جستجوی استاد راهنمای دانشگاهی

Sheng Shenمشاهده پروفایل

استاد

• Nanophotonics
• Nanoscale Energy Transport and Conversion
• Advanced Manufacturing
• +۷ مورد دیگر

Sheng Shen is a Professor in the Mechanical Engineering Department at Carnegie Mellon University (CMU) , with courtesy appointments in the Departments of Electrical and Computer Engineering and Materials Science and Engineering . He earned his Ph.D. in Mechanical Engineering (Minor in Electrical Engineering) from Massachusetts Institute of Technology (MIT) , and his B.S. and M.S. from Huazhong University of Science and Technology in China. Prior to joining CMU in 2011, he conducted postdoctoral research at UC-Berkeley . Education: Ph.D., Mechanical Engineering, MIT (2010) B.S. & M.S., Power Engineering & Engineering Thermophysics, Huazhong University of Science and Technology (2000 & 2003) Research interests include nanophotonics , nanoscale energy transport and conversion , nanofabrication , and advanced manufacturing , with applications in thermal management , light sources and devices , thermal emission control , solar energy conversion , infrared sensing , and multifunctional materials . His work leverages interdisciplinary expertise in thermal and optical measurements , material synthesis , device fabrication , and theoretical modeling . Recent publications highlight advancements in infrared radiation control , thermal interface materials , metasurface engineering , and graphene-based nanosystems . His scientific awards include: NSF CAREER Award DARPA Director's Fellowship DARPA Young Faculty Award Elsevier/JQSRT Raymond Viskanta Award CMU Dean's Early Career Fellowship Philomathia Foundation Research Fellowship Hewlett-Packard Best Paper Award Best Paper Award, Julius Springer Forum Advising spans Ph.D. and postdoctoral researchers in nanoscale energy systems, with alumni contributing to solar energy conversion , infrared sensing , and flexible electronics . His lab receives funding from ARL, DARPA, DOE, DTRA, NASA, NSF, and ONR , and recently secured a DURIP award for instrumentation.

Gianluca Iaccarinoمشاهده پروفایل

استاد

• Scientific Computing
• Uncertainty Quantification
• Turbulence Modeling
• +۶ مورد دیگر

Gianluca Iaccarino is a Professor of Mechanical Engineering at Stanford University and the Robert Bosch Chairholder. He serves as Director of the PSAAP Center and leads large-scale computational research initiatives in uncertainty quantification, exascale computing, and multiphysics simulations. His academic journey includes a PhD in Mechanical Engineering from Politecnico di Bari (2005), postdoctoral work at Stanford's Center for Turbulence Research, and progression from Research Engineer to full Professor. Education : PhD (Politecnico di Bari), MS/BS in Aeronautical Engineering (University of Naples) Research : Computational engineering, turbulence modeling, uncertainty quantification, biomedical fluid dynamics, and exascale-ready algorithms Publications : 15+ recent articles focus on turbulence modeling, data-driven simulations, and uncertainty quantification across diverse applications in aerospace, biomedical, and energy systems Awards : PECASE (2010), APS Fellow (2019), multiple best paper awards (AIAA, ASME), Terman Fellow (2007) Students : Advises doctoral and master's students in mechanical engineering and computational methods Leadership : Director of PSAAP Center (2014-present), Chair of Mechanical Engineering Department (2024-present)

Bradley D. Olsenمشاهده پروفایل

استاد

• Polymer Science
• Materials Engineering
• Soft Condensed Matter Physics
• +۴ مورد دیگر

