Find Academic Supervisors

• Statistical Methodology
• Machine Learning
• Parametric Inference
• +7 more

Claudia Di Caterina is an Associate Professor of Statistics at the University of Verona's Department of Economics. Her research focuses on statistical methodology, including likelihood asymptotics, pseudo-likelihoods, anytime-valid inference, and bias reduction techniques. She holds a PhD in Statistical Sciences from the University of Padova and has conducted research at University College London and the University of Toronto. She teaches courses such as Machine Learning for Economics and Statistics for Business Analysis across various programs. Her academic roles include membership in the Economics Department Council and involvement in committees like the Comitato per le Ricerche con Dati sull'Individuo (CORDI) and Commissione Paritetica Docenti-Studenti. She contributes to research laboratories like VELE (Verona Experimental Lab in Economics) and collaborates with centers such as CRIEP and CIDE. Her publications span topics in high-dimensional data analysis, confidence sequences, and causal inference. She actively engages in public events and third mission activities, including workshops on climate risk and cyber security survey methodologies.

• Stochastic Optimization
• Risk-Averse Decision-Making
• Mathematical Programming
• +7 more

Darinka Dentcheva is a Professor in the Department of Mathematical Sciences at Stevens Institute of Technology, affiliated with the Charles V. Schaefer, Jr. School of Engineering and Science. She holds a PhD in Mathematics from Humboldt University (2006) and an earlier PhD in Mathematics (1989) and MS in Mathematics and Computer Science (1981), also from Humboldt University. Her research focuses on stochastic optimization, risk-averse decision-making, and their applications in machine learning, finance, healthcare, and energy systems. Dr. Dentcheva’s work emphasizes developing mathematical frameworks for managing risk under uncertainty, including stochastic dominance constraints and risk-averse control of Markov systems. She has authored influential books such as *Risk-Averse Optimization and Control: Theory and Methods* (2024) and co-edited volumes on stochastic optimization theory. Her research has been supported by grants from the National Science Foundation (NSF), Office of Naval Research, and Air Force Office of Scientific Research, totaling over $3 million in funding. Her honors include the Davis Memorial Research Award (2007), recognition from the Stevens Board of Trustees for research excellence, and awards from the Bulgarian Ministry of Education. She has held leadership roles, including Chair of the Department of Mathematical Sciences (2018–2021) and co-director of the CRAFT Center (Center for Research toward Advancing Financial Technologies). Her teaching spans courses in nonlinear optimization, stochastic control, and data science. Dr. Dentcheva’s contributions to stochastic optimization theory and applications have been published in over 100 peer-reviewed articles and book chapters. Her recent work explores risk-averse machine learning models, dynamic resource allocation, and bias reduction in sample-based optimization. She actively collaborates with institutions like Rutgers University and serves on editorial boards for journals such as *SIAM Review* and *ESAIM: Control, Optimisation and Calculus of Variations*.

Min WanView profile

Assistant Professor

• Terahertz Imaging Systems
• Optical Imaging Techniques
• Digital Holography
• +5 more

Dr. Min Wan is an Assistant Professor in the Department of Electrical Engineering at Eindhoven University of Technology (TU/e). She holds a Master's degree in Optical Engineering from Beijing University of Technology (2017) and a PhD in Optical Imaging Techniques from University College Dublin (UCD, 2021). Prior to TU/e, she served as an Assistant Professor at UCD and held a visiting position there until 2029. Her research focuses on terahertz imaging systems, digital holography, subpixel displacement estimation, and biomedical applications of THz technology, contributing to UN Sustainable Development Goals related to education and innovation. Education: PhD in Optical Engineering, University College Dublin (2017-2021) Master's in Optical Engineering, Beijing University of Technology (2014-2017) Research Interests: Terahertz full-field imaging and tomography Optical imaging techniques for biomedical applications Subpixel motion estimation algorithms Digital holography and signal processing Optical system design and optimization Her work integrates advanced imaging modalities with computational methods, emphasizing practical implementations in terahertz systems. Recent Research Trends: Dr. Wan’s recent articles emphasize advancements in terahertz full-field imaging, beam illumination optimization, and subpixel tracking algorithms. Her 2025 studies on galvanometric illumination and Bai distribution sampling highlight innovations in imaging precision and signal analysis, while 2024 work on diffractive neural networks explores optical computing applications. Awards & Grants: No specific awards listed, but her research has received significant citations and funding for projects like the RECENTRE program (2024). Labs & Teams: Active in TU/e’s Electrical Engineering labs focusing on terahertz systems and optical imaging. Collaborates internationally on projects involving THz tomography and biomedical applications.

