Alan Wai Hou Lio is an Associate Professor at the Technical University of Denmark's Department of Wind and Energy Systems, specializing in wind turbine control and energy system dynamics. His research develops advanced control strategies to optimize energy capture and reduce structural loads in wind farms. Research Focus: Predictive wind turbine control algorithms LiDAR-assisted flow measurement techniques Offshore floating turbine dynamics Real-time estimation methods for turbulent flows His publications demonstrate consistent focus on experimental validation of control theories, with recent work emphasizing field implementation challenges. Current projects include DigiWind (digital wind energy systems) and PowerKey (wind plant optimization). Education: PhD in Automatic Control (Sheffield, 2017), M.Eng in Electrical Engineering (Imperial College London, 2012).
Donsub Rim is an Assistant Professor in the Department of Mathematics at Washington University in St. Louis. He holds a PhD from the University of Washington, advised by Randall J. LeVeque and Gunther Uhlmann. His research focuses on numerical analysis of PDEs, inverse problems, and low-rank neural network representations (LRNRs) for real-time solutions in geophysics, medical imaging, and plasma physics. Key areas include stability analysis of neural networks for tsunami early warning, fast inversion of the approximate discrete Radon transform (ADRT), and computational applications in geophysical hazard assessment. Rim is on leave during 2024-2025 as a Visiting Scholar at the University of Washington. Previously, he was a visiting scholar at Tohoku University's IRIDeS (2023) and affiliated with the Courant Institute and Columbia University. His work bridges mathematical theory with practical tools like the 'adrt' Python library for signal processing. Research Interests: Numerical methods for PDEs, model reduction techniques, neural network applications in high-consequence domains (e.g., tsunami forecasting), and ADRT-based algorithms for dimensional splitting and sparse approximations. Collaborations include projects on probabilistic tsunami hazard assessment (PTHA), earthquake kinematic effects on tsunami propagation, and meta-learning approaches for physics-informed networks. Publications highlight contributions to real-time prediction systems, stability analysis of neural networks, and manifold approximations for transport-dominated problems. His software developments, such as the ADRT Python package, emphasize open-source tools for scientific computing. Rim has advised or collaborated with researchers at institutions including Columbia University, Tohoku University, and the University of Washington. Grants and future work involve advancing LRNR frameworks for nonlinear hyperbolic problems, improving tsunami early warning systems via GNSS data, and exploring applications of sparse physics-informed backpropagation in geophysics and plasma physics.
Tina Hannemann is a Lecturer in Social Statistics at the University of Manchester's Department of Social Statistics. She serves as director of the MSc program in Social Research Methods and Statistics, and teaches undergraduate and postgraduate modules focusing on survey research methods, demography, and demographic forecasting. Her research explores demographic events in health, mortality, family formation, and socio-economic influences on ethnic minority populations. She has contributed to projects like the EU 'Families and Societies' initiative and NCRM studies on missing data compensation in bio-marker datasets. Education: PhD in Economics from Lund University (2012), MSc in Demography from Rostock University (2007), and advanced studies at INED (Paris). Professional experience includes roles at the Max-Planck Institute for Demographic Research and the CoDE/NCRM research groups. Research emphasizes methodological innovations in handling missing data and applied topics like immigrant fertility patterns and health disparities. She co-organized the Cathie Marsh Institute seminar series and developed training courses on STATA programming and data visualization. Her work addresses global challenges linked to UN SDGs including reduced inequalities and quality education through improved demographic literacy and data accessibility.
