Find Academic Supervisors

Tulika MitraView profile

Professor

• Hardware-Software Co-design
• Real-Time Embedded Systems
• Energy-Efficient Computing
• +7 more

Professor Tulika Mitra is the Dean of the School of Computing and Vice Provost (Special Projects) at the National University of Singapore (NUS). She holds the Provost’s Chair Professor in the Department of Computer Science and has been instrumental in shaping academic policies and strategic initiatives at NUS since joining in 2001. PhD in Computer Science, Stony Brook University (2000) M.E. in Computer Science, Indian Institute of Science (1997) B.E. in Computer Science, Jadavpur University (1995) Her research focuses on hardware-software co-design for energy-efficient computing systems, particularly in real-time embedded systems, heterogeneous architectures, and AI accelerators. She leads major research programs such as the NRF Competitive Research Programme on Low-Power Edge Accelerators and the MOE Tier-3 Programme on Green AI , collaborating with industry leaders like ARM, AMD, and Meta. Her recent publications highlight innovations in CGRA optimization , sparse attention mechanisms , photonic-digital hybrid architectures , and low-power ML inference . These works often integrate compiler techniques, architectural design, and real-time constraints for edge computing applications. Scientific Awards : ESWEEK Test-of-Time Award (2022), ACM SIGDA Distinguished Service Award, IEEE CEDA Outstanding Service Recognition Award, Teaching Excellence Award (2006), and multiple best paper recognitions. Education Leadership : Spearheaded the Computer Engineering (CEG) Programme at NUS, a joint initiative between Engineering and Computing. As a mentor , she has supervised over 25 PhD students , many now in prominent academic or industrial roles. Her research group eCO Lab focuses on embedded computing challenges, while her grant collaborations include projects on 5G base stations, reconfigurable architectures, and IoT-optimized SoCs.

Amin SakzadView profile

Associate Professor

• Lattice-Based Cryptography
• Wireless Communications
• Post-Quantum Cryptography
• +6 more

Dr. Amin Sakzad is an Associate Professor in the Department of Software Systems & Cybersecurity at Monash University's Faculty of Information Technology. His research focuses on lattice-based cryptography, wireless communications, and post-quantum security protocols. He holds a PhD in Applied Mathematics from Amirkabir University of Technology (2011) and has held academic roles at Carleton University and Monash since 2012. Dr. Sakzad’s expertise spans lattice coding theory, MIMO systems, and privacy-preserving technologies for genomic databases and blockchain applications. He leads multiple ARC-funded projects, including work on secure databases (SRDBMS) and post-quantum cryptographic primitives for FinTech and energy sectors. His research has been recognized through awards such as the FIT Dean’s Award for Teaching Excellence (2021). Key collaborations include projects on blockchain security (CollinStar Lab), genomic data privacy, and energy market cybersecurity. His work addresses UN SDGs through contributions to quality education (SDG 4) and industry innovation (SDG 9). Recent publications highlight advancements in lattice-based cryptography (e.g., CRYSTALS-Kyber variants), privacy-preserving energy trading, and secure blockchain protocols like FPPW watchtower systems. His research bridges theoretical cryptography with practical implementations in embedded systems and 5G telecommunications. Grants: 16 active/completed projects including $1.2M in ARC funding Advising: Supervising PhD projects on lattice applications in post-quantum crypto and blockchain Labs: Core member of Monash’s Software Defined Telecommunications (SDT) Lab and CollinStar Lab

• Computer Architecture
• Machine Learning
• Approximate Computing
• +3 more

Hadi Esmaeilzadeh is an Associate Professor at the University of California, San Diego in the Department of Computer Science and Engineering . He leads the Alternative Computing Technologies (ACT) Laboratory and serves as Associate Director of the Center for Machine Integrated Computing and Security (MICS) . Previously, he was an Assistant Professor at Georgia Institute of Technology. Ph.D., Computer Science and Engineering, University of Washington (2013) Research focuses on computer architecture , machine learning acceleration , and approximate computing His work has produced 15+ publications spanning IEEE Micro Top Picks , CACM Research Highlights , and ISCA . Key projects include: Tabla : Cross-stack ML acceleration framework DnnWeaver : Open-source DNN acceleration platform Major honors include: IEEE TCCA Young Computer Architect Award ISCA Hall of Fame Qualcomm Innovation Fellowship Georgia Tech PURA Award Teaching roles: CSE 141: Introduction to Computer Architecture CSE 240D: Accelerator Design for Deep Learning CSE 240A: Principles of Computer Architecture

