Find Academic Supervisors

Andreas LäuchliView profile

Professor

• Quantum Science
• Condensed Matter Physics
• Atomic, Molecular, and Optical (AMO) Physics
• +3 more

Andreas Läuchli is a Professor at the University of Innsbruck and a member of the Erwin Schrödinger Center for Quantum Science & Technology (ESQ). He currently heads the Institute of Theoretical Physics at the University of Innsbruck. His research focuses on the theoretical and computational exploration of correlated quantum matter in condensed matter and AMO systems. Key interests include novel phases of matter, quantum critical phenomena, and non-equilibrium quantum systems, leveraging concepts from condensed matter theory, statistical mechanics, quantum field theory, and quantum information science. He develops innovative computational algorithms for large-scale simulations on high-performance computers. Research Areas: Quantum Phase Transitions Quantum Criticality Many-Body Localization Tensor Network Methods Quantum Simulation Techniques Notable Contributions: His work bridges foundational quantum theory with advanced computational methods, advancing understanding of complex quantum systems. Currently leads a research group at the University of Innsbruck, contributing to Austria's quantum science initiatives through ESQ collaborations.

Katy CloughView profile

Senior Lecturer

• General Relativity
• Numerical Relativity
• Black Hole Physics
• +4 more

Dr. Katy Clough is a Senior Lecturer and STFC Ernest Rutherford Research Fellow at Queen Mary University of London, leading the Research Innovation and External Stakeholders Engagement within the School of Mathematical Sciences. She is a core member of the Centre for Geometry, Analysis and Gravitation. Her research focuses on testing the limits of general relativity and particle physics theories through numerical simulations of black hole environments and early universe inhomogeneities. She has secured significant grants, including £584,718 for her fellowship project addressing cosmological questions in strong gravity, and is part of a £1.6M STFC-funded astronomy research consortium. Her work spans fundamental fields like black hole dynamics, gravitational waves, and modified gravity theories. She actively supervises PhD students, emphasizing enthusiasm and learning over prior coding experience. Notable achievements include developing computational tools like GRFolres and GRChombo for modified gravity simulations, and her contributions to understanding scalar dark matter interactions, cosmic inflation robustness, and warp drive physics. Key awards include the STFC Ernest Rutherford Fellowship, recognizing her innovative research. Her grants highlight her role in advancing numerical relativity and cosmology. She also collaborates globally on projects like LISA gravitational wave mission science and axion star-black hole interactions.

Kaie KubjasView profile

Associate Professor

• Algebraic Statistics
• Nonlinear Algebra
• Matrix and Tensor Decompositions
• +5 more

Kaie Kubjas is an Associate Professor at Aalto University in the Department of Mathematics and Systems Analysis, School of Science. Since 2024, she has held a tenured position, following a tenure-track role from 2017–2024. She earned her PhD in Mathematics at Freie Universität Berlin (2013) under Professors Christian Haase and Klaus Altmann, with postdoctoral research at institutions including the Max Planck Institute and MIT. Her research focuses on applied nonlinear algebra, algebraic statistics, and their applications in biology (e.g., phylogenetics and 3D genome reconstruction), as well as matrix/tensor decompositions. She has organized major events like the European Women in Mathematics General Meeting 2022 and the 2025 workshop on Algebraic Statistics and Multistate Models. Kubjas serves on editorial boards of journals like SIAM Journal on Applied Algebra and Geometry and Annales Fennici Mathematici . Recent work includes advances in log-concave maximum likelihood estimation, 3D genome reconstruction, and structured matrix decompositions. Her students, such as Olga Kuznetsova (Second Place MEGA 2021 Poster Award winner), have contributed to these areas. She regularly contributes to seminars like the Algebra and Discrete Mathematics at Aalto, fostering interdisciplinary collaboration.

