Fahad Khan is a Researcher at Cranfield University's Centre for Robotics and Assembly, part of the Aerospace department. He holds an MSc in Robotics Engineering (Cranfield University) and a B.Tech in Mechatronics (NMIMS University). Currently employed full-time as a Research Assistant and part-time PhD candidate, his work focuses on intelligent robotics systems, human-robot collaboration (HRC), and adaptive automation technologies. Education: MSc Robotics Engineering, Cranfield University (UK) B.Tech Mechatronics Engineering, NMIMS University (India) Current Projects: Smart Cobotics (ISCF) project: Developing adaptive HRC systems using physiological and contextual data. EPSRC-funded research on facial emotion recognition for manufacturing robots. Research interests include IoT integration, motion control, and machine learning applications in robotics. He has published on HRC gesture design, emotion recognition systems, and ROS 2-based industrial frameworks. Awards: None explicitly listed. Grants: Supported by Engineering and Physical Sciences Research Council (EPSRC) through the Made Smarter Innovation project. Technical expertise spans ROS, OpenCV, MATLAB, and PLC programming. His LinkedIn and GitHub profiles showcase contributions to robotic systems and machine learning projects.
Dr. Xi Chen is an Assistant Professor in Finance (Accounting and Finance) at the University of Sussex Business School since November 2019. He holds a PhD from the ICMA Centre, Henley Business School, University of Reading (2014), and has held roles including Visiting Research Associate at the University of Oxford (2015–2017) and Behavioural Scientist at Oxford Risk Ltd (2015–2019). Currently affiliated with Oxford Risk as an associate research fellow. Education: PhD in Finance (2014), University of Reading; MSc in Finance (2010–2011), University of Reading. Certifications: Fellow of the Higher Education Academy (FHEA). Research focuses on financial market dynamics, investor behavior, and FinTech innovations. Key areas include crypto-asset analysis, real options valuation, and machine learning applications. Proficient in Python, R, MATLAB, and Stata. Teaching includes modules like Programming in Finance, Portfolio Management, and FinTech and Financial Transformation. Active in developing new courses for undergraduate and postgraduate programs. Awards: Recognized for academic contributions through Fellow of the Higher Education Academy. Labs/Teams: Collaborates with Oxford Risk on behavioral science applications and maintains affiliations in academic and industry networks.
Dr. Meiqin Li is an Associate Professor of Applied Mathematics at the University of Virginia (UVA) and a Faculty Fellow at the UVA Center for Teaching Excellence. She joined UVA in 2017 after earning her Ph.D. in Applied Mathematics from Texas A&M University. Her research focuses on STEM education innovation, particularly integrating technology into classrooms, fostering inclusive learning environments, and optimizing curricula for engineering students. She also explores numerical computation, optimization, and nonlinear analysis in applied mathematics. Dr. Li’s educational background includes a Ph.D. from Texas A&M University, College Station, TX (2017). Her teaching portfolio spans courses like APMA 3080 (Linear Algebra) and APMA 2501 (Programming in R & MATLAB), reflecting her commitment to computational and pedagogical excellence. Her research interests emphasize active learning strategies, curriculum redesign for equity, and leveraging MATLAB/Autograder tools in education. Recent publications highlight outcomes from integrating precalculus into calculus courses and enhancing linear algebra instruction through technology. Awards: 2025 ASEE Mathematics Division Best Paper Award 2024 SoTL Dissemination Award (UVA Center for Teaching Excellence) 2020 Thomas E. Hutchinson Award Finalist Dr. Li’s work bridges pedagogical innovation and applied mathematics, with grants and recognitions underscoring her impact in teaching and research. She actively contributes to UVA’s engineering education community and promotes student success through inclusive practices.
