Find Academic Supervisors

• Machine Learning
• Artificial Intelligence
• Speech Processing
• +6 more

Saeed Amizadeh is a prominent researcher specializing in artificial intelligence and machine learning, currently affiliated with Microsoft's research division. His work spans multiple domains of AI including speech processing, natural language understanding, computer vision, and time series analysis, with a particular focus on developing novel frameworks that bridge symbolic reasoning with neural approaches. Over the past decade, he has established himself as a significant contributor to the field through publications in top-tier conferences including ICASSP, ICLR, AAAI, KDD, and IJCAI. Dr. Amizadeh's research interests primarily center around advancing the theoretical foundations and practical applications of machine learning systems. His work on neuro-symbolic visual reasoning has contributed to understanding how to effectively disentangle visual perception from logical reasoning in AI systems. He has made significant contributions to differentiable programming, particularly in making classical machine learning pipelines fully differentiable, which enables end-to-end optimization of complex ML workflows. His research on speech enhancement using GANs represents cutting-edge work in audio processing, while his time series anomaly detection frameworks have practical applications in numerous industry settings. Analysis of his publication trends reveals a consistent trajectory from theoretical machine learning foundations toward increasingly applied research with practical industrial relevance. His early work (2010-2015) focused on fundamental algorithms and probabilistic modeling approaches, while his more recent publications (2019-2025) demonstrate a shift toward practical AI systems with direct applications in speech processing, audio separation, and enterprise machine learning. A notable theme throughout his career is the development of frameworks that enable more efficient, scalable, and interpretable AI systems. As a key contributor to Microsoft's ML.NET framework, Dr. Amizadeh has played an important role in developing tools that make machine learning more accessible to enterprise developers. His collaborative work spans academia and industry, with notable partnerships with researchers from various institutions as well as within Microsoft Research.

• Information Retrieval
• XML Search Engines
• Text Search Engines
• +8 more

Shlomo Geva is an Adjunct Professor in the School of Computer Science at Queensland University of Technology's Faculty of Science. His research focuses on information retrieval systems, particularly in specialized areas including XML search engines, text search engines, link discovery, and document computing. His academic work spans multiple disciplines within computer science, with particular emphasis on information retrieval technologies and their applications. Professor Geva's research interests include clustering algorithms, cross language information retrieval, focused information retrieval, information retrieval systems, link discovery mechanisms, search engine technologies, text indexing and retrieval methods, and XML indexing and retrieval techniques. His work demonstrates a consistent focus on improving the efficiency and effectiveness of information access systems across various data formats and domains. His recent publications reveal a trend toward applications of information retrieval techniques in diverse fields including remote sensing, bioinformatics, and data stream processing. The research shows an evolution from traditional information retrieval problems toward more specialized applications requiring advanced clustering algorithms and efficient data processing techniques for large-scale datasets. Professor Geva has successfully supervised numerous doctoral students whose research topics include indoor environment mapping by robots, cross-language information retrieval, natural language query interfaces for XML, evolvable hardware, and autonomous robot behavior systems.

Yuwen GuView profile

Assistant Professor

• High-dimensional statistics
• Variable selection
• Model combination
• +3 more

Yuwen Gu is an Assistant Professor in the Department of Statistics at the University of Connecticut. Their research focuses on high-dimensional statistics, variable selection, model combination, nonparametric methods, causal inference, and optimization techniques. Their recent publications highlight advancements in quantile regression, sparse modeling, and high-dimensional data analysis. Notable trends include applications of kernel methods, distributed computing for large-scale statistics, and regularization techniques for insurance and multi-source data. Yuwen Gu's work also explores interdisciplinary domains, such as bioacoustic signal processing in ecological studies, though their primary contributions remain in statistical methodology and computational efficiency.

