Theory, Linear Algebra, and Discrete Mathematics. Although no specific lab or research team is named, his research output suggests active collaboration and mentorship. His role in organizing academic programs and colloquia further indicates engagement with the broader mathematical community.

• Sequencing and Scheduling
• Combinatorial Optimization
• Computational Complexity
• +6 more

Dr. Yumei Huo is a Professor in the Department of Computer Science at the College of Staten Island, City University of New York (CUNY). Her work spans theoretical and applied domains, including combinatorial optimization, sequencing and scheduling, high performance computing, and medical imaging. She has developed and taught multiple HPC-focused courses and actively supervises undergraduate and graduate research. Education : BS, University of Science and Technology (China) MS, University of Science and Technology (China) PhD, New Jersey Institute of Technology Dr. Huo's research focuses on Sequencing and Scheduling , Combinatorial Optimization , Computational Complexity , Operations Research , High Performance Computing (HPC) , and more recently, Medical Imaging and Big Data . Her recent publications apply deep learning and CNN models to lung nodule detection, polyp classification, and CT image analysis, demonstrating a shift toward biomedical data science. Her recent publications (2018–2021) show a strong trend in applying machine learning to medical imaging, particularly in CT scans for lung and colon analysis. Earlier works focus on theoretical scheduling problems with constraints like machine availability, processing sets, and completion time optimization, indicating a foundation in algorithmic design and complexity theory. Scientific Awards and Recognition : Extensive peer reviewer for over 40 journals and conference proceedings Reviewed more than 50 PSC-CUNY research grant proposals and Chancellor’s Research Fellowship applications Contributed to NSF/IEEE TCPP curriculum initiatives in parallel computing education Dr. Huo has supervised numerous students on research projects and co-authored publications with them. She has also led curriculum development, including launching four HPC courses. She has no known active grants mentioned, but her sustained publication and educational contributions reflect ongoing research support. She is affiliated with research teams in algorithm design and medical imaging AI, particularly collaborating with researchers like H. Zhao, J. Y-T. Leung, and J. Tan.

• Applied Mathematics
• Numerical Relativity
• Computational Astrophysics
• +6 more

Isabel Cordero Carrión is an Associate Professor in the Department of Mathematics at the Faculty of Mathematics, Universitat de València. She is affiliated with two research groups: CompAC (Computational Astrophysics and Cosmology Group) and ANIMS (Numerical Analysis, Images, Multiresolution and Simulation). Institution: Universitat de València School: Faculty of Mathematics Department: Department of Mathematics Research Groups: CompAC, ANIMS Her research focuses on applied mathematics, particularly in numerical relativity and computational astrophysics. She works on the numerical evolution of Einstein's equations, relativistic hydrodynamics, and the development of stable and accurate numerical schemes for general relativistic systems. Her work bridges differential geometry, partial differential equations, and high-performance computing. The recent articles show a consistent trend in advancing numerical methods for general relativity, including formulations of the Einstein and hydrodynamic equations, implicit time integration, and spherical coordinate implementations. Her work emphasizes stability, constraint preservation, and computational efficiency in simulating relativistic systems. No scientific awards are listed in the provided text. She completed her Ph.D. in 2009 at the Universitat de València under the supervision of Dr. José María Ibáñez. While no formal students are listed, her collaborative work includes co-authorship with researchers such as Pablo Cerdá-Durán, Miguel-Ángel Aloy, and Jérôme Novak. She has not received any mentioned grants in the text. She is a member of the CompAC and ANIMS research groups, which focus on computational astrophysics, numerical analysis, and simulation of physical systems using advanced mathematical methods.