Rhine SamajdarView profile

Researcher

• Quantum Physics
• Condensed Matter Physics
• Quantum Computing
• +3 more

Rhine Samajdar is a Researcher at Princeton University, currently serving as a Postdoctoral Research Associate. Their work focuses on quantum many-body systems, quantum simulation, and topological phases of matter. Key research interests include Rydberg atom arrays, quantum spin liquids, and the interplay between topology and quantum dynamics. Research interests encompass quantum spin models, frustrated magnets, and the application of advanced techniques such as Floquet engineering and variational tensor networks. Samajdar's recent studies explore collective quantum phenomena like coarsening dynamics, supersolid phases, and the emergence of Dirac spin liquids in lattice models. Their work bridges theoretical predictions with experimental realizations through programmable quantum simulators. Publications highlight advancements in understanding quantum phase transitions, hyperuniformity in electron liquids, and the role of long-range interactions in Hubbard models. Ongoing efforts involve machine learning approaches to uncover hidden orders and optimize quantum state tomography. Samajdar collaborates with experimental groups to design quantum simulators for probing novel phases, such as those observed in twisted graphene superlattices and moiré materials. Their research contributes to foundational understanding of quantum matter with potential applications in quantum computing and topological materials.

Yajing LiuView profile

Associate Professor

• Earthquake Seismology
• Fault Mechanics
• Seismic Hazard Assessment
• +3 more

Yajing Liu is an Associate Professor and Canada Research Chair in Earthquake Seismology at McGill University's Department of Earth and Planetary Sciences. Her research focuses on earthquake mechanics, fault behavior, and anthropogenic seismicity, particularly induced by hydraulic fracturing. She combines geophysical observations with advanced numerical modeling to improve earthquake hazard prediction. Education: PhD (2007, Harvard University) and BSc (2001, Peking University). Professional experience includes roles at Woods Hole Oceanographic Institution and Princeton University. She leads the SCEC SEAS benchmark project, advancing community code verification for earthquake and aseismic slip simulations. Notable work includes discovering a novel type of slow-slip hydraulic fracturing-induced earthquakes in British Columbia and studying induced seismicity links to methane emissions. Research emphasizes subduction zones, fault geometry effects, and crustal stress dynamics. She has advised PhD student John Onwuemeka (JGR 2021) and secured NSERC strategic grants. Current projects explore seismic hazard mitigation in unconventional energy extraction and fault system interactions. Key awards: Canada Research Chair in Earthquake Seismology Labs/teams: SEAS community code project, McGill seismic hazard research group

Margaret MartonosiView profile

Professor

• Computer Architecture
• Quantum Computing
• Power-Efficient Systems
• +3 more

Margaret Martonosi is the William M. Addy ’82 University Professor of Computer Science at Princeton University, a position she has held since 1994. Her research focuses on computer architecture, with significant contributions to both classical and quantum systems. She has led major initiatives such as co-founding the ACM CARES movement and served as Director of the NSF Directorate for Computer and Information Science and Engineering (CISE) from 2020 to 2024. Her work emphasizes power-efficient designs, quantum hardware/software co-design, and scalable hybrid computing frameworks. Her academic leadership includes roles in broadening participation in computing through the Computing Research Association (CRA-W) and mentorship recognized by awards like the ACM Frances E. Allen Award. She has authored over 200 papers, with impactful contributions in security (e.g., SoCurity frameworks), quantum architectures (e.g., ARQUIN, HetArch), and heterogeneous computing (e.g., DCRA chiplet systems). Her recent research addresses challenges in quantum-classical integration and post-Moore’s Law computing paradigms. Publications highlight innovations like TensorQC for hybrid quantum-classical systems and CutQC for distributed quantum circuit optimization. Her NSF leadership shaped federal computing research priorities, emphasizing quantum science, AI, and cyberinfrastructure. She maintains active roles in technical societies and serves on non-profit boards focused on advancing computing equity and accessibility. Key grants include the $115M quantum science center (with Princeton’s electrical engineering collaboration) and EPiQC (Enabling Practical-Scale Quantum Computation). Her lab develops tools like MosaicSim for agile hardware-software co-design and RTLCheck for verifying hardware consistency models. Leadership Roles: NSF CISE Director (2020–2024), ACM CARES Co-founder, CRA-W Board Key Projects: Quantum-Supercomputing Integration, Power-Efficient Architectures, Formal Verification Frameworks Recent Focus: Quantum Benchmarking (SuperMARQ), Edge Computing Security, Chiplet-Based Systems