Jeffrey D. BlanchardView profile

Professor

• Applied Harmonic Analysis
• Compressed Sensing
• Matrix Completion
• +7 more

Jeffrey D. Blanchard is a Professor in the Department of Mathematics and Statistics at Grinnell College. He also holds the endowed Donald L. Wilson Professor of Enterprise and Leadership title and directs the Donald and Winifred Wilson Center for Innovation and Leadership. His academic journey includes a Ph.D. from Washington University in St. Louis (2007), followed by a VIGRE Postdoctoral Fellowship at the University of Utah (2007–2009) and an NSF International Research Fellowship at the University of Edinburgh (2010). His research focuses on applied and computational harmonic analysis, particularly compressed sensing, matrix completion, composite dilation wavelets, and GPU-accelerated scientific computing. These areas intersect with digital signal processing, high-dimensional geometry, and random matrix theory. He has developed efficient algorithms for sparse recovery and large-scale data processing, often leveraging graphical processing units for performance gains. The recent publications reflect a consistent trend in algorithmic development for compressed sensing and sparse approximation, with increasing emphasis on GPU implementations and structured sparsity. His work bridges theoretical analysis with practical computational solutions, often resulting in open-access software tools like GAGA, GGKS, and GGMS. NSF International Research Fellowship (2010) NSF Grants: DMS 1112612, DMS 1620390, OISE 0854991 Harris Faculty Fellowship (2013–2014) MAA Project NExT Fellow (2008–2009) Department of Homeland Security Fellowship (2003–2006) Blanchard has advised numerous undergraduate research projects in areas such as GPU-based k-selection, joint sparsity, and composite dilation wavelets. His research has been supported by multiple National Science Foundation grants focused on algorithm analysis and high-performance computing. He has taught a wide range of courses including Calculus, Numerical Analysis, and Wavelet Applications. He leads a research group that develops GPU-accelerated algorithms for compressed sensing and sparse recovery. His software packages—GAGA, GGKS, and GGMS—are publicly available and used for high-performance computing tasks such as greedy pursuit and order statistics selection on GPUs.

Jeffrey S. RosenthalView profile

Professor

• Probability theory
• Stochastic processes
• Markov chain Monte Carlo
• +4 more

Jeffrey S. Rosenthal is a Professor in the Department of Statistical Sciences at the University of Toronto, Faculty of Arts and Science. He holds a PhD in Mathematics from Harvard University and a BSc from the University of Toronto. PhD, Mathematics, Harvard University BSc, University of Toronto His research centers on probability theory , stochastic processes , and statistical computation , with a particular focus on Markov chain Monte Carlo (MCMC) algorithms . His work spans theoretical foundations and practical applications, including random walks on groups and interdisciplinary modeling. He is also known for his public engagement in statistics through bestselling books and media appearances. The recent publications reflect a sustained focus on the theoretical underpinnings and convergence properties of MCMC methods, including adaptive and non-reversible algorithms. His work also extends into data science applications, such as analyzing streaks in online chess and the long-term impact of the COVID-19 pandemic on mortality. The keywords span probability, statistics, computational mathematics, and machine learning, indicating a blend of theoretical rigor and applied relevance. Scientific Awards and Honors: CRM-SSC Prize in Statistics COPSS Presidents' Award SSC Gold Medal Fellow of the Royal Society of Canada Fellow of the Institute of Mathematical Statistics Alumnus of Influence, University College Pierre Robillard Award SSC Student Research Presentation Award Savage Award Finalist Academic Supervision and Grants: Professor Rosenthal has supervised a large and diverse group of students, including numerous PhD candidates, MSc students, and post-doctoral fellows, many of whom have gone on to successful academic careers. His research is supported by ongoing publications and collaborations, indicating active grant funding and a vibrant research program. He maintains a well-documented research team and provides extensive resources for students and collaborators. Research Teams and Labs: He leads an active research group in probability and computational statistics, with a documented team of current and past post-doctoral fellows, PhD students, and research assistants. The group maintains a collaborative environment, as evidenced by joint publications and team photos, and focuses on advanced topics in MCMC theory and applications.

Maurizio LeottaView profile

Researcher

• Test Automation
• Empirical Software Engineering
• Requirements Engineering
• +6 more

Maurizio Leotta is a faculty member at the Department of Informatics, Bioengineering, Robotics, and Systems Engineering (DIBRIS) at the University of Genova, Italy. His research focuses on software testing automation, empirical software engineering, requirements engineering, business process modeling, and model-driven software development. He has contributed to advancing end-to-end web testing methodologies through tools like Assessor, STILE, and SleepReplacer-GPT, emphasizing robust locators, thread sleep replacement, and parallel test execution optimization. Primary Affiliation : DIBRIS, University of Genova Research Themes : Test Automation, Gamification in Software Testing, Web Accessibility, IoT Systems Testing In recent years, Leotta has explored the integration of large language models (LLMs) in automated testing, comparing browser automation tools (Selenium, Cypress, Puppeteer, Playwright), and analyzing job market trends post-pandemic. His work spans both practical tool development (e.g., TESTQUEST, Mutta) and empirical studies evaluating testing approaches. He actively participates in program committees for testing workshops and collaborates on gamification initiatives in software development education. The 15 most recent publications reflect his expertise in test automation frameworks, gamification strategies, and accessibility enhancements. Articles cover topics such as explicit wait management in web testing, LLM applications for image captioning, and comparative studies of automation tools. His research also extends to IoT system testing, runtime verification, and job market analysis using large-scale job advertisement data.