Tony Stillfjord is an Associate Professor at the Centre for Mathematical Sciences, Lund University, Sweden. He previously held postdoctoral positions at the Max Planck Institute for Dynamics of Complex Technical Systems and Chalmers/University of Gothenburg. Ph.D. and MSc in Numerical Analysis from Lund University Funded by WASP (Wallenberg AI, Autonomous Systems and Software Program) Research Interests : Numerical methods for partial differential equations, splitting schemes, stochastic optimization, and large-scale differential Riccati equations. His work focuses on developing low-rank approximations and robust optimization algorithms. Recent Publications highlight advancements in Lie/Strang splitting for operator-valued Riccati equations, stochastic descent methods, and GPU-accelerated splitting schemes. Software Contributions : Developed DREsplit (MATLAB package for differential Riccati equations) and BST20_CODE for stochastic optimization experiments. Contact : Office at MH:562E, Lund University. Email: tony.stillfjord@math.lth.se . URL: tonystillfjord.net
Kirk D. Dolan is a Professor at Michigan State University (MSU), holding joint appointments in the Department of Food Science and Human Nutrition (lead) and the Department of Biosystems & Agricultural Engineering within the College of Agriculture & Natural Resources. His research focuses on thermal processing modeling of foods and inverse problems in food science, utilizing advanced computational tools like MATLAB and COMSOL for parameter estimation. He actively contributes to food safety through extension work, including co-teaching the FDA-mandated Better Process Control School and HACCP courses. PhD, Agricultural Engineering, Michigan State University, 1989 MS, Agricultural Engineering, University of California, Davis, 1985 BS, Agricultural Engineering, University of Florida, 1983 Dolan’s research spans thermal processing technologies (canning, drying, aseptic processing), inverse problem solving, and statistical methods for food researchers. His recent publications emphasize antioxidant analysis, kinetic modeling, and computational approaches in food systems. Recent publications highlight advancements in parameter estimation for food processing, including studies on thermal conductivity in cherry pomace, starch viscosity models, and microbial inactivation dynamics. His work bridges computational methods with practical applications in food safety and quality preservation. 2019 CANR Camden Endowed Teacher/Scholar Award Dolan chairs the triennial Inverse Problems Symposia at MSU and co-teaches graduate courses like BE 835 (Modeling Methods in Biosystems Engineering). His extension work supports Michigan food entrepreneurs through FDA product registration assistance and industry training programs.
Ambra Ferrari is a Research Fellow at the Interdepartmental Center for Mind/Brain Sciences (CIMEC) within the University of Trento. Her work focuses on cognitive development, multisensory perception, and neuroimaging techniques. She teaches courses such as Cognitive neuroscience of infant development and contributes to the Cognitive Neuroscience program in the Department of Psychology and Cognitive Sciences. Her research integrates methodologies from neuroscience and psychology to study infant cognition, social development, and sensory integration. She develops computational tools like the WTools MATLAB toolbox for analyzing infant neural data. Ferrari's work bridges developmental psychology, psycholinguistics, and sensory neuroscience, emphasizing how prior expectations guide perception during communication. In teaching, she employs journal clubs and seminars to foster critical analysis of empirical studies and contemporary theories in cognitive development. Her courses aim to equip students with skills to evaluate experimental research and understand neurobiological foundations of cognition. Her laboratory (CIMEC) focuses on adaptive behavior, cross-modal plasticity, and embodied communication. Current projects explore statistical learning mechanisms, attention modulation in multisensory perception, and the role of gesture-prosody interactions in language comprehension.
Alberto Godio is a Full Professor at the Politecnico di Torino, affiliated with the Department of Environmental, Land and Infrastructure Engineering (DIATI). He coordinates Latin America relations under the University Strategic Plan and is a member of the Interdepartmental Center Photonext for Applied Photonics. His research focuses on Applied Geophysics, Geophysical Data Integration, and Glaciology. He graduated in Mining Engineering (1988) and earned a PhD in Underground Resources Engineering (1993). He has been an Associate Professor (2005-2024) and Full Professor (2024-present) at PoliTO, leading projects funded by EU (FP7, LIFE, Horizon), MIUR (PRIN, FIRB), and regional bodies. His recent publications explore geophysical methods for subsurface modeling, glacial systems, and environmental remediation, with keywords spanning Geophysics, Hydrology, and Climate Science. His work emphasizes ground-penetrating radar, seismic noise analysis, and hybrid modeling techniques. Scientific awards include the Best Paper Award at the Near Surface Geoscience Conference (2008). He has supervised PhD students on fiber optic sensors and GPR optimization, advised projects on digital twins, and led EU-funded research on biogas enhancement in landfills.
