جست‌وجوی استاد و پژوهشگر

Gail E. Kaiserمشاهده پروفایل

استاد

• Software Engineering
• Static and Dynamic Program Analysis
• Software Testing
• +۷ مورد دیگر

Gail E. Kaiser is a Professor of Computer Science and the Director of the Programming Systems Laboratory (PSL) in the Computer Science Department at Columbia University. She has been with Columbia University since 1985, becoming a full Professor in 1998. Prof. Kaiser's research spans software engineering, program analysis, software testing, and software security, with recent focus on addressing challenges in AI/ML systems testing and security. Prof. Kaiser received her PhD in Computer Science from Carnegie Mellon University in 1985 and her ScB in Computer Science and Engineering from MIT in 1979. Her dissertation at CMU was titled "Semantics for Structure Editing Environments" under advisor Nico Habermann, and at MIT she completed "Automatic Extension of an Augmented Transition Network Grammar for Morse Code Conversations" under advisor Al Vezza. Prof. Kaiser's research interests primarily focus on software engineering following a systems building approach, with recent emphasis on static and dynamic program analysis techniques to improve software reliability and security. Since 2005, she has investigated testing "non-testable" programs, particularly in machine learning, data mining, and scientific computing applications where traditional testing oracles are insufficient. She has developed novel techniques and tools for detecting bugs and verifying repairs in complex systems. Concurrently, she has worked on collaboration environments for computational scientists, creating knowledge sharing and domain-aware environments to support scientific workflows. Prof. Kaiser's recent publications demonstrate a strong focus on the intersection of software engineering and artificial intelligence. Her work addresses critical challenges in testing AI systems, code understanding through deep learning, vulnerability detection, and educational tools for computational thinking. There's a clear evolution from traditional software engineering topics toward AI/ML applications, with particular emphasis on metamorphic testing for non-testable systems, code similarity analysis, and educational applications. Prof. Kaiser has received numerous prestigious awards throughout her career: Distinguished Journal Award (10 Years) from 18th IEEE International Conference on Software Testing, Verification and Validation (ICST), April 2025 Best Research Paper Award at 24th IEEE International Conference on Source Code Analysis & Manipulation (SCAM), October 2024 Distinguished Reviewer Awards for ASE 2024 and FSE 2024 ACM SIGSOFT Distinguished Paper Award for "CONCORD: Clone-aware Contrastive Learning for Source Code", July 2023 Best Student Paper Award at ICCE 2021 Multiple ACM SIGSOFT Distinguished Paper Awards dating back to 2014 Presidential Young Investigator in Software Engineering and Software Systems from NSF (1988-1993) Prof. Kaiser has chaired Columbia's doctoral program since 1997 and served on editorial boards including IEEE Internet Computing and as a founding associate editor of ACM Transactions on Software Engineering and Methodology. Her lab has been continuously funded by major agencies including NSF, NIH, DARPA, ONR, NASA, and numerous companies. Current grants include significant NSF funding for secure containers architecture, learning semantics of code for software assurance, and finding semantic security bugs. As Director of the Programming Systems Laboratory (PSL), Prof. Kaiser leads research in software systems, program analysis, and software testing. The lab has developed numerous tools and techniques for software reliability and security, with recent focus on challenges in AI/ML systems. Her work bridges theoretical foundations with practical applications, often resulting in deployable tools that address real-world software engineering challenges.

Andrew Childsمشاهده پروفایل

استاد

• Quantum Algorithms
• Quantum Information Processing
• Quantum Simulation
• +۲ مورد دیگر

Andrew Childs is a Professor at the University of Maryland, affiliated with the Department of Computer Science and the Institute for Advanced Computer Studies (UMIACS). He serves as Director of the NSF Quantum Leap Challenge Institute for Robust Quantum Simulation (RQS) and is a Fellow at the Joint Center for Quantum Information and Computer Science (QuICS). His research focuses on quantum algorithms for simulating physical systems, algebraic problems, and quantum walk protocols, with applications in quantum computing and computational complexity. University of Maryland Institute for Advanced Computer Studies (UMIACS) Joint Center for Quantum Information and Computer Science (QuICS) NSF Quantum Leap Challenge Institute for Robust Quantum Simulation Childs' research spans quantum simulation, quantum Fourier transform, phase estimation, and Hamiltonian dynamics. He has developed techniques to reduce quantum computational resources for simulating quantum systems and explored limitations of quantum computers through hidden subgroup problems and non-unitary dynamics. His publications cover diverse areas including quantum walk optimization, Hamiltonian simulation methods, and applications to cryptography and condensed matter physics. Recent works address spatial search algorithms, product formulas for commutators, and quantum routing protocols. As an educator, Childs has taught courses on quantum algorithms and information processing at both the University of Maryland and University of Waterloo, with lecture notes and materials spanning multiple years. Contact: amchilds@umd.edu | Office: ATL 3359 | Affiliated with University of Maryland's quantum research institutes.