• Information Theory
• Cryptography
• Lattice Theory
• +5 more

Cong Ling is a Professor of Information Theory and Cryptography at Imperial College London's Department of Electrical and Electronic Engineering, within the Faculty of Engineering. His research focuses on lattice theory and its applications in coding, cryptography, quantum information, and number theory. Key affiliations include the Academic Centre of Excellence in Cyber Security Research and the Engineering Secure Software Systems group. Education details are not explicitly provided in the text, but his professional experience indicates advanced qualifications in electrical engineering and mathematics. Research interests span lattice-based cryptography, post-quantum security, algebraic coding theory, and quantum-resistant algorithms. His work bridges information theory and number theory, with contributions to MIMO systems, secure communication protocols, and cryptographic protocol design. Recent publications emphasize lattice reduction techniques, quantum algorithms for the shortest vector problem, and advancements in polar codes. Notable trends include exploration of non-commutative algebras for cryptography, Gaussian sampling optimizations, and hybrid quantum-classical approaches to hard integer problems. Over 50+ articles published since 2018 reflect his leadership in lattice-based research and quantum-safe technologies. Awards: None explicitly listed in the text. Grants/Advising: No specific grants or student advisees mentioned; focus remains on collaborative research outputs. Labs/Teams: Associated with Imperial's Cyber Security Research groups and quantum engineering initiatives.

Mikaela IacobelliView profile

Associate Professor

• Analysis
• Kinetic theory
• Statistical mechanics
• +7 more

Mikaela Iacobelli is an Associate Professor in the Department of Mathematics at ETH Zürich. During the 2024-25 academic year, she was a von Neumann Fellow at the Institute for Advanced Study in Princeton. Previously, she held faculty positions at Durham University and a research fellowship at the University of Cambridge. Her educational background includes: PhD in Mathematics from Sapienza University of Rome and École Polytechnique in Paris (2015) Master's degree from Sapienza University of Rome (2012) Bachelor's degree from Sapienza University of Rome (2009) Mikaela's research lies at the interface of analysis, kinetic theory, and statistical mechanics. She studies partial differential equations that model the collective behavior of many-particle systems, with a focus on Vlasov-type plasmas and gravitational dynamics. Her current projects range from quasineutral and singular-limit problems for Vlasov-type systems to quantization of measures, ultrafast diffusion, and gradient-flow structures that link microscopic particle models to macroscopic fluid descriptions. She makes extensive use of PDEs techniques, optimal transport, probability, calculus of variations, and Riemannian geometry in her work. Her recent publications demonstrate a strong focus on Vlasov-type equations, particularly examining quasineutral limits, stability properties, and connections to other physical systems like Euler equations and magnetohydrodynamics. She has made significant contributions to understanding Landau damping, quantization problems on manifolds, and the mathematical foundations of plasma physics. Her notable scientific awards include: SNSF Starting Grant (Swiss ERC) Challenges and Breakthroughs in the Mathematics of Plasmas (2025-2030) von Neumann Fellow at the Institute for Advanced Study, Princeton (2024-2025) Invited speaker at the International Congress of Mathematical Physics (2021) CO-PI of the Germaine de Staël Funding Program for French-Swiss cooperation (2021-2023) L'Oréal prize for Women in Science (2015) Mikaela actively mentors postdocs, PhD, Master's, and Bachelor's students. Her current mentees include postdocs Dennis Chemnitz, Rishabh Gvalani, Stefano Rossi, and Simon Becker, as well as PhD students Thérèse Moerschell, Ata Deniz Aydin, and Antoine Gagnebin. She has served as PI for the Starting Research Grant from the University of Rome Sapienza and is currently the PI for the SNSF Starting Grant. She co-organizes several academic seminars including the Zurich Colloquium in Mathematics, the PDE and Mathematical Physics seminar at ETH Zürich and UZH, and the Analysis Seminar. She also serves on various committees including as Chair of the European Mathematical Society Committee for Women in Mathematics.

