Wavelet-Based Inference Professors and Supervisors | ApplySharif

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Xiuyuan ChengView profile

Associate Professor

Duke UniversityUnited States

Xiuyuan Cheng is an Associate Professor of Mathematics at Duke University, affiliated with the Trinity College of Arts & Sciences. Her expertise lies in applied analysis, focusing on developing theoretical and computational techniques for high-dimensional data analysis, signal processing, and machine learning. She holds a Ph.D. from Princeton University (2013). Her research emphasizes graph-based methods, kernel techniques, and deep learning applications in manifold data analysis. Notable contributions include work on graph Laplacian convergence, optimal transport for single-cell data, and rotation-equivariant neural networks. She has received grants from the National Science Foundation, including a CAREER award (2023–2028) for learning graph diffusion from high-dimensional data. Recent publications explore topics such as bi-stochastic graph normalization, neural tangent kernels, and adversarial defense using basis transformations. Teaching includes advanced courses like Measure and Integration and High-Dimensional Data Analysis. She collaborates extensively in interdisciplinary projects, integrating mathematical theory with computational tools for real-world applications.

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Noah SimonView profile

Associate Professor

University of WashingtonUnited States

Noah Simon is an Associate Professor in the Department of Biostatistics at the University of Washington School of Public Health. His research focuses on high-dimensional statistical methods, machine learning, and their applications in biomedicine. He develops computational tools for genomic and clinical data analysis, including penalized regression techniques and adaptive clinical trial designs. Education: B.A. Mathematics, Pomona College (2008) Ph.D. Statistics, Stanford University (2013), advised by Robert Tibshirani Research Interests: Dr. Simon specializes in high-dimensional estimation, algorithm optimization, and clinical trial methodology. His work addresses challenges in biomarker discovery, imaging-based diagnostics, and genomic data analysis. Key areas include sparse-group lasso regularization, adaptive enrichment designs for personalized medicine, and scalable computational methods for big data. Grants & Funding: NIH Director's Early Independence Award ($250k/year, 2014–2019) Amazon and Google Cloud Computing Grants for biomarker research Awards: Forbes 30 Under 30 in Science (2015) NSF Graduate Research Fellowship Honorable Mention (2010) Weiland Fellowship (2011–2013) Advising: He mentors PhD and MS students in biostatistical methodology and data science, with current advisees including Jean Feng, Brayan Ortiz, and Jeremy Roth. Notable collaborations include work on neural activity detection via calcium imaging (SCALPEL) and nonparametric variable importance assessment using neural networks. Lab & Affiliations: Based at the Hans Rosling Center for Population Health, his group develops open-source software (e.g., sgl , standGL ) and contributes to biomedical data science initiatives at UW.

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Min ChenView profile

Professor

University of Texas at DallasUnited States

Dr. Min Chen is a Professor in the Department of Mathematical Sciences at the University of Texas at Dallas (UTD), affiliated with the School of Natural Sciences and Mathematics. He holds an adjunct professorship at the University of Texas Southwestern Medical Center. His expertise spans statistical genomics, bioinformatics, Bayesian methods, and sampling techniques. He completed his Ph.D. in Statistics and Decision Science at the University of Texas at Austin and a postdoctoral fellowship in statistical genomics at Yale University. Education: B.S. in Computer Science (University of Science & Technology of China, 1994); M.A. in Statistics (University of Pittsburgh, 1999); Ph.D. in Statistics (UT Austin, 2006); Postdoc in Statistical Genomics (Yale University, 2008–2010). Research focuses on statistical methodologies in genomics, including genome-wide association studies, network-based modeling, and Bayesian integrative analysis. He also explores spatial modeling and ranked set sampling. His work addresses challenges in cancer genetics, epigenetics, and single-cell gene regulation. Notable awards include the NIH Career Development Award (2013), David Bruton Fellowship (2006), and R.L. Anderson Student Paper Award (2006). He is a member of the American Statistical Association and International Chinese Statistical Association. He advises graduate students in Data Science, Statistics, and Bioinformatics & Computational Biology (BCBM) programs. His teaching includes courses on advanced statistical methods and data science. Research contributions span over 40 peer-reviewed articles, with recent work in tumor pathology imaging, antibiotic resistance, and Alzheimer’s disease mechanisms.

