جست‌وجوی استاد و پژوهشگر

Sanjiv K. Bhatiaمشاهده پروفایل

استاد

• Image Processing
• Computer Vision
• Digital Terrain Modeling
• +۴ مورد دیگر

Sanjiv K. Bhatia is a Professor and Director of Graduate Computer Science Programs (M.S. and Ph.D.) at the University of Missouri–St. Louis. He holds a Ph.D. in Computer Science from the University of Nebraska-Lincoln (1991), an M.S. from the University of Arkansas (1987), and a B.E. in Computer Science Engineering from Motilal Nehru National Institute of Technology (1983). His research focuses on Image Processing, Computer Vision, Digital Terrain Modeling, and Intelligent Information Retrieval. Notable contributions include the RISE image search engine and face perception databases. He has taught numerous courses, including Operating Systems, Image Processing, and Cloud Computing. His teaching philosophy emphasizes hands-on programming and problem-solving. He is active in ACM and AAAI, and his work spans academic leadership, software development, and interdisciplinary projects in environmental and forensic applications. His recent research explores geospatial analysis, urban decarbonization, and machine learning integration with environmental science. Despite no explicitly listed awards, his extensive publication record and leadership roles highlight his academic contributions.

David Juedesمشاهده پروفایل

استاد

• Algorithms
• Complexity Theory
• Approximation Algorithms
• +۴ مورد دیگر

David Juedes is a Professor of Electrical Engineering and Computer Science and Associate Dean for Academics at Ohio University's Russ College of Engineering and Technology. He also directs the Avionics Engineering Center and is part of the Center for Scientific Computing and Immersive Technologies. Juedes holds a Ph.D. from Iowa State University (1994) and has taught courses ranging from introductory computer science to advanced graduate topics like computational complexity and formal languages. His research focuses on algorithms, complexity theory, approximation algorithms, and computational biology. Notable contributions include work on secure embedded systems (e.g., d-GUARD and DAGGER frameworks) and parameterized algorithm analysis. He has advised multiple graduate and undergraduate students in areas like parameterized algorithms and bioinformatics. Juedes has received awards including ACM Senior Membership (2010) and the Russ College Outstanding Research Paper Award (2004). His work spans 30+ years with over 80 publications in top journals/conferences. He actively promotes STEM education through initiatives like cybersecurity workshops for K-12 teachers and curriculum development for real-time systems and CORBA-based resource management. Labs/Teams: Director of Avionics Engineering Center, Core member of Center for Scientific Computing and Immersive Technologies.

Pamela E. Harrisمشاهده پروفایل

استاد

• Algebraic Combinatorics
• Representation Theory
• Graph Theory
• +۴ مورد دیگر

Pamela E. Harris is a Professor of Mathematics in the Department of Mathematical Sciences at the University of Wisconsin-Milwaukee. She is also a cohost of the podcast Mathematically Uncensored and President and co-founder of Lathisms: Latinxs and Hispanics in the Mathematical Sciences. Education: PhD in Mathematics (2012), University of Wisconsin-Milwaukee; M.S. in Mathematics (2008), University of Wisconsin-Milwaukee; B.S. in Mathematics (2005), Marquette University; Associate Degrees in Sciences and Liberal Arts (2003), Milwaukee Area Technical College. Her research focuses on algebraic combinatorics, particularly representation theory of Lie algebras, with applications to parking functions, graph theory, and discrete mathematics. She is a leading advocate for diversity, equity, and inclusion in mathematics, mentoring underrepresented students and co-organizing symposia for SACNAS and the American Mathematical Society. Dr. Harris has authored over 65 peer-reviewed articles and coauthored books such as Asked And Answered: Dialogues On Advocating For Students of Color in Mathematics and Practices and Policies: Advocating for Students of Color in Mathematics . Her work includes developing community agreements and DEI frameworks for academic settings. Scientific Awards: MAA Deborah and Franklin Tepper Haimo Award (2023), MAA Northeast Section Award (2020), MAA Henry L. Alder Award (2019), Council on Undergraduate Research Mentor Award (2019), Karen Uhlenbeck EDGE Fellow (2020). She actively engages in promoting community-building initiatives and has contributed to national dialogues on mentorship, equity, and the intersection of mathematics with social justice.

