Dr. Agnes Lamacz-Keymling is a researcher at the University of Duisburg-Essen in the AG Optimal Control of Partial Differential Equations. Her research focuses on analysis of PDEs, multiscale problems, homogenization, and wave phenomena. PhD in Mathematics (2011) and Diploma in Mathematics (2008) from TU Dortmund Her work spans homogenization of periodic structures, wave propagation in heterogeneous media, negative index meta-materials, and multiscale modeling. She has received third-party funding through a DFG project on wave propagation in periodic structures and negative refraction. Recent publications highlight trends in Bloch wave homogenization, dispersive wave models, and photonic crystal analysis. Her teaching includes Mathematics E3 and E4 in the winter semester 2021/22. Third-party funding: DFG project on wave propagation in periodic structures and mechanisms of negative refraction (2014)
Keith Blow is a Professor in Electronic Engineering at Aston University, leading the Photonics Research Group and the Adaptive Networks Communications Research Group. He holds a BA (First Class Honours) in Physics and Theoretical Physics from Cambridge University (1978) and a PhD in Solid State Physics from the Cavendish Laboratory (1981). Prior to joining Aston in 1999, he worked at BT Research Laboratories, focusing on optical fiber technologies and nonlinear effects. His research spans photonics, optical networks, adaptive communication systems, and energy-efficient protocols. Key areas include soliton-based transmission, nonlinear optical processing, and wireless sensor networks. He has supervised 6 students and contributed to over 99 publications, with notable work on soliton crystals, optical frequency combs, and FSO channel optimization. Blow serves on the editorial board of the Journal of Modern Optics and reviews for conferences like the Advanced Photonics Congress. His labs focus on advancing optical communication systems, network efficiency, and sensor network applications, emphasizing practical implementations of theoretical findings.
John Butcher is a Teaching Professor at the Aston Pharmacy School, part of the College of Health and Life Sciences at Aston University. He specializes in Neuroscience and Computational Intelligence, focusing on neuroplasticity mechanisms and biologically inspired neural networks. His work bridges neuroscience research and computational models, with applications in forensic science, robotics, and medical diagnostics. He holds a 1st-class degree in Computer Science and Management Science from Keele University (2007), followed by a PhD on reservoir computing applied to nonlinear time-series (2010). Postdoctoral research included astrocyte roles in plasticity (2012) and imaging neuron populations in crab stomatogastric ganglia. Recent projects explore caffeine's effects on adolescent learning and forensic applications of neural networks for age estimation. Teaching roles include co-programme director for the BSc Neuroscience and programme director for MSc Neuroscience for Drug Discovery. He teaches Computational Neuroscience, laboratory skills, and supervises research projects. His research spans astrocyte signaling, reservoir computing, and forensic entomology analysis, with over 14 peer-reviewed publications. Notable contributions include advancing voltage-sensitive dye imaging techniques and reservoir computing applications in structural health monitoring. Collaborations span neuroscience, chemistry, and engineering disciplines.
Dr. Yanhua Hong is a Reader in the School of Computing and Engineering at Bangor University. His research focuses on nonlinear dynamics of semiconductor lasers, chaos theory, and their applications in optical communications and microwave photonics. He leads projects such as 'Microwave Photonics Generation Using Low-Cost VCSELs' and has published extensively in journals like Optics Express and Photonics. Key areas of expertise include semiconductor laser dynamics under optical feedback, secure communication systems leveraging chaotic signals, and the design of photonic microwave generation systems. Hong collaborates internationally with institutions like Southwest University (China) and the Universitat Politècnica de Catalunya (Spain). He actively supervises PhD students and examines external theses. Recent work highlights include high-speed secure stream ciphers using synchronized chaos, optimization of multimode fiber imaging systems, and analysis of intermittent laser dynamics via reservoir computing. His contributions bridge fundamental research with applied photonics, contributing to advancements in optical security, signal processing, and next-generation communication networks.