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Keir K. RogersView profile

Research Fellow

Imperial College LondonUnited Kingdom

Keir K. Rogers is an STFC Ernest Rutherford Fellow at Imperial College London (starting 2025). Previously, he held the Dunlap Fellowship at the University of Toronto and a postdoctoral position at the Oskar Klein Centre for Cosmoparticle Physics, Stockholm University. He earned his PhD from University College London and a Master’s from the University of Oxford. His research focuses on cosmological model physics, leveraging statistical and machine learning techniques to analyze cosmological data. Key areas include testing ultra-light axion models of dark matter, analyzing cosmic microwave background (CMB) and large-scale structure data, and developing computational frameworks like cosmological emulators and Bayesian optimization. He contributed to bounds on dark matter models using Lyman-alpha forest data and pioneered methods for CMB component separation using wavelets. Scientific awards include the STFC Ernest Rutherford Fellowship and Dunlap Fellowship. His work involves collaborations on next-generation surveys like the Dark Energy Spectroscopic Instrument (DESI) and CMB-S4. He leads efforts to improve parameter inference through simulation-based methods and has developed tools for mitigating high-density absorber contamination in Lyman-alpha forest analyses.

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David BaconView profile

Professor

University of PortsmouthUnited Kingdom

Professor David Bacon is a leading academic in cosmology and astrophysics at the University of Portsmouth's Faculty of Technology, serving as Director of the Institute of Cosmology & Gravitation (ICG). His research focuses on cosmology at radio wavelengths, gravitational lensing, and theories of gravity, with significant contributions to dark matter and dark energy studies. Education : PhD in Natural Sciences from the University of Cambridge (2002) Appointments : PPARC/STFC Fellow (2002–2004), University of Edinburgh; Senior Research Fellow/RCUK Academic Fellow (2007), University of Portsmouth; Professor (2018–present) His work leverages data from major surveys like the Dark Energy Survey (DES), Atacama Cosmology Telescope (ACT), and Euclid mission, employing techniques such as weak lensing , galaxy clustering , and machine learning . Recent publications analyze dark energy constraints , galaxy cluster profiles , and cosmological parameter estimation using FRBs and supernovae. Scientific Awards : PPARC Research Fellowship (2002) STFC Advanced Fellowship (2004)

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Marco GrangettoView profile

Professor

University of TurinItaly

Marco Grangetto serves as Full Professor in the Department of Computer Science at the University of Turin, coordinating research in image processing and computer vision. His expertise spans wavelets, image/video coding, data compression, error resilient video coding, and biomedical image processing, with significant contributions to ISO JPEG2000 standardization and editorial roles in IEEE Transactions on Multimedia. His educational background includes a PhD in Electrical and Communications Engineering (2003) and MSc in Telecommunications Engineering (1999), both from Politecnico di Torino. His research integrates Artificial Intelligence and Deep Learning with medical imaging and fundamental compression theory , producing innovations in neural network pruning, capsule networks, and entropy-based models. Recent work focuses on Covid-19 diagnosis from chest X-rays and efficient 3D scene modeling. His publication trends reveal dual trajectories: applied medical AI (Covid-19 diagnostics, lung cancer segmentation) and theoretical advances (learned compression, contrastive learning, bias mitigation). This bridges clinical validation with information-theoretic foundations, particularly in resource-constrained environments. Scientific recognition includes: Premio Optime by Unione Industriale di Torino (2000) Fulbright Grant for research at UC San Diego (2001) He maintains leadership through IEEE editorial positions, ISO standardization participation, and MPAI membership. His research group develops medical datasets (UniToChest, UniToPatho) while advancing neural network efficiency for clinical deployment. Current projects focus on entropy minimization techniques and unbiased representation learning for healthcare applications.

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Costas SmaragdakisView profile

Assistant Professor

University of CreteGreece

Costas Smaragdakis is an Assistant Professor in Numerical Analysis and Scientific Computing at the Department of Statistics and Actuarial - Financial Mathematics, University of the Aegean. He is also a Member of the Institute of Applied and Computational Mathematics (IACM) at FORTH. His work bridges numerical methods, machine learning, and applied mathematics, with a focus on solving complex problems in finance and oceanography. Research Interests : Numerical Analysis Scientific Computing Mathematical Modelling Deep Learning Machine Learning Applications to PDEs/PIDEs Recent Research Trends : His articles highlight a strong focus on integrating deep learning with traditional numerical methods for functional minimization, PDE/PIDE solutions, and financial applications. Earlier work emphasizes acoustic signal processing, inverse problems in oceanography, and wavelet-based analysis. Events : Organized a mini-symposium on Machine Learning Methods in Finance (ICCF24, Amsterdam) and attended international workshops in Canada and Greece. Contact : kesmarag@aegean.gr , kesmarag@iacm.forth.gr , Office A5, Vourlioti Building, Karlovassi, Samos, Greece.

