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Sandjai BhulaiView profile

Professor

• Machine Learning
• Operations Research
• Telecommunications
• +3 more

Prof. Sandjai Bhulai is a Full Professor at Vrije Universiteit Amsterdam's Faculty of Science (Department of Mathematics) and holds affiliations with the Network Institute. He specializes in machine learning, operations research, and their applications in telecommunications, healthcare, and optimization. His research addresses real-world challenges such as suicide prevention through AI-driven systems, signal processing in underwater acoustics, and dynamic dispatch algorithms for logistics. Key research interests include machine learning algorithms for classification and prediction, reinforcement learning for optimization problems, and data-driven decision-making in healthcare and transportation. He has pioneered methods like the 'Dutch Draw' baseline for binary classification and contributed to ambulance dispatch systems and power grid topology optimization. Prof. Bhulai has published over 137 research outputs, including high-impact articles in International Journal of Medical Informatics , Ocean Engineering , and Transportation Science . He received the Best Paper Award (2018) and led projects such as the AI-BIPTO initiative for integrated production optimization. He advises on technical and ethical committees, including roles at NWO and the Mondriaanfonds. His teaching spans advanced courses in machine learning, algebraic geometry, and linear programming.

Éric SchostView profile

Professor

• Symbolic Computation
• Algebraic Geometry
• Computer Algebra Systems
• +5 more

Éric Schost is a Professor in the Cheriton School of Computer Science at the University of Waterloo. His research focuses on symbolic computation, algebraic algorithms, and computational complexity. He specializes in polynomial system solving, algorithm design for algebraic structures, and their applications in mathematics and computer science. Key research interests include: - Symbolic computation and computer algebra systems - Homotopy continuation methods for solving polynomial systems - Efficient algorithms for sparse and structured linear algebra - Complexity analysis of algebraic algorithms - Applications in cryptography and computational algebraic geometry His work bridges theoretical foundations with practical implementations, often published in top venues like the Journal of Symbolic Computation and ISSAC proceedings. Collaborations span topics from algebraic geometry to algorithmic number theory.

Ron SmithView profile

Lecturer

• Business Education
• Engineering Education
• Mechanical Engineering
• +3 more

Ron Smith is a Lecturer and Counselor in the B2B Revenue Readiness Program at Mississippi State University’s College of Business. His work bridges academic instruction with professional development, focusing on business-to-business strategies and student engagement in technical coursework. He holds a strong background in mechanical engineering pedagogy, evidenced by publications on curriculum design and innovative teaching methods such as gamified learning using platforms like Kahoot!. His research spans multiple disciplines including biomedical research (e.g., NGS applications in oncology), thermal systems engineering, and statistical methodologies. Notable contributions include work on heat recovery systems, Markov chain Monte Carlo analysis, and uncertainty quantification in energy systems. His instructional innovations aim to enhance conceptual understanding through historical context and active learning strategies. Ron’s articles reflect a blend of practical engineering solutions and pedagogical advancements, emphasizing real-world applications such as energy efficiency improvements and student motivation techniques. His interdisciplinary approach integrates technical expertise with educational theory to address challenges in both academic and industrial contexts.

Samuel J. LomonacoView profile

Professor

• Quantum Computation
• Topology
• Knot Theory
• +3 more

Samuel J. Lomonaco is a Full Professor of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He holds a Ph.D. in Mathematics from Princeton University (1964) and a B.S. in Mathematics from St. Louis University (1961). His research focuses on quantum computation, topology, knot theory, and their applications in computer science and mathematics. He leads the Quantum Algorithms Research Group, supported by DARPA, and has authored influential books on quantum computation and information theory. Notable contributions include work on quantum hidden subgroup algorithms, topological quantum computing, and the application of knot theory to physics. Dr. Lomonaco has held academic and research roles at institutions such as SUNY Albany, Texas Instruments, and the Institute for Defense Analyses. He is an associate editor of the Journal of Knot Theory and Its Ramifications and has received awards including the 2003 Pot de Vin Award and recognition for contributions to the Ada programming language. His research interests span quantum cryptography, algebraic coding theory, and symbolic computation. He currently advises PhD students and has pioneered new quantum algorithms like the Wandering Shor algorithm. Education: Ph.D., Mathematics, Princeton University, 1964 B.S., Mathematics, St. Louis University, 1961 Research Interests: Quantum computation, topological quantum computing Knot theory and its applications in physics Algebraic coding theory and error-correcting codes Symbolic computation and quantum algorithms Awards & Recognitions: 2003 Pot de Vin Award Award from the U.S. Under Secretary of Defense for Research and Engineering (Ada programming contributions) Labs/Teams: Quantum Algorithms Research Group (DARPA-funded)