Bradley D. Olsen is a full professor in the Department of Chemical Engineering at the Massachusetts Institute of Technology (MIT), where he leads research at the intersection of polymer science, soft matter physics, and bioengineering. His work focuses on designing materials for critical applications in biotechnology, hemostasis, and sustainable polymer development while advancing fundamental understanding of polymer network mechanics and self-assembly. Education: Ph.D. in Chemical Engineering, University of California Berkeley (2007) S.B. in Chemical Engineering, Massachusetts Institute of Technology (2003) Olsen's research spans protein-based materials, block copolymer phase behavior, and mechanochemical hydrogels. He has pioneered methods for quantifying polymer network topology, developing hemostatic nanoparticles, and creating bio-inspired materials for selective biomolecular transport and medical applications. His recent publications emphasize data-driven approaches to polymer characterization and educational outreach in materials science. Scientific Awards: American Physical Society (APS) Fellow (2023) Fulbright Amazonia Scholar (2023) Alexander and I. Michael Kasser Chair in Chemical Engineering (2021) ACS Macro Letters Young Investigator Award (2021) MIT Committed to Caring Honor (2019) AIChE Owens Corning Early Career Award (2019) APS Dillon Medal (2018) Kavli Emerging Leader in Chemistry (2017) ACS Polymer Division Fellow (2016) Camille Dreyfus-Teacher Scholar (2015) Alfred P. Sloan Research Fellow (2014) NSF Career Grant (2013) NIH Postdoctoral Fellowship (2008-2009) Hertz Fellow (2003-2007) Barry M. Goldwater Scholarship (2002) Olsen has received significant grant support including NSF Career (2013) and AFOSR (2012) awards. His teaching activities include innovative international outreach like the 2025 soccer-themed science camp in Brazil. The Olsen Group at MIT explores advanced materials with applications ranging from trauma care to sustainable polymers.

Alexey Gorshkovمشاهده پروفایل

استاد مدعو

• Quantum Optics
• Quantum Information Science
• Condensed Matter Physics
• +۶ مورد دیگر

Alexey Gorshkov is an Adjunct Professor at the University of Maryland (UMD) affiliated with the Joint Quantum Institute (JQI) and the Quantum Information and Computer Science Laboratory (QuICS). His primary academic role is in theoretical physics, focusing on quantum optics, quantum information science, and condensed matter physics. He leads a research group exploring quantum magnetism with alkaline-earth atoms, driven-dissipative systems, topological matter, and strongly interacting photons. His work bridges AMO (atomic, molecular, and optical) systems with high-energy and condensed matter physics, emphasizing quantum simulation and novel quantum technologies like precise clocks and quantum computers. Education details are not explicitly listed, but his research collaborations with institutions like JQI and UMD suggest advanced academic training in theoretical physics. His research interests revolve around understanding and controlling quantum many-body systems, particularly in far-from-equilibrium scenarios, entanglement dynamics, and dissipation effects. He has contributed to studies on Rydberg atoms, quantum routing protocols, and error mitigation in quantum simulators. Recent articles highlight his work on quantum protocols for verifying speedups, time-independent information flow, and entanglement dynamics. His group's achievements include demonstrating one-dimensional anyons and developing methods for correlated noise estimation with quantum sensors. Awards and grants are not explicitly mentioned in the provided text, but his prolific publication record indicates sustained research impact. Labs and teams associated with him include the JQI and QuICS, where he collaborates on experimental and theoretical projects. Graduate student and postdoc positions are available in his group, focusing on areas like quantum magnetism and topological systems. His work often involves close ties with experimental groups, emphasizing practical applications of theoretical breakthroughs.

Ralph H. Colbyمشاهده پروفایل

استاد

• Polymer Physics
• Rheology
• Ionomers
• +۵ مورد دیگر

Ralph H. Colby serves as Professor of Materials Science and Engineering and Chemical Engineering at Pennsylvania State University's College of Earth and Mineral Sciences, holding the Corning Faculty Fellowship. His research focuses on molecular-level dynamics in complex fluids, particularly polymers, ionomers, and liquid crystalline systems. With over 130 publications and authorship of the textbook Polymer Physics (2003), he directs an active research program examining structure-property relationships in soft matter. B.S. in Materials Science and Engineering, Cornell University (1979) M.S. in Chemical Engineering, Northwestern University (1983) Ph.D. in Chemical Engineering, Northwestern University (1985) Professor Colby's research spans polymer physics, rheology, and materials for energy applications. His group employs mechanical rheology, dielectric spectroscopy, and scattering techniques to investigate ion transport in single-ion conductors for batteries, dynamics of glass-forming liquids, and self-assembly in polyelectrolyte systems. Current work emphasizes structure-property relationships in ionomers, liquid crystalline polymers, and branched architectures. Analysis of recent publications reveals consistent focus on ionomer membranes for energy applications, processing-structure relationships in advanced polymers, and fundamental dynamics of complex fluids. Key trends include increasing integration of computational modeling with experimental characterization, expansion into sustainable materials processing, and growing emphasis on applications in battery technology and biomedical materials. Penn State Faculty Scholar Medal for Outstanding Achievement (2022) Bingham Medal, Society of Rheology (2012) American Chemical Society Fellowship Corning Faculty Fellowship in Materials Science and Engineering Professor Colby leads multiple federally funded projects including NSF's 'Fundamental Studies of Flow-Induced Polymer Crystallization' and DOE's 'Conduction mechanisms and structure of ionomeric single-ion conductors'. His group maintains strong industry partnerships with Corning Incorporated and participates in interdisciplinary initiatives like the Penn State Intercollege Graduate Degree Program in Materials Science and Engineering. Current research includes collaborations on breast cancer adherence interventions in Rwanda and conjugated polymer development for flexible electronics. The Colby Research Group operates specialized facilities for rheological characterization, dielectric spectroscopy, and X-ray scattering at Penn State's Materials Research Institute. The team maintains active collaborations with national laboratories and international research groups, focusing on translating fundamental polymer physics discoveries into practical applications for energy storage and advanced manufacturing.

