Simon Masnou is a Full Professor at Université Claude Bernard Lyon 1, affiliated with the Institut Camille Jordan (CNRS UMR 5208). He holds leadership roles as Head of the 'Applied Mathematics, Statistics' Master's degree and Head of the 'M2 Maths in Action' program. Previously, he served as Director of the Camille Jordan Institute (2018-2022). His research focuses on applied mathematics, image processing, shape optimization, and geometric measure theory, with contributions to variational models, geometric flows, and applications in computer vision and materials science. Education: PhD in Mathematics (1998, Paris Dauphine) and HDR (2008, Paris 6). Research projects include ANR STOIQUES (2024-2028), PEPR PDE-AI (2023-2028), and collaborations with industry on topics like defect prediction in aluminum production and high-dimensional data analysis. Teaching includes courses on linear algebra, optimization, and machine learning at undergraduate and graduate levels. Key contributions span phase field models, varifold-based surface approximation, and image inpainting. He supervises PhD students in geometric variational problems and computational methods. His work bridges theoretical mathematics with industrial challenges, addressing issues in materials science, medical imaging, and cultural heritage preservation.
Alan H. Barr is a Professor of Computer Science at the California Institute of Technology (Caltech), affiliated with the Division of Engineering and Applied Science and the Computation & Neural Systems (CNS) department. He is a founding member of the Caltech Computer Graphics Group and a leader in developing mathematically rigorous methods for computer graphics and predictive modeling. His research focuses on enhancing computational modeling accuracy through approaches like interval analysis and constraint-based systems. Notable contributions include deformable models, quaternion interpolation, and cellular simulation frameworks. He has advised over 20 graduate students, many of whom became industry leaders at Pixar, Microsoft Research, and academic institutions like NYU and Brown University. Awards include the ACM SIGGRAPH Achievement Award (1988) and ACM Fellow (1995). Research Interests: Predictive modeling with error bounds Scientific visualization and MRI data analysis Biophysical systems simulation (e.g., cellular organelles) Self-assembling robotic structures for space colonization Mathematically robust computer graphics techniques Key Collaborations: Caltech Biological Imaging Center (Beckman Institute) JPL (Jet Propulsion Laboratory) New computational substrates research (quantum/DNA computing) Recent Work: Expanding into computational biology, medical imaging optimization, and high-confidence systems for managing complex computational interactions. Active in interdisciplinary projects across Caltech divisions.
Luis A. Ricardez-Sandoval is an Associate Professor in the Department of Chemical Engineering at the University of Waterloo and holds a Tier II Canada Research Chair in Multiscale Modelling and Process Systems. His research group develops advanced computational tools for optimizing chemical processes across multiple scales. Doctorate: Chemical Engineering, University of Waterloo (2008) MASc: Chemical Engineering, Instituto Tecnologico de Celaya (2000) BASc: Chemical Engineering, Instituto Tecnologico de Orizaba (1997) The research group focuses on multiscale modelling and process systems engineering , particularly for CO2 capture , energy systems , and heterogeneous catalysis . Their work combines advanced mathematics, machine learning , and uncertainty analysis to optimize chemical processes before physical implementation. Recent publications emphasize dynamic system optimization under uncertainty, multiscale simulation , and CO2 conversion technologies . Key methodologies include probabilistic uncertainty quantification and economic predictive control . Scientific Awards : 1997: First Place, XII National Creativity Contest 1998: Best Student Award, Instituto Tecnologico de Orizaba 1999: Third Place, XIV National Creativity Contest 2000: J.M. Smith Award for Best MASc Student He has collaborated with international institutions like CONACyT-Mexico, China Scholarship Council, and Universidad de Los Andes. His teaching includes graduate courses in process control, optimization, and computer-aided design.