Alice Thompson is a Reader (Associate Professor) in Applied Mathematics at the University of Manchester, specializing in fluid mechanics with emphasis on free-surface phenomena. She leads research on bubble/drop dynamics, thin films, and multiphase flows using mathematical modeling, asymptotic methods, and experimental collaborations. Research Focus: Her work explores interfacial flows in confined geometries, control of liquid films, and pattern formation in deposition processes. Key applications include microfluidics, inkjet printing, and pulmonary flows. Current projects investigate viscoplastic fluid behavior, bubble interactions in microchannels, and airway deposition modeling. Education: PhD in Applied Mathematics (University of Nottingham, 2012) Part III Mathematics (University of Cambridge, 2007) BA Mathematics (University of Cambridge, 2006) Research Trends: Recent publications demonstrate evolving focus from fundamental bubble/drop dynamics (2016-2019) toward controlled flow applications in biomedical/industrial contexts (2020-present), with increasing emphasis on yield-stress fluids and functional surface interactions. Current PhD Students: Jake Harris: Multiphase flow modeling Sammy Ayoubi: Control of disordered systems James Shemilt: Lung deposition modeling
Professor Peter Howard Haynes is a faculty member at the University of Cambridge's Faculty of Mathematics, specializing in Atmosphere-Ocean Dynamics. His research bridges theoretical fluid mechanics, climate modeling, and atmospheric physics, with a focus on stratosphere-troposphere interactions, convective processes, and climate variability. He leads the Atmosphere-Ocean Dynamics research group, contributing to fundamental understanding of geophysical fluid systems. His work emphasizes: Dynamics of convective overshoots impacting stratospheric water vapor Climate sensitivity of Southern Ocean convection Stochastic modeling of atmospheric jets QBO influences on tropical clouds Fluid mechanical analysis of mixing processes Recent publications demonstrate consistent focus on climate change impacts, with 80% of articles addressing warming-related feedbacks, model uncertainties, and hydrological cycle interactions. Methodology combines theoretical frameworks, machine learning techniques, and observational validation.
Oliver D. Street is a Research Fellow at the Grantham Institute - Climate Change and the Environment, Imperial College London, within the Faculty of Natural Sciences. His research focuses on geometric mechanics and stochastic modeling, particularly in fluid dynamics and plasma physics. He holds a PhD from Imperial College London (2022) and an MRes in Mathematics of Planet Earth from Imperial College London and the University of Reading (2019). Key research areas include stochastic differential equations, wave-current interactions, free boundary problems, and plasma dynamics in thin domains. His work bridges geometric methodologies with real-world applications in oceanography and climate science. Recent contributions address soliton behavior, thermal Green-Naghdi models, and Hall magnetohydrodynamics. Street has collaborated with institutions like the UK Met Office and co-authored over 15 peer-reviewed articles. His teaching experience includes analysis and geometric mechanics at undergraduate/postgraduate levels. Technical skills span Python, MATLAB, FORTRAN, and scientific communication.
Rayhaneh Akhavan is an Associate Professor in the Department of Mechanical Engineering at the University of Michigan, Ann Arbor. She holds a tenured position and is actively involved in research and teaching. Her educational background includes: Ph.D. in Mechanical Engineering from the Massachusetts Institute of Technology (1987) M.S. in Mechanical Engineering from the Massachusetts Institute of Technology (1982) B.S. in Engineering and Applied Science from the California Institute of Technology (1980) Professor Akhavan's research focuses on fluid mechanics, with particular emphasis on turbulence physics, modeling, and control. She employs high-fidelity computations and reduced-order modeling for complex turbulent flows involving multi-phase systems, fluid-structure interactions, and viscoelastic effects. Her work also explores biomimetic and bio-inspired concepts for flow control and drag reduction, utilizing lattice Boltzmann, pseudo-spectral, and stochastic methods within high-performance computing frameworks. Applications span energy, environmental, aerospace, and naval disciplines. Her notable recognition includes the Robert T. Knapp Award from the American Society of Mechanical Engineers in 1995 for the best paper on "Dynamics of a Turbulent Jet Interacting with a Free Surface". As an advisor, Professor Akhavan adopts a "hands-on" mentoring style, meeting with students daily to weekly depending on their needs. She expects students to be actively engaged in literature review, coding, simulations, data analysis, and paper writing. Authorship is typically granted with the student as first author when they provide a complete draft. Students are required to publish at least one (preferably up to three) papers in top journals for graduation. Funding for conference attendance (such as APS/DFD) is provided through Rackham Travel Grants and her research funds. She maintains a small research group and does not hold regular group meetings, focusing instead on individual mentoring.
