Omar Rivasplata is a Senior Lecturer in Machine Learning and Robotics, specializing in theoretical and applied machine learning. He is affiliated with the MCAIF: Centre for AI Fundamentals, focusing on advancing foundational aspects of artificial intelligence. His research spans reinforcement learning, Bayesian analysis, neural networks, and generalization bounds. Key research interests include the theoretical underpinnings of deep learning architectures, optimization strategies for large-scale models, and probabilistic methods in machine learning. Notable contributions include work on gradient clipping for wide/deep networks and semi-pessimistic reinforcement learning frameworks. Leading the MCAIF project (2021–2026), a multidisciplinary initiative exploring core AI principles. Active collaborations with institutions worldwide, emphasizing open-access publications (e.g., Transactions on Machine Learning Research). His articles explore topics like PAC-Bayesian theory, convergence of diffusion models, and meta-analyses of Bayesian methods. He advises a diverse cohort of postgraduate researchers in AI fundamentals.
Joel Friedman is a Professor in the Department of Computer Science at the University of British Columbia (UBC), Faculty of Science. His research focuses on Algebraic Graph Theory, Combinatorics, and Theoretical Computer Science, with significant contributions to spectral graph theory, sheaf theory, and computational complexity. He has supervised numerous PhD and Master's students in these areas. Friedman teaches courses such as CPSC 421 (Introduction to the Theory of Computing) and CPSC 531F (Discrete Hodge Theory and Topological Data Analysis). His work bridges pure mathematics and computer science, including studies on Riemann-Roch theorems for graphs, eigenvalue conjectures, and applications of linear algebra in discrete structures. He has authored over 50 publications, with recent contributions on topological data analysis, graph expansions, and algorithmic methods. Awards and honors include the Izaak Walton Killam Memorial Faculty Research Fellowship. Friedman actively engages in graduate supervision, advising students on topics like sparsifier constructions and coded caching problems. His research is affiliated with the Institute of Applied Mathematics at UBC. He has held academic positions since the 1980s, with a consistent record of contributions to theoretical computer science and discrete mathematics.
Susanne Bradley is a Teaching Professor in the Department of Computer Science at the University of British Columbia (UBC). She specializes in algorithm design, parallel computation, and numerical analysis. Her teaching responsibilities include courses such as CPSC 320 (Intermediate Algorithm Design and Analysis) and CPSC 418 (Parallel Computation). Her research focuses on preconditioners for saddle-point systems, eigenvalue analysis, and innovative educational methodologies like inverted two-stage exams. Bradley’s academic contributions span numerical linear algebra, computational methods, and biomechanical simulation. She has published extensively on topics such as eigenvalue bounds for saddle-point systems and the application of machine learning in sensorimotor control. Her work emphasizes practical solutions for complex computational challenges and pedagogical innovations to enhance learning outcomes. Her office is located in ICCS 241, and she can be reached at smbrad@cs.ubc.ca .
Prof. Erhan İnce is a faculty member in the Department of Electrical and Electronic Engineering at Eastern Mediterranean University (EMU), where he has served since 1998. He holds a PhD in Communications from the University of Bradford (1997) and MS/BS degrees in Electrical and Electronic Engineering from the University of Bucknell (1992, 1990). His research focuses on mobile communications, channel coding, OFDM/OFDMA, statistical signal processing, and facial recognition. He has supervised numerous PhD and MS students, including Ramin Bakhshi, Syed Amjad Ali, and Waseem Qassab Bash. Key projects include 'Monitoring KKTCELL's Base Stations' (2018-2019) and 'Background Subtraction and Lane Occupancy Analysis' (2011). He received an award at the 1998 Symposium on Communication Systems & Digital Signal Processing. His work spans peer-reviewed journals (IEEE Access, Digital Signal Processing) and books on video surveillance. He actively contributes to IEEE and other academic communities.
Dr. Henrik Sykora is a Research Fellow at the University of Southampton, specializing in stochastic dynamical systems. His research focuses on numerical methods, data-driven identification, and applications in energy harvesting and control systems. He is affiliated with the East Highfield Campus and has collaborated on projects like 'Stochastic Nonsmooth Analysis For Energy Harvesting' and 'IoT-based ocean pollutant monitoring'. His work bridges computational mathematics and engineering applications. His research interests include stochastic nonsmooth systems, delay differential equations, and noise-induced phenomena in mechanical systems. Notable projects involve analyzing chatter formation in machining processes and developing stochastic modeling techniques for manufacturing processes. His publications span journals like Computers & Structures and International Journal of Robust and Nonlinear Control . No scientific awards are listed. While no current teaching or advising roles are specified, his research projects indicate collaboration with students and colleagues. He contributes to interdisciplinary work at the University of Southampton's research facilities.
