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Professor
Jonathan A. Kelner is a Professor of Applied Mathematics at the Massachusetts Institute of Technology (MIT) and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) . His research bridges pure mathematics and algorithms, focusing on spectral graph theory, combinatorial optimization, and distributed computing.
Professor
Giuseppe Pascazio serves as a Full Professor in the Department of Mechanics, Mathematics & Management at the Polytechnic University of Bari, Italy. His research spans computational fluid dynamics with dual focus on aerospace applications and biomedical engineering, particularly in hypersonic flow phenomena and microwave ablation technologies for cancer therapy. His primary research interests include fluid dynamics, computational methods for high-enthalpy flows, thermochemical non-equilibrium modeling, turbulent boundary layer analysis, and biomedical device optimization. Pascazio develops advanced numerical techniques including high-order schemes, state-to-state kinetics implementations, and GPU-accelerated solvers to address complex flow physics in atmospheric entry and medical applications. His work bridges fundamental gas dynamics with practical engineering solutions for spacecraft thermal protection and minimally invasive cancer treatments. Analysis of his recent publications (2021-2025) reveals three dominant research thrusts: (1) High-fidelity simulation of hypersonic flows with detailed chemistry using state-to-state kinetics, (2) Development of robust numerical methods for shock-capturing in thermochemically non-equilibrium flows, and (3) Biomedical applications focusing on microwave ablation probe design and microcapsule transport in vascular systems. His aerospace work emphasizes atmospheric reentry physics while biomedical research targets cancer therapy optimization. Pascazio has participated in significant research projects including "PrInCE" (Innovative Processes for Energy Conversion) and "INNOVHEAD" (Advanced technologies for reduction of polluting emissions in Heavy Duty engines). His collaborative work involves industrial partnerships in aerospace and medical device sectors, though specific grant details beyond project names aren't provided. He maintains active research output with over 50 publications demonstrating consistent contributions to high-speed aerodynamics and biomedical fluid dynamics.
Professor
Professor Chris J Budd OBE is a distinguished Professor of Applied Mathematics at the University of Bath's Department of Mathematical Sciences, where he serves as Director of Knowledge Exchange for the Bath Institute for Mathematical Innovation (IMI). He is also Professor of Mathematics at the Royal Institution of Great Britain and a former Gresham Professor of Geometry. His leadership extends to directing the Centre for Nonlinear Mechanics and serving as Super Champion of the KE Hub. His educational background includes a gap year with Marconi that profoundly shaped his career, followed by undergraduate studies at Cambridge and a DPhil at Oxford. This industry experience during his formative years established his lifelong commitment to industrial mathematics and knowledge exchange. Budd's research focuses on nonlinear mathematical problems with industrial applications, particularly adaptive moving mesh methods for meteorology and climate modeling, data assimilation, non-smooth dynamical systems, and the mathematics of machine learning. He approaches linear problems as 'for cissies,' preferring the challenges of nonlinear systems that better represent real-world phenomena. His work bridges theoretical mathematics with practical applications across meteorology, environmental science, and engineering. His recent publications reveal a strong trend toward integrating machine learning with traditional numerical methods, particularly in climate modeling and solving partial differential equations. This includes Fourier Neural Operators, adaptive mesh methods enhanced by graph neural networks, and mathematical frameworks for understanding climate tipping points through non-smooth dynamics. OBE for services to mathematics National Teaching Fellowship (NTF) Knowledge Transfer Award for work with the Met Office Fellow of the Institute of Mathematics and its Applications (FIMA) Chartered Mathematician (C Math) British Science Association award for best science festival (2009) As principal investigator of the £3.5M EPSRC Programme Grant 'Maths4DL' on the Mathematics of Deep Learning, Budd leads a major collaborative effort between Bath, Cambridge, and UCL. He actively supervises numerous PhD students across diverse projects including climate modeling, machine learning applications, and industrial mathematics problems. His commitment to knowledge exchange is exemplified through V-KEMS (Virtual Forum for Knowledge Exchange in the Mathematical Sciences), which he co-founded to address challenges like the COVID-19 pandemic through mathematical approaches. Budd directs the Centre for Nonlinear Mechanics at Bath, fostering interdisciplinary research through mathematical modeling of complex systems. He also leads the Bath Institute for Mathematical Innovation's knowledge exchange activities, connecting academic mathematics with industrial and societal challenges. His work with V-KEMS has proven particularly effective during the pandemic, mobilizing teams of mathematicians to address urgent real-world problems.