Dan MikulincerView profile

Assistant Professor

• High-Dimensional Geometry
• Probability Theory
• Statistics
• +10 more

Dan Mikulincer is the Brian and Tiffinie Pang Assistant Professor at the University of Washington in the Department of Mathematics, College of Arts and Sciences. He previously held a postdoctoral Instructor position at MIT Mathematics and earned his Ph.D. from the Weizmann Institute of Science under Ronen Eldan. He completed his B.Sc. in Mathematics and Computer Science at Ben-Gurion University, where he also studied Cognitive Neuroscience. B.Sc.: Ben-Gurion University (Mathematics, Computer Science, Cognitive Neuroscience) Ph.D.: Weizmann Institute of Science, Faculty of Mathematics Postdoc: MIT Mathematics Current: Assistant Professor, University of Washington, Department of Mathematics His research lies at the intersection of high-dimensional geometry, probability, statistics, information theory, and data science. He is particularly focused on normal approximations, Stein's method, stochastic analysis, and dimension-free phenomena. His work explores foundational aspects of learning theory, random matrices, transportation inequalities, and neural networks, often using probabilistic and analytic tools to derive sharp, robust results in high dimensions. The recent publications reflect a consistent focus on probabilistic methods in high-dimensional settings. Key themes include normal approximation via Stein's method, optimal transport, concentration and anti-concentration inequalities, random graph models, and theoretical aspects of machine learning such as learnability and neural network expressivity. The work spans both pure mathematics (e.g., GAFA, PTRF) and top-tier computer science venues (e.g., COLT, STOC, NeurIPS), highlighting interdisciplinary impact. Although no formal scientific awards are listed in the provided text, his publications in premier journals and conferences (Annals of Probability, STOC, NeurIPS, COLT) indicate significant recognition in the theoretical community. Dan Mikulincer has advised or collaborated with several researchers including Yair Shenfeld, Max Fathi, Ronen Eldan, and Sébastien Bubeck. He has served as a TA for 18.650: Statistics for Applications at MIT and taught programming courses (Java, Python, JavaScript) at the Interdisciplinary Center Herzliya. He is also a senior lecturer at WeCode, a nonprofit providing free programming education to underrepresented youth in Israel, indicating a strong commitment to education and outreach. He has been affiliated with research groups at MIT Mathematics, Weizmann Institute, and Microsoft Research AI, where he spent the summer of 2019 hosted by Sébastien Bubeck. These collaborations span theoretical machine learning, stochastic processes, and algorithmic foundations.

Frank VallentinView profile

Professor

• Optimization
• Semidefinite Programming
• Discrete Geometry
• +8 more

Frank Vallentin is a full professor of applied mathematics (computer science) at the Mathematical Institute of the University of Cologne, Germany. He has held academic positions at Technische Universiteit Delft, Centrum Wiskunde & Informatica (CWI), and the Hebrew University of Jerusalem. His research spans optimization, discrete geometry, harmonic analysis, and computational mathematics. Research Interests: His primary mathematical interests include semidefinite programming, combinatorial optimization, harmonic analysis, discrete geometry, combinatorics, geometry of numbers, special functions, computational complexity, and coding and information theory. These areas reflect a deep integration of theoretical mathematics with algorithmic and computational techniques. The 15 most recent publications reveal a strong focus on geometric optimization, lattice problems, energy minimization, and semidefinite programming bounds. Key themes include chromatic numbers of lattices, symplectic capacities, polarization phenomena, and algorithmic solutions to geometric problems. His work often involves recursive SDP hierarchies, extremal configurations, and computational verification of theoretical bounds. Scientific Awards and Grants: SIAG/Optimization Prize (2011, with Christine Bachoc) NWO VIDI Grant (2010–2015): Semidefinite programming and harmonic analysis DFG Project: Symplectic capacities of polytopes (2017–) EU Horizon 2020 MINOA Project: Optimization with limited quantum resources (2017–) DFG Project: Spectral bounds in extremal discrete geometry (2019–) Advising and Grants: Vallentin has advised numerous PhD and master’s students at TU Delft and the University of Cologne, covering topics in discrete geometry, optimization, coding theory, and quantum information. He has secured major research funding from NWO, DFG, and the EU, supporting interdisciplinary projects in algorithmic optimization and mathematical physics. He is actively involved in organizing workshops and summer schools. Labs and Teams: He leads a research group at the University of Cologne focusing on optimization and discrete geometry, with strong collaborations with CWI Amsterdam, TU Delft, and international institutes. His team works on theoretical and computational aspects of geometric optimization, often using symmetry reduction and harmonic analysis.