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Alwell Julius OyetView profile

Professor

Memorial University of NewfoundlandCanada

Alwell Julius Oyet is a Professor of Statistics in the Department of Mathematics and Statistics at Memorial University of Newfoundland. He earned his B.Sc. (1986) and M.Sc. (1992) from the Federal University of Technology, Owerri, Nigeria, and his Ph.D. (1997) from the University of Alberta, Canada. His research focuses on longitudinal data analysis, spatial statistics, time series, statistical computing, multinomial data, and robust methods. He maintains active research collaborations in these areas. Oyet teaches undergraduate and graduate courses including Design of Experiments, Time Series Analysis, Multivariate Analysis, and Computational Statistics. He has extensive experience developing course materials and statistical computing resources.

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Hassan DoostiView profile

Senior Lecturer

Macquarie UniversityAustralia

Hassan Doosti is a Senior Lecturer at the School of Mathematical and Physical Sciences, Macquarie University. His research focuses on statistical methodologies, particularly in flexible modeling techniques for complex datasets, with applications in medical studies and business analytics. He has authored or edited books such as Flexible Nonparametric Curve Estimation and Ethics in Statistics: Opportunities and Challenges . Research Interests Nonparametric estimation including wavelet methods and density estimation Statistical modeling of health-related data (e.g., colorectal cancer, stroke) Development of novel statistical algorithms (e.g., censored regression, numerical dependency analysis) Ethical considerations in data analysis for medical sciences Recent Projects Outside Studies Program (2025) APRIntern: Disease Risk Modelling (2019) Key Contributions His work bridges theoretical statistics with practical applications, including: Development of adaptive wavelet quantile density estimation techniques Statistical analysis of neurological and oncological data Advancing methods for handling censored and zero-inflated datasets Awards Recipient of the Faculty of Science and Engineering Award for Inter-School Collaboration (2023) for collaborative research excellence. Professional Activities Editor of multiple peer-reviewed books and active contributor to interdisciplinary projects involving healthcare, data science, and biostatistics.

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Werner DietlView profile

Associate Professor

University of WaterlooCanada

Werner Dietl is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. His work focuses on programming languages, static analysis, software security, and formal verification techniques. He has contributed to type system design, low-power computing, and approximate data types through projects like EnerJ. His research also addresses challenges in compiler design, cryptographic protocol validation, and runtime enforcement mechanisms. Key research areas include: Type systems for imperative and domain-specific languages Static analysis of implicit control flow (e.g., Java reflection, Android intents) Approximate computing and energy-efficient computation Formal verification of security properties Publications span topics from unit measurement type inference to ownership-based security models, reflecting a focus on practical formal methods. His work emphasizes scalability and precision in type systems while addressing real-world software engineering challenges.

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Giancarlo ManziView profile

Associate Professor

Sapienza University of RomeItaly

Giancarlo Manzi is an Associate Professor of Statistics at the Department of Methods and Models for Economics, Territory, and Finance, University of Rome La Sapienza. He holds a PhD from the University of Milan-Bicocca and conducted thesis research at the University of Toronto. His career includes roles at the Medical Research Council Biostatistics Unit in Cambridge and the University of Milan. University of Rome La Sapienza (Current) Medical Research Council Biostatistics Unit (Former Researcher) University of Milan (Former Researcher and Associate Professor) University of Milan-Bicocca (PhD) University of Toronto (Thesis collaboration) His research spans Machine Learning , Bayesian Statistics , and Smart Mobility , with a focus on Covid-19 analytics , data visualization , and epidemiological modeling . He integrates Multivariate Statistics with Public Health to address complex challenges in health systems and urban environments. The 15 most recent publications highlight expertise in quantile regression , Bayesian networks , time-series analysis , and smart mobility optimization . His methodological contributions include wavelet analysis, cross-correlation models, and SIRD modeling frameworks applied to pandemic dynamics and bike-sharing systems. Scientific awards and honors are not explicitly mentioned in the provided texts. Giancarlo Manzi has taught at the University of Verona, Catholic University of the Sacred Heart, and Universidad Carlos III in Madrid, maintaining strong ties with Italy's academic institutions.