Abdullah Balمشاهده پروفایل

پژوهشگر

• Data Science
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Dr. Abdullah Bal is a researcher at Georgia State University's College of Arts & Sciences, Department of Computer Science, with over 25 years of academic experience. He holds a Ph.D. in Electrical Engineering from Yildiz Technical University (2002) and has taught graduate and undergraduate courses in algorithms, machine learning, and optical pattern recognition. B.Sc., Electronics and Communication Engineering, Istanbul Technical University (1993) M.Sc., Electrical Engineering, Yildiz Technical University (1997) Ph.D., Electrical Engineering, Yildiz Technical University (2002) His research focuses on data science, machine learning, and hyperspectral imaging applications in fields ranging from forensic analysis to historical structure preservation. He has led projects funded by the U.S. Army Research Office and the Scientific and Technological Research Council of Turkey, including real-time target detection systems and digital imaging for historical structures. Recent publications demonstrate his expertise in kernel-based transforms, ensemble learning, and hyperspectral data analysis. His work spans food safety inspection, infrared target tracking, and biometric verification systems. Faculty Outstanding Research Publication Award (2006) Turkish Air Force Academy Science Competition Winner (2009) Best Paper Award at ICFCT (2016) Previously, he chaired YTU's Informatics Department (2009-2016) and participated in academic governance through the Electrical and Electronics College Executive Committee (2012-2015). You can contact him at abal@gsu.edu in room 739, 25 Park Place.

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.

• Nova Outbursts
• Thermonuclear Explosions
• White Dwarfs
• +۷ مورد دیگر

Timothy John O'Brien is a Professor of Astrophysics and Associate Director of the Jodrell Bank Centre for Astrophysics at the University of Manchester, Faculty of Science & Engineering, Department of Physics & Astronomy. He also served as Director of Teaching & Learning in the Department of Physics & Astronomy from 2016-2020 and as Associate Dean for Social Responsibility for the Faculty of Science & Engineering during the same period. His educational background includes a B.Sc. (Hons) in Physics with Astrophysics from the University of London (1985) and a Ph.D. in Astrophysics from the University of Manchester (1990). Professor O'Brien's research concentrates on the study of exploding stars—mainly nova outbursts caused by thermonuclear explosions on the surface of white dwarfs in binary star systems. Over the years, he has developed expertise in a wide range of astrophysical techniques, working across the spectrum from radio waves to X-rays while also carrying out numerical simulations of the aftermath of explosions using his own hydrodynamic codes. Recently, he has developed an interest in the search for extra-terrestrial intelligence (SETI) using radio telescopes. His research fingerprint shows strong activity in Novae (100%), Optical Bursts (94%), Recurrent Novae (56%), Ejecta (45%), Planetary Nebula (38%), Emissions (32%), Classical Novae (32%), and Nebula (28%). His recent publications demonstrate a continued focus on nova outbursts, particularly in symbiotic novae systems like RS Ophiuchi, with high-resolution imaging techniques and multi-wavelength observations. His work spans theoretical modeling, observational astronomy across the electromagnetic spectrum, and data analysis from major telescope facilities. Kelvin Medal of the Institute of Physics (2014) for Public Engagement Professor O'Brien has extensive teaching experience, having worked as a lecturer in three universities since 1988. He has taught a wide range of courses in astronomy & astrophysics, physics, applied mathematics, and computing. From 1999-2009, he directed a distance learning program in astronomy that enrolled over 1,300 students. His current teaching includes Dynamics, Physics of the Solar System, and a course on the Search for Extraterrestrial Life. He has also supervised numerous project students throughout his career. He is actively involved in public engagement, including regular media appearances and events at the Jodrell Bank Centre for Engagement. He was a co-founder of the bluedot festival and contributed to the successful nomination of Jodrell Bank Observatory as a World Heritage Site in 2019.