Eduardo G. AltmannView profile

Professor

• Complex Systems
• Data Science
• Network Analysis
• +5 more

Eduardo G. Altmann is a Professor in the School of Mathematics and Statistics at the University of Sydney. His research focuses on mathematical models and computational methods applied to complex systems, data science, and statistical laws. He is part of the Complex Systems and Data Science group and the Computational Social Science Lab, and contributes to interdisciplinary collaborations in areas like urban scaling and language dynamics. Altmann teaches courses such as MATH3076/3976/4076 (Mathematical Computing) and DATA5441 (Networks and High-Dimensional Inference). He serves on editorial boards for journals including the Journal of Statistical Mechanics and New Journal of Physics. His recent work includes studies on Monte Carlo methods for manifold triangulations, generative models for network communities, and statistical laws in complex systems. He has authored a monograph, 'Statistical Laws in Complex Systems,' published by Springer Nature, and frequently engages in academic outreach via platforms like Bluesky.

Laura GrigoriView profile

Professor

• High-Performance Scientific Computing
• Numerical Linear Algebra
• Communication-Avoiding Algorithms
• +6 more

Laura Grigori is a Professor of Applied and Computational Mathematics at EPFL and Head of the Laboratory for Simulation and Modeling at the Paul Scherrer Institute (PSI), Switzerland. She holds a Ph.D. in Computer Science from Université Henri Poincaré (2001) and has been a leading figure in high-performance numerical algorithms and communication-avoiding methods. Her research focuses on numerical linear algebra, randomized algorithms, and scalable scientific computing for applications in astrophysics, molecular simulations, and exascale systems. Affiliations: EPFL Chair of High-Performance Numerical Algorithms & Simulation; Head, PSI Laboratory for Simulation and Modeling. Education: Ph.D. in Computer Science, Université Henri Poincaré (2001); Postdoctoral Researcher, UC Berkeley/LBNL (2001–2003); Senior Researcher at INRIA (2004–2023). Research Interests: Communication-avoiding algorithms, randomized numerical linear algebra, parallel preconditioners, tensor computations, and exascale computing challenges. Awards: SIAM Fellow (2020), ERC Synergy Grant (2018), SIAM Supercomputing Career Prize (2024), and Best Paper Awards (2016, 2023). Grants & Leadership: PI of the ERC Synergy Project EMC² (Extreme-scale Computational Chemistry); Chair of PRACE Scientific Steering Committee (2021–2022); SIAM SIAG Supercomputing Chair (2016–2017). Labs/Teams: Alpines Group (INRIA/Sorbonne University), EMC² Collaboration (Cancès, Maday, Piquemal).

• Neural Networks
• Medical Image Analysis
• Computer Vision
• +5 more

Zhigang Li is a Professor in the Department of Computer Science at South China University of Technology's School of Computer Science and Engineering. His research spans multiple technical domains with significant contributions to neural networks, medical imaging, computer vision, and sensor technologies. Recent collaborations include work with Northwestern Polytechnical University, Hong Kong University of Science and Technology, and various medical research institutions. Dr. Li's research interests focus on neural network architectures, particularly small-world and feedforward networks for system modeling and medical applications. His work bridges computer science with practical applications in healthcare (EEG analysis, schizophrenia detection, liver transplant allocation), environmental monitoring (wastewater treatment), and engineering systems (CMOS image sensors, UAV networks). His research demonstrates strong interdisciplinary connections between theoretical computer science and real-world problem solving. Analysis of his recent publication trends shows increasing focus on medical applications of AI, with significant work in brain functional network analysis, depression recognition, and schizophrenia detection. His technical contributions include novel neural network architectures, efficient sensor systems, and advanced signal processing techniques. The publications reveal a consistent pattern of high-quality output in top-tier journals across multiple disciplines. Dr. Li has received recognition through publications in prestigious venues including IEEE Transactions, Medical Image Analysis, and Expert Systems with Applications, though specific awards aren't documented in the provided bibliography. His work demonstrates significant impact across multiple fields, particularly in applying computational methods to healthcare challenges. His research program includes collaborations with medical researchers for brain imaging applications, electrical engineers for sensor development, and computer scientists for network architecture design. Current projects appear focused on multi-view brain network analysis, energy-efficient sensor systems, and medical AI applications with potential clinical impact.