Masoud AsgharianView profile

Professor

• Survival Analysis
• Changepoint Problem
• Nonparametric Bayesian Analysis
• +5 more

Masoud Asgharian is a Professor in the Department of Mathematics and Statistics at McGill University. His research focuses on survival analysis, changepoint problems, nonparametric Bayesian methods, and data envelopment analysis. He has contributed to influential studies on dementia survival rates, censored data methodologies, and statistical efficiency measures. His work bridges biostatistics and operations research, with applications in public health and medical sciences. Key contributions include methodologies for prevalent cohort survival analysis, input relaxation efficiency measures in stochastic DEA, and causal inference techniques. Asgharian has collaborated extensively with researchers in epidemiology and biomedical engineering, as evidenced by his co-authored publications on topics ranging from tooth enamel properties to low-precision neural network quantization. His research has been published in high-impact journals such as New England Journal of Medicine , Journal of the American Statistical Association , and Biometrics . Current affiliations include leadership roles in statistical research at McGill, with ongoing projects in computational statistics and healthcare analytics.

• Mathematics
• Machine Learning
• Quantum Topology
• +9 more

Sergei Gukov is the John D. MacArthur Professor of Theoretical Physics and Mathematics at the California Institute of Technology (Caltech), where he has been a faculty member since 2005. He serves in the Division of Physics, Mathematics and Astronomy, with primary affiliation in the Department of Mathematics. His research bridges the fields of mathematics and theoretical physics, focusing on deep connections between geometry, topology, and quantum field theory. Gukov received his B.S. from Moscow Institute of Physics and Technology in 1997, followed by an M.S. and Ph.D. from Princeton University in 2001. He joined Caltech as an Associate Professor in 2005, was promoted to Professor in 2007, and was named the John D. MacArthur Professor in 2021. His research spans several interconnected areas at the frontier of mathematics and physics. A central theme is the exploration of quantum topology and its connections to mathematical physics. He has made significant contributions to the geometric Langlands program, gauge theory, and the categorification of knot and 3-manifold invariants. His recent work increasingly incorporates machine learning approaches to mathematical problems, reflecting his interest in the intersection of traditional mathematical research and modern computational techniques. Gukov's work often reveals deep connections between seemingly disparate areas of mathematics and physics, such as the relationship between Rozansky-Witten geometry and Coulomb branches in supersymmetric gauge theories. Gukov's publications demonstrate a consistent focus on the mathematical structures underlying quantum field theories and their topological implications. His recent work shows an increasing emphasis on computational approaches to mathematical problems, particularly through his interest in mathematics and machine learning. The recurring themes across his research include the application of physical insights to solve mathematical problems and the discovery of new mathematical structures through physical reasoning. He serves on the editorial boards of several prestigious journals including the Journal of Knot Theory and Its Ramifications, Communications in Mathematical Physics, and Letters in Mathematical Physics. Gukov is also active in the academic community, having delivered plenary talks at major conferences such as the First International Congress of Basic Science and presenting at String Math 2023 on the potential impact of AI on mathematical research. Gukov teaches Ma 146 ab, Introduction to Knot Theory and Quantum Topology, a course that reflects his research interests. He also runs a seminar on Mathematics and Machine Learning, held Tuesdays from 2-3pm in East Bridge Conference room 114, demonstrating his commitment to fostering interdisciplinary research at the intersection of mathematics and computational methods.

Bruno ClerckxView profile

Professor

• Wireless Communications
• Signal Processing
• MIMO Wireless Networks
• +7 more

Prof. Bruno Clerckx is a Professor of Wireless Communications and Signal Processing at Imperial College London's Department of Electrical and Electronic Engineering, Faculty of Engineering. He leads the Communications and Signal Processing Group and the Wireless Communications and Signal Processing Lab. Education: M.Sc. and Ph.D. in Electrical Engineering from Université Catholique de Louvain, Belgium Doctor of Science (DSc) from Imperial College London Research Interests: Focuses on wireless communications and signal processing for next-generation networks, including MIMO systems, reconfigurable intelligent surfaces (RIS), rate-splitting multiple access (RSMA), and integrated sensing and communications (ISAC). His work emphasizes 6G technologies, full-duplex systems, and energy-efficient architectures. Key contributions include pioneering research on beyond-diagonal RIS and RSMA prototyping. Awards: 2021 Blondel Medal (France) 2021 Adolphe Wetrems Prize (Royal Academy of Belgium) Fellowships from IEEE and IET IEEE Communications Society Distinguished Lecturer (2021-2023) Labs & Teams: Heads the Wireless Communications and Signal Processing Lab, and is affiliated with the IEEE Special Interest Groups on RSMA and BD-RIS. Collaborates with global institutions including Stanford University, Tsinghua University, and Samsung Electronics. Industry Experience: Former CTO of Silicon Austria Labs and contributor to 4G/5G standards at Samsung. Holds 80+ patents and authored two books on MIMO systems.