• Cryptography
• Information Security
• Cryptographic Automation
• +۳ مورد دیگر

Christina L. Garman is an Assistant Professor in the Department of Computer Science at Purdue University, where she joined in Spring 2018. Her research focuses on practical cryptography and cryptographic automation to make secure system development accessible to non-experts through error-resistant design methodologies. Her educational background includes: Bachelor of Science in Computer Science and Engineering from Bucknell University (2011) Bachelor of Arts in Mathematics from Bucknell University (2011) Master of Science in Engineering in Computer Science from Johns Hopkins University (2013) Doctor of Philosophy in Computer Science from Johns Hopkins University (2017) Professor Garman's work centers on real-world cryptographic system security, spanning protocol analysis (e.g., RC4 in TLS, Apple iMessage flaws), decentralized anonymous systems (Zerocash/ZCash), and cryptographic automation. She pioneered techniques for removing human error in cryptographic deployments through automated tools and frameworks. Her research bridges theoretical cryptography with practical implementation challenges in privacy-preserving technologies and secure infrastructure. Analysis of her 2021-2025 publications reveals expanding research horizons: hardware security vulnerabilities (Rowhammer, SGX), privacy network enhancements (Tor onion services), software supply chain security (SBOM tools), and advanced cryptographic protocols (zkSNARKs, MPC). This evolution demonstrates consistent focus on real-world security impact while diversifying into hardware-software cross-layer threats and formal verification methods for cryptographic implementations. Her major scientific recognitions include: NSF CAREER Award (2021) for cryptographic automation research ACM CCS Best Paper Award (2016) for iMessage security analysis IEEE Test of Time Award (2024) for foundational Zerocash work Professor Garman co-founded ZCash, a privacy-focused cryptocurrency based on her Zerocash protocol, and her NSF CAREER grant supports cryptographic automation development. Her research has received significant media coverage in The Washington Post, Wired, and The New York Times, highlighting real-world relevance. While specific student advising details aren't public, her active publication record indicates ongoing mentorship of graduate researchers in security and cryptography. Her work maintains strong industry connections through ZCash development and Tor network contributions, with recent projects like keyless CDNs demonstrating practical applications of cryptographic automation. She remains a leading voice in cryptographic research communities through conference participation and collaborative projects addressing evolving security challenges.

• Evolutionary Computation
• Machine Learning
• Natural Computing
• +۵ مورد دیگر

Thomas Bäck is a Professor at the Leiden Institute of Advanced Computer Science (LIACS) , Leiden University , Netherlands, and a member of the interdisciplinary programme Society, Artificial Intelligence and Life Sciences (SAILS) . His academic career spans roles at Leiden University (1996–present) and leadership positions at the Center for Applied Systems Analysis in Dortmund (1994–2000). Education : Diplom-Informatiker (Computer Science), Technische Universität Dortmund (1990) Dr. rer. nat. (Computer Science), Technische Universität Dortmund (1994) Research Interests : Dr. Bäck specializes in evolutionary computation , machine learning , and their applications in sustainable smart industry and healthcare . Recent work focuses on integrating large language models (LLMs) and quantum computing into optimization frameworks, with projects like CIMPLO (predictive maintenance), ECOLE (experience-based optimization), and SAPPAO (airline operations optimization). Scientific Contributions : His 526+ publications cover evolutionary algorithms, quantum optimization, and LLM-driven design, with recent trends including: Quantum computing (e.g., quantum approximate optimization, quantum advantage challenges) LLM integration (e.g., hyperparameter tuning, mutation control, code evolution graphs) Healthcare and industry (e.g., predictive maintenance, anomaly detection, melt quality prediction) Algorithm benchmarking (e.g., IOHprofiler, MA-BBOB, explainable benchmarking) Scientific Awards : IEEE Fellow (2022) Royal Netherlands Academy of Arts and Sciences (KNAW) member (2021) Academia Europaea member (2022) IEEE Computational Intelligence Society Evolutionary Computation Pioneer Award (2015) Fellow, International Society of Genetic and Evolutionary Computation (2003) Best Ph.D. thesis award, German Society of Computer Science (GI) (1995) Advising and Grants : He supervises Ph.D. candidates in evolutionary computation and machine learning and has secured 7 major grants from organizations like the Dutch Research Council , European Commission , and The Research Council of Norway . His editorial roles include Editor-in-Chief of the Evolutionary Computation Journal and associate editorships in leading AI journals.