Michael SelzerView profile

Researcher

• Computational Materials Science
• Phase-Field Modeling
• Research Data Management
• +7 more

Dr.-Ing. Michael Selzer is a Group Leader in Research Data Management at the Karlsruhe Institute of Technology (KIT), specifically within the Institute of Nanotechnology. He leads the Kadi4Mat project, focusing on FAIR (Findable, Accessible, Interoperable, Reusable) research data infrastructure for materials science. His work bridges computational modeling and digital research workflows. Research Interests: Michael Selzer specializes in computational materials science , with a strong emphasis on phase-field modeling for simulating microstructure evolution, fracture mechanics, and multiphase systems. His research extends to materials informatics , digital workflows , and research data management , particularly in the context of battery materials, porous media, and solid-state systems. He integrates machine learning and data science to analyze and optimize materials properties. Publication Trends: His recent publications (2023–2025) highlight a growing focus on FAIR data infrastructure (e.g., Kadi4Mat, KadiStudio), large language models in battery science (LISA), low-code simulation platforms (MUSICODE), and reproducibility in bioprinting . These reflect a strategic shift toward digitalization and automation in materials research, while maintaining a strong foundation in physics-based modeling. Scientific Contributions: Developed and advanced phase-field models for grain growth, crack propagation, and interfacial phenomena. Contributed to the development of Kadi4Mat, a research data infrastructure for materials science. Integrated machine learning and AI into materials characterization and battery research. Published extensively in journals such as Acta Materialia , Computational Materials Science , and Scientific Data . Advising and Grants: While no direct students are listed, his leadership role in Kadi4Mat suggests mentorship and team supervision. He has likely secured funding for digital infrastructure and computational materials projects, evidenced by sustained publication output and collaborative work with major institutions. His research is highly collaborative, involving teams across KIT and international partners. Labs and Teams: He leads the Research Data Management group under the Kadi4Mat initiative at KIT. This team focuses on developing digital tools for materials research, including electronic lab notebooks (KadiWeb), workflow automation (KadiStudio), and ontology-based data integration. The group operates at the intersection of computational science, data engineering, and materials discovery.

Suiping ZhouView profile

Professor

• Distributed Virtual Environments (DVEs)
• Parallel and Distributed Systems
• Human Behavior Modeling
• +5 more

Suiping Zhou is a Professor at the School of Science and Technology, Middlesex University. He previously held academic positions at Teesside University (UK) and Nanyang Technological University (Singapore). He earned his B.Eng., M.Eng., and Ph.D. in Automatic Control from Beijing University of Aeronautics and Astronautics, followed by engineering experience at Beijing Simulation Center and a postdoctoral fellowship at the Weizmann Institute of Science (Israel). Research Focus: Large-scale distributed interactive applications (e.g., MMOGs), parallel/distributed systems, and human behavior representation in modeling and simulation. Key Contributions: Over 100 peer-reviewed publications on topics including QoS optimization, crowd simulation, distributed virtual environments, and agent-based modeling. Technical Expertise: OpenFlow, genetic programming, convolutional neural networks, and simulation consistency models. His work bridges theoretical modeling with practical applications in virtual environments and networked systems.

• Computational Linguistics
• Natural Language Processing
• Machine Translation
• +5 more

Pablo Gamallo Otero is a Professor at the University of Santiago de Compostela (USC), affiliated with the Faculty of Philology and the Department of Spanish Language and Literature. He leads research in the LComp Research Group in Computational Linguistics and is part of the Center for Research in Intelligent Technologies (CITIUS). His work focuses on computational linguistics, natural language processing (NLP), and language technologies for minority languages like Galician and Portuguese. He has extensively contributed to projects like the Nós initiative, developing large-scale corpora (e.g., CorpusNÓS) and models for low-resource languages. Key research interests include sentiment analysis, hyperpartisan news detection, neural machine translation (NMT), and cross-lingual paraphrasing. His work bridges theoretical linguistics and practical NLP applications, with a focus on preserving and revitalizing Galician through advanced AI tools. He has collaborated on international initiatives such as the Iberobench benchmark and the SemEval competition, advancing multilingual AI evaluation frameworks. His articles often address challenges in machine translation for Galician, leveraging Portuguese resources via transliteration strategies. He has also explored diachronic language distance measurement, lexical resources for mental health analysis, and dependency-based compositional semantics. His contributions span conferences like PROPOR and TREC, reflecting his role in both academic research and applied technology development.