Ergin Tönük serves as an Associate Professor in the Mechanical Engineering Department at Middle East Technical University (METU) in Ankara, Turkey. With a career deeply rooted at METU where he earned all his degrees, he maintains an active research profile with office B-319 and contact details tonuk@metu.edu.tr and +90 312 210 5293. His academic credentials include: Bachelor's Degree (B.Sc.) from METU, 1990 Master's Degree (M.Sc.) from METU, 1992 Ph.D. from METU, 1998 Professor Tönük's research spans Pneumatic Tires, Finite Element Analysis, Mechanism Synthesis, Soft Tissue Biomechanics, and Railway Vehicles. His biomechanics work focuses on computational modeling of biological systems including joint mechanics, tissue behavior, and medical device development. Tire and railway research leverages his mechanical engineering expertise for transportation applications, while his mechanism design studies address complex kinematic problems. Analysis of his 2016-2025 publications reveals a strong biomedical trajectory with emphasis on orthopedic applications. Key themes include hand/knee joint modeling, tissue engineering scaffolds, gait/posture analysis systems, and viscoelastic material characterization. His methodological signature combines finite element analysis with experimental validation, particularly in soft tissue mechanics and surgical simulation. While specific advising records and grant details aren't provided, his 70+ publications indicate substantial graduate mentorship and research funding acquisition. His work aligns with biomechanics and computational mechanics groups within METU's Mechanical Engineering Department, though dedicated laboratory facilities aren't specified in available sources.
Bjoern Menze is a Professor and Rudolf Mößbauer Tenure Track Chair at the Technical University of Munich (TUM), leading the Image-based Biomedical Modeling Group within the Munich School of Bioengineering. His research focuses on medical image computing, tumor growth modeling, and computational physiology, with applications in clinical neuroimaging and personalized radiotherapy design. He holds a Ph.D. in Computer Science from Heidelberg University and has held positions at ETH Zurich, INRIA Sophia Antipolis, MIT, and Harvard Medical School. His academic journey includes a postdoc at MIT’s CSAIL and Harvard Medical School, followed by roles at ETH Zurich and INRIA. His work bridges biomedical imaging with machine learning, emphasizing model-driven analysis of physiological processes. He has been a visiting professor at Maastricht University and contributes to initiatives like the Center for Translational Cancer Research at TUM. Key research areas include tumor growth modeling, quantitative imaging biomarkers, and integrating mathematical models with clinical data. His awards include the MICCAI Young Scientist Award (2014), Leopoldina Fellowship (2009), and DFG Research Fellowship (2008). He advises on medical AI, leads interdisciplinary projects, and publishes extensively in top journals like Nature Neuroscience and IEEE Transactions on Medical Imaging. His lab’s work spans applications such as glioblastoma radiotherapy optimization, whole-body bone lesion detection, and neural connectivity imaging. Collaborations include institutions like Harvard, MIT, and ETH Zurich. He emphasizes translating computational methods into clinical practice for personalized healthcare solutions.
Gary Grewal is an Associate Professor at the School of Computer Science , University of Guelph. His research focuses on developing intelligent Computer-Aided Design (CAD) tools for Field Programmable Gate Arrays (FPGAs) , integrating classical optimization techniques with machine learning and deep learning to address challenges in placement and routing for heterogeneous devices. He has received the Michal Servit Award (2017, 2018) for outstanding FPGA research and the University of Guelph Faculty Association Distinguished Professor Award for Excellence in Teaching (2017) . Grewal has held NSERC Discovery Grants annually from 1999 to 2023. Co-founder of the Guelph FPGA CAD Group Key collaborator with institutions like Ryerson University , University of Toronto , and University of British Columbia His work extends to health technology through the IronTracker mobile app , developed with Andrew Hamilton-Wright and students (A. D'Angelo, J. Carter, F. Liu, R. Pattison) to manage Hereditary Hemochromatosis (HHC) . The app, available in four languages and adopted in 100+ countries, was recognized at Parliament Hill and the Ontario Legislature. Scientific Awards : Michal Servit Award (2018) Michal Servit Award (2017) Distinguished Professor Award for Teaching (2017) NSERC Discovery Grants (1999-2023) His recent publications highlight trends in machine learning for FPGA CAD , including reinforcement learning for partitioning, deep learning for congestion estimation, and adaptive algorithms for placement. Grewal remains active in teaching courses like Discrete Optimization (CIS*6070) and Digital Systems I (CIS*3120).