Didem Gözüpekمشاهده پروفایل

استاد

• Graph Theory
• Approximation Algorithms
• Network Optimization
• +۲ مورد دیگر

Didem Gözüpek is Professor in Computer Engineering at Gebze Technical University and Director of the Graph Theory and Network Optimization (GRANET) Research Laboratory. Her research examines combinatorial optimization problems in communication networks using graph-theoretic approaches. Her award-winning work includes contributions to equimatchable graph characterization, reload cost optimization, and cognitive radio network scheduling. Recent publications advance knowledge in packing coloring and structural graph theory. She leads TUBITAK-funded projects on domination in graphs and combinatorial optimization applications to urban systems.

Timothy Sunمشاهده پروفایل

استادیار

• Graph Theory
• Combinatorics
• Theoretical Computer Science
• +۳ مورد دیگر

Timothy Sun is an Assistant Professor of Computer Science at San Francisco State University (SFSU), serving as Graduate Coordinator and Advisor. His research focuses on combinatorics and theoretical computer science, particularly topological graph theory. He holds a Ph.D. (2019) and B.S. (2013) in Computer Science from Columbia University, advised by Xi Chen and Rocco Servedio in the Theory Group. He previously held positions at Emory University (2019–2020) and Columbia University (2018). His Erdős number is 3. Education: Ph.D. Computer Science, Columbia University (2019); B.S. Computer Science, Columbia University (2013, magna cum laude) Research emphasizes graph embeddings, genus calculations, and algorithmic design. Notable contributions include work on complete graph embeddings, cycle-finding algorithms, and Rubik’s Cube group commutators. Teaching includes Analysis of Algorithms I/II at SFSU and courses at Emory/Columbia. Publications span graph theory and topology, with recent work on Jungerman ladders, nonorientable genus calculations, and algorithm visualization tools like AAnim. Awards include three bronze medals in speedcubing at the 2009 World Championships. Advising activities include graduate student coordination at SFSU. No listed grants are detailed in the provided texts. Maintains a lab focused on theoretical computer science and algorithmic visualization.

Tom Hayesمشاهده پروفایل

دانشیار

• Theoretical computer science
• Machine learning
• Markov chains
• +۷ مورد دیگر

Tom Hayes is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo, School of Engineering and Applied Sciences. His research focuses on theoretical computer science and machine learning, with particular emphasis on convergence rates for Markov chains, sampling algorithms, physics of algorithms, and distributed algorithms for radio-enabled sensor networks. Dr. Hayes holds a PhD in Computer Science from the University of Chicago. His academic journey has led him to specialize in probabilistic methods and theoretical foundations of computing. His research interests span theoretical computer science with applications to machine learning. He investigates fundamental questions about how algorithms behave, particularly focusing on Markov chains, random combinatorial structures, and distributed systems. His work bridges theoretical computer science with practical applications in networked systems and optimization problems. His research has significant implications for understanding algorithmic behavior in complex systems. Analysis of his recent publications reveals a consistent focus on theoretical aspects of distributed computing, graph algorithms, and probabilistic methods. His work shows increasing attention to energy efficiency in wireless networks and optimal mixing properties of Markov chains. The research demonstrates strong connections between theoretical computer science and practical applications in network design and analysis. Dr. Hayes actively contributes to the academic community through publications at major theoretical computer science conferences. His work appears regularly in proceedings of conferences like APPROX/RANDOM, reflecting his standing in the theoretical computer science community. He serves as an educator in the Computer Science department, teaching foundational courses including CSE 191 (Intro Discrete Structures) and CSE 331 (Algorithms and Complexity). His teaching emphasizes theoretical foundations while connecting concepts to practical applications in computer science.