Oscar P. Brunoمشاهده پروفایل

استاد

• Partial Differential Equations
• Computational Science
• Numerical Analysis
• +۵ مورد دیگر

Oscar P. Bruno is a Professor of Applied and Computational Mathematics at the California Institute of Technology (Caltech). He holds a Licenciado from the University of Buenos Aires (1982) and a Ph.D. in Mathematics from New York University's Courant Institute (1989). Since 1998, he has been a Professor at Caltech, previously serving as Associate Professor (1995–98) and Executive Officer for Applied Mathematics (1998–2000). His research focuses on developing high-performance numerical methods for solving partial differential equations (PDEs), addressing challenges in complex geometries, singularities, and high-frequency phenomena. Key contributions include the Fourier Continuation (FC) method and integral-equation techniques, enabling solutions to previously intractable PDE problems in science and engineering. Prof. Bruno's expertise spans computational electromagnetics, computational fluid dynamics (CFD), solid mechanics, and mathematical physics. His work integrates numerical analysis, multiphysics modeling, and computational science to solve real-world problems in geophysics, optics, and fluid dynamics. He has received numerous awards, including membership in the National Academy of Sciences of Argentina (2020), the Vannevar Bush National Security Science and Engineering Fellowship (2016), and SIAM Fellow (2013). Bruno serves on editorial boards for journals like SIAM Journal on Scientific Computing and SIAM Journal on Applied Mathematics, and participates in national science advisory roles. His teaching includes advanced courses on applied mathematics methods (ACM/IDS 101 ab), emphasizing theoretical foundations and numerical techniques for PDEs. His research group develops cutting-edge solvers with applications in shock dynamics, optical tomography, and geophysical fluid dynamics.

• Scientific Machine Learning
• Numerical Linear Algebra
• Numerical Multilinear Algebra
• +۷ مورد دیگر

Peter Benner is a Professor and Director at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, where he leads the Computational Methods in Systems and Control Theory group. He also holds an Honorarprofessor position for Mathematics at Otto-von-Guericke Universität Magdeburg since 2011. Benner has previously served as Managing Director of the Max Planck Institute during multiple periods (2013-2014, 2021-2022, and 2025-2026), demonstrating his leadership in the field. Benner's research focuses on Scientific Machine Learning, Numerical Linear and Multilinear Algebra, Model Order Reduction and Reduced-order Modeling, Numerical Methods in Systems and Control Theory, PDE Constrained Optimization, High-performance and Power-aware Computing, and Mathematical Software development. His work bridges theoretical mathematics with practical engineering applications, particularly addressing challenges in large-scale dynamical systems. Analysis of his recent publications reveals a strong emphasis on developing efficient computational methods for complex systems. Benner has pioneered approaches combining model order reduction with tensor methods to tackle high-dimensional problems in uncertainty quantification and PDE-constrained optimization. His work shows a consistent trend toward integrating data-driven techniques with traditional model-based approaches, particularly for nonlinear and parametric systems. Throughout his career, Benner has actively mentored students and collaborated with researchers worldwide, delivering numerous invited talks at prestigious institutions and conferences across Europe, North America, and Asia. His research has received significant funding, supporting the development of mathematical software and computational methods for industrial applications. Benner leads the Computational Methods in Systems and Control Theory department at the Max Planck Institute, which focuses on developing and implementing advanced numerical methods for large-scale dynamical systems. The group maintains strong connections with both theoretical mathematics and practical engineering applications, particularly in fluid dynamics, energy systems, and control theory.

