جست‌وجوی استاد و پژوهشگر

Jiangping Chenمشاهده پروفایل

استاد مهمان

• Data Analytics
• Digital Libraries
• Information Retrieval
• +۵ مورد دیگر

Jiangping Chen is the Interim Executive Associate Dean and Visiting Professor at the University of Illinois Urbana-Champaign's School of Information Sciences (iSchool). Previously, she was a Regents Professor and Chair of the Department of Information Science at the University of North Texas (UNT). She leads the UNT Intelligent Information Access (IIA) Lab, focusing on data science, health informatics, and multilingual digital libraries. Her research emphasizes data analytics for information problems, cross-language retrieval, and information services. Dr. Chen holds a PhD in Information Transfer from Syracuse University, a Master's in Information Science from the Chinese Academy of Sciences, and a Bachelor's from Wuhan University. She has served as Editor-in-Chief of The Electronic Library and chaired the 2018 Joint Conference on Digital Libraries (JCDL). Her research spans data analytics, digital libraries, information retrieval, natural language processing, computational linguistics, and privacy/security. Notable achievements include the LIBINF Top 10 Cited Article award (2022) and the ACM Recognition of Service Award (2018). She has authored over 150 peer-reviewed publications and a monograph on multilingual digital libraries. Her recent work addresses misinformation detection, interdisciplinary knowledge discovery, and sustainable archiving practices. She co-chairs the ALISE Council of Deans and actively contributes to academic leadership and policy initiatives in information science.

• Wireless Communication
• Networking
• Military & Secure Communication
• +۵ مورد دیگر

John M. Shea is a Professor and Associate Chair for Faculty Affairs in the Department of Electrical & Computer Engineering at the University of Florida. His research focuses on wireless communication & networking, military & secure communication systems, cross-layer design, and networked autonomous vehicles. He has collaborated with organizations like AFOSR, AFRL, DARPA, and industry partners Harris and Samsung Electromechanics. Education: PhD, MS, and BSEE in Electrical Engineering from Clemson University (1993-1998). Awards include the Summer Faculty Fellow (ONR, 2012/2014), MILCOM Technical Achievement Award (2012), and the Fred W. Ellersick Award (1996). Recent research trends emphasize cybersecurity in autonomous systems, resilient consensus algorithms for multi-agent networks, and privacy-preserving localization techniques. His work on jammer placement strategies and dynamic spectrum sharing has been supported by defense-related grants. Advising and grants: Extensive industry-academic partnerships, with grants from AFOSR, NSF, and DoD. His research group explores cutting-edge applications in military communication systems and autonomous vehicle networks.

Guy E. Blellochمشاهده پروفایل

استاد

• Parallel Computing
• Programming Languages
• Algorithms and Complexity
• +۵ مورد دیگر

Guy E. Blelloch is the U.A. and Hellen Whitaker University Professor at Carnegie Mellon University , affiliated with the Computer Science Department . He focuses on the intersection of algorithms and programming languages within parallel computing , with significant experimental and theoretical contributions. His recent work includes the PSCICO project (with Gary Miller, Bob Harper, Peter Lee) exploring high-level programming constructs in geometric algorithms, and continued development of the NESL programming language for parallel computation. Research spans Parallel Garbage Collection Work-Efficient Scheduling Deterministic Parallel Algorithms Processing-in-Memory Models Key publications since 2022 show trends in dynamic data structures , cache-oblivious algorithms , and concurrent memory management , with applications in graph processing (PIM-tree), geometric computation (tree-based sorting), and multiversioning systems. His work often bridges theory and practice, demonstrated in frameworks like Ligra , GBBS , and Aspen for large-scale graph analysis. Scientific awards include ACM Paris Kanellakis Theory and Practice Award (2023) IEEE Charles Babbage Award (2021) Best Paper Awards at PPoPP 2022, SPAA 2021 SCS Doctoral Dissertation Award (Honorable Mention) He has advised numerous PhD students including Magdalen Dobson (current student on nearest-neighbor problems), Daniel Anderson (parallel batch-dynamic algorithms), and Julian Shun (graph processing frameworks). His teaching includes advanced courses on Parallel and Sequential Data Structures and Parallel and Concurrent Algorithms .

