جست‌وجوی استاد و پژوهشگر

Aleksandar Nikolovمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Algorithm Design
• High-Dimensional Geometry
• +۶ مورد دیگر

Aleksandar Nikolov is an Associate Professor in the Department of Computer Science at the University of Toronto, holding the Canada Research Chair in Algorithms and Private Data Analysis. He is affiliated with the Vector Institute and a faculty fellow at the Schwartz Reisman Institute. His research focuses on theoretical computer science, algorithm design, and their connections to high-dimensional geometry, differential privacy, discrepancy theory, and approximation algorithms. Nikolov completed his PhD at Rutgers University under Muthu's advisement and was a postdoctoral researcher at Microsoft Research. Research Interests: Nikolov explores high-dimensional geometry's applications in algorithm design, particularly in private data analysis (differential privacy), discrepancy theory, and experimental design. His work bridges geometric tools with computational challenges, including nearest neighbor search and geometric optimization. He also delves into approximation algorithms and sublinear/parallel algorithms for large-scale data analysis. Awards: Nikolov received the Best Paper Award at SoCG 2015 and a $100 prize from Joel Spencer for resolving Beck's 3-permutations conjecture. His contributions span theoretical computer science with a focus on algorithmic foundations and privacy. Supervision and Education: He has mentored several graduate and undergraduate students, including current PhD candidates Haohua Tang and Lily Li. Teaching includes advanced courses like CSC2420 (Algorithm Design) and a discrepancy theory course. His research spans over 50 publications, with recent work emphasizing differential privacy and geometric algorithms. Affiliations: Beyond his academic role, Nikolov collaborates with industry and academic institutions, contributing to Canada's AI ecosystem through Vector Institute and interdisciplinary efforts at Schwartz Reisman.

Belgin Ergenç Bostanoğluمشاهده پروفایل

دانشیار

• Database Systems
• Data Mining
• Privacy-Preserving Techniques
• +۳ مورد دیگر

Belgin Ergenç Bostanoğlu is an Associate Professor in the Computer Engineering Department at Izmir Institute of Technology (Turkey). Her research focuses on query optimization in distributed databases, association rule mining, privacy-preserving data mining, and graph-based algorithms. She leads the Dworld research laboratory and has held academic and industry roles since the 1980s. Education: B.Sc. in Computer Engineering, Middle East Technical University (1983) M.Sc. in Computer Engineering, Izmir Institute of Technology (2002) Ph.D. in Computer Engineering, Paul Sabatier University, France (2008) Research Interests: Dynamic frequent itemset mining and hiding under multiple support thresholds Subgraph mining in evolving graphs Federated query processing over linked data Privacy-preserving techniques in distributed databases Medical NLP applications (e.g., TurkMedNLI dataset) Her recent work emphasizes large-scale graph analysis, medical NLP dataset development, and adaptive join operators for federated SPARQL queries. She has contributed to over 30 peer-reviewed publications and led projects like the TÜBİTAK ARDEB 3501 platform for dynamic frequent itemset mining. Teaching: Courses include Advanced Database Management Systems, Knowledge Discovery, and Privacy-Preserving Data Mining. Labs & Projects: Manages Dworld lab and coordinates projects such as 'Turkish Medical NLP Model Development' (BAP-funded) and the Behavioral Next Generation Wireless Networks COST Action.

David M. Nicolمشاهده پروفایل

استاد

• Cyber-security
• Network Security
• Parallelized Discrete-Event Simulation
• +۵ مورد دیگر

David M. Nicol is the Herman M. Dieckamp Endowed Chair and Professor of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. He directs the Information Trust Institute, Advanced Digital Sciences Center (Singapore), and Critical Infrastructure Resilience Institute. As a leader in cyber-security and large-scale system simulation, he oversees national centers funded by DHS, DOE, and NSA. Nicol’s research focuses on network security, infrastructure resilience, and parallel simulation, with contributions to startups like Network Perception and textbooks like Discrete-Event Systems Simulation . Education: B.A. Mathematics (Carleton College, 1979), M.S./Ph.D. Computer Science (University of Virginia, 1983/1985) Affiliations: Director of CIRI, CREDC, and ADSC; Co-PI of NSA Science of Security Lablet His research interests include cyber-risk assessment, mathematical modeling, and security policy analysis. Nicol has advised numerous PhD students and received accolades for teaching excellence at UIUC, Dartmouth, and William & Mary. Notable awards include ACM and IEEE Fellowships, and the Titan of Simulation distinction. He teaches courses on computer systems analysis and information assurance, and has led major conferences like Winter Simulation Conference (2006 General Chair). His work bridges academia and industry through grants totaling millions in cyber-security innovation.