Philipp KanskeView profile

Professor

• Clinical Psychology
• Behavioral Neuroscience
• Social Cognition
• +5 more

Prof. Philipp Kanske is Chair of Clinical Psychology and Behavioral Neuroscience at TU Dresden, leading the Institute of Clinical Psychology and Psychotherapy since 2020. He holds a PhD (summa cum laude) from Leipzig University and completed postdoctoral research at the Central Institute of Mental Health and Max Planck Institute for Human Cognitive and Brain Sciences. His work focuses on social cognition, emotion regulation, and neuroimaging in psychiatric disorders like bipolar disorder. He has held prestigious roles including Young Academy member at the Leopoldina and BMBF Health Research Forum. Education: M.Sc. Psychology (University of Oregon, 2004), Diploma in Psychology (TU Dresden, 2005), Dr. rer. nat. (Leipzig, 2008), and psychotherapist license (2011). Research emphasizes neural mechanisms of empathy, theory of mind, and stress responses using fMRI/EEG. Editor roles include Cognition and Emotion and Scientific Reports . Key awards include the Otto Hahn Medal (2008) and Heinz Maier-Leibnitz Award (2017). His 78+ peer-reviewed papers explore social brain networks, with a SCOPUS h-index of 25. Collaborations span clinical, neuroimaging, and interdisciplinary projects addressing mental health vulnerability and neuroplasticity in interventions.

Jan LellmannView profile

Professor

• Variational Image Processing
• Optimization on Manifolds
• Image Registration
• +7 more

Jan Lellmann is a Professor at the Institute of Mathematics and Image Computing of the University of Lübeck, with affiliations to Fraunhofer MEVIS . His research focuses on variational image processing , emphasizing the systematic formulation of prior knowledge into energy functions for improved accuracy and data efficiency. Applications span medical imaging , earth sciences , and biological data analysis . He develops non-smooth optimization methods for problems with combinatorial aspects like image segmentation . His recent work includes manifold-constrained optimization and quantum algorithms for imaging tasks. He has contributed software libraries like MFOPT (for manifold optimization) and COAL (for convex energy minimization). Scientific Awards: Best Student Paper Award at SSVM 2021 Honorable Mention at CVPR 2016 Recent Research Trends: Integration of quantum computing with classical image registration. Advancements in Riemannian geometry for protein dynamics and cryo-EM. Development of meta-learning frameworks for adaptive image alignment. Focus on non-smooth and higher-order regularization for sparse data reconstruction.

Olga KloppView profile

Professor

• Nonparametric Estimation
• High-Dimensional Inference and Sparsity
• Network Models
• +3 more

Olga Klopp is a Professor of Statistics at ESSEC Business School and a member of the CREST Statistics Department. Her research focuses on nonparametric estimation, high-dimensional inference and sparsity, network models, and matrix completion. She has made significant contributions to statistical theory in low-rank modeling and missing data problems, with applications to networks, epidemiology, and machine learning. Nonparametric Estimation and High-Dimensional Inference : Key areas in her work, including theoretical guarantees and adaptive methods. Network Models and Graphons : Addressing dynamic networks, change-point detection, and graphon games with missing links. Matrix Completion : Pioneering work on robust and collective matrix completion with low-rank constraints. Her recent publications highlight advancements in tensor decomposition, topic modeling via projections, and sparse network estimation. She advises PhD students in statistics and network analysis.