Chang SuView profile

Assistant Professor

• Bioinformatics
• Biostatistics
• Genomics
• +6 more

Chang Su serves as Assistant Professor in the Department of Biostatistics and Bioinformatics at Emory University, with a secondary appointment in the Department of Human Genetics, School of Medicine. His research develops statistical methodologies for single-cell genomics and genetics data analysis. His educational background includes: Ph.D. from Yale University B.S. from Sun Yat-sen University Chang Su's research focuses on gene regulation in specific contexts and the genetics basis of single-cell genomics, with applications to neurodegenerative diseases, lung diseases, and immune disorders. His work bridges biostatistics and biological discovery through computational innovation. His recent publications (2022-2025) demonstrate consistent advancement in single-cell multimodal data analysis, including cell-type-specific enhancer mapping, co-expression network inference, and applications to Parkinson's and Alzheimer's disease. Methodological contributions span microbial network analysis and functional connectome metrics related to aging and cognition. Chang Su teaches BIOS 555 - High Throughput Data Analysis R&B and maintains active research collaborations, though specific grant details or student mentorship information are not provided in available materials.

Julia WrobelView profile

Assistant Professor

• Bioinformatics
• Biostatistics
• Public Health Informatics
• +4 more

Julia Wrobel is an Assistant Professor in the Department of Biostatistics and Bioinformatics at Emory University. Her research focuses on integrating biostatistical methods with bioinformatics and public health challenges, particularly in spatial data analysis, cannabis pharmacology, and functional data modeling. She holds a BA from Swarthmore and a PhD from Columbia University. Education: BA, Swarthmore PhD, Columbia University Research interests span spatial proteomics, cannabis effects on human physiology, and computational methods for single-cell and multiplex imaging data. Her work includes developing tools like scSpatialSIM and GammaGateR for analyzing spatial molecular patterns. Recent studies explore immune cell clustering in tumors and THC's impact on driving performance. Her 2024 Dean’s Pilot Innovation Award supported research into novel statistical methods for spatial and functional data. Collaborations focus on translational applications in oncology and public health, with publications bridging biostatistics, computational biology, and clinical toxicology. Grants/Advising: Recipient of 2024 Dean’s Pilot Innovation Award Focus on interdisciplinary collaborations in bioinformatics Labs/Teams: Active in Emory’s Biostatistics and Bioinformatics core, specializing in multiplex imaging analytics and cannabis-related health studies.

Alessandro FedrizziView profile

Professor

• Quantum Technology
• Photonics
• Quantum Communication
• +3 more

Alessandro Fedrizzi is a Professor at Heriot-Watt University, affiliated with the School of Engineering & Physical Sciences and the Institute of Photonics and Quantum Sciences. He holds an EPSRC Early Career Quantum Technology Fellowship. His research focuses on photonic quantum technology, including quantum communication, computation, and metrology, with an emphasis on scalable hybrid quantum systems. Fedrizzi completed his PhD in 2008 under Anton Zeilinger at the Institute for Quantum Optics and Quantum Information in Vienna. Prior to joining Heriot-Watt in 2015, he worked at the University of Queensland, Australia, on an ARC Discovery Early Career Award. Education: PhD in Quantum Optics (2008, Vienna) Research Interests: Fedrizzi develops photonic quantum technologies for experimental quantum information processing. His work bridges foundational research and applied areas like quantum networks, satellite communication, and hybrid systems. Key themes include scalable architectures, quantum entanglement, and photonics-based quantum devices. Recent projects explore programmable quantum networks, quantum-dot light purification, and anonymous quantum conferencing. Articles Overview: His recent publications address quantum networking protocols, photonics-based entanglement, and experimental validations of quantum communication systems. Notable works include programmable photonic networks and entanglement-induced many-body interference studies. Awards: EPSRC Early Career Fellowship (2015). Advising & Grants: Fedrizzi accepts PhD students in Experimental Quantum Networking and Satellite Quantum Communication. His fellowship supports quantum technology commercialization efforts. Labs & Teams: Active in the Institute of Photonics and Quantum Sciences, collaborating internationally on quantum network architectures and photonics systems.