Lila FontesView profile

Associate Professor

• Theoretical Computer Science
• Privacy-Preserving Algorithms
• Communication Complexity
• +3 more

Lila Fontes is an Associate Professor in the Computer Science Department at Swarthmore College. She holds a Ph.D. and M.Sc. from the University of Toronto (supervised by Stephen Cook and Toniann Pitassi) and a B.A. in Mathematics from Harvard University (supervised by Peter Koellner). Her research focuses on theoretical computer science, particularly privacy, communication complexity, and algorithm design, exploring trade-offs between privacy and computational efficiency. She teaches courses such as Data Structures and Algorithms, Theory of Computation, and Algorithms. Notable publications include works on communication complexity, privacy-preserving algorithms, and lower bounds analysis. Fontes has received awards including the Best Student Paper Award (PEARC 2023) and Best Paper Award (SIROCCO 2023). She actively serves on academic committees, including the College Judiciary Committee and faculty hiring committees.

Fei XueView profile

Associate Professor

• Scientific computing
• Numerical linear algebra

Fei Xue is Associate Professor in the Department of Mathematical Sciences at Clemson University. His research specializes in numerical linear algebra and scientific computing, particularly developing efficient algorithms for large-scale eigenvalue problems and linear systems. Research focuses on preconditioned iterative methods, Krylov subspace techniques, and numerical solutions to partial differential equations. Recent work has developed novel algorithms for eigenvalue computation, including the Block Preconditioned Harmonic Projection method for nonlinear eigenproblems and a Chebyshev-based Locally Optimal Block Preconditioned Conjugate Gradient method. Applications span quantum physics, materials science, and computational finance. Dr. Xue has received sustained NSF funding since 2011 for his work on eigenvalue solvers and matrix computations. Teaching responsibilities include graduate courses in numerical linear algebra and scientific computing.

• Computational Mathematics
• Numerical Analysis
• Fluid Dynamics
• +3 more

Leo Rebholz is the Dean's Distinguished Professor of Mathematical Sciences and Division Lead for Mathematics at Clemson University's College of Science. He holds a PhD in Mathematics from the University of Pittsburgh (2006). His research focuses on computational and applied mathematics, particularly nonlinear solvers for PDEs and data assimilation techniques. He has published over 130 papers and 5 books, with recent work emphasizing acceleration methods for Navier-Stokes equations and fluid dynamics simulations. His contributions include Anderson acceleration algorithms, reduced-order modeling, and numerical stability analysis. Rebholz has supervised 12 PhD students, 14 M.S. students, and 15 undergraduates. He serves on editorial boards for the Journal of Numerical Mathematics and International Journal of Numerical Analysis and Modeling . His research bridges theoretical mathematics and practical applications, combining rigorous proofs with computational efficiency. Notable areas of focus include turbulence modeling, vorticity-based formulations, and energy-preserving discretizations. Recent work highlights include advancing Anderson acceleration for fluid problems, developing physically constrained reduced-order models, and analyzing data assimilation methods for geophysical flows. His studies emphasize long-time accuracy, stability, and computational efficiency in numerical simulations.

William EricksonView profile

Research Fellow

• Representation Theory
• Lie Groups
• Invariant Theory
• +3 more

Dr. William Erickson is a Postdoctoral Research Fellow in the Department of Mathematics at Baylor University, affiliated with the College of Arts & Sciences. He holds a Ph.D. from the University of Wisconsin-Milwaukee (2022) and a B.A. from the University of Notre Dame (2010). Before pursuing his doctorate, he taught middle/high-school mathematics and Latin for eight years, while also managing a local pool as a lifeguard and swim instructor. His research focuses on the representation theory of Lie groups from a combinatorial perspective, invariant theory, and algebraic statistics, with notable contributions to combinatorial algebra and geometric probability. Educations: Ph.D., Mathematics, University of Wisconsin-Milwaukee, 2022 (Advisor: Jeb Willenbring) B.A., Mathematics, University of Notre Dame, 2010 Research Interests: Dr. Erickson explores the intersection of algebra, combinatorics, and geometry, with a focus on Lie groups and their representations. His work often employs combinatorial tools to study invariant rings, Young tableaux, and algebraic statistics. Recent projects include analyzing the earth mover’s distance in probabilistic contexts and developing new methods for reconstructing Young tableaux via minors. Publications Trends: His articles span combinatorial algebra, representation theory, and applied statistics. Notable topics include Demazure products, palindromicity in generating functions, and geometric interpretations of statistical metrics. His work frequently bridges pure and applied mathematics, with applications ranging from optimization to geometric analysis. Scientific Awards & Grants: No awards or grants explicitly listed in the provided text. Advising & Teams: No formal advisees or lab teams explicitly mentioned. His collaborative work includes co-authors like Rebecca Bourn, Jan Kretschmann, and Markus Hunziker.