Zi-Xia Songمشاهده پروفایل

استاد

• Graph Theory
• Combinatorics
• Structural Graph Theory
• +۴ مورد دیگر

Zi-Xia Song is a Professor in the Department of Mathematics at the University of Central Florida (UCF), where she has served since 2005 and was promoted to Full Professor in 2019. She advises both graduate and undergraduate students while maintaining active research and editorial roles. Education: Ph.D. in Algorithms, Combinatorics and Optimization from Georgia Institute of Technology (2004) under Robin Thomas Postdoctoral: Zassenhaus Assistant Professor at The Ohio State University Research Interests: Dr. Song's work centers on Graph Theory and Combinatorics , with deep specialization in structural and extremal graph theory. Her current investigations include: Hadwiger’s conjecture and related structural problems Ramsey theory (co-critical graphs, Gallai-Ramsey numbers) Erdos-Lovasz Tihany conjecture Star edge-colorings Turan and anti-Ramsey numbers in planar graphs Awards: Two Teaching Incentive Program (TIP) awards for instructional excellence One Research Incentive Program (RIA) award for field-advancing publications Advising and Funding: As sole Principal Investigator, Dr. Song has secured major external funding including: National Science Foundation Grant DMS-2153945 (current) National Science Foundation Grant DMS-1854903 (past) National Security Agency Grant H98230-09-1-0055 (past) She also serves on NSF review panels and mentors students across degree levels while advancing her research agenda through journal editorial work.

Alexander Golovnevمشاهده پروفایل

استادیار

• Computational Complexity
• Algorithms
• Pseudorandomness
• +۲ مورد دیگر

Alexander Golovnev is an Assistant Professor at Georgetown University's Computer Science Department. His research focuses on computational complexity, algorithms, pseudorandomness, learning theory, and cryptography. He received his PhD from New York University in 2017, advised by Oded Regev and Yevgeniy Dodis, with postdoctoral positions at Columbia University, Yahoo Research, and Harvard University. His current teaching includes Matrix Rigidity (Spring 2025) and Introduction to Algorithms. He has developed a 5-Course Specialization on Discrete Mathematics and organized workshops on Matrix Rigidity and Fine-Grained Cryptography. Service includes program committees for CSR'22, FOCS'22, STOC'24, CCC'24, and ITC'25. Research interests span quantum computing reductions, lattice problems, circuit complexity, and fine-grained cryptography. His recent publications explore worst-case to average-case reductions, function inversion tradeoffs, and SNARK constructions. Awards include NSF CAREER Award (2024), Rabin Postdoctoral Fellowship (2018-2020), and IPEC excellent student paper award (2012). Current PhD/Master's advisees: Sidhant Saraogi (joint with Justin Thaler), Karthik Gajulapalli, Samuel King, and Satyajeet Nagargoje.