Professor
Qiang (Steven) Wang is a Professor in the School of Mathematics and Statistics at Carleton University. His academic career spans several decades with a strong focus on finite fields and their applications in cryptography and coding theory. He maintains an active teaching schedule including courses like Abstract Algebra, Rings and Fields, and Fields and Coding Theory. Professor Wang's research interests center on Finite Fields and Applications in Cryptography and Coding Theory, Combinatorics, Algebra and Number Theory. His work particularly focuses on polynomials and functions over finite fields, sequences over finite fields, and applications in Coding Theory and Cryptography. He has made significant contributions to the study of irreducible polynomials, permutation polynomials, primitive polynomials, planar functions, Boolean functions, LFSR and NLFSR sequences, de Bruijn sequences, stream ciphers, pseudorandom generators, and linear complexity. His research bridges theoretical mathematics with practical cryptographic implementations. His extensive publication record shows a consistent and growing research program, with numerous publications in top-tier journals including Designs, Codes and Cryptography, Finite Fields and Their Applications, and Cryptography and Communications. His most recent work (2022-2024) has increasingly focused on compositional inverses of permutation polynomials, stable polynomials, additive codes with few weights, and generalized cyclotomic mappings, while maintaining strong connections to cryptographic applications. Editorial board member of Applicable Algebra in Engineering, Communication and Computing, Springer Editorial board member of Finite Fields and their Applications, Elsevier Professor Wang actively contributes to the academic community through his editorial work and seminar organization. He coordinates the Carleton Finite Fields eSeminar and participates in the Ottawa-Carleton Combinatorics and Optimization Seminar, fostering research collaboration in discrete mathematics and related fields. His teaching portfolio is diverse, covering both undergraduate and graduate courses that reflect his broad expertise across algebraic structures, coding theory, and discrete mathematics.
Assistant Professor
Ryoma Hattori is an Assistant Professor at the University of Florida, based at the UF Scripps Biomedical Research campus in Jupiter, FL. His laboratory, the Hattori Lab, focuses on neural mechanisms underlying cognitive functions, learning, and their disruption in autism. Dr. Hattori received his educational degrees from prestigious institutions: Ph.D. in Molecular and Cellular Biology from Harvard University (2016) A.M. in Molecular and Cellular Biology from Harvard University (2012) B.S. in Biophysics and Biochemistry from the University of Tokyo (2010) His research interests center on decision making, reinforcement learning, and number sense, using systems and computational approaches. The lab employs techniques such as in vivo 2-photon imaging, optogenetics, virtual reality behaviors, and machine learning to investigate neural activity and plasticity dynamics in mice. A significant focus is understanding how these processes are impaired in autism spectrum disorder. Analysis of his recent publications reveals a strong emphasis on computational neuroscience and neural circuit mechanisms. His work spans from developing advanced imaging and analysis tools to uncovering fundamental principles of value coding and meta-reinforcement learning, with applications in both basic neuroscience and artificial intelligence. Dr. Hattori has received numerous scientific awards, including: Outstanding Mentor Award 2025 from Society of Research Fellows, UF Scripps SFARI Bridge-to-Independence Award 2022-Current from Simons Foundation Warren Alpert Distinguished Scholar Award 2021-2024 from Warren Alpert Foundation Postdoctoral Grant Award 2021-2022 from The KANAE Foundation And several fellowships during his postdoctoral and graduate training. As a principal investigator, Dr. Hattori leads multiple active grants, including the Shenoy Undergraduate Research Fellowship in Neuroscience (2025-2026) and a project on "Neural activity and plasticity dynamics for reinforcement learning in autism" funded by the Simons Foundation. His mentorship has been recognized with the Outstanding Mentor Award. The Hattori Lab is a dynamic research group utilizing cutting-edge technologies to explore the neural basis of cognition, with a particular interest in translational implications for autism and related disorders.
Associate Professor
Kyun Ho Lee is an Associate Professor in the Department of Aerospace Engineering at Sejong University, specializing in space propulsion systems, satellite thermal engineering, and computational fluid dynamics (CFD). His career spans academic research and practical development in aerospace technologies. Ph.D., KAIST (2009) M.S., Yonsei University (2000) B.S., Yonsei University (1998) His research focuses on cutting-edge aerospace technologies, including Space Propulsion , Thermal Engineering , and Inverse Heat Analysis . Recent work explores CFD modeling of propulsion plumes, rarefied gas dynamics , and optimization of FEEP thrusters for small satellites. Applications extend to green propulsion systems, waste-to-fuel technologies, and advanced emitter designs. The latest publications highlight trends in ionic monopropellants , gallium-based FEEP systems , and thermal cracking of plastic waste for sustainable aviation fuels. Collaborations span computational modeling, propulsion system development, and environmental stress testing for spacecraft.
