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Jacqueline MessingView profile

Lecturer

• Language Revitalization
• Multilingualism
• Indigenous Communities
• +4 more

Jacqueline Messing is a Lecturer in the Department of Anthropology at the University of Maryland (UMD) since 2010, specializing in sociolinguistics, ethnography, and education. She holds a PhD and MA in Anthropology from the University of Arizona and a BA in BDIC/Linguistics & Anthropology from the University of Massachusetts-Amherst. Research Interests: Her work focuses on language revitalization, multilingualism, and identity in Mexico and the U.S., particularly among indigenous communities. She examines how language policies, education systems, and sociocultural factors influence language shift and cultural preservation. Teaching: Courses include Educational Anthropology, Language and Identity, and Native American Languages. She actively engages students through innovative methods like Twitter hashtags for classroom discussions (#UMDLangIDRacism202X). Publications: Her research spans sociolinguistic studies in Tlaxcala, Mexico, and broader analyses of racism in language use. Key works include Language Acquisition, Shift, and Revitalization in Latin America (2016) and a tribute to linguist Jane H. Hill (2019). Advising/Grants: No formal advisee list or grants are mentioned in the provided materials. Her contributions are primarily through teaching and research.

Jeyakesavan VeerasamyView profile

Professor

• Wireless Networks
• Algorithms
• Computer Science Education
• +4 more

Dr. Jeyakesavan Veerasamy is a Professor of Instruction in Computer Science and Director of the Center for CS Education & Outreach at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. His roles include teaching computer science courses, directing outreach programs for K-12 students, and organizing summer research experiences and coding camps. He oversees initiatives such as cyber defense workshops, robotics, game design, and app development programs aimed at fostering computer science literacy in the Dallas area. Education: PhD in Computer Science, University of Texas at Dallas (1999) MS in Computer Science, University of Texas at Dallas (1993) B.E. in Electronics and Communication Engineering, Anna University (1990) Research focuses on wireless networking optimization, approximation algorithms, and computer science education. His work includes patents on wireless network efficiency and contributions to theoretical computer science, such as approximation algorithms for postman problems. He received the Best PhD Dissertation Award in Computer Science (1998-99) for his work on approximation algorithms. Outreach efforts include teacher training programs, professional development workshops, and partnerships with schools to deliver coding classes taught by UTD graduate students. His programs emphasize practical applications of computing through robotics, cybersecurity, and app development.

Dexin ShiView profile

Associate Professor

• Psychometrics
• Statistical Methodology
• Causal Inference
• +3 more

Dr. Dexin Shi is an Associate Professor in the Department of Psychology at the University of South Carolina, affiliated with the McCausland College of Arts and Sciences. He holds expertise in quantitative psychology, focusing on statistical methodology development and application. Dr. Shi earned his Ph.D. in Quantitative Psychology from the University of Oklahoma (2016) and completed a postdoctoral fellowship at the University of South Carolina. His research emphasizes psychometrics, causal inference, missing data analysis, and model selection, with applications in educational, clinical, and health psychology. He has published over 50 peer-reviewed articles in top journals like Psychological Methods and Structural Equation Modeling . Notable awards include the 2021 Rising Star Award from the Association for Psychological Science and the McCausland Faculty Fellowship (2021–2024). Dr. Shi teaches courses such as PSYC 220 (Psychological Statistics) and advanced quantitative methods like PSYC 815 (Causal Inference). He has received grants for innovative pedagogy and open educational resources. His research trends highlight advancements in statistical techniques, including Bayesian methods, machine learning applications in equating, and causal pathway identification. He actively explores methodological challenges in psychological assessment and data analysis.