• Computer Vision
• Data Science
• Data Visualization
• +۵ مورد دیگر

Michael D. Grossberg is an Assistant Professor in the Department of Computer Science at the City College of New York (CUNY). He is affiliated with the NOAA-CREST Institute and the Computational Science of Data Intensive Remote Sensing (CS:dir$) group. His research spans interdisciplinary fields including computer vision, data science, remote sensing, climate science, and medical imaging. Grossberg holds a BA in Mathematics from the University of Pennsylvania, a PhD in Mathematics from MIT, and has held academic positions at Columbia University before joining CCNY. Education: Bachelor of Arts in Mathematics, University of Pennsylvania Doctor of Philosophy in Mathematics, Massachusetts Institute of Technology Postdoctoral Fellow at Hebrew University (Jerusalem) Assistant Professor of Mathematics, Columbia University Instructor and Research Scientist in Computer Science, Columbia University Research Interests: Grossberg's work focuses on applying computational methods to solve challenges in science and engineering. Key areas include: Quantitative image restoration for satellite data (e.g., MODIS, VIIRS) Machine learning applications in climate modeling and sea ice classification Semi-automatic medical image segmentation for radiation treatment planning Data visualization tools for climate and remote sensing data Lossless compression algorithms for Earth science imagers His research is supported by grants from NIH, NOAA, NASA, and the Office of Naval Research. Grants & Collaborations: NIH P20 grant for medical image segmentation NOAA-funded projects on satellite data restoration and compression Climate research collaborations with Columbia University and Memorial Sloan Kettering Cancer Center Labs & Teams: Grossberg leads the CCNY research lab focusing on interdisciplinary projects. Collaborators include Irina Gladkova (NOAA-CREST), Gikas Mageras (MSKCC), and climate scientist Niko Krakauer. His lab supports student projects in web development, data science, and medical imaging through programs like the NOAA-CREST HIRES initiative.

• Quantum Topology
• Skein Modules
• Character Varieties
• +۴ مورد دیگر

Adam Sikora is a Professor of Mathematics at the State University of New York at Buffalo, affiliated with the Department of Mathematics in the College of Arts and Sciences. His research focuses on quantum topology, skein modules, character varieties, and their interactions with algebra, number theory, and geometry. Education: PhD in Mathematics from the University of Maryland, College Park. Research Interests: Quantum topology, including skein modules and knot invariants Character varieties and their moduli spaces Interactions between topology, algebra, and number theory Low-dimensional topology and geometric group theory Recent work emphasizes categorification of skein modules, SL(n)-skein algebras, and applications to quantum invariants. His research also explores connections between topological structures and algebraic geometry. No scientific awards are explicitly listed in the provided materials. Advising and grants are not detailed in the texts, though his publications indicate active collaboration with researchers like J.H. Przytycki and Thang T.Q. Le.

• Sparse Matrix Computation
• Optimization Algorithms
• High-Performance Computing
• +۲ مورد دیگر

Shahadat Hossain is Professor and Program Chair of Computer Science at the University of Northern British Columbia. His research develops optimization algorithms for sparse matrix problems, complex networks, and high-performance computing. Key contributions include: DSJMToolkit for sparse Jacobian matrices Graph-based methods for matrix estimation Quantum-classical optimization hybrids He has supervised over 20 graduate students and secured NSERC Discovery Grants for optimization research. Hossain holds a Dr.Scient. from University of Bergen and completed visiting positions at Cornell University and Humboldt University. His 15 most-cited publications demonstrate consistent focus on sparse matrix computation, graph algorithms, and computational optimization techniques with applications in scientific computing and network analysis.

Paola Leccaمشاهده پروفایل

استادیار

• Graph Theory
• Dynamical Networks
• Control Theory
• +۴ مورد دیگر

Paola Lecca is an Assistant Professor at the Free University of Bozen-Bolzano's Faculty of Engineering, specializing in theoretical and applied research on graph theory, dynamical networks, control theory, and causal inference. She is a Senior Professional Member of the Association for Computing Machinery and leads projects at the Smart Data Factory laboratory, focusing on technology transfer to industry. Her research integrates mathematical methods for complex systems in biological contexts, including systems biology, biophysics, and biochemistry. She develops computational approaches for network dynamics, simulation, and analysis, aiming to understand emergent properties in multi-agent systems. Courses taught include Mathematics and Statistics for Data Science and Preparatory Mathematics. She actively contributes to conferences and editorial boards, including Frontiers in Bioinformatics and Big Data Analysis for Medical Sciences.