Amanda Watson is an Assistant Professor in Electrical and Computer Engineering at the University of Virginia, with joint appointments in Computer Science. She leads the Watson Research Lab within the UVA Link Lab, a multidisciplinary center for Cyber-Physical Systems (CPS) and Internet of Medical Things (IoMT) research. Her work bridges wearable technology with healthcare and athletic performance applications, focusing on noninvasive monitoring, physiological signal analysis, and safety-critical medical devices. She is also the cofounder and CEO of Luminosity Wearables, commercializing a noninvasive continuous glucose monitor. Education: PhD in Computer Science (2020) - College of William & Mary MSc in Computer Science (2016) - College of William & Mary Bachelors in Computer Science and Mathematics (2014) - Drury University Her research spans multiple domains including: Wearable spectroscopy for nutrition and skin health Machine learning for drug overdose and fall risk detection Biomechanical monitoring in sports medicine Wearable support for visual and neurological impairments IoMT device integration and analytics Recent publications (2024-2025) show strong emphasis on calibration-free physiological monitoring systems, with technical contributions in spectral analysis , multi-wavelength sensing , and rapid prototyping for healthcare wearables. Applications range from maternal health to gerontological social isolation detection. Lab and Team: The Watson Research Lab at UVA develops wearable solutions for clinical and athletic contexts, with ongoing collaborations in the PRECISE Center at University of Pennsylvania and LENS lab at William & Mary alumni network. She works with multidisciplinary teams including engineers, clinicians, and data scientists.
Zachary Tatlock is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington, where he leads the Programming Languages & Software Engineering Group (PLSE) and the SAMPL Group. His research spans programming languages, formal verification, compilers, and computational fabrication. He is also an Amazon Scholar with AWS's Automated Reasoning Group and previously advised OctoML. Tatlock's work bridges theoretical foundations with practical systems, focusing on making it easier to write tricky code while ensuring correctness through rigorous proofs and measurements. PhD in Computer Science & Engineering, University of California, San Diego (2014) Thesis: Reducing the Costs of Proof Assistant Based Formal Verification Advisor: Sorin Lerner BS in Computer Science (Honors) and Mathematics, Purdue University (2007) Professor Tatlock's research focuses on the intersection of programming languages, formal methods, and systems. His work in compilers and formal verification aims to make it easier to write tricky code while ensuring correctness through rigorous proofs. He explores computational fabrication techniques that bridge digital design with physical manufacturing. His recent work on equality saturation (via the egg framework) has transformed program optimization and synthesis. Tatlock also investigates floating-point numerics, distributed systems verification, and hardware/software co-design, always seeking to balance theoretical rigor with practical implementation. Tatlock's recent publications demonstrate a strong focus on equality saturation techniques (egg framework), computational fabrication, and verified systems. His work increasingly integrates machine learning with program analysis and synthesis. There's a clear trajectory toward more practical applications of formal methods in real-world systems, particularly in numerical computing and fabrication. His research group has made significant contributions to e-graph technology, floating-point accuracy, and the verification of distributed systems. Distinguished Paper Award for Rewrite Rule Inference Using Equality Saturation (OOPSLA 2021) Spotlight Paper Award for Dynamic Tensor Rematerialization (ICLR 2021) Distinguished Paper Award for egg: Fast and Extensible Equality Saturation (POPL 2021) Faculty Appreciation for Career Education & Training (FACET) Award (2020) NSF CAREER Award: Verifying Distributed System Implementations (2017) Distinguished Paper Award for Automatically Improving Accuracy for Floating Point Expressions (PLDI 2015) Distinguished Teaching Award Nomination (2015) Professor Tatlock has advised numerous doctoral, master's, and undergraduate students who have gone on to prominent positions in academia and industry, including faculty positions at the University of Utah and Brown University, and leadership roles at companies like OctoML and Certora. His research is supported by significant funding from NSF, DARPA, DOE, and industry partners, totaling millions of dollars. Current grants include projects on computer-aided reasoning, formal verification, computational fabrication, and machine learning systems. He has served on numerous program committees and organized workshops including FPTalks, EGRAPHS, and PNW PLSE. As co-leader of the Programming Languages & Software Engineering (PLSE) research group and affiliate of the SAMPL Group at the University of Washington, Tatlock has developed influential tools including egg (an equality saturation toolkit), Carpentry Compiler, and Odyssey. His group actively collaborates with industry partners including Amazon Web Services, where he serves as an Amazon Scholar. The group has made significant contributions to equality saturation, floating-point accuracy, program synthesis, and computational fabrication, with applications ranging from compiler optimization to 3D printing.