Tosiron AdegbijaView profile

Associate Professor

• Computer Architecture
• Embedded Systems Design
• Low-Power Embedded Systems
• +14 more

Tosiron Adegbija is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Arizona, where he serves as Director of Graduate Studies and Thomas R. Brown Endowed Fellow. He is a member of the Graduate Faculty and actively contributes to research and teaching in computer architecture and embedded systems. Education: PhD in Electrical and Computer Engineering, University of Florida, 2015 MS in Electrical and Computer Engineering, University of Florida, 2011 BS in Electrical Engineering, University of Ilorin, Nigeria, 2005 His research centers on energy-efficient computing with a focus on bio-inspired computer architecture , including spiking neural network (SNN) accelerators and in-memory computing. He also explores domain-specific architectures , adaptable memory systems , and microprocessor optimizations for IoT . His work leverages novel memory technologies like STT-RAM to enhance performance and reduce energy consumption in embedded and resource-constrained systems. Recent publications highlight trends in hybrid SNN acceleration, domain-specific accelerator generation, and system-level design space exploration. His research is increasingly focused on neuromorphic computing, automated hardware design, and ultra-efficient architectures using emerging materials like antiferromagnetic tunnel junctions. Scientific Awards: National Science Foundation (NSF) CAREER Award (2019) Elected IEEE Senior Member (2020) Best Paper Award at IEEE ISVLSI (2014) Teaching Award, University of Arizona (2018) ACM GLSVLSI Travel Award (2015) He advises numerous graduate and undergraduate students, many of whom have pursued careers at institutions like Pacific Northwest National Labs, Micron Technology, and Amazon. He has secured significant funding, including a $1.9M NSF FuSE2 grant for energy-efficient computing. His lab collaborates with UA Physics, CMU, and UNL. He has also developed educational tools for Chipyard and RISC-V, supporting hands-on learning in computer architecture. Labs and Research Teams: Leads a research group focused on bio-inspired and domain-specific computing, fostering innovation in energy-efficient hardware. The lab emphasizes hardware/software co-design, neuromorphic engineering, and real-world deployment in IoT and biomedical applications.

• Machine Learning
• Dynamical Systems
• Optimization
• +3 more

Michael Muehlebach leads the independent Learning and Dynamical Systems research group at the Max Planck Institute for Intelligent Systems in Tuebingen, Germany. His interdisciplinary work bridges machine learning, dynamical systems theory, and control engineering to develop algorithms for cyber-physical systems with theoretical guarantees and practical implementations. Dr. Muehlebach received his B.Sc. and M.Sc. in Mechanical Engineering from ETH Zurich in 2010 and 2013, specializing in robotics and control systems. He completed his Ph.D. at ETH's Institute for Dynamic Systems and Control under Prof. R. D'Andrea in 2018, followed by postdoctoral research with Prof. Michael I. Jordan at UC Berkeley. His research focuses on constrained optimization, reinforcement learning, and control theory with applications in robotics. He pioneered approaches that express constraints in terms of velocities rather than positions, enabling more efficient optimization algorithms. His work spans theoretical foundations to physical implementations, including the One-Wheel Cubli balancing robot and electromagnetic navigation systems. Recent publications reveal a strong trend toward physics-informed machine learning, particularly for robotics applications requiring real-time performance and safety guarantees. Dr. Muehlebach has received numerous prestigious awards: Outstanding D-MAVT Bachelor Award Willi-Studer prize for best Master's degree ETH Medal and HILTI prize for doctoral thesis Branco Weiss Fellowship (2018) Emmy Noether Fellowship (2020) Amazon Fellowship (2024) He actively mentors doctoral researchers including Hao Ma, Melis Ilayda Bal, and Onno Eberhard, with research supported by multiple grants. His group maintains strong collaborations with Bernhard Schölkopf's Empirical Inference group at the Max Planck Institute. The Learning and Dynamical Systems group develops innovative hardware and software platforms, including Floaty (a wind-harnessing flying robot), advanced electromagnetic navigation systems, and data-efficient learning methods for robotic table tennis. Their approach combines rigorous theoretical analysis with practical validation on physical systems, emphasizing the integration of known physical structure into machine learning algorithms to improve sample efficiency and ensure generalization.