Shuchin AeronView profile

Associate Professor

• Data Science
• Statistical Signal Processing
• Inverse Problems
• +8 more

Shuchin Aeron is an Associate Professor in the Department of Electrical and Computer Engineering at Tufts School of Engineering, with joint appointments in the Departments of Computer Science and Mathematics. He holds a Ph.D. from Boston University (2009) and completed postdoctoral research at Schlumberger Doll Research, focusing on borehole acoustic signal processing. His research spans statistical signal processing, machine learning, compressed sensing, and information theory, with applications in geophysics, bioengineering, and imaging. Aeron has authored over 175 publications and holds patents in acoustic signal processing. He received the NSF CAREER Award (2016) and is a Senior Member of the IEEE. Educations: Ph.D., Electrical Engineering, Boston University, 2009 M.S., Electrical Engineering, Boston University, 2004 B.Tech., Indian Institute of Technology, 2002 Research Interests: Statistical signal processing (SSP), inverse problems, compressed sensing, information theory, convex optimization Machine learning applications in geophysical signal processing, imaging, and bioengineering His work emphasizes optimal sampling and recovery of multidimensional signals, with contributions to compressed sensing architectures and generative models for particle physics experiments. He leads NSF-funded projects on data science and domain generalization, and collaborates with industry partners like Schlumberger and Mitsubishi Electric Research Labs. Awards: NSF CAREER Award (2016) Mitsubishi Electric Research Lab Research Gift (2015) Grants and Funding: NSF HDR TRIPODS (2019–2023) AFOSR: Enabling Trusted Human-Like Artificial Teammates (2018–2023) NSF: Optimal Sampling and Recovery for Multilinear Signals (2013–2016) Aeron teaches advanced courses in probabilistic systems analysis, information theory, and machine learning. He directs the Tufts Data Science undergraduate and graduate programs, and serves on editorial boards of journals including Frontiers in Signal Processing and IEEE Transactions on Geoscience and Remote Sensing .

Yufei ZhaoView profile

Associate Professor

• Combinatorics
• Extremal combinatorics
• Probabilistic combinatorics
• +4 more

Yufei Zhao is an Associate Professor of Mathematics at the Massachusetts Institute of Technology (MIT). His research focuses on extremal, probabilistic, and additive combinatorics, with applications to graph theory, discrete geometry, and computer science. Dr. Zhao received his S.B. in Mathematics and Computer Science and Engineering from MIT in 2010, followed by an M.A.St. in Mathematics with Distinction from Cambridge University in 2011. He completed his Ph.D. in Mathematics at MIT in 2015 under the supervision of Jacob Fox. His research interests span a broad range of combinatorial mathematics, with particular emphasis on the interplay between structure and randomness. Dr. Zhao has made significant contributions to extremal graph theory, additive combinatorics, and the theory of pseudorandom graphs. His work often connects different areas of mathematics through innovative applications of combinatorial methods. Dr. Zhao's publications demonstrate a consistent focus on fundamental problems in combinatorics, with recent work exploring equiangular lines, spherical codes, extremal set theory, and the connections between graph theory and additive combinatorics. His research has been recognized with prestigious awards including the Fulkerson Prize (2024), NSF CAREER award (2021), Sloan Research Fellowship (2019), and Dénes König Prize (2018). Fulkerson Prize (2024) NSF CAREER award (2021) Sloan Research Fellowship (2019) Dénes König Prize (2018) Dr. Zhao actively mentors students, currently advising Travis Dillon, Dingding Dong, and Nitya Mani. His former PhD students include Benjamin Gunby, Jonathan Tidor, Aaron Berger, Ashwin Sah, and Mehtaab Sawhney. He has also authored the influential textbook "Graph Theory and Additive Combinatorics: Exploring Structure and Randomness" (Cambridge University Press, 2023), which has received high praise from leading mathematicians including Terry Tao and Ben Green.