Carlos BorgesView profile

Associate Professor

• Inverse Scattering Problems
• Numerical Analysis
• Domain Decomposition
• +4 more

Carlos Borges is an Associate Professor in the Department of Mathematics at the University of Central Florida (UCF), part of the College of Sciences. He holds a Ph.D. in Mathematics from Worcester Polytechnic Institute (2013) and has held postdoctoral positions at the Oden Institute (University of Texas-Austin) and the Courant Institute (NYU). His research focuses on inverse scattering problems, numerical analysis, and neural networks, with applications in computational engineering and mathematical physics. Education: PhD in Mathematics (Worcester Polytechnic Institute, 2013), MS in Mathematics (Instituto de Matematica Pura e Aplicada, 2007), BEng in Computer Engineering (Instituto Militar de Engenharia, 2002). Research interests include inverse scattering (acoustic, electromagnetic), domain decomposition methods, fast solvers, and machine learning integration in numerical analysis. His work emphasizes high-resolution parameter reconstruction and multi-frequency data utilization for obstacle identification. He advises four students: PhD candidates Isabela Vasconcellos Viana and Nick Arustamyan, and undergraduates Tina Tran and Eli Rivera-Sanchez. His recent publications (2023–2025) highlight advances in neural network-driven inverse scattering and robust algorithms for irregular geometries. He organizes the Scientific Computing Seminar at UCF, focusing on numerical analysis and computational methods. His software contributions include solvers for inverse scattering problems, available on GitHub. He is active in professional societies like SIAM and IEEE, and regularly presents at conferences such as SIAM CSE 2025.

Abey López-GarcíaView profile

Associate Professor

• Analysis
• Multiorthogonal Polynomials
• Potential Theory
• +4 more

Abey López-García is an Associate Professor in the Department of Mathematics at the University of Central Florida (UCF). He joined UCF in August 2017 and has previously worked as a postdoc at the University of Leuven (2010-2013) and at the University of South Alabama (2013-2017). He holds a bachelor’s degree from Universidad Complutense de Madrid and a doctoral degree from Vanderbilt University. López-García is a collaborator of the GAMA research group at Universidad Carlos III de Madrid. Education : Bachelor in Mathematics, Universidad Complutense de Madrid, Spain PhD in Mathematics, Vanderbilt University His research focuses on Analysis, particularly multiorthogonal polynomials , minimal energy problems , potential theory , and connections to random matrices and random polynomials . His publications explore asymptotics, recurrence relations, and equilibrium problems in these areas. Recent work includes studies on Nikishin systems, greedy energy sequences, and lattice path models. Teaching : Courses include Calculus I-III, Ordinary Differential Equations, Linear Algebra, and advanced topics like Random Matrix Theory and Special Functions. Office hours are held on Mondays and Wednesdays. Collaborative ties with Universidad Carlos III de Madrid’s GAMA group and participation in international conferences (e.g., Constructive Functions 2025, Baylor Analysis Fest) highlight his academic engagements.

Peter DologView profile

Associate Professor

• Recommender Systems
• Web Engineering
• Machine Learning
• +5 more

Peter Dolog is an Associate Professor at the Department of Computer Science, Aalborg University, Denmark. He is affiliated with The Technical Faculty of IT and Design. His research focuses on Recommender Systems, Web Engineering, Machine Learning, and Health Informatics. He leads projects such as the MEco Medical Ecosystem and the IWIS Intelligent Web and Information Systems initiative. His work emphasizes explainable AI, knowledge graphs, and social media analytics for public health monitoring. Notable contributions include hypergraph-based recommendation models and sensitive information detection in legal documents. Projects include MEco (medical event surveillance) and IWIS (web systems personalization), funded by EU and national grants. His research spans 148 publications, with recent work on explainable recommendation systems and neural networks for medical data analysis. Collaborations include the University of Vienna and international conferences like ECIR and RecSys. Media coverage highlights his work on using social media for epidemic detection. Key research areas include recommendation systems with attention mechanisms, knowledge graph integration, and ethical AI applications in healthcare. His methodologies bridge machine learning with user-centric design, emphasizing transparency and explainability in AI-driven systems.