Tiffani L. Williamsمشاهده پروفایل

استاد آموزشی

• Computer Science Education
• Computational Biology
• Bioinformatics
• +۳ مورد دیگر

Tiffani L. Williams is a Teaching Professor and Director of Onramp Programs in the Department of Computer Science at the University of Illinois at Urbana-Champaign, and serves as a Dean’s Fellow in Inclusion, Belonging, and Engagement in the Grainger College of Engineering. Previously, she held roles as Director of Computer Science Programs and Professor of the Practice at Northeastern University-Charlotte (2017–2020) and was a faculty member at Texas A&M University’s Department of Computer Science and Engineering (2005–2017). Her research focuses on computer science education , computational biology , and inclusive science communication , with a particular emphasis on phylogenetic tree inference , high-performance computing , and diverse educational pathways . She pioneered programs like the iCAN accelerator to broaden participation in computing for non-CS graduates. Her awards include the Denice Denton Emerging Leader ABIE Award , Radcliffe Institute Fellowship , and multiple teaching excellence honors from Texas A&M and Illinois. Her work bridges computational science and equitable education , with notable contributions to phylogenetic algorithms and reproducible research practices. Recent courses include advanced computer science fundamentals and excursions in computing. She leads initiatives to address systemic inequities in STEM through curriculum design and inclusive pedagogy.

Berkant Savasمشاهده پروفایل

دانشیار

• Scientific computing
• Numerical linear and multilinear algebra
• Large-scale graph/network computations
• +۳ مورد دیگر

Berkant Savas is an Associate Professor at Linköping University's Department of Science and Technology (ITN) within the Physics, Electronics and Mathematics school. His research focuses on scientific computing, numerical linear/multilinear algebra, large-scale graph/network computations, and tensor analysis. He has developed influential algorithms like Clustered Matrix Approximation (CMAPP) and Grassmann manifold computational tools. His work bridges theoretical foundations with practical applications in data science and engineering. Key contributions include scalable methods for massive graphs, low-rank tensor approximations, and optimization on manifolds. Savas' MATLAB-based tools (e.g., Grassmann classes, tensor approximation packages) are widely used in academic and industrial research. He has published extensively in top journals and conferences such as SIAM Journal on Matrix Analysis and IEEE transactions. Current research explores applications in link prediction, recommendation systems, and high-dimensional data analysis. Savas collaborates internationally, with notable co-authors like Inderjit Dhillon and Lars Eldén. His work emphasizes computational efficiency and memory optimization for large-scale problems arising in networks and information retrieval. Though no explicit awards are mentioned, his impactful contributions highlight recognition within the computational mathematics community.

• Computational Fluid Dynamics
• Aerospace Engineering
• Turbulence
• +۵ مورد دیگر

Bernardini Matteo is an Associate Professor at the Department of Civil and Environmental Engineering, Sapienza University of Rome. His research focuses on high-fidelity computational fluid dynamics (CFD), with particular emphasis on compressible turbulent flows, shock wave/boundary layer interactions, and exascale computing. He leads the development of the STREAmS solver, a high-performance compressible flow solver optimized for exascale architectures. His work addresses challenges in aerospace engineering, including planetary entry systems, cryogenic tank pressurization, and rocket nozzle dynamics. Key research areas include direct numerical simulation (DNS) and large eddy simulation (LES) of supersonic/hypersonic flows, aeroacoustics, and flow control using microramps and surface modifications. His numerical methods are applied to study phenomena such as liquid sloshing in spacecraft, aeroelastic interactions in parachutes, and thermal effects in boundary layers. He collaborates on projects involving solid rocket motors, wind turbine aeroelasticity, and HPC optimization for exascale platforms. Publications from 2023–2025 highlight advancements in exascale-ready CFD, cryogenic system modeling, and turbulence characterization under extreme conditions. His work bridges fundamental fluid dynamics research with aerospace engineering applications, emphasizing high-performance computing and multiphysics simulation.