Rogério de Lemosمشاهده پروفایل

مدرس ارشد

• Resilient AI
• Self-adaptive software systems
• Authorisation infrastructures
• +۷ مورد دیگر

Rogério de Lemos is a Senior Lecturer in Computing Science and Director of Postgraduate Research (PGR) at the School of Computing, University of Kent. He previously served as an invited assistant professor at the University of Coimbra, Portugal, and as a Senior Research Associate at the Centre for Software Reliability (CSR) at the University of Newcastle upon Tyne, UK. Dr. de Lemos' research focuses on architecting resilient systems, particularly in resilient AI, self-adaptive software systems, and authorization infrastructures. He belongs to both the Programming Languages and Systems Group and the Cyber Security Group at the University of Kent. His specific research interests include: Software engineering for self-adaptive systems assurances and resilience evaluation Dynamic generation of processes Handling insider threats using self-adaptive authorization Architectural abstractions for fault tolerance Verification and validation of dependable software architectures Software development for safety-critical systems Dependability and bioinspired computing His publication trends show increasing emphasis on practical applications of self-adaptive systems in cyber security contexts, with recent work spanning network traffic analysis, cryptographic function detection, and cloud-edge security architectures. His research bridges theoretical foundations with practical implementations, particularly in cyber security and resilient systems architecture. Dr. de Lemos currently leads the "Collaborative and Confidential Information Sharing and Analysis for Cyber Protection" project funded by the European Union's Horizon 2020 Programme. His past projects include "ADAAS: Assuring Dependability in Architecture-based Adaptive Systems" and multiple collaborations with NCR on sensor fusion and fault tolerance. As Director of Postgraduate Research, he oversees the School of Computing's research degree programs and likely supervises PhD students in resilient systems and cyber security, though specific student names are not listed in the available information.

Matthew W. Kunzمشاهده پروفایل

دانشیار

• Astrophysical plasma dynamics
• Instability and turbulence in plasmas
• Transport in weakly collisional plasmas
• +۲ مورد دیگر

Matthew W. Kunz is an Associate Professor of Astrophysical Sciences at Princeton University, serving as Associate Chair of the Department of Astrophysical Sciences and Director of Graduate Studies for the Program in Plasma Physics. He holds a B.S. in Astronomy-Physics and B.A. in Music from the University of Virginia (2003), and a Ph.D. in Physics from the University of Illinois at Urbana-Champaign (2009). His research focuses on astrophysical plasma dynamics, including instability, turbulence, and transport in weakly collisional and poorly ionized plasmas, with applications to galaxy clusters, accretion disks, and the solar wind. Dr. Kunz's work employs analytical and numerical methods to study multi-scale plasma dynamics, aiming to understand angular momentum transport in accretion disks, kinetic turbulence cascades, and magnetic field evolution. His research has been recognized with several awards, including an NSF CAREER Award (2020-25), Alfred P. Sloan Research Fellowship (2017-20), and NASA Einstein Postdoctoral Fellowship (2011-14). He teaches courses on plasma astrophysics (AST 521), irreversible processes in plasmas (AST 554), and astrophysical research methods (AST 303). His publications demonstrate a consistent focus on plasma turbulence, magnetic reconnection, and cosmic ray propagation, with recent work emphasizing collisionless plasma dynamics and high-energy astrophysical phenomena.