Jeff EdmondsView profile

Professor

• Complexity Theory
• Scheduling
• Proof Systems
• +4 more

Jeff Edmonds is a Professor in the Department of Electrical Engineering & Computer Science at York University. He received his PhD from the University of Toronto in 1992, with a thesis on time-space tradeoffs. His postdoctoral work at ICSI in Berkeley focused on secure data transmission for multimedia applications. He joined York University in 1995. His research interests include complexity theory, scheduling, proof systems, probability theory, combinatorics, and machine learning. He is affiliated with the Theory Group at York University and teaches advanced courses such as COSC6111 (Advanced Algorithm Design and Analysis), CSE3101 (Algorithms), and CSE2101 (Fundamentals of Data Structures). Edmonds has developed notable algorithms, including collaborative work with Kirk Pruhs on randomized cake-cutting protocols, which address fair division problems with applications beyond dessert, such as asset distribution and international negotiations. His teaching emphasizes foundational algorithmic techniques, including loop invariants, recursion, dynamic programming, and randomized algorithms. His research extends to network flow, linear programming, entropy, and computational complexity topics like NP-completeness and computability. He is also active in machine learning fundamentals, covering topics such as gradient descent and neural networks.

• Systems Engineering
• Control Engineering
• Discrete Event Systems
• +7 more

Mariagrazia Dotoli is a Full Professor in Systems and Control Engineering at the Polytechnic University of Bari, Department of Electrical and Information Engineering, where she has been serving since 1999. She previously held the position of Vice Rector for Research (2011-2013) and served as a member elect of the Academic Senate (2012-2015). Currently, she coordinates the interuniversity PhD course in Industry 4.0 between the Polytechnic University of Bari and the University of Bari Aldo Moro. Her research interests span across multiple domains of systems engineering, with particular focus on discrete event industrial systems, Petri nets, manufacturing systems, supply chains, logistics and transportation systems, traffic networks, and energy systems. Her work bridges theoretical control systems with practical industrial applications, especially in the context of Industry 4.0 and smart manufacturing. Her extensive publication record demonstrates consistent contributions to automation science and engineering, with recent work focusing on warehouse optimization, collaborative robotics, supply chain management, and smart energy systems. Her research shows a clear trend toward integrating classical control theory with modern computational approaches, including machine learning and optimization algorithms for industrial applications. Prof. Dotoli maintains significant editorial responsibilities as Senior Editor of the IEEE Transactions on Automation Science and Engineering and Associate Editor for multiple IEEE journals. She has organized and chaired numerous international conferences including CASE2024, MED2021, and CODIT2020, demonstrating leadership in the automation community. Her academic career shows continuous progression from Assistant Professor (1999) to Full Professor, with additional leadership roles in university administration and international professional organizations. She remains actively engaged in both theoretical research and practical industrial applications of control systems engineering.

Saad MneimnehView profile

Associate Professor

• Scheduling Algorithms
• Load Balancing
• Computational Biology
• +4 more

Saad Mneimneh is an Associate Professor in the Department of Computer Science and the Department of Mathematics and Statistics at Hunter College, part of The City University of New York (CUNY). He holds a PhD in Computer Science from MIT and has held academic positions at Southern Methodist University and as a visiting scholar at various institutions. His research focuses on algorithmic techniques applied to scheduling, load balancing, and computational biology, particularly RNA interaction and protein secondary structure modeling. He has received awards such as the Cecil Insdorf Award for Excellence in Teaching and grants from NSF and PhRMA. Education: PhD in Computer Science from MIT (2002), SM and BE degrees from MIT and American University of Beirut, respectively. Professional roles include doctoral faculty at CUNY's Graduate Center and leadership in initiatives like CoSSMO. His teaching spans courses in discrete mathematics, algorithms, and computational biology. Research interests include network algorithms, combinatorial optimization, and interdisciplinary projects like the 'Counting with Code' educational initiative. He has supervised PhD students in computational biology and algorithm design. Grants include funding for RNA interaction studies and network analysis. Collaborations span bioinformatics, social network modeling, and mathematical pedagogy. Key contributions include work on RNA interaction algorithms, protein secondary structure models, and educational outreach. His work bridges theoretical computer science with applications in biology and mathematics education.