Dr. Mathukumalli Vidyasagar is the Cecil & Ida Green Chair in Systems Biology Science at The University of Texas at Dallas (UT Dallas), serving as Professor of Bioengineering and Head of the Bioengineering Department. He holds a part-time appointment as Distinguished Professor at the Indian Institute of Technology Hyderabad through the Jawaharlal Nehru Science Fellowship (2015–present). His academic journey includes roles as Director of the Centre for Artificial Intelligence and Robotics (1989–2000) and Executive Vice President at Tata Consultancy Services (2000–2009). He earned B.S., M.S., and Ph.D. degrees in Electrical Engineering from the University of Wisconsin–Madison (1965–1969). His research focuses on control theory, systems biology, and computational biology, with applications to cancer diagnostics and machine learning. Notable contributions include work on robust control, L1-optimal control, and statistical learning theory. Dr. Vidyasagar has authored over 140 peer-reviewed papers and 11 books. His accolades include Royal Society Fellowship (2012), IEEE Control Systems Award (2008), and the Rufus Oldenburger Medal (2012). He has advised 14 Ph.D. and 12 M.S. students, and sponsored 8 postdoctoral scholars.
Dr. Emad Chaparian is a Lecturer in Mechanical & Aerospace Engineering at the University of Strathclyde, part of the Engineering Faculty of Engineering. He holds a PhD in Mechanical Engineering from the University of British Columbia (UBC) and has held postdoctoral and visiting positions at institutions including KTH Royal Institute of Technology (Sweden) and the University of Waterloo (Canada). His research focuses on complex fluids, rheology, multiphase flows, and porous media, with applications to environmental sustainability and industrial processes. He is a Fellow of the Institute of Mathematics and its Applications (FIMA) and has led projects funded by the Royal Society and the EPSRC. Education: PhD (Mechanical Engineering, UBC), Postdoc (KTH), Research Fellow (UBC Mathematics) Professional Activities: Visiting researcher at UBC (2025), EPSRC IM3AGES workshop participant (2024) Research interests include computational rheometry, viscoplastic fluid dynamics, and innovative measurement techniques. Recent work explores particle manipulation, bubble dynamics in yield-stress fluids, and flow modeling in porous media. His 2025 publications reflect advancements in nonlinear rheology and multiphase system analysis. He actively contributes to both experimental and numerical studies, emphasizing sustainable industrial applications. Awards: FIMA (2023), Sir Anderson Visiting Professorship (2024) Grants/Projects: Principal Investigator for EPSRC-funded projects on tailings pond harvesting (2022–2023) and elastoviscoplastic fluid mechanics (2024–2024) Laboratory activities involve collaboration with interdisciplinary teams, leveraging advanced computational tools and experimental setups like 3D-printed rheometry fixtures.
Dr. Axel Hutt is a senior researcher (Directeur de Recherche) at INRIA Grand Est in Strasbourg, France, leading the MIMESIS team. His work focuses on neural systems modeling, nonlinear dynamics, and data assimilation in complex systems. He holds a PhD from the University of Stuttgart (2001) and an HDR (Habilitation) from the University of Nice (2013). Hutt’s research bridges neuroscience, mathematics, and engineering, with contributions to anesthesia modeling, neurostimulation, and EEG analysis. He has held roles at the Max Planck Institute, Humboldt University Berlin, and the University of Ottawa, and received the Schloessmann Fellowship (2000) and ERC Starting Grant (2011). His current projects include PhD supervision of T. Nette and participation in high-profile conferences like Re:publica and ICMNS. Recent work explores myelination effects, closed-loop neurostimulation, and AI ethics. Education: Physics Diploma (Stuttgart, 1997), PhD (Stuttgart, 2001), HDR (Nice, 2013) Key Roles: INRIA Team Leader (MIMESIS, 2019–present), NeuroSys Team Head (2013–2015) Research Themes: Neural field theory, stochastic processes, clinical neurostimulation applications Publications: Over 180 peer-reviewed articles across journals like Communications Physics and PLoS Computational Biology , focusing on noise-driven dynamics, EEG modeling, and computational psychiatry. Awards: ERC MATHANA Grant (2011), Schloessmann Fellowship (2000)