• Applied and Computational Mathematics
• Parallel Scientific Computing
• Computational Physics
• +8 more

Juan Antonio Acebrón Torres is an Assistant Professor in the Department of Information Science and Technology at ISCTE-Instituto Universitário de Lisboa (ISCTE-IUL), Portugal, and a researcher at INESC-ID. He is affiliated with the School of Technology and Architecture and actively contributes to research in computational mathematics and high-performance computing. PhD in Mathematical Engineering, Universidad Carlos III de Madrid, 2000 MS in Theoretical Physics, Universidad Autónoma de Madrid, 1994 His primary research interests include applied and computational mathematics, parallel scientific computing, Monte Carlo methods, probabilistic domain decomposition, and nonlinear physics. He develops scalable algorithms for solving partial differential equations and stochastic systems, with applications in computational physics and linear algebra. His work emphasizes efficiency and parallelization for petascale and distributed computing environments. The most recent publications demonstrate a consistent focus on Monte Carlo-based numerical solvers, particularly for matrix functions and PDEs. Key themes include probabilistic domain decomposition, multilevel Monte Carlo methods, random trees, and high-performance implementations. These works appear in top-tier journals like Journal of Computational Physics , SIAM Journal on Scientific Computing , and Future Generation Computer Systems , indicating strong interdisciplinary impact. Associate Editor, Axioms Acebrón has supervised multiple postdoctoral, PhD, and Master’s students, including Francisco Bernal Martinez and Angel Rodriguez-Rozas. His research has been supported through institutional affiliations and collaborative projects. He has not received any explicitly mentioned scientific awards in the provided text. He leads a research group focused on probabilistic numerical methods and high-performance computing, with ongoing projects involving Monte Carlo solvers for matrix functions and PDEs. A postdoctoral position is currently open in his team, indicating active research expansion.

Michał PrzewoźniczekView profile

Professor

• Evolutionary Computation
• Multi-objective Optimization
• Many-objective Optimization
• +12 more

Michał Przewoźniczek is a Professor at Wrocław University of Science and Technology (PWr), actively contributing to the fields of evolutionary computation, optimization, and artificial intelligence. He is a key member of multiple research teams, including the Metaheuristics Team, Machine Learning Team, and Advanced Data Analysis Methods Team, reflecting his broad interdisciplinary impact. His research focuses on Evolutionary Computation , Multi- and Many-objective Optimization , Linkage Learning , Problem Decomposition , and Hybridization of metaheuristic methods, with applications in industrial process planning, network optimization, and real-world decision-making. He leads significant research projects such as greybox optimization (2023–2026) and multi-objective evolutionary methods with linkage learning (2021–2026), demonstrating sustained grant support and scientific leadership. The analysis of his recent publications reveals a consistent trend in advancing parameter-less evolutionary algorithms, empirical linkage learning, and scalable distributed implementations using containerization (e.g., Docker). His work bridges theoretical algorithm development with practical applications in optical networks, manufacturing, and distributed computing. His scientific honors include: Best Paper Nomination at the Genetic and Evolutionary Computation Conference (GECCO) Associate Editor, IEEE Transactions on Evolutionary Computation (since January 2023) Prof. Przewoźniczek supervises graduate students as a thesis advisor and collaborates extensively with researchers such as Piotr Dziurzanski, Marcin Komarnicki, Krzysztof Walkowiak, and Leandro Soares Indrusiak. He is actively involved in major conferences like GECCO, CEC, and CORES, and his work is indexed in Google Scholar, ResearchGate, and ORCID (0000-0003-2446-6473). He is affiliated with research groups focusing on Machine Learning, Computer Networks, Advanced Data Analysis, and Metaheuristics, indicating a strong collaborative and team-based research environment.

Hans A. WintherView profile

Associate Professor

• Theoretical Cosmology
• Large-Scale Structure Formation
• Modified Gravity
• +3 more

Hans A. Winther is an Associate Professor at the Institute of Theoretical Astrophysics, University of Oslo, specializing in theoretical cosmology with a focus on large-scale structure formation. He holds a Master’s (2010) and PhD (2013) from the University of Oslo, followed by postdoctoral research at the University of Oxford and the University of Portsmouth. His work bridges cosmological theory with advanced numerical simulations, emphasizing modified gravity, dark matter models, and N-body codes. Winther leads courses such as AST5220 Cosmology II and AST1010 Introduction to Astronomy , integrating computational tools like FML and MG-PICOLA . His research includes developing simulation frameworks for modified gravity (e.g., ISIS , SCALAR ) and axion-like dark matter. He actively contributes to the Euclid mission through simulations and data analysis, addressing nonlinear cosmological structures and parameter constraints. Notable projects include the FML C++ library for parallel grid-particle algorithms and the MG-PICOLA code for fast modified gravity simulations. His work underscores the interplay between theory and computation in cosmology, aiming to test gravity and dark matter hypotheses using observational data from surveys like Euclid and LSST.