Michael Schweinbergerمشاهده پروفایل

استاد

• Network Science
• Statistics
• Machine Learning
• +۷ مورد دیگر

Michael Schweinberger is a Professor of Statistics at The Pennsylvania State University (Penn State), affiliated with the Eberly College of Science. Previously, he served on the faculty of Rice University and held visiting positions at the University of Washington, Seattle, and the University of Missouri, Columbia. He also held postdoctoral positions at Penn State and the University of Washington. Schweinberger earned his Ph.D. in Statistics from the University of Groningen, Netherlands. His research focuses on the statistical science of networks, including applications to artificial intelligence, neuroscience, genetic diseases, epidemiology, social media, and economics. He emphasizes developing scalable models and methods for large networks, causal inference in networked systems, and high-dimensional data analysis. His work has been funded by the U.S. National Science Foundation (NSF), the U.S. Department of Defense (DoD), and the Netherlands Organisation for Scientific Research (NWO). While no specific scientific awards are listed, his contributions to network statistics are recognized through editorial roles with journals such as the Journal of Computational and Graphical Statistics and the Journal of Statistical Software. He also served as a reviewer for prestigious institutions like the European Research Council (ERC) and the German Research Foundation (DFG). Schweinberger has advised two Ph.D. students: Jonathan R. Stewart and Sergii Babkin. He has also served on 17 Ph.D. committees. His teaching portfolio includes courses on statistical learning with networks, stochastic modeling, and mathematical statistics.

Vitalii Konarovskyiمشاهده پروفایل

استاد

• Stochastic Analysis
• Interacting Particle Systems
• Stochastic Partial Differential Equations
• +۳ مورد دیگر

Prof. Dr. Vitalii Konarovskyi is a Professor of Stochastic Processes and their Applications at the University of Hamburg's Department of Mathematics within the Faculty of Mathematics, Informatics and Natural Sciences. He holds a W2 professorship and has held academic roles at institutions such as Universität Leipzig, Bielefeld, and Jena. His research focuses on stochastic analysis, interacting particle systems, stochastic PDEs, measure-valued diffusions, random graphs, and machine learning. He actively teaches advanced courses in stochastic processes, mathematical statistics, and related fields, including recent modules like 'Stochastic Differential Equations' and 'Large Deviations.' Education & Career: PhD in Mathematics (2010-2015), Institute of Mathematics of NAS of Ukraine W2 Professor, University of Hamburg (2023–present) Postdoc roles at Universität Bielefeld, Leipzig, and the Max Planck Institute Research Interests: His work bridges stochastic processes with applications in physics and machine learning, emphasizing systems with singular interactions, measure-valued dynamics, and coalescing/fragmenting particle systems. Recent focus includes the Dean-Kawasaki equation, stochastic gradient descent dynamics, and stochastic block models. Awards & Recognition: No specific awards mentioned, but his contributions to stochastic analysis and coalescing particle systems are internationally recognized through collaborative research centers like SFB 1283 at Bielefeld. Teaching & Supervision: Extensive teaching portfolio includes stochastic calculus, large deviations, and mathematical methods for physicists. Active in supervising PhD students through research groups and seminars on topics like machine learning and stochastic flows. Labs/Teams: Collaborates with groups like Benjamin Gess's team at Bielefeld and Max von Renesse's group, contributing to projects on fluctuating hydrodynamics and mean-field limits.

• Random Graphs
• Design and Average Case Analysis of Algorithms
• Evolutionary Dynamics on Graphs
• +۱ مورد دیگر

Raptopoulos Christoforos is an Assistant Professor at the Department of Mathematics of the University of Patras , Greece. His academic qualifications include a Ph.D. (2009), M.Sc. (2005), and B.Sc. (2002) in Computer Engineering & Informatics from the same university. His research focuses on Random Graphs , Design of Algorithms , and Evolutionary Dynamics on Networks , with applications spanning computer science, probability theory, and computational biology. Key contributions include work on temporal graph algorithms, network majority determination, and evolutionary processes on graphs. His teaching portfolio covers Discrete Mathematics , Numerical Solutions of Ordinary Differential Equations , and Data Structures at the undergraduate level, alongside Algorithm Analysis and Data Structures for the MSc in Computational and Analytical Statistics in Data Science. While no specific scientific awards are listed, his 35+ publications since 2004 in journals like Theoretical Computer Science and Journal of Computer and System Sciences highlight his prolific output. He has collaborated extensively with Paul Spirakis, Sotiris Nikoletseas, and George Mertzios.