Berrak SismanView profile

Assistant Professor

• Artificial Intelligence
• Speech Processing
• Machine Learning
• +3 more

Berrak Sisman is an Assistant Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, affiliated with the Data Science and AI Institute and the Center for Language and Speech Processing (CLSP). She leads the Speech & Machine Learning Lab (SmILe Lab), focusing on AI-driven speech technologies. She received her PhD from the National University of Singapore in 2020 and was previously a tenure-track faculty member at the University of Texas at Dallas (2022–2024). Research Interests: Her work spans artificial intelligence, speech synthesis, voice conversion, emotion analysis in speech, medical speech applications, and secure speech technology. She develops neural models for expressive and adaptive speech processing. Publications: Her recent articles (2024–2025) emphasize speech emotion recognition, zero-shot prosody control, accent conversion, and disentangled representations in TTS, reflecting a focus on cross-modal learning, robustness, and real-world applications. Awards & Grants: NSF CAREER Award (2024) Amazon Faculty Research Award (2022) Singapore Ministry of Education Award (2021) A*STAR Singapore International Graduate Award (2016–2020) Leadership: She directs the SmILe Lab, recruiting PhD/Master’s students for projects in neural speech modeling. Her grants include NSF and Amazon funding for voice conversion and emotion synthesis research.

• Electrical and Electronic Engineering
• Computer Hardware
• Computer Software
• +7 more

Professor Peter Y. K. Cheung is a Professor of Digital Systems at Imperial College London, holding dual affiliations within the Department of Electrical and Electronic Engineering and the Dyson School of Design Engineering. His work focuses on reconfigurable systems, FPGA architectures, and high-level synthesis tools. He co-founded one of the UK's leading FPGA research groups with Professor Wayne Luk, addressing challenges in variability mitigation, reliability, and application-specific FPGA deployments. His research spans Field-Programmable Gate Arrays (FPGAs) Reconfigurable computing Neural network acceleration Cryptographic protocols Embedded systems He has pioneered techniques such as logic shrinkage for FPGA-based neural networks and developed frameworks like LUTNet for efficient inference. His contributions also include fault-tolerant FPGA designs and methodologies for distributed computation protocols. Key collaborations include work with the Department of Computing on FPGA-based AI acceleration and cybersecurity applications. His recent work explores edge computing, secure decentralized systems, and pandemic modeling using adaptive control strategies. Notable projects include the DSCS protocol for secure distributed computation, acceleration of gravitational wave detection algorithms, and energy-efficient CNN implementations. His research bridges hardware-software co-design with real-world applications in healthcare, finance, and aerospace.

Charles A. BoumanView profile

Professor

• Computational Imaging
• Statistical Image Models
• Tomographic Image Reconstruction
• +13 more

Charles A. Bouman is the Showalter Professor of Electrical and Computer Engineering and Biomedical Engineering at Purdue University, with a courtesy appointment in Mathematics. He is a leading researcher in computational imaging, integrating statistical signal processing, physics, and computation for applications in healthcare, scientific, and industrial imaging. Education: B.S.E.E., University of Pennsylvania, 1981 M.S., University of California at Berkeley, 1982 Ph.D. in Electrical Engineering, Princeton University, 1989 His research focuses on computational imaging , including statistical image models, multiscale techniques, tomographic reconstruction, and fast algorithms. Key areas include Model-Based Iterative Reconstruction (MBIR), Plug-and-Play priors, document processing, and multiscale segmentation. His work has led to foundational contributions in total variation regularization and sparse-view reconstruction. The recent publications highlight a strong trend in integrating machine learning with physical models for image reconstruction, particularly through Plug-and-Play methods. His work spans optical tomography, halftoning, image scaling, and document compression, demonstrating consistent innovation in both theory and practical software implementation. Scientific Awards and Honors: Member, National Academy of Inventors Life Fellow, IEEE Fellow, IS&T; Honorary Member (2022); Service Award (2023) Fellow, SPIE and AIMBE IEEE Signal Processing Society Claude Shannon-Harry Nyquist Award (2021) Electronic Imaging Scientist of the Year (2014) SIAM Imaging Science Best Paper Prize (2020) Founder, IS&T Computational Imaging Conference (2003) Co-Founder, IEEE Transactions on Computational Imaging Vice President, IS&T; Former VP of Publications (2000–2004) Bouman has advised numerous graduate students and leads a vibrant research group developing open-source tools like MBIRJAX , SVMBIR , and OpenMBIR . His research has been supported by the National Science Foundation, General Electric, Intel, Xerox, Hewlett-Packard, and the State of Indiana 21st Century Fund. He maintains an active presence through tutorials, conference leadership, and educational resources including video lectures and a textbook on Foundations of Computational Imaging. Labs and Research Teams: His group develops cutting-edge software for tomographic reconstruction, clustering, segmentation, and dynamic sampling. Projects include Gaussian Mixture modeling (GMCluster), Plug-and-Play implementations, Sparse Matrix Transforms, and UAV sensing datasets. The research is highly interdisciplinary, bridging engineering, mathematics, and biomedical applications.