Dr. Omar Bashar is a Senior Lecturer in Economics at Deakin Business School, Faculty of Business and Law, Deakin University, Australia. He is based at the Geelong Waterfront Campus and actively contributes to research, teaching, and academic leadership. He has served as Economics Discipline Coordinator, Director of Teaching in Economics, and Deputy Course Director for the Bachelor of Business program. PhD in Economics – University of Melbourne Master of Economics – Thammasat University, Thailand Bachelor of Social Science (Economics) – University of Chittagong, Bangladesh His research centers on Applied Macroeconomics , with a focus on housing markets , energy and environmental economics , monetary and fiscal policy , and time series econometrics . He employs advanced empirical techniques to analyze macroeconomic shocks, policy impacts, and sectoral dynamics in both developed and developing economies, particularly Australia and Bangladesh. The trend in his recent publications shows a strong emphasis on housing market dynamics, asymmetric policy effects, and the macroeconomic implications of energy and resource shocks. His work combines rigorous econometric modeling with real-world policy relevance, frequently appearing in journals such as Economic Modelling , Journal of Macroeconomics , and Accounting and Finance . His scientific recognition includes: Departmental award: Outstanding Unit Chair (MAE203) in T2, 2019 Member, Economic Society of Australia Member, Econometric Society Dr. Bashar has secured competitive research funding, including a current grant from the Transport Accident Commission (TAC) on evaluating best practices in TAC protocols. He actively supervises postgraduate research students, with two completed PhD supervisions. His teaching portfolio includes core economics units such as Macroeconomics, The Global Economy, and Analytical Methods in Economics and Finance. He is also engaged in major conferences, including the Australian Conference of Economists and the Econometric Society Australasian Meeting. He leads or participates in several research projects, including studies on transformative services, asymmetric spillovers in financial markets, carbon emissions and business cycles, and sectoral energy-economy interactions.
Angela Juana Torres Iglesias is a Professor at the University of Santiago de Compostela , affiliated with the Faculty of Medicine and Dentistry and the Department of Psychiatry, Radiology, Public Health, Nursing and Medicine . Her research focuses on depression in non-professional caregivers , preventive mental health interventions , and mathematical modeling in medical contexts . Research Trends : 2019-2015: Analyzed psychometric properties of depression assessment tools, prevalence of clinical depression in caregivers, and long-term efficacy of problem-solving interventions. 2000-2006: Developed nonlinear models for aneurysm rupture prediction, epidemic dynamics, and blood flow instabilities. Caregiver Mental Health : Leading trials on preventive cognitive-behavioral and problem-solving therapies for caregivers with depressive symptoms. Systematic reviews of psychosocial interventions and meta-analyses of caregiver depression trends. Collaboration and Impact : Affiliated with the GRISAMP Mental Health and Psychopathology Research Group and Clinical Psychiatry, Social Psychiatry and Psychotherapy . Her work bridges psychiatry, public health, and applied mathematics.