Petros Drineas is the Department Head and Professor of Computer Science at Purdue University's College of Engineering. His research focuses on randomized numerical linear algebra (RandNLA), algorithms for large-scale data analysis, computational genomics, and machine learning. He leads the ENIGMA-TS initiative for global neuroimaging genetics collaboration and has developed tools like TeraPCA for tera-scale genotype analysis. Drineas has held leadership roles in NSF and BSF-funded projects, including studies on Tourette syndrome, Alzheimer's disease, and algorithmic efficiency. His work bridges theoretical computer science with applications in genetics, neuroscience, and optimization. Key contributions include matrix sketching frameworks, randomized algorithms for PCA, and methods for GWAS and polygenic risk score analysis. His grants include funding for RandNLA applications in inverse problems, fast least squares solvers, and collaborative research on genetic variation. He has pioneered methods for handling massive datasets in genetics, such as the MaSk-LMM framework for linear mixed models and Patch2Self2 for self-supervised denoising.
Roberto De Marchis is a Researcher at Sapienza University of Rome, affiliated with the Department of Methods and Models for Economics, Territory, and Finance. His academic career focuses on quantitative approaches to financial and mathematical problems, with teaching responsibilities in Financial Mathematics for Economics and Finance programs. Role: Ricercatore (Researcher) Department: Methods and Models for Economics, Territory, and Finance Email: roberto.demarchis@uniroma1.it Research Interests: His work spans optimization models, data quality, and knowledge management. Specific areas include numerical methods for integral equations, financial option pricing, ruin probability analysis, and fractional calculus applications. Publication Trends: He specializes in mathematical finance and applied mathematics, particularly in computational approaches for financial derivatives and risk modeling. His publications demonstrate interdisciplinary applications of mathematical theory to economic and financial systems. Teaching: Coordinates Financial Mathematics courses (channels N-Z and A-L) and co-authored a key textbook in the field.
Stefano Patri' serves as a Professor in the Department of Methods and Models for Economy, Territory and Finance at Sapienza University of Rome. He teaches foundational mathematics courses for business sciences, advanced mathematics for finance (in English for the FINASS Master's program), and computer science laboratories for the MANIMP Master's degree. His institutional affiliation is consistently maintained through Sapienza University's academic structure. Professor Patri's research centers on deterministic and stochastic optimization theory with applications in economic modeling. He specializes in mathematical programming techniques including Hamilton-Jacobi-Bellman equations and Kuhn-Tucker conditions applied to economic problems. His work spans public debt optimization, game-theoretic approaches to environmental agreements, and innovative applications in insurance mathematics and urban mobility systems. His recent publications demonstrate strong interdisciplinary connections between mathematics and economics, with notable contributions in 2023 on public debt correction mechanisms and pay-as-you-drive insurance models. The research consistently bridges theoretical mathematics with practical economic policy applications, particularly in fiscal management and strategic decision-making frameworks. Teaching activities remain central to his role, with current responsibilities including: Mathematics Foundation Course for Business Sciences Mathematics for Finance (Master's Degree FINASS in English) Laboratory of Computer Science (MANIMP Master's Degree) Mathematics for PhD School Physics Curriculum Student engagement occurs through scheduled office hours by appointment and comprehensive digital resources including video lessons and exercise materials hosted on his institutional web page.
Dr Jennifer Pestana is a Senior Lecturer in the Department of Mathematics and Statistics at the University of Strathclyde, Faculty of Science. She has been a faculty member since 2015 and is actively involved in research and academic leadership. She is a principal investigator on multiple grants and serves as an editor for SIAM publications. Education: DPhil, University of Oxford, 2012 Postdoctoral positions: University of Manchester, University of Oxford Her research centers on numerical linear algebra and its applications in scientific computing . She investigates the convergence of Krylov subspace methods , develops preconditioners for specific applications, and explores the use of tropical algebra for matrix pre-scaling. Current projects involve fast solvers for fractional diffusion and analysis in broadband signal processing . Her work bridges theoretical numerical analysis with practical computational challenges. The recent publications reflect a strong focus on iterative methods, matrix analysis, and signal processing. Common themes include Toeplitz matrices , eigenvalue decomposition , preconditioning , and covariance estimation , demonstrating her expertise in both algorithmic development and application-driven numerical methods. Scientific Awards: Best Student Paper Award (2018) Dr Pestana actively supervises students and leads research projects, including EPSRC-SFI and Strathclyde ISP Joint PhD initiatives. She has secured significant funding for work on Krylov methods and efficient solvers. Her professional service includes organizing major conferences such as the Biennial Conference on Numerical Analysis and serving on program committees, including for the International Linear Algebra Society. She is also an invited speaker and visiting researcher, demonstrating national and international recognition. She is involved with research teams focused on numerical analysis and scientific computing, contributing to collaborative efforts in developing novel preconditioned iterative solvers. Her lab and project groups work on both theoretical and applied aspects of linear algebra in PDEs and signal processing.