• Numerical Modeling
• Computational Simulation
• Materials Science
• +۵ مورد دیگر

Assoc Prof Ng Teng Yong is an Associate Professor at the School of Mechanical & Aerospace Engineering (NTU), specializing in numerical modeling and simulation. With a background as Research Manager at A*STAR Institute of High Performance Computing, his work spans materials science, nanotechnology, and aerospace engineering. Current focus on graphene-based desalination membranes Expertise in molecular dynamics simulations Investigates nanoscale fluid mechanics and structural dynamics Recent publications highlight advancements in energy-efficient electrodialysis, smart robotics, and nonlinear vibration analysis. His interdisciplinary approach integrates computational methods with experimental validation in additive manufacturing and soft material mechanics.

Christopher M. Wolvertonمشاهده پروفایل

استاد

• Computational Materials Science
• Energy Storage Materials
• First-Principles Quantum Mechanics
• +۷ مورد دیگر

Dr. Christopher M. Wolverton is a Professor of Materials Science and Engineering at Northwestern University , where he leads the Wolverton Research Group . His work focuses on computational materials science with applications in energy sustainability , particularly in batteries , hydrogen storage , and thermoelectrics . PhD in Physics from University of California, Berkeley BS in Physics (summa cum laude) from University of Texas, Austin His research leverages first-principles quantum mechanical simulations and machine learning to enable virtual materials synthesis before laboratory testing. The group specializes in hybrid computational methods integrating Density Functional Theory (DFT) , Monte Carlo simulations , and phase-field microstructural models . The article portfolio shows leadership in energy storage materials , with recent work on data-driven nanoparticle facet control , mixed-anion semiconductors , and machine learning-accelerated discovery . Publications span top journals including Nature Energy , Nature Materials , and Science . 2006 Ford Motor Company Technical Achievement Award 2005 Ford Patent & Publication Awards 2003 Ford Environmental/Physical Sciences Recognition As advisor to PhD candidates Zhenpeng Yao , Shiqiang Hao , and Shane Patel , he fosters interdisciplinary research connecting materials informatics with experimental validation . The group maintains active collaborations with Argonne National Lab and MIT/Harvard teams.

• Plasma Physics
• Nonlinear Dynamics
• Electromagnetic Theory
• +۳ مورد دیگر

Thomas M. Antonsen Jr. is a Distinguished University Professor at the University of Maryland, holding joint appointments in the Department of Electrical and Computer Engineering and the Department of Physics. He is affiliated with the Institute for Research in Electronics & Applied Physics (IREAP), Maryland Energy Innovation Institute, and the Institute of Physical Science and Technology. His research focuses on plasma physics, nonlinear dynamics, and high-power coherent radiation sources. Antonsen earned his B.S., M.S., and Ph.D. in electrical engineering from Cornell University (1973–1977) and has held visiting positions at institutions such as the University of California, Santa Barbara, and the École Polytechnique in France. **Education:** B.S., Electrical Engineering, Cornell University, 1973 M.S., Electrical Engineering, Cornell University, 1976 Ph.D., Electrical Engineering, Cornell University, 1977 **Research Interests:** Antonsen’s work spans magnetically confined plasmas, laser-plasma interactions, and advanced vacuum electronics. He has pioneered adjoint methods for optimizing beam-wave interaction systems and contributed to the development of high-power microwave amplifiers. His recent projects include wave chaos in complex systems and machine learning applications in nonlinear dynamics. **Awards & Honors:** James Clerk Maxwell Award (American Physical Society, 2023) IEEE Marie Sklodowska-Curie Award (2022) University of Maryland Distinguished University Professor (2017) IEEE Fellow (2012) **Teaching & Mentorship:** Antonsen teaches courses such as Physics 132 (Biophysics), Electrodynamics, and Plasma Physics. He mentors graduate students in plasma physics and vacuum electronics through his research groups at IREAP and the Bright Beams Collective. **Labs & Collaborations:** His research is supported by grants from the Department of Energy, NASA, and the Office of Naval Research. Key collaborations include the National Institute of Standards and Technology (NIST) and the European XFEL facility.