• Energy systems integration
• Grid modeling and simulation
• High performance computing
• +5 more

Deepthi Vaidhynathan is a Researcher IV in the Computational Science Center at the National Renewable Energy Laboratory (NREL), where she works in the Complex System Simulation and Optimization Group. Her research focuses on energy system integration, grid modeling and simulation, and high performance computing for scientific applications in power systems and building energy management. Her educational background includes: Bachelor of Electronics and Communication Engineering from Anna University Master of Electrical and Computer Engineering from the University of Colorado Boulder Deepthi's research spans energy systems integration, grid modeling, high performance computing, computer architecture, and control theory. She applies quantum computing principles and microprocessor optimization to develop advanced simulation tools for modern power grids with high renewable energy penetration. Her work bridges theoretical algorithms with practical energy system challenges. Analysis of her 2024-2025 publications reveals concentrated expertise in hybrid simulation techniques for inverter-dominated power grids and AI-enhanced control frameworks. She specializes in partitioning algorithms for large-scale electromagnetic transient simulations and neural-network-based predictive control for building energy systems, addressing critical needs in grid stability and renewable integration. Deepthi is an active contributor to NREL's Complex System Simulation and Optimization Group, which develops high-fidelity tools for modeling next-generation energy infrastructure. The group's work supports U.S. Department of Energy initiatives for grid modernization and renewable energy deployment. No scientific awards are documented in available sources. Public records indicate no formal student advising roles or disclosed grant funding details.

Hassane SadokView profile

Professor

• Numerical Analysis
• Iterative Methods
• Linear Algebra
• +5 more

Hassane Sadok is a Professor and President at the University of the Littoral Côte d'Opale (ULCO), France. He is affiliated with the Department of Mathematics within the College of Science, where he contributes to teaching and research in numerical analysis and scientific computing. Position: Professor Institution: University of the Littoral Côte d'Opale (ULCO) Administrative Role: President since 2016 Department: Mathematics School: College of Science His research focuses on numerical linear algebra, particularly iterative methods for solving large linear systems, Krylov subspace methods (such as GMRES, Arnoldi, and CMRH), and regularization techniques for ill-posed problems. He has made significant contributions to the convergence analysis of minimal residual and orthogonal projection methods. His work bridges theoretical numerical analysis with practical computational algorithms. The recent publications reflect a strong trend in developing and analyzing block and global iterative solvers for multiple linear systems, convergence theory for eigenvalue problems, and regularization strategies like the L-curve and greedy Tikhonov methods. These works are grounded in applied mathematics and computational science, with applications in engineering and data analysis. Although no formal awards are listed in the provided texts, his leadership as university president and sustained research output indicate recognition in both academic and administrative domains. He advises graduate students in numerical analysis and computational mathematics, though specific names are not listed. He has not disclosed information on research grants. His teaching includes Master’s level courses in interpolation, quadrature, and numerical resolution of nonlinear equations. While no specific research lab or team is named, his collaborations with researchers like K. Jbilou, Y. Saad, and L. Reichel suggest active participation in a research group focused on numerical methods, likely within the Laboratoire de Mathématiques Pures et Appliquées (L.M.P.A.) at ULCO.

• Distributed Computing
• Database Systems
• Big Data
• +7 more

YongChul Kwon is a researcher affiliated with the University of Washington , focusing on distributed computing, database systems, and big data optimization. His work addresses critical challenges in MapReduce frameworks, including skew management, fault tolerance, and performance efficiency. Key research contributions include SkewTune for dynamic skew mitigation in MapReduce, SnipSuggest for SQL autocompletion, and comparative analyses of Hadoop's scalability. He has published in venues like VLDB , SIGMOD , and IEEE Data Engineering Bulletin , often collaborating with Magdalena Balazinska and Bill Howe. Recent work (2013–2016) explores iterative MapReduce frameworks, Hadoop's workload evolution, and parallel processing of scientific user-defined functions. Earlier contributions (2008–2010) include fault-tolerant stream processing, clustering algorithms for astrophysics, and collaborative query management systems. His research bridges distributed systems, database engineering, and scientific computing, with implications for cloud infrastructure and large-scale data analytics.