Yuede Jiمشاهده پروفایل

استادیار

• High-Performance Computing (HPC)
• Security
• Graph AI
• +۵ مورد دیگر

Dr. Yuede (YJ) Ji is an Assistant Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington (UTA), leading the Graph Lab. His research focuses on graph-centric security, learning, and computing, intersecting High-Performance Computing (HPC), Security, Graph AI, and Graph Analytics. Prior to UTA, he was an Assistant Professor at the University of North Texas (2021–2024). He earned his Ph.D. from George Washington University in 2021 under Dr. Howie Huang. His work has been recognized with awards such as the Best Paper Award at NPC 2014 and the Best Poster Award at SMCDC 2023. Education: Ph.D., Computer Engineering, George Washington University (2021) M.S., Computer Science & Technology, Jilin University (2015) B.E., Software Engineering, Jilin University (2012) Research Interests: Graph-centric security, HPC, Graph AI, malware detection, and privacy-preserving systems. His research is funded by NSF, DOE, and Google. Grants & Fundings: NSF SHF: Small: An Accelerated Computation Architecture (2024–2027) NSF CICI: Secure Containers in HPC Infrastructure (2023–2026) Students & Advising: Advising Ph.D., M.S., and undergraduate students. Current advisees include Chaoqun Li, Emmanuel Emakhue Oseghale, and Xiao Hu. Awards: Best Editor Award, IEEE OJCOMS (2024) CSE Junior Faculty Research Award Nominee (2024) Labs & Teams: The Graph Lab develops scalable solutions for HPC security and graph analytics. Recent projects include BINGO (EuroSys ‘25) and TANGO (SC ‘23).

John R. Kleinمشاهده پروفایل

استاد

• Algebraic Topology
• Geometric Topology
• K-Theory
• +۳ مورد دیگر

John R. Klein is a Professor in the Department of Mathematics at Wayne State University, which is part of the College of Liberal Arts and Sciences. His office is located in room 1213 FAB, and he can be contacted at klein@wayne.edu or ae9462@wayne.edu. Dr. Klein's primary research interests include: Algebraic Topology Geometric Topology K-Theory Poincaré Duality Homotopy Theory Quantum Information Theory Dr. Klein's research spans several decades with a consistent focus on algebraic and geometric topology. His work often explores the intersection of topology with other mathematical disciplines and physical applications. Recent publications demonstrate an expanding interest in applications to quantum information theory and mathematical physics, particularly in the analysis of quantum states and systems. His research combines deep theoretical insights with practical applications in areas such as quantum computing and statistical mechanics, showing a clear evolution from foundational topological work to interdisciplinary applications. Dr. Klein has taught a variety of mathematics courses at Wayne State University, including Calculus I-III, Differential Equations and Matrix Algebra, and Algebra I. He is scheduled to teach Calculus III and Algebra I in the Fall Term 2025, indicating his ongoing active role in the department. Dr. Klein maintains an extensive collaborative network, working with prominent mathematicians including Bruce Williams, Thomas Goodwillie, and Vladimir Y. Chernyak. These collaborations have resulted in significant contributions to homotopical intersection theory, embedding theory, and the application of topological methods to physical systems. His work bridges theoretical mathematics with practical applications in quantum information science.