Eugene Demlerمشاهده پروفایل

استاد

• Theoretical Condensed Matter Physics
• Atomic and Molecular Physics
• Quantum Optics
• +۱ مورد دیگر

Eugene Demler is a Full Professor at the Department of Physics, ETH Zurich. Previously, he held academic positions at Harvard University from 1998 to 2021, including Assistant Professor (2001-2004), Associate Professor (unspecified dates), and Full Professor (2005-2021). His work bridges theoretical condensed matter physics, atomic and molecular physics, quantum optics, and quantum simulations. Education: MSc in Physics, Moscow Institute of Physics and Technology (1993) Diploma work, Lebedev Physics Institute (1992-1993) PhD in Theoretical Physics, Stanford University (1998), supervised by S.C. Zhang Demler's research focuses on strongly correlated quantum systems, spintronics, quantum sensing, and photo-induced phase transitions. His recent publications explore topics such as quantum polarons, Josephson plasmons, magnon dynamics, and terahertz spectroscopy in superconductors. He has pioneered hybrid quantum-classical methods for electron-phonon systems and cavity-mediated quantum materials. His Google Scholar articles (2023-2025) span theoretical and experimental domains, with keywords including Quantum Physics , Condensed Matter Physics , and Quantum Optics . Subfields include Quantum Control , Superconductivity , Spin Waves , Quantum Sensing , Non-Equilibrium Dynamics , and Quantum Simulation . Scientific Awards: Hamburg Prize for Theoretical Physics (2021) Simons Investigator (2021) Moore Distinguished Scholar (2020) Hanna Visiting Scholar (2019) Highly Cited Researcher (2017-2020) Senior Fellow at ETH Zurich's Institute for Theoretical Studies (2015) Simons Fellowship (2015) Distinguished Scholar at Max Planck Institute of Quantum Optics (2015) Thomson Reuters Highly Cited Researcher (2014) Siemens Research Award (2006) Johannes Gutenberg Lecture Award (2006) NSF Career Award (2002) Sloan Fellowship (2002) Demler teaches courses such as Statistical Physics and Strongly Correlated Systems in Atomic and Condensed Matter Physics . His work integrates theoretical modeling with experimental collaborations, particularly in quantum optics and condensed matter systems.

Stephen A. Vavasisمشاهده پروفایل

استاد

• Continuous Optimization
• Data Science
• First-Order Methods
• +۴ مورد دیگر

Stephen A. Vavasis is a Professor in the Department of Combinatorics and Optimization at the University of Waterloo, part of the Faculty of Mathematics. He holds a PhD in Computer Science from Stanford University (1989) and has held academic positions at Cornell University (1989–2006) before joining Waterloo. His research focuses on continuous optimization, data science, first-order methods, scientific computing, and computational mechanics. Current teaching includes courses on convex optimization and portfolio optimization methods. He has served as Associate Dean of Computing (2017–2020) and Interim Director of Data Science graduate programs. His work is supported by NSERC grants. Notable awards include the Hertz Fellowship, Churchill Scholarship, and Guggenheim Fellowship. Vavasis's research emphasizes applications of optimization to clustering, machine learning, and fracture mechanics. His publications span convex optimization frameworks, algorithmic analysis of gradient methods, and numerical methods in mechanics. Recent work explores unifying analyses of first-order optimization algorithms and robust optimization techniques for high-dimensional data problems.

Mike Kirbyمشاهده پروفایل

استاد

• Scientific Computing
• Physics-Informed Machine Learning
• Computational Science and Engineering
• +۴ مورد دیگر