Ralf ZimmermannView profile

Associate Professor

• Numerical Linear Algebra
• Matrix Analysis
• Scientific Computing
• +4 more

Ralf Zimmermann is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark. His research focuses on Numerical Linear Algebra, Matrix Analysis, and Scientific Computing, with a particular emphasis on Reduced Order Modelling and Manifold Geometry. He has contributed significantly to the development of efficient algorithms for dynamical systems and geometric optimization on matrix manifolds such as the Stiefel and Grassmann manifolds. His work bridges computational mathematics and engineering applications, including aerodynamics and model reduction techniques. Key research interests include the theoretical foundations of manifold geometry, algorithm design for high-dimensional data, and the application of these methods to real-world problems. He has published extensively on topics like Riemannian metrics, injectivity radii, and curvature analysis. His recent work explores adaptive probabilistic reduced-order models and gradient-enhanced Kriging methods for high-dimensional systems. Recipient of the Best Paper Award (2021) and DFG Scholarship (2014) Organized workshops such as the Nordic Numerical Linear Algebra Meeting (2024) Contributed to projects like Optimal Structure-Preserving Model Reduction and the Danish Data Science Academy His teaching includes courses on numerical analysis, computational physics, and differential equations. Zimmermann actively collaborates internationally, contributing to conferences and peer review.

Longxiu HuangView profile

Assistant Professor

• Data/Signal Processing
• Applied Harmonic Analysis
• Machine Learning
• +4 more

Longxiu Huang is an Assistant Professor at Michigan State University (MSU), holding dual appointments in the Department of Computational Mathematics, Science and Engineering (CMSE) and the Department of Mathematics. Prior to this, she was an Assistant Adjunct Professor at UCLA's Department of Mathematics. Her research focuses on Data/Signal Processing, Applied Harmonic Analysis, Machine Learning, Random Matrix/Tensor Analysis, Dynamical Sampling, and Optimization. Education : Ph.D. in Mathematics, Vanderbilt University (2014-2019) M.S. in Mathematics, Fudan University (2012-2014) B.S. in Mathematics, Sun Yat-Sen University (2009-2012) B.S. in Software Engineering, Sun Yat-Sen University (2008-2009) Research Interests : Huang's work bridges theoretical mathematics and applied computational methods. She explores signal reconstruction in dynamical systems, tensor decompositions for high-dimensional data, robust matrix/tensor completion, and optimization algorithms for ill-conditioned problems. Her contributions include CUR-based methods for low-rank approximations and sampling strategies for sparse signals on graphs. Professional Activities : She co-organizes the One World MINDS Seminar, a global initiative in mathematical data science. Her research has been published in top journals like SIAM Journal on Imaging Sciences and Applied and Computational Harmonic Analysis, with a focus on theoretical guarantees and practical applications in imaging and machine learning. Lab/Teams : Active collaborator in interdisciplinary projects at MSU, particularly in computational mathematics and machine learning applications.

Matteo PozziView profile

Professor

• Risk Analysis
• Civil Infrastructure Systems
• Probabilistic Modeling
• +7 more

Matteo Pozzi is a Professor in the Civil and Environmental Engineering Department at Carnegie Mellon University (CMU). His research focuses on probabilistic risk analysis and decision optimization for civil infrastructures, integrating sensor data and engineering models to enhance resilience against extreme events. He holds a Ph.D. in Structural Engineering from the University of Trento (Italy) and completed a post-doctoral fellowship at UC Berkeley. His work emphasizes probabilistic models for seismic vulnerability, maintenance scheduling, and smart infrastructure systems. Education: Ph.D., Structural Engineering, University of Trento (2007) Laurea (M.S. + B.S.), Civil Engineering, University of Trento (2003) Research Interests: His research spans advanced infrastructure systems, climate-resilient technologies, and intelligent engineered systems. Key areas include sensor integration for structural health monitoring, probabilistic modeling for infrastructure resilience, and optimal planning for mitigating extreme events. Awards & Grants: NSF CAREER Award (2017) Siebel Energy Institute Research Grant (2017) PITA Grant for Microreactor Technology (2019) Multiple NSF grants (SES-DRMS, ENG-CMMI-IMEE, GEO-ICER-PREEVENTS) Labs & Affiliations: He is affiliated with the Pennsylvania Smart Infrastructure Incubator and collaborates with the Scott Institute for Energy Innovation. His lab develops computational tools for integrated risk assessment and decision-making in civil systems.