Dr. Grey Ballard is an Associate Professor in the Department of Computer Science at Wake Forest University . He earned a B.S. in Math and Computer Science (2006), M.A. in Math (2008) from Wake Forest, and PhD in Computer Science (2013) from the University of California, Berkeley. He was a Truman Fellow at Sandia National Laboratories before joining Wake Forest. Research Focus: Ballard develops communication-optimal algorithms for high-performance computing , particularly in tensor decompositions , symmetric matrix computations , and nonnegative matrix factorization . His work combines numerical linear algebra with parallel algorithm design to reduce data movement costs in distributed systems. Publications demonstrate expertise in communication lower bounds , randomized tensor rounding , and visualization tools for parallel algorithms. He has contributed software packages such as TuckerMPI , GentenMPI , and PLANC for large-scale data compression and clustering. Scientific Awards: Wake Forest Excellence in Research Award NSF CAREER Award SIAM Linear Algebra Prize Three Conference Best Paper Awards (SPAA, IPDPS, ICDM) C.V. Ramamoorthy Distinguished Research Award (UC Berkeley) ACM Doctoral Dissertation Award – Honorable Mention Teaching: Courses include Introduction to Computer Science , Numerical Linear Algebra , and Parallel Algorithms . He has developed educational tools using the Thread-Safe Graphics Library to visualize parallel dynamic programming and collective communication.
Jiajia Yu is a Phillip Griffiths Assistant Research Professor in the Department of Mathematics at Duke University, affiliated with the Trinity College of Arts & Sciences. She holds a Ph.D. in Mathematics from Rensselaer Polytechnic Institute (2023) and a B.S. in Mathematics and Applied Mathematics from Beijing Normal University (2017). Her research focuses on applied and computational mathematics, with a particular emphasis on numerical methods for mean-field games, optimal transport, and inverse problems. She collaborates with prominent researchers such as Professors Hongkai Zhao, Xiuyuan Cheng, and Jian-Guo Liu. Recent work includes convergence analysis of fictitious play in mean-field games, zeroth-order optimization methods, and bilevel optimization frameworks for inverse mean-field games. Her contributions span computational methods on manifolds and trajectory regularization in machine learning contexts. Upcoming engagements include presentations at the IPAM Workshop on Scientific Machine Learning (2025) and Duke Math+ Program leadership on optimal transport topics. Her GitHub repositories showcase MATLAB implementations of FISTA algorithms and computational tools for mean-field games on manifolds.
Stefano Scialo' is an Associate Professor in the Department of Mathematical Sciences "G.L. Lagrange" (DISMA) at the Polytechnic University of Turin. He is also a member of the Interdepartmental Center Ec-L - Energy Center Lab and serves as the contact person for the Bachelor's Degree Program in Mathematics for Engineering (L3). His academic journey began with a Master’s in Aerospace Engineering (2007), followed by a PhD in Mathematics for Engineering (2014), both from the same institution. After his PhD, he held postdoctoral and Assistant Professor positions at DISMA before being promoted to Associate Professor. Education: PhD in Mathematics for Engineering, Politecnico di Torino, 2014 Master in Aerospace Engineering, Politecnico di Torino, 2007 His research focuses on advanced numerical methods for partial differential equations, particularly in the context of complex multiscale and multiphysics systems. Key areas include the Virtual Element Method (VEM), domain decomposition techniques based on PDE-constrained optimization, and the simulation of flows in fractured porous media. He has made significant contributions to 3D-1D coupled problems, with applications in geosciences and biomedical modeling such as tumor-induced angiogenesis. His methodological work emphasizes robustness, scalability, and applicability to non-conforming and polygonal meshes, enabling high-performance computing solutions. The trend in his recent publications reveals a strong emphasis on developing and analyzing mixed virtual element methods, optimization-based coupling strategies, and their applications to engineering and biological systems. His work bridges theoretical numerical analysis with practical implementations in fluid dynamics and subsurface flow. Scientific Contributions: Principal Investigator of the FREYA project (2023–2026) on hybrid numerical approaches for fault reactivation. Coordinator of the INdAM-GNCS research project (2018–2019). Member of the research group "Numerical Analysis and Scientific Computing" at DISMA. Stefano Scialo' actively supervises doctoral students, including Matteo Trombini in the PhD program in Mathematical Sciences. He teaches a range of courses such as Advanced Scientific Programming in MATLAB, Numerical Methods and Scientific Computing, and specialized topics on Virtual Element Methods. He also contributes to curriculum development and academic governance through roles in doctoral colleges and degree program committees, including those for Mathematical, Mechanical, Aerospace, and Automotive Engineering. Laboratories and Research Groups: Member, Interdepartmental Center Ec-L - Energy Center Lab Research Group: Numerical Analysis and Scientific Computing (DISMA)