Professor
Matthias Baitsch serves as Professor of Construction Informatics and Numerical Methods in the Department of Civil and Environmental Engineering at Bochum University of Applied Sciences, where he concurrently heads the BIM Institute. His academic trajectory includes research assistant and senior engineer roles at Ruhr-University Bochum (2000-2009), academic coordination at the Vietnamese-German University (2009-2012), and an acting professorship at the University of Kassel (2012-2014). His educational foundation comprises: Civil Engineering studies at the University of Dortmund (1991-1997) under the interdisciplinary "Dortmund Model" Doctorate from Ruhr-University Bochum (2003) on geometric imperfection-based optimization of compressive beam structures Professor Baitsch's research integrates computational mechanics with civil engineering practice, specializing in construction informatics, numerical optimization, and high-order finite element methods. His work pioneers distributed optimization frameworks, structural health monitoring for wind energy infrastructure, and BIM-based construction informatics. Key methodological contributions include hp-FEM implementations, parallel optimization algorithms, and mobile structural analysis tools. Analysis of his recent publications reveals three dominant research trajectories: (1) Advanced numerical methods for structural optimization under uncertainty, (2) Health monitoring-driven lifetime prediction for wind turbine systems, and (3) Computational modeling of tunnel environments using viscoacoustic inversion techniques. These threads demonstrate consistent focus on robust numerical implementations and real-world civil engineering applications. As Head of the BIM Institute, he leads institutional efforts in digital construction technologies, fostering industry-academia collaboration on building information modeling standards and applications. His teaching portfolio spans foundational mathematics, numerical methods, and computer science for civil engineering students, emphasizing practical computational skills.
Professor
Malay K. Das is a Professor in the Department of Mechanical Engineering at the Indian Institute of Technology Kanpur . With a PhD from PennState, his career spans advanced research in thermofluid science, focusing on energy systems, carbon capture, and battery thermal management. B. E. (University of Calcutta), M. Tech. (IIT Kanpur), PhD (PennState) Teaches graduate-level courses like Machine Learning for Engineers and Mathematics for Engineers Leads two research laboratories: Energy Conservation and Storage Laboratory and Gas Hydrate Research Laboratory Research Interests: Computational Fluid Dynamics (CFD) applications in energy systems Physics-informed machine learning for thermofluid applications CO2 Sequestration and Methane Hydrate Reservoirs Thermal Management of Batteries and Fuel Cells Modeling Transport Phenomena in Porous Media Recent Publication Trends: His work focuses on energy conversion , gas hydrate dynamics , and advanced materials for electrochemical systems . Key areas include Lattice Boltzmann Methods , viscoelastic flow analysis , and nanofluid applications in carbon capture. Advising: Currently supervising PhD students Sourav Dhawan (CO2 Hydrates), Randeep Ravesh (Methane Recovery), Ayaj A. Ansari (Coalbed Methane), and Pawan K. Pandey (Cerebral Aneurysm Flow). Labs and Teams: Leads the Energy Conservation and Storage Laboratory (8 PhD graduates, 3 in progress) and Gas Hydrate Research Laboratory (2 PhD graduates, 1 in progress). Research teams work on fuel cells , CO2 sequestration , and graphene-based nanomaterials for energy applications.
Professor
Mark Iwen is a Full Professor with a dual appointment in the Department of Mathematics and the Department of Computational Mathematics, Science and Engineering (CMSE) at Michigan State University. His research bridges theoretical mathematics and computational science, focusing on high-dimensional data analysis, signal processing, and numerical algorithms. Research Interests: His work centers on computational harmonic analysis, mathematical data science, and algorithms for large-scale data. Key areas include sparse spectral methods, phase retrieval, tensor decomposition, compressive sensing, sublinear-time algorithms, and Johnson-Lindenstrauss embeddings. He develops provably accurate and efficient algorithms for problems in high dimensions, such as solving PDEs, function approximation, and manifold learning. Publications and Trends: His recent publications (2023–2025) emphasize sparse and low-rank tensor methods, phase retrieval, and high-dimensional PDE solvers. There is a strong focus on algorithmic efficiency, provable guarantees, and applications in imaging and data science. Many works involve collaborative efforts and include open-source code. Scientific Awards: Scholarship established in honor of Benjamin (Ben) Segal, reflecting his mentorship and academic impact Advising and Grants: He advises numerous students, including undergraduates and graduate researchers, many of whom are co-authors on publications and code repositories. While specific grants are not listed, his sustained research output and collaborative projects suggest active funding. He co-founded the One World MINDS Seminar, demonstrating leadership in the academic community. Labs and Teams: He leads a research group focused on algorithm development for high-dimensional problems, with strong ties to computational mathematics and data science. His team develops and shares code for sparse FFTs, phase retrieval, tensor methods, and PDE solvers, indicating a collaborative and open research culture.
