Nonlinear Computational Models on Strings, Trees and Graphs Professors and Supervisors

Guillaume Rabusseauمشاهده پروفایل

دانشیار

University of MontrealCanada

Guillaume Rabusseau is an Associate Professor at Mila and the Department of Computer Science and Operations Research (DIRO) at Université de Montréal , holding a Canada CIFAR AI Chair since 2019. His research spans machine learning, theoretical computer science, and multilinear algebra. Education : PhD in Computer Science (2016) from Aix-Marseille Université , MSc in Fundamental Computer Science from AMU, BSc in Computer Science (distance learning) from AMU. Research Interests : Tensor methods for machine learning, spectral learning algorithms, connections between weighted automata, tensor networks, and RNNs, low-rank regression, and nonlinear computational models on structured data. Publication Trends : Recent work focuses on tensor train decomposition, temporal graph benchmarks, quantum-inspired ML, spectral regularization, and formal methods for sequence modeling. Collaborative papers address dynamic graphs, foundational models for molecular learning, and high-order pooling in GNNs. Scientific Awards : Canada CIFAR AI Chair (2019–present, renewed) Advising : Supervises PhD students like Maude Lizaire and Pascal Tikeng Notsawo, and MSc students such as Soroush Omranpour. Past advisees include Andy Huang (now at Oxford) and Tianyu Li (Samsung).

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Przemysław Gordinowiczمشاهده پروفایل

استادیار

Lodz University of TechnologyPoland

Dr. Przemysław Gordinowicz serves as an Assistant Professor at Lodz University of Technology within the Department of Applications of Contemporary Mathematical Analysis. Based in room 161 with contact details (+48) 42 631-36-33 and pgordin@p.lodz.pl, he maintains an active research profile in discrete mathematics. His research centers on Graph Theory and Combinatorics, specializing in graph coloring extendability problems for planar and outerplanar graphs, pursuit-evasion games (cops and robbers, localization games), and polynomial method applications. He also investigates random graph models, discrete Laplacian problems, and extremal graph theory, employing combinatorial and algebraic techniques to solve complex structural problems. Analysis of his 15 most recent publications (2015-2025) reveals consistent focus on three interconnected themes: (1) polynomial method applications to list coloring extendability, (2) theoretical advances in pursuit-evasion games across diverse graph classes, and (3) firefighter problem variants including surviving rate calculations. His work bridges theoretical combinatorics with algorithmic applications, frequently appearing in top-tier journals like Journal of Graph Theory and Electronic Journal of Combinatorics. He has supervised PhD student Paweł Twardowski, whose 2023 dissertation addressed graph polynomial applications to list coloring extendability in planar graphs. No information about research grants or collaborative teams is available in the provided materials.

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Asai Asaithambiمشاهده پروفایل

استاد

University of North FloridaUnited States

Asai Asaithambi is a Professor and Graduate Program Director in the School of Computing at the University of North Florida (UNF), where he also served as School Director from 2011-2015. With 37+ years of academic experience, he previously held leadership roles including Professor/Chair at the University of South Dakota (2004-2011), Associate Professor/Chair at Saint Louis University (1997-2004), and Assistant/Associate Professor positions at Lincoln University and Mississippi State University. His academic credentials include: PhD in Computer Science, University of Wisconsin-Madison (1985) MS in Computer Science, Indian Institute of Technology-Madras (1980) Bachelor of Technology in Electrical Engineering, Indian Institute of Technology-Madras (1977) Professor Asaithambi's research spans artificial intelligence, computational science, and high-performance computing, with significant contributions to quantum computing algorithms, biomedical image analysis, and numerical methods for differential equations. His work demonstrates consistent focus on applying computational techniques to real-world problems in healthcare, cybersecurity, and engineering, while maintaining strong commitment to broadening participation of underrepresented groups in computing. Analysis of his 15 most recent publications (2019-2025) reveals dominant themes in medical imaging AI (lung X-ray and brain MRI analysis), bioinformatics algorithms for genome rearrangement, quantum computing security implications, and multi-robot coordination systems. His methodology consistently combines theoretical algorithm development with practical implementation, often leveraging advanced optimization techniques and parallel computing approaches. Key research projects include: Getting American Indians to Information Technology (GAIN-IT) as Co-PI (2008-2013) A Computational Intelligence to Gene Structure Prediction as PI (2007-2008) South Dakota Biomedical Research Infrastructure Network as Co-PI (2001-2012) Throughout his career, Professor Asaithambi has directed numerous doctoral dissertations and Master's theses while teaching diverse courses from introductory programming to quantum computing. His teaching philosophy emphasizes critical thinking development for both traditional and non-traditional student populations, with particular attention to mathematical preparation challenges in computational courses.

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