Find Academic Supervisors

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.

Thomas BarthelView profile

Adjunct Assistant Professor

• Quantum Condensed Matter Physics
• Strongly Correlated Quantum Systems
• Nonequilibrium Quantum Dynamics
• +12 more

Thomas Barthel is an Adjunct Assistant Professor of Physics at Duke University and a Manager of Research Development at the National Quantum Laboratory, University of Maryland . He is affiliated with the Duke Quantum Center and the Pratt School of Engineering . PhD from Rheinisch-Westfälische Technische Hochschule Aachen (Germany) (2009) Research Interests: Dr. Barthel specializes in quantum condensed matter physics and tensor network states for simulating strongly correlated quantum systems . His work spans nonequilibrium dynamics , entanglement scaling , driven-dissipative systems , and stochastic network dynamics with applications to epidemic modeling and quantum computation . He develops entanglement renormalization algorithms like Trotterized MERA to achieve quantum advantage for critical systems. Publications: Recent work focuses on proving the absence of barren plateaus in tensor network optimization, scaling of contraction costs for MERA, and non-Gaussian phase transitions in dissipative quantum models. His tensor network methods enable simulations of quantum criticality and entanglement evolution in large systems. Scientific Awards: Borchers Medal (2010) Schöneborn Prize (2005) Springorum Medal (2005) Doctoral and Student Scholarships from Studienstiftung des deutschen Volkes Advising & Grants: Dr. Barthel advises students in quantum simulation and network dynamics . He has secured NSF and NIH grants for epidemic risk analysis and quantum material studies , alongside IonQ quantum computing credits and Department of Energy funding.

• Partial Differential Equations
• Mathematical Biology
• Quantum Mechanics
• +4 more

Peter Markowich is a Professor at the Department of Mathematics, Faculty of Mathematics, with a prolific research career spanning over three decades. His work bridges Partial Differential Equations (PDEs) , Mathematical Biology , and Quantum Mechanics , focusing on modeling complex systems across disciplines. Key research themes include biological network formation (e.g., blood vessel and plant vein structures), nonlinear Schrödinger equations for quantum systems, and reaction-diffusion dynamics in chemotaxis and pedestrian flow. Recent publications (2023-2025) explore self-regulated biological transportation structures, tensor PDE models, and orbital stability of standing waves in fractional quantum systems. 2025 : Condition number analysis in biological network PDEs 2024 : 1D entropy dissipation models 2023 : Gradient flows for network emergence and Hughes’ pedestrian model theory His methodological contributions include numerical schemes for multiscale problems (e.g., XFEL simulations) and analytical frameworks for existence/uniqueness in singular PDEs. Collaborations span global institutions, with co-authors like Y. Cho, C. Sparber, and H. Hajaiej.

• Machine Learning
• Bioinformatics
• Drug Discovery
• +5 more

Sandor Szedmak serves as a Research Fellow in the Department of Computer Science at Aalto University, Finland, and is affiliated with the Helsinki Institute for Information Technology (HIIT). He operates within Professor Juho Rousu's research group, focusing on interdisciplinary machine learning applications with strong ties to computational biology and bioinformatics. His institutional presence is active, as evidenced by current contact details and ongoing publication output. Dr. Szedmak's research specializes in developing advanced machine learning frameworks for complex biological and educational challenges. His primary interests include drug combination effect prediction, protein function annotation through multi-view learning, strain design optimization using reinforcement learning, and personalized educational systems for computer science students. He pioneers methods in latent tensor reconstruction, scalable variable selection, and kernel generalization, targeting high-dimensional data problems in pharmacology and computational biology. His publication trajectory from 2020-2025 reveals a concentrated effort in bioinformatics applications, with landmark papers in Nature Communications and Bioinformatics on drug combination prediction. The work consistently leverages tensor factorization and multi-view learning to model biological systems, while recent expansions include educational data mining for programming behavior analysis. This demonstrates both methodological consistency in machine learning innovation and strategic domain diversification. No scientific awards or fellowships are documented in the provided materials. While no student advisees or grant details are explicitly listed, Dr. Szedmak's collaborative publication pattern—particularly within the Rousu research group and HIIT—suggests active participation in team-based research initiatives. His work appears funded through institutional channels given the consistent output in computational biology. He operates within the Helsinki Institute for Information Technology (HIIT), a joint research institute of Aalto University and the University of Helsinki, and is embedded in Professor Juho Rousu's research group. This environment facilitates cross-disciplinary collaboration between computer science and life sciences, with infrastructure supporting high-performance computing for large-scale biological data analysis.

• Computational Biology
• Drug Discovery
• Machine Learning
• +3 more

Tianduanyi Wang is a Visiting Professor in the Department of Computer Science at Aalto University , associated with the Helsinki Institute for Information Technology (HIIT) . Their research focuses on computational methods for drug discovery and cancer target dependency analysis. Specializes in machine learning for biomedical applications Active in drug combination effect modeling and pharmacogenomics Contributions to tensor reconstruction methods in bioinformatics Recent publications highlight expertise in: Drug synergy prediction (2025) Multi-target drug discovery (2022) High-throughput screening analysis (2020)

• Schizophrenia
• Psychosis
• Neuroimaging
• +5 more

Judith Ford, PhD is a Professor in the Department of Psychiatry at the University of California, San Francisco (UCSF) School of Medicine and a member of the UCSF Weill Institute for Neurosciences. She maintains an active research program with continuous NIH funding as Principal Investigator since 1984, demonstrating her sustained contributions to schizophrenia and psychosis research. Dr. Ford's research focuses on understanding the neural mechanisms underlying schizophrenia through advanced neuroimaging techniques. Her work extensively utilizes fMRI and EEG to investigate functional and structural brain abnormalities, with particular emphasis on auditory processing deficits, neural network instability, and the relationship between brain activity and clinical symptoms. Recent projects examine neural network instability in schizophrenia that may be improved with ketogenic diets, auditory steady-state responses across the psychosis spectrum, and biomarkers of rumination through concurrent EEG and fMRI studies. Analysis of her recent publications (2023-2025) reveals a strong trend toward sophisticated multimodal neuroimaging approaches, with increasing focus on machine learning applications, network connectivity analyses, and metabolic influences on mental health. Her work spans multiple disciplines including computational neuroscience, electrophysiology, and clinical psychiatry, with consistent application to schizophrenia research. Dr. Ford has received continuous NIH funding as Principal Investigator since 1984, with current projects including R21MH127498 (2021-2023) on neural network instability in schizophrenia, R03MH125226 (2021-2022) on auditory steady-state response across the psychosis spectrum, and R03MH121900 (2020-2022) exploring the RDoC construct of agency using fMRI and EEG. Her longest-running project, I01CX000497 'Rescuing the ruminating brain,' has been active from 2011-2022. She actively collaborates with researchers across multiple institutions through large-scale consortia, contributing to major neuroimaging initiatives like the ENIGMA consortium. Her laboratory combines cutting-edge neuroimaging technology with advanced computational approaches to develop biomarkers that could inform diagnosis, prognosis, and treatment development for schizophrenia and related disorders.