Prof. Dr.-Ing. H. Siegfried Stiehl is a retired Senior Professor (until Sept 2021) at the Department of Informatics, University of Hamburg. He previously held roles including Dean of the Faculty of Mathematics, Computer Science, and Natural Sciences (2001–2006), Vice President for Research (2007–2013), and Head of the Image Processing Research Group. His academic journey includes a PhD from TU Berlin (1980) and a Habilitation in Computer Vision (1987). Education: 1973: Ing. Degree in Ingenieur-Informatik, Fachhochschule Furtwangen 1976: Diploma in Computer Science, TU Berlin 1980: Dr.-Ing. Dissertation on medical image processing, TU Berlin Research focuses on Computer Vision , Computational Neuroscience , and Cognitive Science , with contributions to medical image registration, 3D landmark detection, and biomechanical modeling. Key projects include the EU-funded 'COVIRA' consortium (1989–1995) and leadership in the SFB 950 'Manuscript Cultures' project (2015–2019). His 110+ publications span biomedical image registration, elastic deformation algorithms, and real-time signal processing. Notable collaborations include work with institutions like the University of Pennsylvania, University of Birmingham, and Philips Research. Leadership roles include organizing scientific events, serving on editorial boards (e.g., Biological Cybernetics), and founding the Interdisciplinary Nanoscience Center Hamburg (INCH) in 2001. His research has addressed challenges in neurosurgical interventions, VLSI implementation of neural networks, and interdisciplinary education.
Roland N. Horne is the Thomas Davies Barrow Professor of Earth Sciences at Stanford University and Senior Fellow at the Precourt Institute for Energy. He holds positions in the Department of Energy Science & Engineering and is an Affiliate at the Stanford Woods Institute for the Environment. With degrees from the University of Auckland (BE, PhD, DSc), Horne has established himself as a leading expert in geothermal reservoir engineering and energy production optimization. His research focuses on inverse problems in reservoir modeling, including tracer analysis of fractures, computer-aided well test analysis, production schedule optimization, and automated history matching. Horne has made significant contributions to understanding geothermal reservoir engineering and multiphase flow of boiling fluids through porous materials and fractures. The analysis of his recent publications (2023-2025) reveals a strong emphasis on enhanced geothermal systems (EGS), with particular focus on flexible operations, economic modeling, and advanced characterization techniques. His work increasingly incorporates machine learning approaches for reservoir analysis and has expanded into microbial tracing methods for interwell connectivity assessment. There's also significant attention to US geothermal resource potential and integration into the broader energy transition. Honorary Member of the Society of Petroleum Engineers Member of the US National Academy of Engineering Multiple SPE Distinguished Lecturer appointments (1998, 2009, 2020) John Franklin Carl Award recipient Five Best Paper awards from Geothermal Resources Council Patricius Medal from German Geothermal Society Core Values Award from Women in Geothermal (2023) Horne has supervised 60 PhD and 135 MS students throughout his career. His current teaching includes undergraduate and graduate courses in Fundamentals of Energy Processes, Geothermal Reservoir Engineering, Mass and Energy Transport in Porous Media, and Well Test Analysis. He previously served as President of the International Geothermal Association (2010-2013) and Technical Program Chair for multiple World Geothermal Congress events. Horne maintains active research collaborations worldwide, including with the University of Tokyo (where he was a Fellow of the School of Engineering in 2016) and China University of Petroleum. His current research group focuses on advancing EGS technologies and developing more accurate reservoir characterization methods for geothermal applications.