Dr. Małgorzata Cudna is an Assistant Professor in the Department of Teaching Mathematics and Informatics at the Faculty of Mathematics, Physics and Computer Science, Maria Curie-Skłodowska University in Lublin, Poland. She holds a PhD in Mathematics, having defended her doctoral thesis in 2012 on numerical issues related to determining effective parameters of random media. Her educational background includes: Doctoral studies completed in 2012 with thesis "Certain numerical issues related to determining effective parameters of random media" Dr. Cudna's research spans both mathematical sciences and computational linguistics, demonstrating an interdisciplinary approach to academic inquiry. Her primary focus is on mathematics and computer science education , with significant contributions to numerical analysis and computational linguistics . Her work includes research on homogenization for nonlinear Hamilton-Jacobi equations and corpus-based studies of Polish language constructions. She has developed educational platforms like "Umiem Matmę" and organizes robotics workshops with Ozobots and LEGO Mindstorms, demonstrating her commitment to innovative STEM teaching methods across all educational levels. Dr. Cudna actively participates in educational projects promoting the Institute of Mathematics, including postgraduate studies programs in "Programming and Databases" and "Programming and Computer Science in School" for teachers, funded by the Ministry of Science and Higher Education. She also organizes the "Zrozum, Zalicz, Zostań Matematykiem" competition and conducts specialized training for talented students. Her scientific output shows a distinctive interdisciplinary pattern, bridging mathematical theory with practical educational applications and linguistic analysis. She maintains an active teaching schedule with consultations on Tuesdays from 11:30-13:30 during the 2024/2025 summer semester, with additional consultations available by email arrangement.
Dr. Helen Durand is an Associate Professor in the College of Engineering at Wayne State University, where she holds a joint appointment in Electrical and Computer Engineering. With a PhD from UCLA (2017), she leads the Durand Lab focusing on cyberphysical systems for next-generation manufacturing. Current academic roles: Associate Professor of Chemical Engineering and Materials Science (Primary), Secondary Appointments in Electrical and Computer Engineering Education: PhD, MS, BS in Chemical Engineering from UCLA Research Interests span four key areas: Advanced Control Theory for nonlinear systems, including Lyapunov-based economic model predictive control (EMPC) Cybersecurity frameworks for industrial control systems, cyberattack detection/handling Quantum Computing integration in control algorithms and cyberattack prevention Digital Twin Development for dynamic process modeling and virtual testing Recent Publications demonstrate her interdisciplinary approach combining chemical engineering, control theory, and quantum technologies. Key trends include Quantum algorithm applications for control optimization (2022-2024) Image-based control simulation environments using Blender (2024) CFD modeling for semiconductor manufacturing (2023) Directed randomization security architectures (2023) Lyapunov-based attack detection systems (2020-2022) Scientific Recognition Featured in AIChE's 35 Under 35 list (2023) Invited speaker at 12 institutions (2023-2024) 5 keynote presentations at international conferences Teaching includes graduate courses in Advanced Engineering Mathematics and undergraduate Product/Process Design. The lab recruits multiple funded PhD students annually, with active research in Detroit's manufacturing ecosystem.
Eckehard Olbrich is a Group Leader and Researcher at the Max Planck Institute for Mathematics in the Sciences (MiS) in Leipzig, Germany. His work bridges mathematics, information theory, and social science with a focus on complex systems analysis. He has coordinated major European research projects including SoMe4Dem (Social Media for Democracy) and ODYCCEUS (Opinion dynamics and cultural Conflict in European Spaces). His educational background includes a PhD in theoretical solid-state physics from the Technical University Dresden (1995), followed by postdoctoral work at the Max Planck Institute for the Physics of Complex Systems in Dresden and research at the University of Zürich. Since 2004, he has been affiliated with the Max Planck Institute for Mathematics in the Sciences. Olbrich's research spans computational social science, information theory, and complex systems. He applies information-theoretic approaches to analyze social media data, complex networks, and human sleep EEG patterns. His work on information decomposition, multi-level systems, and time series analysis has produced significant contributions to understanding complex phenomena across disciplines. He has developed methods for analyzing polarization, opinion dynamics, and network structures in social systems. His publication record shows a strong trend toward interdisciplinary research combining information theory with social and biological systems. Recent work focuses on computational social science applications, particularly analyzing polarization and issue alignment on social media platforms, while maintaining connections to fundamental information theory and complex systems research. Olbrich has collaborated extensively with researchers including Sven Banisch (Karlsruhe Institute for Technology), Peter Achermann (University of Zürich), David Wolpert (Santa Fe Institute), and Jürgen Jost at MiS. His research has been supported by major funding programs including Horizon Europe, Horizon 2020, and the DFG. He has taught courses on Complex Systems Methods and Data Analysis and Modeling at the University of Potsdam, and has contributed to the development of TISEAN, free software for nonlinear time series analysis. His current research continues to explore the intersection of information theory, network science, and computational social science with applications to understanding democratic processes in the digital age.