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Yongmiao HongView profile

Professor

Cornell UniversityUnited States

Yongmiao Hong serves as the Ernest S. Liu Professor of Economics and International Studies in the Department of Economics at Cornell University. He holds dual appointments as Professor of Statistics and field member in both the Department of Statistical Sciences and Center for Applied Mathematics. Professor Hong's research spans model specification testing, nonlinear time series analysis, financial econometrics, and empirical studies of Chinese economic systems. His methodological innovations include generalized spectral analysis for capturing nonlinear dependencies, semiparametric specification tests using orthogonal series/kernel methods, and autoregressive conditional interval (ACI) models for interval-valued time series data. His work demonstrates how interval data (e.g., daily stock price ranges) provides superior econometric estimation compared to point-valued observations. His publication record reveals consistent contributions to top-tier journals including Econometrica , Annals of Statistics , and Review of Financial Studies . Key trends show evolutionary progression from foundational specification testing (1990s) to sophisticated nonlinear time series tools (2000s), with recent focus on interval-valued modeling and multivariate conditional distribution validation. His research consistently bridges theoretical econometrics with financial market applications. Professor Hong advises doctoral students in economics and statistics, though specific advisee names aren't publicly listed. His research has been supported by grants enabling extensive empirical work on Chinese financial markets and continuous-time model validation. He previously served as President of the Chinese Economists Society in North America (2009-2010). His laboratory work centers on time series methodology development, particularly spectral analysis extensions and interval data modeling. Current projects involve refining ACI models for crude oil price forecasting and developing wavelet-based covariance matrix estimators robust to heteroskedasticity.

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Andrei VolodinView profile

Professor

University of ReginaCanada

Andrei Volodin is a Professor in the Department of Mathematics and Statistics at the University of Regina, Canada. He serves as the Co-op Work/Study Coordinator and has an extensive publication record spanning probability theory, statistical inference, and applied statistics. His research focuses on limit theorems, bootstrap methods, and distributional analysis with applications to quality control and healthcare economics.

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Marcus A. BrubakerView profile

Associate Professor

York UniversityCanada

Marcus A. Brubaker is an Associate Professor at York University's Department of Electrical Engineering and Computer Science within the Lassonde School of Engineering. He also holds an Adjunct Professor status at the University of Toronto's Department of Computer Science and serves as a Research Scientist at Google DeepMind. His primary affiliations include co-directorship of the Computational Vision and Imaging Lab (CVIL) at York University and membership in the Vector Institute as a Faculty Affiliate. Brubaker's research spans Computer Vision, Machine Learning, Statistics, and their applications to problems like cryo-electron microscopy (cryo-EM) and 3D molecular reconstruction. Education: He earned a Ph.D. in Computer Science from the University of Toronto (2011), with a thesis on physical models of human motion. His M.Sc. (2006) and B.Sc. (2004) in Computer Science were also from the University of Toronto. Research focuses include cryo-EM structure determination (notably the cryoSPARC software), normalizing flows, and probabilistic modeling. His work bridges computational imaging, statistical inference, and deep learning. Key contributions include advancements in cryo-EM algorithms, generative models, and camera noise analysis. He co-founded Structura Biotechnology and has contributed to open-source tools like Stan's math library. Publications emphasize novel view synthesis, neural radiance fields, sensor noise modeling, and cryo-EM reconstruction techniques. His research trends highlight integration of physics-based models with machine learning for solving complex imaging problems. Awards and recognition include impactful contributions to cryo-EM through cryoSPARC, a Nature Methods paper in 2017, and leadership in academic-industrial collaborations. Brubaker's labs and teams focus on advancing imaging technologies and their applications in structural biology and computer vision.

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Michael MultererView profile

Associate Professor

University of Italian SwitzerlandSwitzerland

Michael Multerer is an Associate Professor at the Faculty of Informatics, Università della Svizzera italiana (USI). His research focuses on multiresolution methods, scattered data analysis, and numerical analysis with applications in computational mathematics and engineering. He leads projects such as the SNSF Starting Grant on multiresolution methods for unstructured data, emphasizing nonlinear approximation and kernel-based techniques. Research Interests: Development of fully discrete multiresolution methods for unstructured data Wavelet theory and kernel matrix algebra Uncertainty quantification in partial differential equations Scattered data compression and approximation Key Software Contributions: FMCA: Fast multiresolution covariance analysis for scattered data Bembel: Boundary element library for solving Laplace and Helmholtz equations SPQR: Anisotropic sparse grid quadrature in MATLAB Funding: Holder of the SNSF Starting Grant (2025) for advancing multiresolution techniques in unstructured data processing. Labs/Teams: Active in the research group at USI’s Faculty of Informatics, collaborating with institutions like TU Darmstadt and University of Basel on numerical methods and engineering applications.

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