Dr. Jia Liu is a Professor and Chair of the Department of Mathematics and Statistics at the University of West Florida, within the Hal Marcus College of Science and Engineering. She has been a key academic figure at UWF since 2006, leading both research and departmental initiatives in computational and applied mathematics. Her educational background includes a Ph.D. in Mathematics from Emory University, funded by the National Science Foundation, focusing on preconditioned Krylov subspace methods for incompressible flow problems. She earned her M.S. and B.A. in Mathematics from Central China Normal University, where her bachelor's thesis received the highest honor. Dr. Liu's research lies at the intersection of numerical linear algebra, scientific computing, and interdisciplinary applications. Her primary interests include: Numerical solvers for large sparse linear systems Krylov subspace iterative methods Preconditioning techniques Applications to Navier-Stokes and optimization problems Geometric and topological analysis of ellipsoids Complex networks and community detection via spectral clustering Machine learning for disease prediction and biological modeling The trends in her publications reflect a consistent focus on robust numerical algorithms with applications across fluid dynamics, network science, and biomedical modeling. Her work emphasizes both theoretical development and practical implementation in high-performance computing environments. Dr. Liu has served as an editor and editorial board member for several peer-reviewed journals and regularly reviews submissions. While specific awards are not listed, her sustained publication record in prestigious venues such as SIAM Journal on Scientific Computing and Journal of Biological Dynamics underscores her scholarly impact. As department chair and professor, she plays a central role in academic advising, curriculum development, and research mentorship. She teaches core courses including Differential Equations, Numerical Analysis, and Real Analysis, contributing significantly to both undergraduate and graduate education. Her leadership extends to managing research grants and fostering collaborations across disciplines. Though no formal lab name is mentioned, her research activities suggest involvement with computational modeling groups, likely associated with applied mathematics and data science initiatives at UWF. Her ongoing work continues to advance numerical methods for complex systems in science and engineering.
Mihaela Vajiac is a Professor and Program Director for Mathematics at Chapman University's Schmid College of Science and Technology. She serves as Director of the Center of Excellence in Complex and Hypercomplex Analysis (CECHA) and organizes the Math/Physics/Computation Seminar. Her educational background includes a Ph.D. from Boston University and a B.S. from the University of Bucharest. Dr. Vajiac's research spans: Complex/Hypercomplex Analysis : Investigating Dirac-type operators, quaternionic systems, and applications in physics and engineering. Algebraic Computational Methods : Developing algebraic tools for PDEs including Maxwell and Cauchy-Fueter systems. Differential Geometry : Exploring integrable systems, curvature invariants, and symplectic structures. Her recent publications focus on bicomplex tensor products, spectral factorization in hypercomplex spaces, and geometric invariants, reflecting sustained innovation in operator theory and Clifford analysis. She co-organizes international workshops like IWOTA 2021 and maintains active collaborations with global researchers.