• Databases
• Programming Languages
• Compilers
• +4 more

Amir Shaikhha is an Associate Professor (Reader) in the School of Informatics at the University of Edinburgh, specializing in data-analytics systems. Previously, he served as a Departmental Lecturer at the University of Oxford (2019-2020) and Assistant Professor at Edinburgh (2020-2024), following his 2018 PhD from EPFL where he received a Google Ph.D. Fellowship and thesis distinction award. His research spans: Databases Programming Languages Compilers Machine Learning Software Engineering with emphasis on high-performance system design through techniques like tensor algebra optimization, domain-specific languages, and probabilistic programming. Recent publications (2023-2025) reveal strong trends in quantum simulation synthesis, sparse tensor processing, and compiler-driven database optimization, evidenced by top-venue acceptances at PLDI, SIGMOD, and OOPSLA. His scientific recognition includes: Dahl-Nygaard Junior Prize (2025) Google Research Scholar Award (2025) Most Influential Paper Award at GPCE (2024) Best Paper Award at GPCE (2017) He actively mentors students including PhD graduate Hesam Shahrokhi (now at Huawei Research), award-winning MSc candidates like Youning, and CGO competition winners Jingwen and Callum. His service includes PC chair roles for GPCE 2023 and DBPL 2025, alongside program committee work for SIGMOD, VLDB, and ECOOP. As leader of the Data Analytics Lab (DAL), he develops open-source systems like StructTensor and VecHT for tensor processing, contributing to Edinburgh's Institute for Computing Systems Architecture with focus on bridging theoretical foundations with practical data-engineering solutions.

• Quantum Computing
• Theoretical Computer Science
• Combinatorics
• +5 more

Mario Szegedy is a Professor in the Department of Mathematics at Rutgers, The State University of New Jersey, and a member of the university’s Graduate Faculty. His research integrates quantum computing , theoretical computer science , combinatorics , and algorithms . A dominant theme is elucidating the power and limits of quantum algorithms relative to classical counterparts. Representative directions include: Quantum Monte Carlo methods and speed-ups Locally testable codes and quantum error correction Combinatorial optimization and approximation algorithms Graph-theoretic processes and Markov chains Communication complexity and decision-making under constraints Across more than two decades, Szegedy’s publications reveal a steady trajectory toward deeper quantum–classical separations, practical quantum algorithm design, and structural insights into hard combinatorial problems. His 2024–2025 work pushes further into non-linear quantum Monte Carlo and exotic quantum codes, while earlier contributions laid foundational results on query complexity and randomized algorithms. Although the provided text does not enumerate awards or funded grants, the breadth and longevity of publication in top venues implicitly signal sustained research funding and recognition within the theoretical computer-science community. Laboratory or center affiliations are not specified in the text, but his presence in the Mathematics Department and Graduate Faculty suggests active involvement in mentoring PhD students and postdocs in discrete mathematics and quantum information.

• Operating Systems
• Embedded Systems
• Networking
• +7 more

Matthew Lentz is an Assistant Professor of Computer Science at Duke University, where he has been employed since Fall 2021. He is affiliated with the Duke Systems Group within the Department of Computer Science under Trinity College of Arts & Sciences. Prior to joining Duke, he served as a Postdoctoral Researcher at VMware Research Group and maintains an affiliation as an Affiliated Researcher . Education: Ph.D. in Computer Science from University of Maryland, College Park (2020), advised by Bobby Bhattacharjee External Relationships: Collaborations with Broadcom and ongoing postdoctoral work with VMware Dr. Lentz's research operates at the intersection of systems, networking, and security. His work includes: Developing abstractions and tools for secure, trustworthy software systems Improving performance in modern networking and machine learning applications Exploring network performance measurement and security threat analytics Advancing GPU-as-a-Service infrastructure (via NSF CC* Compute grant 2024-2026) Research trends show consistent focus on: System security (endpoint security, verification, privacy-preserving communication) Networking optimization (service mesh, collective communication, mobile networks) Machine learning systems (inference optimization, MoE training, heterogeneous computing) Mobile computing (Bluetooth beacons, peripheral control, contact tracing) Scientific Awards: NSF CC* Compute grant for GPUs-as-a-Service infrastructure (2024) Advising & Grants: Advises current PhD students Alexander Du, Chenyang Liu, and Luka Duranovic while serving as co-advisor for Yongji Wu (now at UC Berkeley). Collaborates with Danyang Zhuo and Kartik Nayak.