Krzysztof Spalikمشاهده پروفایل

استاد

• Phylogenetics
• Evolutionary Biology
• Plant Taxonomy
• +۳ مورد دیگر

Prof. Krzysztof Spalik is a distinguished academic serving as Dean of the Faculty of Biology at the University of Warsaw and Head of the Scientific Council of the Biological-Chemical Research Centre. He leads the Committee of Environmental and Evolutionary Biology at the Polish Academy of Sciences (PAS). His research focuses on phylogenetics, evolution, taxonomy, and biogeography of Apiaceae, with particular emphasis on economically important plants like carrots and medicinal genera such as Ferula and Thapsia. His current project investigates ecological factors shaping secondary woodiness in Apioideae. Spalik has held numerous leadership roles, including deputy head of the Committee for Evolutionary and Theoretical Biology PAS and membership in expert panels for the National Science Centre Poland. He is a recipient of the 2013 Academic Teacher of the Year award for his contributions to education in exact and natural sciences at UW. Spalik actively engages in educational research, collaborating with the Institute of Educational Research and serving on the Central Examination Board. His teaching portfolio includes Botany, Ecological Education, Evolutionism, and Taxonomy, reflecting his commitment to both research and pedagogy. His research has led to groundbreaking taxonomic revisions (e.g., Laserocarpum in Greece) and advancements in phylogenetic methodologies, such as the Fast mvSLOUCH tool for analyzing multivariate trait evolution. Spalik’s work bridges molecular systematics with ecological adaptation, addressing topics like dispersal mechanisms, drought resistance, and inflorescence evolution in Apiaceae. Over 50 peer-reviewed publications and active roles in international collaborations underscore his influence in plant evolutionary biology.

Damek Davisمشاهده پروفایل

استادیار

• Optimization
• Machine Learning
• Signal Processing
• +۴ مورد دیگر

Damek Davis is an Assistant Professor in the Department of Mathematics at Cornell University, affiliated with both the College of Engineering and the College of Arts and Sciences. He holds a Ph.D. in Mathematics from the University of California, Los Angeles (2015). His research focuses on developing and analyzing optimization algorithms for large-scale, nonconvex, and nonsmooth problems arising in machine learning and signal processing. He emphasizes theoretical guarantees on algorithm performance and practical implementations that leverage modern computing architectures. His work spans stochastic optimization, subgradient methods, and convergence analysis, with applications to computational microscopy, phase retrieval, and decentralized systems. Recent contributions include advancements in adaptive stepsize methods, sharpness-aware optimization, and global optimality conditions for mixture models. Davis collaborates on theoretical foundations and algorithmic innovations in high-dimensional statistics, nonlinear optimization, and parallel computing. His scholarly output includes over 50 peer-reviewed articles, with recent highlights in top venues such as SIAM Journal on Optimization, Mathematical Programming, and IEEE Transactions on Signal Processing. His research is supported by grants from NSF and AFOSR, focusing on topics like robust statistical estimation and algorithmic scalability.

Todd Martinezمشاهده پروفایل

استاد

• Theoretical Chemistry
• Photochemistry
• Mechanochemistry
• +۳ مورد دیگر

Todd Martinez is the David Mulvane Ehrsam and Edward Curtis Franklin Professor of Chemistry and Professor of Photon Science at Stanford University, affiliated with SLAC National Accelerator Laboratory. His research focuses on theoretical and computational chemistry, with emphasis on photochemistry, mechanochemistry, and quantum mechanical methods. He earned his PhD in Chemistry from UCLA in 1994, followed by postdoctoral work at UCLA and the Hebrew University in Jerusalem before joining Stanford in 2009. His group develops novel simulation tools, including GPU-accelerated quantum chemistry software (e.g., TeraChem) and virtual reality interfaces (InteraChem and MolAR) for interactive molecular modeling. Key interests include predicting molecular behavior under light and mechanical force, designing self-healing materials, and advancing reaction discovery through first-principles methods. Awards include the MacArthur Fellowship and recognition as a Fellow of the American Academy of Arts and Sciences. Education: PhD in Chemistry (UCLA, 1994); Postdoctoral Studies at UCLA and Hebrew University (Jerusalem) Research Themes: Molecular modeling, excited-state dynamics, mechanochemical reactions, and computational tools Publications span ultrafast spectroscopy, quantum mechanical modeling, and algorithm development for large-scale simulations. The Martinez Group’s work bridges theory and application, aiming to make molecular design predictive and accessible through interdisciplinary collaborations.