Bogdan Epureanu is a Professor of Mechanical Engineering at the University of Michigan, Ann Arbor, holding the Roger L. McCarthy Professorship and the Arthur F. Thurnau Professorship. He serves as the Director of the Automotive Research Center, a key institution for vehicle technology development for the U.S. Army. His educational qualifications are: Ph.D. in Mechanical Engineering, Duke University, 1999 Graduate Studies, University of Valladolid, 1994 M.S. in Mechanical Engineering, Galati University, 1993 Graduate Studies, École Nationale Supérieure des Mines de Paris, 1992 Professor Epureanu's research spans biological and epidemiological systems, aerospace and automotive structures, and turbomachinery. His work involves creating novel mechano-chemical dynamic models, developing sensitive diagnosis techniques, forecasting tipping points in complex systems, and advancing reduced order models and control methodologies for multi-physics systems. His numerous accolades include: Roger L. McCarthy Professorship (2024) N.O. Myklestad Award (2022) Archie Higdon Distinguished Educator Award (2021) Arthur F. Thurnau Professorship (2019) National Science Foundation CAREER Award (2004) 1938E Award, College of Engineering, University of Michigan (2007) He actively mentors Ph.D. students, several of whom have received prestigious awards, and leads significant research initiatives funded by major grants, including a $100 million award from the U.S. Army for the Automotive Research Center through 2028. Professor Epureanu directs the Epureanu Research Group and the Automotive Research Center, fostering innovation in autonomous vehicle technologies and complex systems modeling.
Anna Stefanopoulou is a Professor at the University of Michigan College of Engineering , holding the William Clay Ford Professor of Manufacturing and courtesy appointments in Naval Architecture & Marine Engineering and Electrical Engineering & Computer Science . Her work focuses on estimation and control of electrochemical systems including batteries, fuel cells, and internal combustion engines. Education : Ph.D. in Electrical Engineering & Computer Science (1996), M.S. in Electrical Engineering & Computer Science (1994), M.S. in Naval Architecture & Marine Engineering (1992), and Diploma in Naval Architecture & Marine Engineering from National Technical University of Athens (1991). Her research spans energy storage systems for automotive applications, emphasizing lithium-ion battery modeling , fuel cell water management , and hybrid powertrain optimization . She has pioneered adaptive observers for battery state-of-charge estimation and thermal management strategies for cold-temperature battery operation. Her 15 most recent publications (2013-2017) explore electrochemical degradation , mechanical stress in battery cells , and fuel cell dynamics , with applications in electric vehicles and microgrids . Key scientific awards include: Fellow, SAE (2018) IEEE Control Systems Technology Award (2016) Rackham Distinguished Graduate Mentor Award (2018) ASME Gustus L. Larson Memorial Award (2009) NSF CAREER Award (1997) She advises doctoral students through modeling, lab work, and authorship prioritization , supporting internships post-second year and requiring 4+ conference papers per student . Her Battery Control Group collaborates on automotive electrification and energy storage safety .
Charikleia Stoura is a Researcher at the Chair of Structural Mechanics and Monitoring within the Department of Civil, Environmental and Geomatic Engineering at ETH Zürich . Her work focuses on leveraging data from traversing trains to monitor railway infrastructure, including tracks and bridges. Diploma : Civil Engineering, National Technical University of Athens (2015) Ph.D. : Civil and Environmental Engineering, Hong Kong University of Science and Technology (2021) Postdoctoral Fellowships : ETH Zürich (2022), Marie Skłodowska-Curie Global (2024) Her research integrates Bayesian filtering , data-driven modeling , and on-board sensor networks to develop cost-effective solutions for continuous structural health monitoring (SHM) of transport infrastructure. This includes applications for rail roughness profiling and bridge modal identification using vibration data from trains. Key publication themes include: Imitation learning from observation for input estimation Vehicle-bridge interaction analysis under seismic loads Markov-Chain Monte Carlo Bayesian parameter tuning Virtual sensing techniques for railway infrastructure Reduced-order modeling for rail roughness identification Notable scientific awards include: ETH Zürich Postdoctoral Fellowship Marie Skłodowska-Curie Global Postdoctoral Fellowship
Jürg Alexander Schiffmann is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL), leading the Laboratory for Applied Mechanical Design (LAMD). His work focuses on gas-lubricated bearings, small-scale turbomachinery, and automated design methodologies for energy systems. Academic Affiliation: EPFL School of Engineering Key Roles: Teaching, PhD program committee member (Energy & Robotics), Lab Director Research Interests His research bridges mechanical design optimization and small-scale energy systems , with a focus on gas bearings for turbocompressors, Organic Rankine Cycles for waste heat recovery, and herringbone grooved journal bearings . He pioneers AI-driven tools like DARTS-NETGAB for real-time turbomachinery simulation and surrogate modeling for robust design. Recent Publications span 2025–2023, emphasizing neural networks in optimization, experimental validation of gas bearings, and thermal management in high-speed turbomachinery. Trends highlight cross-disciplinary integration of AI and energy systems. Scientific Awards SwissElectric Research Award (PhD work) Advising includes supervising 25+ PhD students (e.g., Abramishvili Anna, Massoudi Soheyl) on topics like scroll expanders , rotordynamics , and haptics in automated driving . His grants involve collaborations with MIT, CERN, and industry partners like Fischer Engineering Solutions.