Anthony Niblettمشاهده پروفایل

استاد

• Artificial Intelligence
• Law
• Economics
• +۶ مورد دیگر

Anthony Niblett is a Professor and Canada Research Chair in Law, Economics, & Innovation at the University of Toronto Faculty of Law. He is an Affiliate Researcher with the Vector Institute for Artificial Intelligence and co-founder of Blue J, a startup leveraging AI for tax and legal professionals. Education: Ph.D. in Economics, Harvard University (2009) M.A. in Economics, Harvard University (2006) LL.B. (Honours), University of Melbourne (2003) B.Com. (Honours in Economics), University of Melbourne (2003) Bigelow Fellow, University of Chicago His research bridges artificial intelligence , innovation , and legal theory , with a focus on contract law , competition policy , and judicial behavior . He explores how machine learning transforms legal practice, regulatory frameworks, and judicial decisions. His work includes self-driving contracts , computational antitrust , and the personalization of law . Recent publications analyze AI’s role in legal disagreement , gender gaps in employment law , and computational merger reviews . His articles emphasize the intersection of technology , economics , and legal reform . Scientific Awards: Canada Research Chair in Law, Economics, & Innovation Bigelow Fellow, University of Chicago He serves as an Academic Advisor at the Future of Law Lab and contributes to Blue J , advancing AI applications in legal domains. His teaching includes Contract Law , Torts , and Economic Analysis of Law .

Jay Straderمشاهده پروفایل

استاد

• Compact Objects
• Black Holes
• Neutron Stars
• +۵ مورد دیگر

Jay Strader is a Professor in the Department of Physics and Astronomy at Michigan State University, where he serves as Graduate Director for the astronomy PhD program and Associate Chair for astronomy. His research focuses on compact objects, particularly black holes and neutron stars in globular clusters, neutron star binaries in Fermi gamma-ray sources, and intermediate-mass black holes. He has received a Packard Fellowship for Science and Engineering and grants from NSF and NASA. His research group includes postdoc Ryan Urquhart, graduate students Thomas Do and Rebecca Kyer, and several undergraduates, with past students like Teresa Panurach (now director of NoVEL Consortium) and Samuel Swihart (NRC fellow at Naval Research Lab). Education: PhD in Astronomy, UC-Santa Cruz/Lick Observatory Awards: Packard Fellowship Collaborations: Member of Rubin Observatory's Stars, Milky Way, and Local Volume science collaboration since 2008 Previous Positions: Hubble Fellow and Menzel Fellow at Harvard-Smithsonian Center for Astrophysics (2007-2012) Program Initiatives: Co-founder of PAREDS program for early research opportunities at MSU His work has been supported by NSF and NASA grants, and he has contributed to studies on black holes in M22, hypervelocity globular clusters around M87, and transitional millisecond pulsars. His group collaborates with Laura Chomiuk and contributes to data catalogs like the M31 globular cluster velocity dispersion database.

August Evrardمشاهده پروفایل

استاد

• Cosmology
• Astrophysics
• Galaxy Clusters
• +۲ مورد دیگر

Professor August Evrard is a distinguished academic at the University of Michigan, holding the Arthur F. Thurnau Professorship in Physics and Astronomy. He is affiliated with the Department of Physics within the College of Literature, Science, and the Arts. Known for his contributions to cosmology and astrophysics, he pioneered the Problem Roulette tool, recognized with the Provost's Teaching Innovation Prize. His research focuses on galaxy clusters, dark matter, and cosmological surveys like the Dark Energy Survey (DES) and XXL Survey. He has been honored as an AAS Fellow (2025) and has contributed to advancements in physics education through innovative teaching methods and technologies. In research, Prof. Evrard explores topics such as dark matter halo dynamics, galaxy cluster properties, and weak lensing analyses. His work spans observational cosmology, computational modeling, and multi-wavelength astronomy. Notable projects include studies on galaxy cluster mass distributions, the relationship between X-ray emissions and velocity dispersions, and the application of machine learning to astrophysical data analysis. His contributions to education highlight the integration of AI-driven tools to enhance learning, as seen in initiatives like the Problem Roulette and course recommendation systems. Prof. Evrard's awards include the Provost's Teaching Innovation Prize for Problem Roulette and his AAS Fellowship. His academic leadership and innovative approaches to both research and education solidify his role as a pivotal figure in astrophysics and STEM pedagogy.