Victoria E. HowleView profile

Professor

• Numerical Linear Algebra
• Fluid Dynamics
• Preconditioning Methods
• +2 more

Victoria E. Howle is a Professor in the Department of Mathematics & Statistics at Texas Tech University. Her office is located in Mathematics 117-D, and she can be contacted via email at victoria.howle@ttu.edu. Her research focuses on applied mathematics with specialization in numerical linear algebra, particularly developing scalable algorithms for scientific computing problems. Her primary research interests include physics-based preconditioning for incompressible fluid flow (especially Navier-Stokes simulations and extensions), efficient preconditioners for implicit Runge-Kutta methods applied to time-dependent PDEs, and machine learning algorithms. Her work addresses computational challenges in engineering and scientific systems requiring stability and efficiency. Analysis of Dr. Howle's publications reveals consistent focus on numerical methods for PDEs, with recent expansion into machine learning applications in toxicology and quantum computing. Her work consistently addresses computational efficiency through innovative preconditioning techniques across fluid dynamics, thermal convection, and optimization problems. Dr. Howle maintains an active research group, currently advising three PhD students (Colton Mikes, Austin Nelson, and Pavithra Venkatachalapathy) and has supervised numerous graduate students to completion, including 16 PhD graduates and 8 Master's students across mathematics, engineering, and interdisciplinary fields.

Werner NuttView profile

Professor

• Data Management
• Knowledge Representation
• Intelligent Information Extraction
• +3 more

Werner Nutt is a Professor at the Free University of Bozen-Bolzano , Italy, affiliated with the Faculty of Computer Science since 2005. He previously held academic positions as a Reader at Heriot-Watt University (2000-2005), Visiting Professor at Hebrew University of Jerusalem, and Research Scientist at DFKI (1992-2000). Current Role: Professor, Free University of Bozen-Bolzano Past Roles: Reader (Heriot-Watt), Visiting Professor (Hebrew University), Research Scientist (DFKI) Research Interests focus on Data Management , Knowledge Representation , and Intelligent Information Extraction , with emphasis on modeling Construction Processes and ensuring Data Quality . His work bridges Semantic Web technologies with Business Process Management , notably through the COCkPiT project (2017-present) for construction process optimization. Key Contributions include foundational work on Query Completeness in databases, SPARQL Reasoning , and Semantic Diagnostics . He has published extensively in venues like ISWC , BPM , and CIKM , with an h-index of 35 on Google Scholar. His 154+ publications span topics from Probabilistic XML to Construction Process Modeling .

Bedřich SousedíkView profile

Associate Professor

• Applied and Computational Mathematics
• Numerical Analysis
• Scientific Computing
• +5 more

Bedřich Sousedík is an Associate Professor in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC). He joined UMBC in 2014, transitioning from roles as a Research Associate at the University of Southern California and University of Maryland, College Park. His academic journey includes a Ph.D. in Applied Mathematics (2010, University of Colorado Denver) and a Ph.D. in Civil Engineering (2008, Czech Technical University in Prague). Education: Ph.D. in Applied Mathematics, University of Colorado Denver (2010) Ph.D. in Civil Engineering, Czech Technical University (2008) M.Eng. in Mechanical Engineering, Czech Technical University (2001) Research Interests: Focuses on applied and computational mathematics, including numerical analysis, scientific computing, uncertainty quantification, stochastic finite element methods, and domain decomposition techniques. His work emphasizes scalable algorithms for complex systems with parametric uncertainty. Professional Contributions: Author of ~43 refereed publications (24 journals, 12 conferences) with an h-index of 15 (Google Scholar) Principal Investigator on NSF grants totaling $219,999 for multilevel methods research Recipient of prestigious awards including the 2010 Prof. Babuška Prize and 2020 Early Career Excellence Award Advising & Teaching: Supervised 8 Ph.D. and Master’s students since 2014 Mentored undergraduate research projects in computational fluid dynamics and epidemiological modeling

• Numerical Analysis
• Optimal Control
• Finite Element Methods
• +9 more

Enrico Bertolazzi is an Associate Professor in Numerical Analysis at the University of Trento's Department of Industrial Engineering. He specializes in numerical methods for optimal control, finite element methods, sparse matrix computations, and symbolic computation. His work integrates advanced programming skills in C/C++, MATLAB, and other languages to develop computational tools for engineering and mathematical challenges. Bertolazzi's research addresses complex systems such as vehicle dynamics, trajectory optimization, and real-time simulations, with applications in automotive engineering, renewable energy, and robotics. Research Interests: Numerical Optimal Control and Trajectory Planning Finite Element/Finite Volume Methods for PDEs Sparse Matrix Algorithms Clothoid Curve Modeling Symbolic-Numeric Hybrid Techniques Vehicle Dynamics and Autonomous Systems Publications: Recent work focuses on real-time tire models, DAE index reduction, and autonomous vehicle path planning. His contributions span algorithm development, computational toolboxes (e.g., TrussMe-Fem), and interdisciplinary applications combining mathematics with engineering. Grants & Advising: Advises on optimal control, robotics, and simulation projects. Active in developing educational software like the virtual racing car championship platform. Labs/Teams: Involved in research groups focused on intelligent vehicles, computational mechanics, and numerical methods for industrial applications.