Research Fellow
Dr. Polly Smith is a research-focused academic affiliated with the Department of Mathematics and Statistics at the University of Reading, within the School of Mathematical, Physical and Computational Sciences. She has been actively publishing since 2007, with a strong emphasis on data assimilation techniques applied to environmental and geophysical systems. Her research interests center on data assimilation , parameter estimation , and model predictability in complex dynamical systems. These include sea-ice models, fluvial inundation forecasting, morphodynamic modeling of coastal systems, and strongly coupled atmosphere-ocean models. Her work combines advanced numerical methods with real-world environmental data to improve forecasting accuracy and model reliability. The recent publications show a trend toward interdisciplinary applications, integrating satellite remote sensing, image-based monitoring, and hybrid variational-ensemble data assimilation methods. Her work spans climate science, hydrology, and coastal engineering, demonstrating a consistent focus on improving predictive capabilities in Earth system modeling. Scientific Awards: No awards explicitly mentioned in the provided text. Dr. Smith has collaborated extensively with leading researchers such as Sarah L. Dance, Nancy K. Nichols, and Andrew S. Lawless. While no formal students or advising roles are listed, her frequent first-author status and technical reports suggest a leadership role in research projects. There is no mention of specific grants, but her work aligns with major environmental modeling initiatives. She has contributed to both peer-reviewed journals and conference proceedings, including the International Conference on Coastal Engineering. Dr. Smith's research is supported by the computational and mathematical infrastructure at the University of Reading. Her work is part of a larger effort in environmental prediction, likely involving collaborations within the university’s meteorology and climate research groups. While no dedicated lab is named, her research falls within the scope of data-driven environmental modeling teams at Reading.
Associate Professor
Audrey Repetti is an Associate Professor in the Department of Actuarial Mathematics and Statistics within the School of Mathematical and Computer Sciences at Heriot-Watt University in Edinburgh, UK. She also holds a dual affiliation with the Institute of Sensors, Signals, and Systems in the School of Engineering and Physical Sciences, and is part of the Maxwell Institute for Mathematical Sciences - Edinburgh. Her research spans mathematical imaging, optimization, and computational methods with applications across astronomy, medical imaging, and optical engineering. Dr. Repetti's research focuses on developing advanced mathematical frameworks for solving imaging inverse problems. Her work centers on optimization algorithms, Bayesian uncertainty quantification, and the integration of machine learning with traditional mathematical approaches. She has made significant contributions to radio interferometric imaging, computational optical imaging with photonic lanterns, and uncertainty quantification in medical imaging. Her research bridges theoretical mathematics with practical applications in astronomy, healthcare, and engineering. Analysis of her recent publications reveals a clear trajectory toward integrating traditional mathematical imaging approaches with modern machine learning techniques. Her work increasingly focuses on 'hybrid' methodologies that combine data-driven models with optimization frameworks. Key themes include plug-and-play algorithms, uncertainty quantification in imaging, and the development of efficient computational methods for high-dimensional inverse problems. Her research demonstrates strong interdisciplinary connections between mathematics, signal processing, astronomy, and medical imaging. Dr. Repetti is actively involved in academic service, including co-organizing the 2026 ICMS Workshop on Imaging inverse problems and generating models. She has received research funding supporting her work in computational imaging and inverse problems, though specific grant details aren't listed in the provided materials. Her teaching portfolio includes advanced courses in scalable inference, deep learning, and statistics for sciences. She leads several research projects with associated software toolboxes including BUQO (Bayesian Uncertainty Quantification by Optimization), SARA-COIL (Compressive optical imaging with a photonic lantern), and CALIM (Self direction-dependent effect calibration and imaging in radio-interferometry). These projects demonstrate her commitment to developing practical computational tools that advance both theoretical understanding and real-world applications in imaging science.