Henry D. Pfisterمشاهده پروفایل

استاد

• Information Theory
• Error-Correcting Codes
• Quantum Computing
• +۳ مورد دیگر

Henry D. Pfister is the Addy Family Professor of Electrical and Computer Engineering at Duke University, with a secondary appointment in Mathematics. He holds affiliations with the Pratt School of Engineering and the Duke Quantum Center. His research focuses on information theory, error-correcting codes, quantum computing, and machine learning applications in communications. Pfister earned his Ph.D. from UC San Diego and has held prior roles at Texas A&M University, École Polytechnique Fédérale de Lausanne, and Qualcomm. Education: Ph.D. in Electrical Engineering, UC San Diego (2003); M.S. degrees in Public Policy and Environmental Management from Duke University; J.D. and additional degrees from UNC Chapel Hill. Research interests include Reed-Muller codes, quantum error correction, neural decoders for DNA storage, and capacity-achieving coding schemes. Recent work highlights include proving Reed-Muller codes achieve capacity on binary-erasure channels and developing quantum-enhanced classical communication protocols. Publications span topics like polar codes for quantum channels, belief-propagation algorithms, and neural network-based decoding. Notable grants include NSF funding for DNA storage coding and quantum simulation projects. Pfister has advised over 20 graduate students and is a recipient of the STOC Best Paper Award and NSF CAREER Award.

• Materials Science
• Metallurgy
• Ceramic Materials
• +۳ مورد دیگر

Joakim Odqvist is a Professor and Head of Department at the Structures unit within the Royal Institute of Technology (KTH). He specializes in materials science, with a focus on phase separation in alloys, nanostructure evolution, and the mechanical behavior of advanced materials. His research integrates experimental techniques like small-angle neutron scattering and atom probe tomography with computational modeling to study materials such as stainless steels, cemented carbides, and cast irons. Odqvist teaches courses in ceramic materials, material design, and materials structures, mentoring students at both undergraduate and graduate levels. His work addresses challenges in corrosion resistance, fatigue, and additive manufacturing, contributing to the development of high-performance materials for industrial applications. Research Interests: Phase separation mechanisms, spinodal decomposition in Fe-Cr alloys, nanostructure evolution in duplex stainless steels, diffusion kinetics, and the application of statistical models to hydrogen diffusion. His studies often bridge fundamental material science with practical engineering solutions, emphasizing predictive simulations and material design. Recent Articles: His publications focus on controlling nanostructures in super duplex steels, functional gradient carbides, and statistical models for hydrogen diffusion. Key themes include optimizing heat treatments to mitigate embrittlement, understanding precipitation kinetics in additively manufactured materials, and leveraging phase field modeling to predict material behavior. These studies highlight his role in advancing materials for harsh environments, such as aerospace and energy sectors.

Martin Burgerمشاهده پروفایل

استاد

• Inverse Problems
• Applied Mathematics
• Mathematical Modelling
• +۱۵ مورد دیگر

Prof. Dr. Martin Burger is a leading scientist at DESY and a Full Professor in the Department of Mathematics at Universität Hamburg, where he leads the Computational Imaging Group. His research bridges applied mathematics, imaging sciences, and machine learning, with a focus on inverse problems, mathematical modeling, and partial differential equations. He has held professorial positions at Universität Münster and FAU Erlangen-Nürnberg prior to his current dual appointment. Full Professor, Universität Hamburg (2023–present) Leading Scientist, DESY, Hamburg (2023–present) Full Professor, FAU Erlangen-Nürnberg (2018–2023) Full Professor, Universität Münster (2006–2018) His research interests include inverse problems, variational regularization, optimal transport, kinetic models, and mathematical modeling in biology and social sciences. He has made significant contributions to imaging reconstruction, sparse neural networks, and the analysis of transformer architectures. His work often integrates theoretical analysis with computational methods, influencing both pure and applied mathematics. The most recent articles reflect a strong trend toward interdisciplinary applications, combining deep learning with PDE-based modeling, analyzing social and biological systems via kinetic and mean-field models, and advancing mathematical imaging through graph-based and optimal transport methods. His publications span high-impact venues in applied mathematics and computational science. Calderon Prize, Inverse Problems International Association (IPIA) ERC Consolidator Grant (2014) Invited speaker at ECM (2021), ICM (2022), and ICIAM (2023) Editor-in-Chief, European Journal of Applied Mathematics (since 2017) Prof. Burger has supervised numerous PhD students and postdoctoral researchers, many of whom appear as co-authors in his publications. His research is supported by major grants, including funding from the German Federal Ministry of Education and Research (BMBF). He is actively involved in collaborative projects across mathematics, physics, and engineering disciplines. He leads the Computational Imaging Group at DESY, fostering a collaborative environment for developing novel mathematical tools in imaging science. The group works on both theoretical foundations and practical implementations, contributing to advancements in tomography, machine learning, and data analysis.