Victor DeBrunnerView profile

Professor

• Signal Processing
• Image Processing
• Pattern Recognition
• +6 more

Victor DeBrunner is a Professor in the Department of Electrical & Computer Engineering at Florida A&M University–Florida State University. He holds a Ph.D. in Electrical Engineering from Virginia Tech (1990), an M.S. from Virginia Tech (1986), and a B.S. from Auburn University (1984). His research focuses on signal processing, image processing, and pattern recognition, with a strong emphasis on hardware-efficient implementations of convolution algorithms and transforms like the Hirschman Transform. His work often explores FPGA-based optimizations for real-time applications, low-latency systems, and efficient digital signal processing architectures. DeBrunner’s recent publications (2018–2025) highlight trends in convolution algorithm design, transform-based signal analysis, and hardware-software co-design for DSP systems. He has contributed to advancements in filter sensitivity prediction, sparse algorithms for DFT computations, and reduced-complexity convolution methods. Notable topics include FPGA-optimized architectures for 2D convolution, efficient neural network implementations using Hirschman transforms, and latency-reduction strategies in linear convolution systems. His research extends to pedagogical innovations in introductory circuits education, emphasizing student engagement through active learning methods. While no awards are explicitly listed in the provided texts, his prolific publication record indicates sustained academic impact in signal processing and hardware design. DeBrunner’s work frequently involves collaborations on structural health monitoring systems, bridge displacement measurement via accelerometers, and adaptive filtering techniques for noise cancellation. He has also explored applications in weather radar signal processing and biomedical imaging quality metrics.

Istvan TomonView profile

Associate Professor

• Discrete Mathematics
• Extremal and Probabilistic Combinatorics
• Geometry

Istvan Tomon is an Associate Professor in the Department of Mathematics and Mathematical Statistics at Umeå University in Sweden. His research focuses on discrete mathematics, extremal and probabilistic combinatorics, and geometry. He leads research in combinatorial structures and graph theory, with recent work exploring hereditary families, symmetric chain decompositions, and Zarankiewicz problems. His publications demonstrate broad expertise in combinatorial optimization, hypergraph theory, and geometric combinatorics. Recent articles show consistent focus on extremal problems in set systems, matrix combinatorics, and incidence geometry.

Sogol JahanbekamView profile

Associate Professor

• Extremal Graph Theory
• Structural Graph Theory
• Algorithms in Graph Theory
• +4 more

Sogol Jahanbekam is an Associate Professor in the Department of Mathematics and Statistics at San José State University (SJSU), affiliated with the College of Science. She specializes in graph theory and combinatorics, with a focus on extremal, structural, and algorithmic aspects of graph theory. Her research includes probabilistic methods in combinatorics and algebraic approaches to graph problems. Education: She holds a Ph.D. in Mathematics from the University of Illinois at Urbana-Champaign (2013), advised by Douglas B. West; an M.S. in Mathematics from Sharif University of Technology (2008), advised by Saieed Akbari; and a B.S. in Applied Mathematics from Shiraz University (2006). Research interests span extremal graph theory, structural properties of graphs, algorithm design for graph problems, and applications of combinatorial methods to engineering design challenges. Her work bridges theoretical advancements with practical applications, such as graph coloring techniques for optimizing manufacturing systems and additive manufacturing processes. Publications highlight contributions to dynamic graph coloring, antimagic labeling, and graph partitioning. Recent work explores applications of graph theory to design flexibility and physical integration problems in engineering. Her collaborative projects include NSF-funded research on improving additive manufacturing through graph-based design concepts. Awards: No awards explicitly mentioned in provided texts. Teaching: She has taught undergraduate and graduate courses in discrete mathematics, graph theory, linear algebra, and combinatorics at SJSU, RIT, and the University of Colorado Denver. Courses include MATH 129A (Linear Algebra), MATH 179 (Graph Theory), and MATH 42 (Discrete Mathematics). Labs/Teams: Active in interdisciplinary collaborations between mathematics and engineering, particularly in applying combinatorial methods to design optimization problems.