Emily Riehl is the Kelly Miller Professor of Mathematics at Johns Hopkins University , serving as Director of Graduate Studies. She previously held positions at Harvard University as a Benjamin Peirce Postdoctoral Fellow and NSF Postdoctoral Fellow . Her research focuses on category theory , homotopy theory , and homotopy type theory , with particular emphasis on foundational aspects of ∞-categories. She co-authored Elements of ∞-Category Theory (with Dominic Verity) and authored Categorical Homotopy Theory and Category Theory in Context . Education : - Ph.D. in Mathematics (2011), University of Chicago, advised by J. Peter May - Part III of the Mathematical Tripos (Distinction, 2007), University of Cambridge - A.B. in Mathematics (magna cum laude, 2006), Harvard University Research Interests : Her work bridges foundational category theory with homotopy-theoretic applications, emphasizing model-independent approaches to ∞-categories. She explores synthetic formalization frameworks in homotopy type theory and collaborates on projects involving computer-aided proof systems like Lean and Rzk. Key Contributions : - Developed the ∞-cosmoi framework for model-independent ∞-category theory - Co-created synthetic foundations for ∞-categories within homotopy type theory - Authored influential textbooks shaping modern category theory education Awards & Recognition : - AWM Birman Research Prize (2021) - AMS Fellow (2022) - Simons Fellowship (2022) - Johns Hopkins President’s Frontier Award (2020) Service & Outreach : - Co-host of the n-Category Café blog - Co-founder of Spectra (LGBTQ+ mathematicians association) - Organized major conferences and summer schools on ∞-categories and homotopy type theory - Advocate for inclusive academic practices and AI ethics in mathematics
Mark Ainsworth is a Francis Wayland Professor of Applied Mathematics at Brown University and holds a joint faculty appointment with Oak Ridge National Laboratory. He obtained his PhD from Durham University (1989) and has held prominent roles such as Director of the Centre for Numerical Algorithms and Intelligent Software (2011-2012). His research focuses on numerical analysis, particularly finite element methods for partial differential equations, a posteriori error estimation, and high-performance computing challenges like resiliency on exascale systems. Education: PhD in Mathematics, Durham University, 1989 BSc in Mathematics, Durham University, 1986 Research Interests: Numerical approximation of PDEs A posteriori error estimation and adaptive methods High order finite element methods Resiliency of numerical algorithms on emerging architectures Fractional PDEs and scientific data compression Awards: SIAM Fellow (2014) FIMA (2010) Whitehead Prize (2004) J.L. Lions Prize (2004) Fellow of Royal Society of Edinburgh (2003) Grants & Leadership: Co-PI for ARO MURI on fractional PDEs (2015-2020) Directed NAIS center (2011-2012), a £5M multi-institutional initiative Organized major international conferences on computational mathematics Labs/Teams: Collaborations include Oak Ridge National Lab and international research networks in numerical analysis and scientific computing.
Laurent Caraffa is a Researcher at Université Gustave Eiffel, working at the LaSTIG laboratory of IGN (National Institute of Geographic and Forest Information). His research focuses on large-scale 3D data processing, including surface reconstruction from point clouds and images, leveraging triangulated structures and implicit methods. His work also covers indexing and searching within point clouds for large-scale place recognition, with applications in urban environments and navigation systems. Caraffa's research interests span 3D Data Processing, Surface Reconstruction, Point Cloud Processing, Large-scale Place Recognition, Indexing and Retrieval, Big Data, Cloud Computing, Mathematical Optimization, 3D Mapping, and Photogrammetry in degraded conditions. His work bridges theoretical computational geometry with practical applications in geographic information systems and autonomous navigation. His publication record demonstrates significant contributions to distributed 3D processing, particularly through advancements in Delaunay triangulation, watertight surface reconstruction, and neural radiance fields. Recent work shows a clear trajectory toward more efficient and scalable methods for processing massive 3D datasets, with growing emphasis on implicit representations and learning-based approaches for 3D reconstruction. Caraffa actively participates in the scientific community through organizing events like the Big Data Day 2023 at IGN and contributing to major research projects. His work has resulted in publications in top-tier conferences including ICLR, CVPR, ISPRS, and IEEE Big Data, establishing him as a significant contributor to the field of large-scale 3D data processing. As a research supervisor, Caraffa currently co-supervises four PhD students working on projects funded by AID, Criteo, and Huawei, focusing on large-scale place recognition, implicit representations for 3D reconstruction, and 3D reconstruction in degraded conditions. He is also the co-founder of ExtraLabs, a company developing distributed computing solutions for cooperative digital twins, demonstrating the practical impact of his research.