Eric J. SchmutzView profile

Professor

• Discrete mathematics
• Probabilistic methods
• Combinatorial number theory

Eric J. Schmutz is a tenured Professor in the Department of Mathematics at Drexel University, actively contributing to combinatorial mathematics research and academic instruction. His career demonstrates sustained engagement in theoretical and applied probabilistic methods within discrete structures. His academic foundation includes: PhD in Mathematics from the University of Pennsylvania (1988) AB in Mathematics from Princeton University (1981) Professor Schmutz's research centers on discrete mathematics with specialized expertise in probabilistic combinatorics and combinatorial number theory . His work systematically explores asymptotic behaviors in random combinatorial structures, including permutation orders, partition statistics, and matrix properties. This interdisciplinary approach bridges pure mathematics with stochastic modeling, yielding insights applicable to computational complexity and algorithmic design. Analysis of his publication timeline reveals consistent contributions to combinatorial probability since the early 1990s, with recent work (2021) extending classical permutation theory. Key thematic threads include the statistical properties of random permutations, structural analysis of integer partitions, and number-theoretic functions—often involving collaborations with prominent mathematicians like Paul Erdős and William Goh. While no specific advising relationships or grant funding are documented in the source text, his extensive publication record spanning three decades implies significant mentorship of graduate researchers. The absence of laboratory or team affiliations suggests primarily theoretical work conducted through mathematical collaboration rather than experimental facilities.

• Algorithms
• Networks
• Parallel Computing
• +3 more

Sandeep Bhatt is a Teaching Professor in the Department of Computer Science at Stevens Institute of Technology, within the Charles V. Schaefer, Jr. School of Engineering and Science. He holds a PhD, MS, and BS in Computer Science from MIT. His research focuses on Algorithms, Networks, Parallel Computing, and Theory of Computation, with recent contributions to Cybersecurity and Enterprise IT Risk Analysis. Dr. Bhatt's professional experience spans academia and industry, including roles as Director at Akamai Technologies, Bell Communications Research, and Yale University. He has contributed to numerous patents, such as hierarchical image segmentation and threat exchange security methods. Notable awards include the 2023 Outstanding Teacher Award from Stevens Alumni Association. His academic career emphasizes teaching and research in theoretical computer science, with courses like Algorithmic Complexity and Theory of Computation. His publications span cybersecurity frameworks, network analysis, and algorithmic advancements, reflecting a blend of theoretical rigor and practical industry application.

Paliath NarendranView profile

Professor

• Automated Reasoning
• Formal Verification
• Pattern Matching
• +4 more

Dr. Paliath Narendran is a Professor in the Department of Computer Science at the University at Albany, State University of New York, within the College of Nanotechnology, Science, and Engineering. He has been at UAlbany since 1988 after working at General Electric Corporate Research and Development in Schenectady, New York. His educational background includes: PhD in Computer Science from Rensselaer Polytechnic Institute (1984) MTech in Computer Science from Indian Institute of Technology, India (1980) BTech in Electrical Engineering from Indian Institute of Technology, India (1978) Dr. Narendran's research focuses on automated reasoning, formal verification, and pattern matching , with particular emphasis on unification theory and its applications in cryptographic protocol analysis. His work bridges theoretical computer science with practical security applications, developing formal methods to verify the correctness and security of cryptographic protocols. He has made significant contributions to understanding the theoretical foundations of rewrite systems, equational theories, and their applications in security protocol analysis. His recent publications (2020-2025) demonstrate continued progression from foundational theoretical work to increasingly applied security contexts. The research shows strong focus on knowledge problems, unification theory, and their applications to access control models and cryptographic analysis. His work consistently combines deep theoretical insights with practical applications in information security, particularly in developing decision procedures and algorithms for complex equational theories relevant to security protocols. Dr. Narendran teaches graduate courses including Theory of Computation (ICSI 609), Advanced Programming Concepts, Computational Logic, and the Master's Research Project, reflecting his expertise across theoretical and applied computer science.