Annabel Lathamمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence in Education
• Ethics and Trust of AI
• Computational Intelligence
• +۸ مورد دیگر

Dr. Annabel Latham is a Senior Lecturer in Computer Science at Manchester Metropolitan University within the Faculty of Science and Engineering’s Computing and Mathematics department. She holds a PhD in Artificial Intelligence, an MSc in Computing, a Postgraduate Certificate in Academic Practice, a CIM Diploma in Marketing, and a BSc(Hons) in Computation. As a Fellow of the Higher Education Academy (FHEA) and Senior Member of IEEE (SMIEEE), she contributes extensively to AI research and education. PhD in Artificial Intelligence MSc in Computing PGC Academic Practice CIM Diploma in Marketing BSc(Hons) Computation FHEA (2015) SMIEEE (2018) Research Interests include Artificial Intelligence in Education , Ethics of AI , and Computational Intelligence . She specializes in conversational agents, intelligent tutoring systems, and public trust in AI. Her work addresses fairness, accountability, and accessibility in AI-driven education, leveraging technologies like large language models and fuzzy logic. Research Outputs span 15 years, with a focus on explainable AI, educational applications of conversational agents, and ethical frameworks. Recent work (2024-2025) explores postdigital citizen science, trustworthy AI implementation, and XAI usability for non-specialists. 2023 IEEE Region 8 Outstanding Women in Engineering Volunteer Award 2019 IEEE Region 8 Outstanding Women in Engineering Affinity Group of the Year 2018 Outstanding Peer Reviewer, Elsevier: Computers & Education Senior Member IEEE (2018) FHEA (2015) Teaching and Supervision includes undergraduate Databases, postgraduate units in Information Systems and AI Ethics. She supervises MSc and PhD students in areas like explainability-aware machine learning, data responsibility, and AI trustworthiness. Her grants involve collaborations with international funding bodies such as NAFOSTED (Vietnam) and Croatia’s National Council for Science. Labs and Groups include the Computational Intelligence Lab, Machine Intelligence research group, and the Data and AI Ethics research group, where she co-leads initiatives on ethical AI in education.

Anthony P. Austinمشاهده پروفایل

استادیار

• Numerical Linear Algebra
• High-Performance Computing
• Approximation Theory
• +۳ مورد دیگر

Anthony P. Austin is an Assistant Professor in the Department of Applied Mathematics at the Naval Postgraduate School . His research focuses on numerical linear algebra, high-performance computing, approximation theory, and GPU-accelerated algorithms. He has held prior positions at Virginia Tech and Argonne National Laboratory as a J. H. Wilkinson Fellow. His work includes contributions to Chebfun, a software system for numerical computation. Education: D.Phil. in Mathematics , University of Oxford Mathematical Institute (2016) Ph.D. studies with active development in the Chebfun project Research Interests : Numerical methods for large-scale problems, high-order methods for PDEs, and computational techniques leveraging GPUs. His work emphasizes algorithmic innovation and stability in numerical simulations. Recent Contributions : Publications include advancements in spectral factorization, adversarial signal perturbations, and parallel algorithms for partial spectral computations. His seminar co-organization highlights collaboration in early-career faculty research initiatives. Professional Activities : Co-organizer of the NPS Junior Faculty Research Seminar (AY2022/Q3), focusing on fostering interdisciplinary collaboration among early-career faculty.

• Databases and Information Systems
• Big Data
• Data Management
• +۳ مورد دیگر

Dr.-Ing. Julia Othlinghaus-Wulhorst is a Researcher in the Department of Databases and Information Systems at the University of Würzburg, Faculty of Computer Science and Mathematics. She specializes in databases, data engineering, and Big Data. Her work focuses on query processing, data quality, and cloud resource optimization in distributed systems. Education: Diplom-Informatikerin (FH) (FH Diploma in Computer Science). Research Interests: Databases and Information Systems, Big Data, Data Management, Data Engineering, Data Science, Data Quality. She explores scalable database systems, data quality frameworks, and cloud-based resource allocation. Recent Work Trends: Her recent articles address large-scale distributed databases, data quality in Big Data environments, and cloud resource optimization. These reflect a focus on improving system efficiency and data reliability in modern computing landscapes. Awards: Best Paper Award at ICDE 2023 Best Paper Award at ICDE 2022 Outstanding Reviewer Award at ICDE 2022 Advising & Grants: Advises PhD students in data systems and participates in DFG-funded research projects. Active in collaborative initiatives on database optimization and Big Data analytics. Labs & Teams: Member of the Information Systems Lab and Big Data Analytics Group at the University of Würzburg.