• Molecular Simulations
• High Performance Computing
• Polymer Dynamics
• +4 more

Georgios Vogiatzis is a University Researcher at Eindhoven University of Technology (TU/e) in the Department of Mechanical Engineering, specifically within the Processing and Performance group (Van Breemen group) of the Polymer Technology research area. His work focuses on computational approaches to understanding polymer behavior at molecular scales. Education: Ph.D. in Chemical Engineering (2015), National Technical University of Athens M.Sc. in Microsystems and Nanodevices (2012), National Technical University of Athens Diploma in Chemical Engineering (2010), National Technical University of Athens Dr. Vogiatzis specializes in molecular simulations and high performance computing, with particular expertise in addressing the challenge of simulating the wide spectrum of length and time scales in polymer (nanocomposite) melts and glasses. His research investigates slow polymer dynamics at polymer-solid interfaces and the structure of interfaces formed during the directed self-assembly of diblock copolymers. His computational framework development enables the simulation of glassy polymer responses to deformation using only chemical constitution as input. His publication record demonstrates consistent contributions to understanding structural transitions in glassy polymers, particularly atactic polystyrene, with research spanning energy landscape analysis, transition-state theory applications, and computational frameworks for simulating glassy states. His work bridges theoretical models with experimental observations in polymer science. Dr. Vogiatzis collaborates extensively within the Processing and Performance research group at TU/e, working with researchers including Lambèrt C.A. van Breemen and Markus Hütter. His research has been supported by institutions including the Dutch Polymer Institute (DPI). He is affiliated with the Polymer Technology group at TU/e, where he contributes to advancing computational methods for polymer science and engineering applications.

• Signal Processing
• Biomedical Engineering
• Image Processing
• +3 more

Prof. Dr. Mustafa Ersel Kamaşak is a Professor at the Department of Computer Engineering , Istanbul Technical University . His academic journey includes a PhD in Electrical and Computer Engineering from Purdue University and a MA in Electrical and Electronic Engineering from Bogazici University . His research interests span Signal Processing , Biomedical Engineering , Image Processing , Machine Learning , and Total Variation Regularization , with a focus on applications in Medical Imaging , Geospatial Analysis , and Logistics Optimization . His recent work includes 2024 studies on barcode detection and mandibular morphology analysis , 2023 research on Antarctic sea level monitoring , and 2022 projects on fluorescence microscopy restoration . Notable projects led by him include Automatic Detection and Transfer of Document Types (TTO, 2021), Telehealth Equipment Development (TTO, 2019-2021), and Directional Total Variation Model (TUBITAK, 2016-2017). His work has been cited 18 times (Scopus h-index: 22).

Yangyang XuView profile

Associate Professor

• Stochastic Optimization
• Nonlinear Programming
• Computational Optimization
• +4 more

Yangyang Xu is an Associate Professor in the Department of Mathematical Sciences at Rensselaer Polytechnic Institute (RPI), where he conducts research at the intersection of optimization theory and computational methods. His work bridges theoretical foundations with practical applications in machine learning and data science. Dr. Xu's research focuses on several interconnected areas: Continuous optimization : specializing in stochastic programming, distributed optimization, and tensor optimization Optimization methods : developing stochastic approximation techniques, first-order methods, block update methods, and operator splitting approaches Computational frameworks : advancing parallel and distributed computing implementations for large-scale optimization problems Applications : applying optimization techniques to machine learning, signal processing, compressed sensing, and data mining challenges His recent publications (2023-2025) demonstrate a clear progression toward increasingly sophisticated optimization frameworks, particularly in decentralized and stochastic settings. Xu has made significant contributions to nonconvex optimization, minimax problems, and constrained optimization, with his work showing strong theoretical foundations coupled with practical implementations. Dr. Xu serves on the editorial board of Mathematics of Operations Research (since January 2023), reflecting his standing in the optimization community. He actively mentors six PhD students and has successfully guided multiple students to completion, with graduates moving to positions at organizations including Evonik, SAS, and UT Austin. His teaching includes graduate courses such as Stochastic Optimization and Reinforcement Learning and Introduction to Optimization. Dr. Xu leads a research group focused on advancing optimization theory while developing practical algorithms for real-world applications, particularly in distributed computing environments where communication efficiency is critical.