Mikhail BelkinView profile

Professor

• Machine Learning
• Deep Learning
• Feature Learning
• +5 more

Mikhail Belkin is a Professor at the Halicioglu Data Science Institute and the Computer Science and Engineering Department at the University of California San Diego (UCSD), as well as an Amazon Scholar. His research focuses on foundational aspects of machine learning, particularly deep learning and large language models, emphasizing understanding phenomena like feature learning, over-parameterization, and generalization. He serves as editor-in-chief of the SIAM Journal on Mathematics of Data Science (SIMODS) and was elected an ACM Fellow in 2023. Research Interests: Generalization theory in deep learning Optimization dynamics and convergence Feature learning mechanisms in neural networks Kernel methods and their connections to neural networks Double descent phenomenon and model complexity Key Contributions: Belkin's work has advanced understanding of interpolation in over-parameterized models, revealing how zero-training-error models can generalize well despite classical statistical concerns. His analyses of optimization landscapes and loss surfaces have clarified why gradient-based methods succeed in deep learning. He introduced the Average Gradient Outer Product (AGOP) framework to explain feature learning in neural networks, bridging classical kernel methods and modern architectures. Publications: His recent work explores feature learning in neural networks, convolutional architectures, and the theoretical underpinnings of LLMs. Over 15 recent papers are listed, spanning topics like kernel regression, matrix completion, and the mathematical foundations of deep learning. Awards & Grants: ACM Fellow (2023) NSF/Simons Collaboration on Theoretical Foundations of Deep Learning PI in the TILOS AI Institute (NSF-funded) Advising & Teams: Belkin collaborates with interdisciplinary teams at UCSD and institutions like the Broad Institute’s Eric and Wendy Schmidt Center, focusing on AI’s societal implications and scientific applications. His work emphasizes bridging theory and practice, with algorithms like Recursive Feature Machines (RFM) and EigenPro showcasing scalable, interpretable machine learning solutions.

• Artificial Intelligence
• Data Science
• Machine Learning
• +6 more

Hamid Bouchachia is a Professor of Data Science and Intelligent Systems at Bournemouth University, leading the Machine Intelligence Research Group within the Department of Computing and Informatics, Faculty of Science and Technology. He holds an Engineering degree, Master, PhD, and habilitation from reputable institutions, with a Post-Doc at the University of Alberta, Canada. Education: Engineering Degree Master’s Degree PhD in Data Science Habilitation Degree Postdoctoral Research at University of Alberta (Computer & Electrical Engineering) Research Interests: Bouchachia focuses on scalable machine learning for data streams, medical imaging with ML, smart environments (e.g., smart homes, industrial monitoring), and assistive technologies. His work emphasizes online learning, active learning, and distributed AI applications in healthcare and energy sectors. He develops algorithms for real-time pattern recognition, high-performance computing, and adaptive systems. Key Contributions: His research spans incremental learning frameworks, evolving fuzzy classifiers, and energy-efficient neural networks. Recent work includes lightweight RNN models for forecasting and deep learning for abnormal behavior detection in dementia care. Advising & Students: Supervises PhD students on topics like deep learning for healthcare, cloud migration, and smart home systems. Notable advisees include Damla Arifoglu (abnormal behavior detection) and Saad Mohamad (active learning). Labs & Teams: Heads the Machine Intelligence Research Group, collaborating on projects involving AI-driven medical imaging, smart energy systems, and ambient intelligence.

Alexei MiasnikovView profile

Professor

• Combinatorial group theory
• Geometric group theory
• Algorithmic group theory
• +4 more

Alexei Miasnikov is a Professor in the Department of Mathematical Sciences at Stevens Institute of Technology's Charles V. Schaefer, Jr. School of Engineering and Science. He holds a Ph.D. from an undisclosed institution and serves as Department Director. Miasnikov is an elected Fellow of the American Mathematical Society. His research spans combinatorial, geometric, algorithmic and asymptotic group theory; algorithmic and model theoretic algebra; mathematical logic and recursion theory; average and generic computational complexity; cryptography; and statistical methods in pure mathematics. Miasnikov's work connects abstract algebra with computational complexity and logic. Recent publications address Diophantine problems in algebraic groups, model-theoretic aspects of exponential groups, and elementary equivalence in metabelian groups. His scholarship demonstrates consistent advancement in understanding algorithmic decidability within group-theoretic frameworks and structural properties of algebraic systems. Miasnikov directs the graduate program and serves on editorial boards for Applied Discrete Mathematics, Siberian Mathematical Journal, and Groups, Complexity, Cryptology. He organized major international conferences including 'Groups, Logic, and Computation' at Stevens Institute in 2024.