Jeong-Hyon Hwangمشاهده پروفایل

دانشیار

• Databases
• Distributed Systems
• Graph Database Systems
• +۵ مورد دیگر

Jeong-Hyon Hwang is an Associate Professor at the University at Albany, State University of New York , affiliated with the College of Nanotechnology, Science, and Engineering and the Department of Computer Science . As Director of the Data Management Systems (DMS) Lab, he focuses on scalable graph databases, trajectory data compression, and real-time stream processing. His work on the G* graph database system, funded by the NSF CAREER award IIS-1149372 , enables efficient storage and analysis of distributed dynamic graphs. PhD in Computer Science, Brown University (2008) MS in Computer Science, Brown University (2003) MS in Computer Science and Engineering, Korea University (2000) BS in Computer Science and Engineering and Mathematics Education, Korea University (1998, 1994) Dr. Hwang’s research spans Databases and Distributed Systems , with specific emphasis on graph database systems , trajectory data management , and fault-tolerant stream processing . His publications highlight advancements in Internet-scale data management , real-time analytics , and load balancing for dynamic environments. The 15 most recent publications reflect trends in graph algorithms , stream processing reliability , and trajectory compression . Key subfields include distributed graph storage , centrality estimation , non-relational stream models , and high-availability solutions for wide-area networks. Scientific Awards : NSF CAREER award (2012) Best Poster Award, IEEE ICDE (2014) Best Poster Runners-Up, ACM SIGSPATIAL GIS (2010) IBM Open Collaborative Faculty Award (2010) National Scholarship, South Korea (2001-2005) New Software Award, South Korea (2001) Dr. Hwang leads the DMS Lab , developing open-source systems like G* for graph storage and querying. He has authored patents, co-authored Korean translations of technical books, and contributed to foundational research in high-availability algorithms and stream processing engines .

• Neuroscience
• Neuroimaging
• Substance Use Disorders
• +۷ مورد دیگر

Valentina Lorenzetti is a Professor at the School of Behavioural and Health Sciences within the Faculty of Health Sciences . Her research focuses on understanding the neurobiological and behavioral impacts of substance use disorders, particularly cannabis use, through advanced neuroimaging techniques like fMRI and MRI. Key Affiliations ENIGMA Addiction Working Group International Cannabis Toolkit (iCannToolkit) Consortium Collaborative studies across multiple countries Research interests center on cannabis-related neuroanatomical changes , brain functional connectivity , addiction neuroscience , and standardization of cannabis dosage for public health policy. She investigates how chronic cannabis use affects brain morphology (hippocampus, amygdala), cognitive function, and mental health outcomes. Recent studies include systematic reviews on real-time neurofeedback for addiction , THC unit standardization , and cross-disorder comparisons of brain abnormalities . Her work addresses sex differences in substance use , transdiagnostic impulsivity/compulsivity , and neuroimaging methodologies . Collaborations span institutions studying alcohol dependence , pathological gambling , and obesity-related brain changes . She contributes to policy frameworks for legal cannabis markets and has developed tools like the iCannToolkit to standardize cannabis use measurement across cultural contexts.

Jan Nagelمشاهده پروفایل

استاد

• Probability Theory
• Random Walks
• Random Matrices
• +۱ مورد دیگر

Jan Nagel is Professor of Stochastics at TU Dortmund University's Department of Mathematics. His research explores probability theory with focus areas including random walks in random environments, random matrix theory, and stochastic processes. Recent publications investigate the dynamic behavior of viscoelastic structures with random material properties, sum rules in mathematical physics, and functional central limit theorems for random matrices. Dr. Nagel completed his doctorate in Mathematics at Ruhr University Bochum (2010) following a Diplom degree (2008). His academic trajectory includes positions as Assistant Professor at TU Dortmund (2019-2023), research fellowship at TU Eindhoven, and visiting professorships at LMU Munich. His work bridges theoretical mathematics with applications in statistical mechanics and disordered systems.

David Penmanمشاهده پروفایل

مدرس ارشد

• Probabilistic Combinatorics
• Random Graphs
• Paley Graphs
• +۵ مورد دیگر

David Penman is a Senior Lecturer at the University of Essex's School of Mathematics, Statistics and Actuarial Science (SMSAS). He holds a first degree and Part III Mathematics from Cambridge, followed by an M.Sc. and Ph.D. in probability from Sheffield University. His research focuses on probabilistic combinatorics, random graphs, additive combinatorics, and graph theory, with notable work on Paley graphs and fixed point polynomials of permutation groups. He has supervised eight research students, including six Ph.D. candidates, and currently serves as Editor of the British Combinatorial Bulletin. Education : Bachelor's and Part III (Master's equivalent) in Mathematics, University of Cambridge M.Sc. and Ph.D. in Probability (random graphs), University of Sheffield Research Interests : Probabilistic combinatorics with correlation structures Paley graphs and pseudo-random graphs Extremal Ramsey graphs and poset analysis Quicksort algorithms and sandpile group dynamics Professional Roles : Deputy Dean (Education), Faculty of Science and Health (2013–2019) Editor of the British Combinatorial Bulletin Teaching : Current module: Graph Theory (MA314). Publications : Over 10 peer-reviewed articles in journals like Discrete Mathematics , Acta Arithmetica , and Electronic Journal of Combinatorics . His work bridges theoretical mathematics and applied combinatorics, with contributions to additive number theory and algorithmic analysis. Collaborations include projects on large deviations in random graphs and fixed point polynomials in permutation groups.