Ammar Hakim is a Lecturer and Principal Research Physicist at Princeton University's Computational Sciences Department. He holds a Ph.D. from the University of Washington (2006) and specializes in computational plasma physics, fusion energy, and high-energy astrophysics. His research focuses on tokamak turbulence, machine learning for PDEs, and electromagnetic simulations using advanced numerical methods like discontinuous Galerkin schemes. Roles: Deputy Head of Computational Sciences, Instructor for AST 560 (Computational Plasma Physics) Key Expertise: Plasma turbulence, kinetic processes, fluid dynamics, and high-performance computing Tools: Gkeyll simulation framework, discontinuous Galerkin methods, gyrokinetic theory Research interests include plasma-material interactions, multi-fluid moment models, and hybrid kinetic-fluid approaches. His recent work addresses challenges in fusion edge plasmas, relativistic astrophysical systems, and formal verification of PDE solvers.
Professor Helen Milroy is the Perth Children's Hospital Foundation Professor in Child and Adolescent Psychiatry at the University of Western Australia (UWA), holding dual roles as a Commissioner with the National Mental Health Commission and Honorary Research Fellow at the Telethon Kids Institute. She specializes in holistic medicine, child mental health, trauma recovery, and Indigenous knowledge systems. Her research focuses on culturally competent mental health care models, trauma-informed practices, and Indigenous health curriculum development. She has contributed to landmark initiatives such as the Royal Commission into Institutional Responses to Child Sexual Abuse (2013–2017) and serves on boards including the AFL Commission and Gayaa Dhuwi Australia. Education: MB BS (UWA), CertChildPsych WA, FRANZCP. Awards include Member of the Order of Australia (2023), WA Australian of the Year (2021), and Australian Indigenous Doctor of the Year (2018). Current projects include $2.8M grants for culturally competent mental health care models and trauma-informed care frameworks. Her work aligns with UN SDGs 3 (Good Health) and 10 (Reduced Inequalities).
Valerio Roberto Maria Lo Verso is an Associate Professor at Politecnico di Torino's Department of Energy (DENERG), a member of the Interdepartmental Center Ec-L - Energy Center. His research focuses on advanced building envelopes, circadian lighting, daylighting, and environmental comfort. He teaches courses in energy engineering, architecture for sustainability, and related fields. PhD Program Roles: Member of the Design and Technology. People, Environment, Systems program (2024–present). Editorial Board: Editor-in-chief of the Journal of Daylighting (since 2014). Research Interests: His work integrates daylighting studies, responsive façade systems (e.g., microalgae-based shading, electrochromic glazing), and electric lighting systems to enhance energy efficiency and occupant comfort. Projects include the ODALINE initiative on OLED lighting and a current commercial project on zenithal lighting systems (2024–2026). He has been recognized with the Leon Gaster Memorial Award (2011). Teaching Responsibilities: He leads courses such as Energy Audit and Certification of Buildings , Lighting and Noise Control Systems , and contributes to programs in architecture, energy engineering, and sustainable design.
Jack Xin is a Chancellor's Professor at the University of California, Irvine, specializing in Applied and Computational Mathematics. His research focuses on machine learning, deep learning, optimization, and fluid dynamics, with applications in neural networks, turbulence modeling, and computational biology. He leads interdisciplinary projects involving stochastic algorithms, particle methods, and numerical analysis of partial differential equations. His work integrates advanced mathematical techniques with computational tools to address challenges in data science, combustion theory, and disease modeling. Notable contributions include developing efficient attention mechanisms for transformers, analyzing turbulent burning velocity laws, and creating particle-based algorithms for chemotaxis systems. Jack Xin has secured grants such as the NSF ATD program for robust spatio-temporal forecasting and collaborative research on deep learning algorithms. His research emphasizes computational efficiency, theoretical rigor, and real-world applications in fields ranging from epidemiology to climate science. His team explores cutting-edge methods in compressed sensing, neural network quantization, and graph-based models, aiming to bridge gaps between mathematical theory and practical machine learning systems.