Zev Woodstockمشاهده پروفایل

استادیار

• Optimization
• Nonlinear Analysis
• Signal Processing
• +۹ مورد دیگر

Zev Woodstock is an Assistant Professor in the Department of Mathematics & Statistics at James Madison University (JMU). His research focuses on nonsmooth optimization, splitting algorithms, machine learning, and monotone operator theory, with applications in data science, signal/image/audio processing, and large-scale optimization. He holds a PhD in Mathematics (2021) and MS in Applied Mathematics (2018) from North Carolina State University, and a BS in Mathematics (2016) from JMU. Previously, he was a postdoctoral researcher at the Interactive Optimization and Learning Lab at TU Berlin and Zuse Institute Berlin. Education PhD in Mathematics, North Carolina State University, 2021 MS in Applied Mathematics, North Carolina State University, 2018 BS in Mathematics, James Madison University, 2016 Research Interests Dr. Woodstock’s work spans optimization theory and applications, including convex/nonconvex optimization, fixed point algorithms, and signal recovery. He develops methods for solving nonlinear inverse problems using firmly nonexpansive operators, with contributions to variational inequality models, projection-based techniques, and block-iterative schemes. His applied work includes audio signal restoration, large-scale optimization, and machine learning infrastructure. Key Research Trends Recent publications emphasize efficient algorithms for linear minimization, asynchronous optimization frameworks, and robust signal recovery from inconsistent nonlinear data. His work bridges theoretical operator theory with practical applications in imaging, audio processing, and distributed computing. Awards and Grants Recipient of National Science Foundation Grants DGE-1746939 and CCF-1715671 for collaborative research in approximation theory and optimization. Labs and Collaborations Current affiliations include the Department of Mathematics & Statistics at JMU. Previously, he collaborated with the Interactive Optimization and Learning Lab at TU Berlin, focusing on convex optimization and algorithmic design.

JOSE RAMON RIOS VIQUEIRAمشاهده پروفایل

استاد

• Spatial Data Management
• Environmental Data Integration
• Geographic Information Systems (GIS)
• +۶ مورد دیگر

Jose Ramon Rios Viñuela is a Professor at the Department of Electronics and Computing, University of Santiago de Compostela, affiliated with the Higher Technical School of Engineering. He holds a PhD from the University of A Coruña (2003) for his thesis on spatio-temporal data management. His research focuses on spatial databases, environmental data integration, smart city technologies, geographic information systems (GIS), and spatio-temporal data modeling. Key projects include TRAFAIR (traffic-air quality analysis), COGRADE research group (computer graphics and data engineering), and GeoHbbTV frameworks for geospatial interactive media. He leads efforts in distributed spatial data processing, semantic interoperability, and sensor network integration for environmental monitoring. Notable contributions include frameworks like TAQE (traffic-air quality modeling), GeoSPARQL query systems, and MeteoSIX meteorological platforms. His work bridges earth sciences with public accessibility through smart environmental infrastructures, emphasizing citizen engagement and open data dissemination. Current research trends explore big data analytics for geospatial applications, federated environmental data systems, and low-cost sensor networks for urban sustainability. He collaborates on projects involving air quality forecasting (GRAL model), spatial join optimization in large data lakes, and semantic mediation of environmental observation systems. His academic contributions span over 60 peer-reviewed publications across GIS, database systems, and environmental informatics.

François Orieuxمشاهده پروفایل

استادیار

• Inverse Problems
• Bayesian Methods
• Signal Processing
• +۵ مورد دیگر

François Orieux is an Assistant Professor at the University Paris-Saclay, affiliated with the Laboratoire des Signaux et Systèmes (L2S) within CentraleSupélec. His research focuses on Bayesian methods for inverse problems, computational imaging, and signal processing, with applications in astronomy, microscopy, and embedded AI systems. He holds a PhD in Astrophysical Instrumentation (2009) and an HDR in Computational Imaging (2024). Key research areas include: Development of optimization algorithms for hyperspectral and multispectral fusion High-dimensional sampling techniques for large-scale data GPU-accelerated convolution methods for real-time processing Instrument modeling for radio astronomy and terahertz spectroscopy Recent work emphasizes hardware-aware AI implementations on embedded systems and radio interferometric imaging pipelines. He collaborates with institutions like the Institut d'Astrophysique de Paris and leads projects funded by ANR and EU initiatives. Teaching activities include computational imaging and signal processing courses at CentraleSupélec. His open-source contributions include the Q-MM Python toolbox for optimization algorithms.