Ahmad Mohammadpanah serves as a Lecturer in the Department of Mechanical Engineering within the Faculty of Applied Science at the University of British Columbia (UBC). Holding a Professional Engineer (P.Eng.) designation, he earned his PhD and MASc from UBC alongside an MSc from Sharif University of Technology. His academic profile is anchored in mechanical engineering with specialized expertise in dynamic systems and manufacturing technologies. His educational qualifications include: PhD in Mechanical Engineering, University of British Columbia MASc in Mechanical Engineering, University of British Columbia MSc in Mechanical Engineering, Sharif University of Technology Dr. Mohammadpanah's research centers on Dynamics, Vibrations, and Modal Analysis , with pioneering work in Spherical Parallel Robots and Light Weight Lattice Design for Additive Manufacturing . His exploration of Generative Design & Topology Optimization bridges computational methods with practical manufacturing solutions. Current investigations focus on thermal management in metal extrusion additive manufacturing and vibration control in industrial saw systems, demonstrating strong alignment with industry needs in wood processing and advanced manufacturing. Analysis of his 10 publications (2012-2024) reveals consistent focus on vibration phenomena in rotating machinery and additive manufacturing defect mitigation. His work shows progressive evolution from fundamental dynamics research (2012-2018) toward applied industrial solutions (2021-2024), particularly in metal additive manufacturing and smart lumber production. The integration of machine learning in wood quality prediction (2023) highlights his adaptation to emerging computational techniques. No scientific awards were documented in the provided materials. Regarding academic mentorship, Dr. Mohammadpanah actively supervises undergraduate capstone projects (MECH 45X) and laboratory courses, though formal graduate student advisement isn't specified. His teaching portfolio spans foundational courses like MECH 223 (Introduction to Mechanical Design) through advanced topics including MANU 465 (AI in Manufacturing), reflecting comprehensive engagement with mechanical engineering education. His research operates through the "Intelligent Engineering" initiative, focusing on practical industrial applications rather than dedicated laboratory facilities. Current work emphasizes real-world implementation of vibration control systems and additive manufacturing optimization, with strong industry connections evident through Fpinnovations collaboration and patent development.
Martin Dalgaard Ulriksen is an Associate Professor at Aarhus University's Department of Mechanical and Production Engineering, specializing in system dynamics and affiliated with the Mechatronics and Dynamics section. His work focuses on vibration theory, system identification, and control theory with applications in offshore structures and wind turbines. His research encompasses fault detection, parameter estimation, and digital twin technologies. Key projects include True Digital Twin (2024-2025) for wind turbine design and CP-SENS (2023-2026) for cyber-physical sensing in structural monitoring. Publications highlight methodologies like modal expansion, basis pursuit, and eigenstructure assignment for damage localization and system identification. Martin teaches vibration theory and system identification courses at both bachelor's and master's levels while supervising thesis projects. Current collaborations with researchers like D. Bernal demonstrate his emphasis on interdisciplinary approaches. Contact: mdu@mpe.au.dk | +45 93 50 88 66.
Minghao Rostami is an Associate Professor in the Department of Mathematics and Statistics at Binghamton University. His research focuses on interdisciplinary problems at the intersection of applied mathematics, computational fluid dynamics, and biophysics, often leveraging machine learning and numerical methods. His recent work spans topics such as mixed noise removal in signal processing, data-driven fluid trajectory prediction, and multi-scale modeling of actin polymer dynamics. Publications highlight collaborations between mathematical theory and biological applications, particularly in biofluid mechanics. The trends in his publications from 2012 to 2025 emphasize scalable numerical algorithms for dense/sparse matrices, stability analysis in incompressible flows, and hybrid modeling frameworks combining machine learning with physical simulations. Specific subfields include multigrid methods, kernel regularization, and bio-inspired fluid pumping optimization.
Prof. Dr.-Ing. Ulrich Königorski is a Professor of Control Systems and Mechatronics at the Technical University of Darmstadt's Institute of Automatic Control and Mechatronics. He holds a doctorate from TU Karlsruhe and has extensive industry experience at Robert Bosch GmbH and Thyssen Henschel. His research focuses on modeling nonlinear systems, mechatronic systems control, vehicle dynamics, and microgrid control. Königorski leads the Control Systems and Mechatronics Laboratory and has contributed to projects like Proreta 3 (driver assistance systems) and FLORIDyn (wind turbine modeling). His work bridges theoretical control methods with practical applications in automotive, robotics, and renewable energy sectors. Education: Electrical Engineering (1977-1983, TU Karlsruhe), PhD (1987, TU Karlsruhe) on state feedback design. Professional roles include Dean of the Faculty of Electrical Engineering and spokesperson roles in academic committees. Research interests emphasize system modeling, robust control, and mechatronics applications. Notable areas include automated driving trajectory planning, sensorless motor control, and inverter-based microgrid synchronization. His recent work addresses challenges in unsupervised scenario extraction for autonomous systems and gait phase estimation for prosthetics. Labs/Teams: Head of the Control Systems and Mechatronics Laboratory. Collaborations include industry partnerships for automotive and renewable energy projects. Ongoing research focuses on enhancing safety in nonlinear systems, optimizing energy grids, and advancing human-centric automation technologies.