Mario Annunziato is a Researcher in Mathematics at the Department of Physics, University of Salerno, since 2004. His work focuses on numerical methods for stochastic processes and optimal control. Institution: University of Salerno Department: Department of Physics Academic Rank: Researcher Research Interests include numerical solutions of PDEs and integral equations for stochastic processes, probability density function optimization, and modeling random phenomena. His work addresses positivity, monotonicity, and conservation in discrete PDFs. Article Trends span stochastic control frameworks, computational finance, biophysics applications, and numerical methods for jump-diffusion processes. Key topics involve Fokker-Planck equations, Hamilton-Jacobi-Bellman formulations, and splitting methods. Advising and Grants include teaching Numerical Analysis until 2013 and securing funding from the University of Salerno's FARB program, INdAM-GNCS, and the European Science Foundation's OPTPDE grants. He participated in the STRIKE Marie Curie ITN network. Labs & Teams : Collaborated with Prof. Alfio Borzì at Würzburg University and contributed to open-source tools like MATLAB Central File Exchange for PDP solvers.
Dr. Patrick Macnamara is a Senior Lecturer in Economics at the University of Manchester, holding a PhD from University of Rochester (2013). His research focuses on quantitative macroeconomics, firm dynamics, inequality, and heterogeneity. He serves as an adjunct professor at the University of Western Australia. Macnamara's published work examines tax policy impacts on income dynamics, financial frictions in resource allocation, and business cycle mechanisms. Recent publications analyze marginal tax rate effects using structural estimation and capital structure decisions under taxation constraints. His methodological approach combines computational modeling with econometric analysis of firm-level data. Macnamara supervises PhD students working with heterogeneous agent models and microdata analysis. He requires doctoral candidates to develop programming skills in Matlab, C++, or Fortran for solving economic models.
Dr. Ir. Wouter Schakel is a full-time O&O researcher at Delft University of Technology's Faculty of Civil Engineering and Geosciences, specializing in Transport & Planning. His research focuses on microscopic simulation of driver behavior, particularly lane change modeling and traffic flow optimization. He has developed the LMRS lane change model and contributes to OpenTrafficSim. His academic roles include teaching programming courses for transport engineering students and supervising BSc/MSc projects. Education: Civil Engineering (BSc & MSc) from TU Delft, followed by a PhD on freeway driving advice systems. Research highlights include the Greenshields prize-winning LMRS model and work on in-car advisory systems. He teaches courses like 'Programming and MATLAB' and 'Intelligent Vehicles Design and Assessment'. Current projects involve urban traffic simulation validity improvements and lane change strategy extensions.
Lukas Weimann is a PhD candidate at Utrecht University's Copernicus Institute of Sustainable Development, specializing in the Energy & Resources department. His research focuses on the optimization of multi-energy systems and their synergies with carbon capture technologies to decarbonize industries like cement and steel production. He holds a BSc and MSc in Chemical Engineering from ETH Zurich, with a research stint at MIT under Prof. Klavs F. Jensen, where he developed expertise in mathematical optimization. His skills include Matlab (programming language) Modeling . Lukas supervises Master's thesis projects on topics such as: Development of MILP models for carbon capture units Optimization of multi-energy systems for carbon-neutral fuels Thermochemical storage modeling He teaches courses like Energy Conversion Technologies I and Advanced Energy Analysis, emphasizing heat integration and energy system analysis.
Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.