Mike Kirby is a Professor at the Kahlert School of Computing, University of Utah. He also holds adjunct professorships in the Department of Bioengineering and the Department of Mathematics. His current roles include leadership in scientific computing and informatics initiatives, including former directorships of the Utah Informatics Initiative (2019-2023) and the Multi-Scale Multidisciplinary Modeling of Electronic Materials (MSME) Collaborative Research Alliance (2016-2022). He has extensive experience in strategic research initiatives, including serving as Assistant Vice President for Research (2024-2025). Education: Dr. Kirby earned a PhD in Applied Mathematics (2002) and MS in Computer Science (2001) from Brown University, and a BS in Applied Mathematics and Computer Science from Florida State University (1997). Research Interests: Focus on large-scale scientific computing, physics-informed machine learning, computational science and engineering, high-order numerical methods, and visualization. His work bridges applied mathematics and computer science to address real-world engineering challenges. Publications: Over 150 peer-reviewed articles, including high-impact contributions in journals like Journal of Computational Physics and SIAM Journal on Scientific Computing . Recent work emphasizes machine learning for differential equations, topology optimization under uncertainty, and multi-fidelity modeling. Awards: Recognized for leadership in computational science and informatics, including contributions to University of Utah’s Clery Compliance Program. Advising & Grants: Supervised over 50 graduate students and postdocs. Secured funding from NSF, DOE, and industry partnerships, totaling millions in research grants. Active in interdisciplinary collaborations across engineering, materials science, and medicine. Labs/Teams: Scientific Computing and Imaging (SCI) Institute, Utah Informatics Initiative, and the Center for Multiscale Modeling of Electronic Materials (MSME).

Cezary Kaliszykمشاهده پروفایل

استاد

• Automated Reasoning
• Formal Methods
• Formalized Mathematics
• +۷ مورد دیگر

Cezary Kaliszyk is a Professor in Theoretical Computer Science at the University of Melbourne, previously affiliated with the University of Innsbruck. He is actively involved in research and leadership in formal methods, automated reasoning, and machine learning for theorem proving. Research Interests: Automated Reasoning and Interactive Theorem Proving Formalized Mathematics and Proof Automation Machine Learning for Logic and Theorem Proving Integration of AI with Proof Assistants (Coq, Isabelle) Dependent Type Theory and Higher-Order Logic His recent publications (2023–2025) span topics in dependently-typed logic, learning for proof guidance, formalization of surreal numbers, and blockchain-based formal methods. The works consistently bridge formal logic with machine learning, emphasizing automation, explainability, and cross-system integration. Scientific Leadership and Projects: Principal Investigator, ERC project FormalWeb3 Lead Developer, CoqHammer , Tactician , ProofWeb WG5 Leader, COST Action EuroProofNet (until 2024) Contributor to HOL(y)Hammer , Isabelle Enigma He supervises multiple PhD students and has mentored several graduates in formal methods and AI. He teaches courses in theoretical computer science, logic, and machine learning. There are no listed awards in the provided data, but his extensive publication record and project leadership indicate significant recognition in the field. Labs and Research Groups: He leads a research group focused on formal methods and learning-based reasoning, collaborating internationally on projects involving proof automation, formal libraries, and semantic technologies.

Paul Goulartمشاهده پروفایل

استاد

• Robust and high-speed optimisation
• Control engineering
• Convex optimization
• +۴ مورد دیگر

Professor Paul Goulart is a full Professor of Engineering Science at the University of Oxford and Tutorial Fellow at St Edmund Hall, positions he has held since 2014. He leads research and teaching in robust optimization, control systems, and high-speed numerical methods, with applications spanning fluid flows, traffic networks, and economics. Education SB & MSc, Aeronautics and Astronautics – Massachusetts Institute of Technology (MIT) PhD, Control Engineering – University of Cambridge (Gates Scholar, 2007) Research Interests Professor Goulart’s work lies at the intersection of control engineering and optimization . His core expertise includes: Robust and high-speed convex optimization Model predictive control (MPC) and control barrier functions Neural-network-based control and system identification Optimization over traffic and economic networks Real-time and embedded optimization solvers These interests are reflected in prolific publication output and active supervision of doctoral researchers. Publications & Trends From 2020 to 2025 Professor Goulart has co-authored more than thirty papers. A dominant theme is the development of fast, reliable algorithms for conic optimization and robust control , often leveraging machine-learning techniques to enhance scalability and real-time performance. Recent works emphasize safety certificates, GPU-accelerated solvers, and neural-network controllers for uncertain systems. Awards & Honors Gates Cambridge Scholar (2003) Advising & Grants Professor Goulart actively seeks DPhil students in control engineering and optimization . He leads the Control Group within the Department of Engineering Science and has been involved in multiple industrially funded projects, although specific grant identifiers are not provided in the supplied text. Laboratory & Teams He is a member of the Control Group , Department of Engineering Science, University of Oxford, and serves as Secretary to the Governing Body of St Edmund Hall (Michaelmas Term 2024).