Núria Agell Jané is a Full Professor at ESADE Business School , Universitat Ramon Llull, specializing in Artificial Intelligence and Decision-Making Systems. She leads the JUICE (Judgements and Decisions in the Market Place) research group and the ESADE D3 - Institute for Data-Driven Decisions . Doctorate in Applied Mathematics (Qualitative Reasoning Modelling), UPC-BarcelonaTech Bachelor's in Mathematics, University of Barcelona Her research focuses on Artificial Intelligence , Decision-Making Systems , and Fuzzy Logic , with applications in Business, Marketing, and Sustainability. Recent publications emphasize Hesitant Fuzzy Linguistic Term Sets , Consensus Modeling , and AI in Sustainable Development . She coordinates multiple publicly and privately funded projects applying AI to Business and Marketing challenges. As PhD Programme Director (2005-2013) and current Department Director of Operations, Innovation and Data Sciences , she has shaped academic and research strategies at ESADE. Her work spans collaborations with institutions like LAAS-CNRS (France) and University of Edinburgh Business School , with over 40 journal publications and 50 conference contributions. She has directly supervised 11 PhD students in AI and Decision Sciences.
Niklas Mattsson-Carlgren serves as an Associate Professor and Senior Lecturer at Lund University's Faculty of Medicine, Department of Clinical Sciences. He holds multiple significant roles including Deputy Research Team Manager and Principal Investigator for several major research projects. His affiliations extend to the Wallenberg Centre for Molecular Medicine (WCMM), LU Profile Area: Proactive Ageing, and MultiPark: Multidisciplinary Research Focused on Parkinson's Disease. Dr. Mattsson-Carlgren's research primarily focuses on Alzheimer's disease and other neurodegenerative conditions, with additional work on acute brain injuries such as those following cardiac arrest. His expertise spans neurochemistry, including the regulation of pain and sleep. He employs biochemical measurements, neuroimaging, and cognitive testing to study disease processes in vivo across the spectrum from preclinical disease to advanced dementia. His work aims to improve diagnostic and prognostic methods in clinical practice, enhance clinical trial design for novel therapies, and deepen understanding of disease mechanisms. His recent publications demonstrate a strong emphasis on plasma biomarkers for Alzheimer's disease detection and monitoring, with particular focus on tau and amyloid pathology. The research shows increasing integration of machine learning approaches with traditional biomarker analysis, reflecting a trend toward more sophisticated diagnostic tools that combine multiple data sources for improved accuracy in predicting disease progression. As Principal Investigator for multiple significant projects including 'Longitudinal plasma biomarkers, cognitive data and amyloid PET to improve early prevention of Alzheimer's disease' and 'Validation of an MTBR tau immunoassay for CSF and plasma,' he leads substantial research efforts funded by organizations including Familjen Rönströms stiftelse, Eli Lilly and Company, and the Knut and Alice Wallenberg Foundation. He also serves as supervisor for PhD students, notably for 'Studies of induced neuronal cells in Alzheimer's disease.' Dr. Mattsson-Carlgren is actively involved in the neuroscience community, serving on the program committee for Neuroscience Day 2025. His research contributes to UN Sustainable Development Goals related to health and wellbeing. His work has gained significant attention, with multiple papers being picked up by news outlets and referenced across social media platforms including X (Twitter) and Bluesky.