Assistant Professor
Max Willsey is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, since 2024. He specializes in program optimization, leveraging techniques from programming languages, databases, and systems to develop robust and accessible compiler frameworks. His research focuses on equality saturation, E-Graphs, and the integration of Datalog with compiler optimizations. He has contributed to advancements in unifying algorithmic approaches, enabling faster and more extensible program analysis. Teaching: CS 164 (Programming Languages and Compilers, Spring 2025), CS 265 (Compiler Optimization, Fall 2024), and CS 294-260 (Declarative Program Analysis and Optimization, Spring 2024). Research Highlights: Development of the egg and egglog projects, co-organizing the EGRAPHS workshop, and leading the EGRAPHS Community for e-graphs researchers. His recent articles highlight trends in unifying traditional hash joins with worst-case optimal joins, applying equality saturation to diverse domains like Datalog and tensor graph optimization, and advancing E-Graphs for program synthesis and formal verification. Scientific Awards: SIGMOD Record Research Highlight, 2024 MIT PL Review Selection, 2024 Distinguished Paper, OOPSLA 2021 and POPL 2021 NSF Graduate Research Fellowship Honorable Mention, 2018 Qualcomm Innovation Fellow, 2019 Service: Committee Member, PLDI 2025, POPL 2025, ASPLOS 2025 Co-organizer, EGRAPHS 2024 and 2023 workshops Interviewer, UC Berkeley Graduate Admissions Committee, 2024
Research Professor
Irina Rish is a Research Professor at Mila - Quebec AI Institute, affiliated with Université de Montréal, and maintains a significant research affiliation with IBM Research. With an extensive publication record spanning decades and continuing through 2025, she is an active leader in artificial intelligence research. Primary Affiliation: Mila - Quebec AI Institute, Université de Montréal Secondary Affiliation: IBM Research Research Focus Areas: Machine Learning, AI Systems, and Applications Dr. Rish's research interests encompass machine learning theory and applications, with particular expertise in neural networks, language models, reinforcement learning, and efficient AI systems. Her work bridges theoretical understanding with practical implementations across multiple domains including healthcare, time series analysis, and multimodal systems. Analysis of her recent publications (2023-2025) reveals a strong trend toward efficient AI architectures, with significant contributions to model quantization (particularly ternary models), scaling laws in language models, and novel techniques for continual learning. Her research demonstrates how theoretical insights can translate into practical improvements in model efficiency and performance. Key Publication Trends: Model efficiency, scaling laws, continual learning Application Areas: Healthcare (EEG analysis), time series forecasting, vision-language systems Dr. Rish collaborates extensively with researchers across academia and industry, working with notable colleagues including Guillermo A. Cecchi, Djallel Bouneffouf, Eugene Belilovsky, and Matthew Riemer. Her work appears regularly in top-tier conferences (NeurIPS, ICML, ICLR, AAAI) and journals like Transactions of Machine Learning Research, demonstrating both the quality and impact of her research contributions. She leads research in federated learning techniques, reinforcement learning systems, and multimodal AI architectures, with recent work focusing on making AI systems more robust, efficient, and applicable across diverse real-world scenarios. Her research program demonstrates consistent growth in impact and scope, with increasing publication output in recent years.
Assistant Professor
Vincent Sitzmann is an Assistant Professor at the Massachusetts Institute of Technology (MIT) in the Department of Electrical Engineering and Computer Science (EECS), where he leads the Scene Representation Group at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). His research focuses on building machines that learn to understand and interact with the world autonomously through 'world models' - mental simulators that enable agents to predict environmental outcomes and the consequences of their actions. His educational background includes a PhD from Stanford University under Gordon Wetzstein and a Bachelor's degree from the Technical University of Munich. Sitzmann's research spans computer vision, graphics, and robotics, with pioneering contributions to neural scene representations. He introduced Scene Representation Networks (SRNs) that enable continuous 3D-structure-aware scene modeling from 2D images. His work on implicit neural representations with periodic activation functions has become foundational to the field. Recent research focuses on scaling 3D reconstruction techniques, improving generative models for visual content, and developing methods for robot control through neural Jacobian fields. His approach emphasizes both theoretical rigor and practical applications across multiple domains. His publication record shows a clear progression toward more sophisticated diffusion models applied to video generation, robotics, and 3D reconstruction. The 2025 Nature paper on robot control via Jacobian fields demonstrates his expanding influence beyond traditional computer vision into robotics. His work consistently bridges theoretical advances with practical implementations, as evidenced by the CVPR 2023 Best Paper Runner-Up for pixelSplat, which offers scalable solutions for 3D reconstruction. His notable scientific achievements include: CVPR Best Paper Runner-Up (2023) for 'pixelSplat' Multiple papers with 'Spotlight' or 'Oral' presentations at NeurIPS and CVPR 2023 Amazon Research Award for '2D and 3D Animation via Image-Conditional Generative Flow Models' NeurIPS Outstanding New Directions Honorable Mention (2019) As leader of the Scene Representation Group, Sitzmann mentors researchers working at the intersection of computer vision, graphics, and AI. The group has secured funding from prestigious sources including Amazon Research Awards. Their work has practical applications in virtual reality, robotics, and content creation industries. Sitzmann teaches advanced courses at MIT, including 'Advances in Computer Vision' (6.8300). The Scene Representation Group focuses on developing novel methods for 3D scene understanding and manipulation. Current projects include research on neural radiance fields, diffusion models for 3D content creation, and methods for autonomous scene understanding. The group maintains active collaborations with industry partners and academic institutions to advance visual computing research.