Rui TuoView profile

Assistant Professor

• Data Science
• Computer Experiments
• Uncertainty Quantification
• +4 more

Rui Tuo is an Assistant Professor in the Department of Industrial & Systems Engineering at Texas A&M University. He is also affiliated with the Department of Statistics and serves as a Texas A&M Institute of Data Science Career Initiation Fellow. His research focuses on data science, computer experiments, uncertainty quantification, probabilistic machine learning models, and nonparametric statistics. Dr. Tuo holds a Ph.D. in Statistics from the University of Chinese Academy of Sciences (2013). His work emphasizes methodological advancements in statistical modeling and computational efficiency, particularly in high-dimensional and complex systems. His research interests include Gaussian process models, computer model calibration, kernel methods, and their applications in engineering and data-driven decision-making. Recent publications explore topics like multi-fidelity optimization, privacy-aware machine learning, and distributed Gaussian processes. Key achievements include the Career Initiation Fellowship (2022) and contributions to foundational works in calibration theory and uncertainty quantification. His research bridges statistical theory and practical applications, addressing challenges in simulation-based inference and scalable algorithms.

• Microfluidics
• Nanotechnology
• Biomedical Engineering
• +3 more

Vania Silverio is an Assistant Professor at the Department of Physics within the Instituto Superior Técnico of the University of Lisbon. Her research focuses on microfluidics, nanotechnology, and biomedical engineering applications, with particular expertise in metrology standards for microfluidic systems. She teaches courses such as Microfluidics and Micro and Nanofabrication Techniques. Her work integrates experimental methods with computational modeling to address challenges in fluid dynamics and device fabrication. Education: Vania holds a PhD in Mechanical Engineering from Instituto Superior Técnico (2016) and a 5-year Bachelor's Degree in Technological Chemistry from the Faculty of Sciences of the University of Lisbon (2003). Her doctoral research investigated micro-cooling technologies for power systems, receiving Distinction. Her undergraduate work included reaction calorimetry studies, earning top grades. Research interests span microfluidic device development, magnetic cytometry, and thermal management solutions. Her recent projects include portable bioaerosol samplers (SOLARIS), high-throughput screening systems, and metrology standards for lab-on-a-chip technologies. She collaborates with INESC Microsystems and Nanotechnologies, advancing applications in biomedical diagnostics and environmental monitoring. Notable contributions include pioneering leakage testing protocols for microfluidics and integrating neural networks with magnetic sensors for cytometry. Her work bridges fundamental research with practical applications, emphasizing reproducibility and precision in microscale systems.

Hamid UsefiView profile

Professor

• Machine Learning
• Deep Learning
• Computer Vision
• +7 more

Hamid Usefi is a Professor of Mathematics and Statistics at Memorial University of Newfoundland, with a cross-appointment to the Department of Computer Science. He earned his PhD in Mathematics from the University of Western Ontario in 2006, followed by postdoctoral fellowships at the University of British Columbia and the University of Toronto. His research focuses on algebra, machine learning, cryptography, and their applications in bioinformatics, genetics, finance, and industry. He has organized sessions on machine learning at Canadian Mathematical Society meetings and serves as an editor for Nature: Scientific Reports . Dr. Usefi's research interests include deep learning, computer vision, and medical imaging, alongside theoretical algebraic studies. His work bridges pure mathematics and applied domains, with contributions to biomarker discovery, CO2 adsorption modeling, and disease prediction using genomic data. He has been recognized with an NSERC Postdoctoral Fellowship and a MITACS NCE Fellowship. As an educator, he has supervised numerous graduate and undergraduate students in mathematics, statistics, and computer science, mentoring over 20 students since 2010. His publications span journals like Computational Statistics & Data Analysis , Scientific Reports , and Journal of Algebra , demonstrating expertise in both theoretical and applied research. Awards: NSERC Postdoctoral Fellowship (UBC), MITACS NCE Fellowship (University of Toronto) Service: Guest Editor for Scientific Reports (2024 collection on population health modeling) Conferences: Co-organizer of CMS sessions on mathematics of machine learning (2021–2024)