Dan Ralescuمشاهده پروفایل

استاد

• Probability Theory
• Uncertainty Theory
• Fuzzy Sets
• +۵ مورد دیگر

Dan Ralescu is a Professor of Mathematics at the University of Cincinnati since 1980, affiliated with the Department of Mathematical Sciences in the College of Arts and Sciences. He holds editorial roles in 10 journals and has delivered plenary lectures globally. His research focuses on probability theory, fuzzy sets, and uncertainty modeling, with applications in finance, engineering, and decision theory. Education: PhD in Statistics (Indiana University, 1980), M.A. Mathematics with Honors (University of Bucharest, 1972), and additional M.A. in Mathematics/Statistics (Indiana University, 1979). Research interests include limit theorems for random sets, mixed uncertainty models, Bayesian robustness, and large data set management. His work bridges statistical methodologies with fuzzy logic systems, particularly in financial modeling and risk analysis. Key contributions include the Liu Process in uncertainty theory, portfolio optimization models using fuzzy time series, and risk index frameworks for uncertain systems. Awards include the 2014 Lifetime Achievement Award from China’s Society for Uncertainty and IFSA Fellowship. Active in grants since 1985, including NSF-funded projects on decision-making under uncertainty and collaborative efforts with international institutions like Tokyo Institute of Technology. He has supervised numerous research collaborations and publishes extensively in top-tier journals.

Inne Singgihمشاهده پروفایل

استادیار

• Graph Theory
• Graph Labeling
• Discrete Mathematics
• +۳ مورد دیگر

Inne Singgih is an Assistant Professor Educator in the Department of Mathematical Sciences at the University of Cincinnati. They hold a Ph.D. in Mathematics from the University of South Carolina (2020), an M.S. in Applied and Computational Mathematics from the University of Minnesota Duluth (2015), and a B.Sc. in Mathematics from Universitas Indonesia (2009). Their research focuses on Graph Theory, particularly graph labeling, including magic/antimagic labelings, DNA graph labeling, and genome rearrangement models. They have published extensively in top journals like Theoretical Computer Science and Electronic Journal of Combinatorics . Key research interests include exploring structural properties of graphs, algorithmic approaches to genome rearrangement, and combinatorial optimization. Recent work bridges graph theory with bioinformatics, such as analyzing pairwise rearrangement tractability in genome models. Their Erdős number is 2, reflecting strong collaborative networks in discrete mathematics. Teaching excellence is evident through awards like the 2020 Outstanding Graduate Student Instructor and multiple teaching grants. Active in pedagogical development, they led initiatives like the Jupyter NoteBook Calculus Classroom Resources project and hold certifications in inclusive teaching practices. Current projects include antimagic labeling collaborations and undergraduate research on DNA graph characterization. Notable grants include the 2021 University of Cincinnati Research Launch Award and teaching grants focused on improving calculus instruction. Their work often involves interdisciplinary teams, such as collaborations with bioinformaticians on genome rearrangement algorithms and combinatorialists on labeling theory advancements.

Nicola Santoroمشاهده پروفایل

استاد پژوهشی

• Algorithms
• Communication Networks
• Data Structures
• +۸ مورد دیگر

Nicola Santoro is a Distinguished Research Professor in the School of Computer Science at Carleton University, Ottawa, Canada. His academic career spans over four decades, with a focus on distributed computing, algorithms, and network systems. He holds a Ph.D. from the University of Waterloo (1979) and a D.Sc. from the University of Pisa (Italy). His research interests include algorithms, communication networks, distributed computing, robotics, graph theory, and wireless communication. He has contributed extensively to the theoretical foundations of distributed systems, mobile robotics, and fault-tolerant computing. Santoro's work emphasizes scalable and efficient solutions for dynamic networks and multi-agent systems. Key contributions include studies on black hole search in dynamic networks, distributed algorithms for autonomous robots, and network decontamination. His publications span over 400 articles in top-tier journals and conferences. He has also authored influential textbooks, such as Design and Analysis of Distributed Algorithms (Wiley, 2007) and Distributed Computing by Mobile Entities (Springer, 2019). Prof. Santoro has advised numerous graduate students and researchers, shaping the next generation of computer scientists. His work bridges theory and practice, with applications in robotics, sensor networks, and parallel computing.