Dinesh Mehtaمشاهده پروفایل

استاد

• Algorithms
• Data Structures
• VLSI Design Automation
• +۴ مورد دیگر

Dinesh Mehta is a Professor in the Department of Computer Science at Colorado School of Mines, where he has been since 2000. His research focuses on algorithms and data structures applied to interdisciplinary fields such as VLSI design automation, cheminformatics, computational materials, and cyber-physical systems. He holds a B.Tech. from the Indian Institute of Technology Bombay (1987), an M.S. from the University of Minnesota (1990), and a Ph.D. from the University of Florida (1992). His research emphasizes algorithmic solutions for big graph analytics, including parallel algorithms, subgraph pattern matching, and network science applications. He has contributed to advancements in data structures for cheminformatics and VLSI layout optimization. His work bridges computer science with domains like materials science and geophysics, demonstrating strong interdisciplinary impact. Key research themes include optimizing algorithms for large-scale networks, predictive modeling in fracture mechanics, and efficient graphlet counting. His recent papers reflect a focus on parallel computing, machine learning applications in materials science, and algorithmic competitions in education. No significant scientific awards are listed in the provided texts. His advising and grants are not detailed, but his extensive publication record indicates active research and collaboration. He has no listed labs or teams, though his work likely involves interdisciplinary teams across Mines and industry partners.

Maggie Chengمشاهده پروفایل

استاد

• Machine Learning
• Complex Networks Analysis
• Applied Statistics
• +۱۱ مورد دیگر

Maggie Cheng is a Professor of Applied Mathematics and Director of the Center for Interdisciplinary Scientific Computation at Illinois Institute of Technology (IIT). She holds a Ph.D. from the University of Minnesota at Twin Cities. Her research focuses on interdisciplinary applications of data science and network science, including machine learning for power grids, cybersecurity, and complex systems analysis. She leads NSF-funded projects on real-time data analytics for energy systems and human dynamics inference from data streams. Key areas of expertise include machine learning, complex networks analysis, applied statistics, optimization algorithms, and wireless sensor networks. Her work bridges theoretical foundations with practical applications in power systems, communications, and biomedical informatics. She serves as Associate Editor for Frontiers in Big Data , IEEE Journal of Biomedical and Health Informatics , and International Journal of Sensor Networks . Current sponsored research includes projects on energy cyber-physical systems, network security analysis, and big data inference methodologies. She actively mentors Ph.D. students in applied mathematics and interdisciplinary computational science, emphasizing novel machine learning approaches for critical infrastructure resilience.

Meghana Kshirsagarمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Evolutionary Computation
• +۴ مورد دیگر

Meghana Kshirsagar serves as Assistant Professor in the Department of Computer Science & Information Systems at the University of Limerick, with dual appointments at Lero – the Irish Research Centre for Software and the Limerick Digital Cancer Research Centre. Her interdisciplinary work bridges computer science with healthcare innovation, focusing on real-world applications of advanced computational techniques. Her research spans Machine Learning, Artificial Intelligence, Evolutionary Computation, Blockchain, Digital Health, Large Language Models, and Digital Twins. She develops novel algorithms like grammatical evolution for hyperparameter optimization and blockchain frameworks for secure healthcare data sharing, with applications in cancer risk stratification, hip fracture pathway modeling, and financial portfolio optimization. Her work consistently addresses ethical AI implementation and sustainable computing practices. Analysis of her 52+ publications (2011-2025) reveals three dominant trends: (1) Evolutionary computation techniques for symbolic regression and digital circuit design, (2) Blockchain applications in healthcare data security and scientific collaboration, and (3) Responsible AI implementations in medical diagnostics and finance. Her recent focus emphasizes mitigating algorithmic bias in cancer diagnostics and sustainable hyperparameter optimization. As an active member of Lero and the Limerick Digital Cancer Research Centre, she collaborates with clinicians, data scientists, and industry partners to translate research into clinical tools. Her work contributes to UN Sustainable Development Goals through digital health innovations that improve healthcare accessibility and efficiency.