Roles: Professor of Mechanical and Ocean Engineering at MIT, Director of the Laboratory for Acoustics, Sensing, and Undersea Remote Sensing (LASURS). Affiliations: Center for Ocean Engineering, Department of Mechanical Engineering. Education: SB in Physics (MIT), PhD in Ocean Engineering (MIT). Research Interests: Focuses on ocean acoustics and remote sensing, particularly Ocean Acoustic Waveguide Remote Sensing (OAWRS) for monitoring marine life and environments. Explores applications in hurricane dynamics, planetary science (e.g., Europa's ice shell), and musical instrument acoustics. Develops theoretical frameworks for signal processing, scattering, and perception laws. Active in policy initiatives, such as addressing the New England Fisheries Crisis and UN Floating City Program for sea-level rise. Key Contributions: Pioneered OAWRS to image fish/marine mammal populations at continental shelf scales. Demonstrated hurricane destructive power estimation via underwater sound. Advanced theories on Weber’s Law in perception, violin acoustics evolution, and nonlinear scattering. Authored over 60 peer-reviewed papers and presented at global research forums. Awards: Secretary of the Navy Scholar, William I. Koch Professorship, Bose Research Fellowship. Grants/Projects: NASA Science Definition Team for Jupiter Icy Moons Orbiter, MIT Center for Ocean Engineering leadership, NSF-funded studies on acoustic sensing. Labs/Teams: Laboratory for Acoustics, Sensing, and Undersea Remote Sensing (LASURS) at MIT.
Denis Chetverikov is a Professor of Economics at the University of California, Los Angeles (UCLA). His research focuses on econometric theory, with emphasis on high-dimensional models, empirical process theory, bootstrap methods, and applications to asset pricing and policy analysis. He has published in top journals such as Econometrica , Review of Economic Studies , and Annals of Statistics . Education: PhD from the Massachusetts Institute of Technology (MIT). His work bridges theoretical econometrics and computational methods, addressing challenges in modern data analysis. Key contributions include advancements in nonparametric estimation, rank-based inference, and regularization techniques for high-dimensional datasets. Research trends in his articles emphasize methodological innovations for handling complex economic data structures, including factor models, quantile regression, and robust inference frameworks. He has developed statistical software tools like the csranks R package for rank-based analysis. His grants and lab affiliations (if any) are not explicitly detailed in the provided text.
Xin Zhou is an Associate Professor in the Department of Mathematics at Duke University, recognized with the George Polya Prize in 1998. His expertise lies in partial differential equations, inverse scattering theory, and Riemann-Hilbert problems. He has collaborated extensively with notable researchers such as Percy Deift, Alexander Its, and Stephano Venakides. Education: M.S. in Physics from the Chinese Academy of Sciences Ph.D. in Mathematics from the University of Rochester Research Interests: Development of Riemann-Hilbert methods for integrable systems Analysis of Painleve equations and random matrix models Applications of inverse scattering theory to nonlinear PDEs His work bridges mathematical analysis, theoretical physics, and applied mathematics, with contributions to asymptotic analysis and nonlinear wave dynamics. Key Collaborations: Richard Beals (Yale University) Percy Deift (Courant Institute, NYU) A.S. Fokas (Imperial College) Alexander Its (Indiana University-Purdue University) Awards: George Polya Prize (1998) Research Contributions: Pioneering work on steepest descent methods for oscillatory Riemann-Hilbert problems Advances in understanding asymptotic behavior of integrable systems Development of unified frameworks for orthogonal polynomials with varying weights Labs/Teams: Active collaborations across institutions, including work with the Duke Mathematics Department and international research groups.