Valter MorettiView profile

Professor

• Algebraic Quantum Field Theory
• Black Holes
• Functional Analysis
• +10 more

Valter Moretti is a Full Professor in the Department of Mathematics at the University of Trento. His academic career spans roles from Research Fellow to Full Professor, focusing on Mathematical Physics and Quantum Field Theory (QFT) in curved spacetime. He earned an MSc in Physics from Genova University and a PhD in Theoretical Physics from Trento University. Research Interests : Algebraic QFT, General Relativity, Quantum Mechanics, Operator Algebras, and Spectral Theory. His work bridges mathematical rigor with physical applications, particularly in quantum localization, entanglement, and curved spacetime phenomena. Publications : Authored 15+ recent papers on topics like quantum particle localization, entanglement certification, and QFT on curved backgrounds. Collaborated on a 2022 patent for generating entangled photon states. Awards : Holds a patent for a quantum-certified random number generator (2022). Supervision : Advised 8 PhD students, including N. Pinamonti, L. Franceschini, and C. van de Ven. Coordinated national and international research projects (e.g., H2020-MSCA-COFUND-2015). Labs & Collaborations : Affiliated with INFN, TIFPA-INFN, and Q@TN (Quantum@Trento). Organized conferences like Quantum Physics and Geometry (2014) and Quantum Machine Learning (2023). Teaching : Lectures on Analytical Mechanics, Quantum Relativistic Theories, and Special Relativity. Authored textbooks on Spectral Theory and Quantum Mechanics.

Patrick HuberView profile

Professor

• Condensed Matter Physics
• Materials Science
• Nanoporous Materials
• +6 more

Prof. Dr. Patrick Huber is a leading physicist and Institute Director at the Hamburg University of Technology (TUHH) , heading the Institute for Materials and X-Ray Physics (M-2) . He also leads the High-Resolution X-Ray Analytics of Materials group at DESY through a cooperative professorship. His research spans condensed matter physics , nanoporous materials , and X-ray analytics , with significant contributions to molecular water science and soft matter in confinement . Education: PhD in Physics (1999, Saarland University), Diploma in Physics (1995, Saarland University) Professional Career: Full Professor at TUHH (2020-present), Member of CRC 1615 (2023-present), Spokesperson for CMWS (2024-present), Cluster of Excellence BlueMat (2025) Research Interests focus on multi-scale material behavior under extreme confinement, particularly hierarchical porous silicon and silica systems . His work examines adsorption-induced deformation , elastocapillarity , fluid transport in nanopores, and metamaterial design principles using electrolytes , polymers , and liquid crystals . Fundamental studies include fluid interface thermodynamics and microscopic hydrodynamics . Scientific Awards include the Top Reviewer Award (2018) from Applied Physics Letters and the Dr.-Eduard-Martin Award (2000) for his dissertation. He contributes to 130+ publications with an h-index of 36 (2021). Advising and Grants involve supervising 18 doctoral and master's students , including Manuel Brinker , Marc Thelen , and Stella Gries . He participates in Collaborative Research Centre CRC 1615 , Cluster of Excellence EXC 3120 BlueMat , and the United Nations University Hub on Climate Engineering . Laboratory and Teams include the Institute for Materials and X-Ray Physics (M-2) at TUHH, the High-Resolution X-Ray Analytics group at DESY, and contributions to the Centre for Hybrid Nanostructures (CHyN) .