Professor
Marcelo Pereyra is a Professor in Statistics at the School of Mathematical & Computer Sciences of Heriot-Watt University and the Maxwell Institute for Mathematical Sciences in Edinburgh, UK. His academic journey began with a double M.Eng. degree from ITBA (Argentina) and INSA Toulouse (France), followed by a M.Sc. from INSA Toulouse in 2009. He earned his Ph.D. in Signal Processing from the University of Toulouse in 2012, after which he served as a Research Fellow in Statistics at the University of Bristol from 2012 to 2016. In 2017, he joined Heriot-Watt University as an Assistant Professor in Statistics, was promoted to Associate Professor in 2019, and subsequently to Professor in Statistics in 2023. His educational background includes: Ph.D. in Signal Processing, University of Toulouse (2012) M.Eng. (double degree) from ITBA (Argentina) and INSA Toulouse (France), with M.Sc. from INSA Toulouse (2009) Professor Pereyra's research advances the statistical foundations of quantitative and scientific imaging. He has made important contributions to Bayesian imaging sciences and developed significant connections between statistical, variational, and machine learning approaches to imaging. His specific interests include robust uncertainty quantification in imaging inverse problems, automatic calibration and verification of statistical image models, scalable Bayesian computation algorithms derived from stochastic diffusion processes, and applications of imaging with high social or environmental value. His work sits at the intersection of statistics, computational mathematics, and imaging science, with a strong emphasis on developing mathematically rigorous methods that provide reliable uncertainty quantification alongside point estimates. His recent publications demonstrate a clear trajectory toward integrating modern machine learning techniques, particularly diffusion models and generative approaches, with traditional Bayesian statistical methods for imaging problems. The research spans applications from medical imaging to astronomical observations and industrial inspection, with consistent emphasis on uncertainty quantification. His work increasingly focuses on developing scalable computational methods that can handle the high-dimensional nature of modern imaging problems while maintaining statistical rigor. Professor Pereyra has received numerous prestigious awards throughout his career: SIAM SIGEST Award in Imaging Sciences for contributions to proximal Markov chain Monte Carlo methodology Marie Curie Intra-European Fellowship for Career Development (2013) Brunel Postdoctoral Research Fellowship in Statistics (2012) Postdoctoral Research Fellowship from French Ministry of Defence (2012) Leopold Escande PhD Thesis award from the University of Toulouse (2012) INFOTEL R&D award from the Association of Engineers of INSA Toulouse (2009) ITBA R&D award from the Buenos Aires Institute of Technology (2007) Professor Pereyra is deeply committed to developing early career talent, currently supervising five PhD students and two Postdoctoral Research Associates (PDRAs), having previously supervised four PhD students and three PDRAs to completion. His research has received significant support from Heriot-Watt University and the UK Engineering and Physical Sciences Research Council (EPSRC). He is known for fostering multidisciplinary collaboration, having organized eleven international interdisciplinary research meetings in the UK since 2012 and chaired the IMA Conference on Inverse Problems in Edinburgh (2022). As a leader in his field, Professor Pereyra has held Invited Professor positions at prestigious institutions including Institut Henri Poincaré (Paris, 2019), Ecole Normale Supérieure Lyon (2023), and Université Paris Cité (2024). He frequently delivers invited talks at leading mathematical centers worldwide (CIRM, BIRS, IHP, Flatiron, Hausdorff School, INI, and ICMS) to promote multidisciplinary collaboration in imaging sciences.
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