Find Academic Supervisors

• Quantum Information Theory
• Quantum Computing
• Quantum Error Mitigation
• +3 more

Dr. Balint Koczor is an Associate Professor in the Mathematical Institute at the University of Oxford, leading the Quantum Information, Computation and Cryptography Group. He holds a Future Leaders Fellowship, serves as Associate Editor for npj Quantum Information , and organizes the SEEQA conference series. His work bridges theoretical quantum computing with industry applications through AppliedQC.org. His academic credentials include an MSc and Dr rer. nat. from Technical University of Munich (2019). Dr. Koczor's research centers on making quantum computers practical for real-world use, specializing in quantum error mitigation techniques for near-term devices. He develops hybrid quantum-classical algorithms to maximize hardware performance, with emphasis on error correction, efficient circuit design, and quantum simulation. His work directly addresses hardware limitations through close collaboration with experimental teams and quantum technology companies. Analysis of his 15 most recent publications reveals a cohesive research trajectory focused on practical quantum computing solutions. Key themes include classical shadows for efficient state estimation, virtual distillation for error suppression, and CoVaR-based optimization methods. His work consistently targets near-term device constraints while advancing foundational quantum algorithms across simulation, spectroscopy, and cryptography applications. His scientific recognition includes: Future Leaders Fellowship As a Future Leaders Fellow, Dr. Koczor directs significant research funding toward quantum error mitigation and algorithm development. He actively collaborates with quantum hardware companies to optimize application-hardware integration, with research supported through his fellowship and industry partnerships. His group develops tailored solutions for current quantum platforms while mentoring junior researchers. He leads the Quantum Information, Computation and Cryptography Group at Oxford's Mathematical Institute, which operates in close coordination with AppliedQC.org. This team focuses on translating theoretical advances into practical quantum computing frameworks, emphasizing error-resilient algorithms and hardware-aware implementations for near-term quantum devices.

Randal BurnsView profile

Professor

• Data Science
• Machine Learning
• Cloud Storage Systems
• +4 more

Randal Burns is a Professor and the Bill and Lisa Stromberg Head of the Department of Computer Science in the Whiting School of Engineering at Johns Hopkins University. He is also affiliated with the Data Science and AI Institute and co-founder of NeuroData. His research focuses on scalable data systems for scientific applications, spanning storage technologies, cloud infrastructure, and graph/sparse-matrix engines for machine learning. Key themes include data-intensive science, neuroscience imaging analysis, and adaptive performance management. Recent publications highlight trends in edge-parallel graph encoders masked matrix operations for sparsity decentralized foundation model training storage optimization for evolving AI models batch-parallel data structures turbulence data analysis These reflect his work on scalable systems for machine learning and scientific computing. Scientific awards include NSF CAREER Award DOE Early Career Principal Investigator Kavli Fellowship IBM Outstanding Innovation Award Advising: Mentors PhD students in scalable data systems and machine learning. Grants: Supported by NSF, DOE, and DARPA initiatives. Labs: Core member of the Johns Hopkins Turbulence Database Group and NeuroData team, developing open platforms for neuroscience and turbulence research.

Irini MoustakiView profile

Professor

• Latent Variable Models
• Structural Equation Models
• Categorical Data Analysis
• +5 more

Irini Moustaki is a full-time Professor in Social Statistics at the Department of Statistics , London School of Economics and Political Science . Her work focuses on latent variable models, structural equation models, and categorical data analysis with applications in education, psychology, and social sciences. She has co-authored influential books such as Latent Variable Models and Factor Analysis: A Unified Approach (2011) and Analysis of Multivariate Social Science Data (2008). Research Interests : Latent variable models, structural equation modeling, categorical data analysis, missing values, outliers, longitudinal data, composite likelihood estimation, and applications in social sciences. Recent research trends include: Composite likelihood and stochastic approximation methods Bayesian structural equation modeling Outlier detection in latent variable frameworks Network meta-analysis for intervention hierarchies Advances in DIF analysis and sparse factor modeling Scientific Awards : Honorary Doctorate, University of Uppsala Honorary Professor, The Education University of Hong Kong LSE Excellence in Education Award Advising & Grants : Supervised 8 PhD students and contributed to major grants from ESRC, MRC, and British Academy. Key roles in editorial boards of Psychometrika , Computational Statistics and Data Analysis , and Structural Equation Modeling .

Karystinos GeorgiosView profile

Professor

• Telecommunications theory and systems
• Coding theory
• Adaptive signal processing
• +7 more

George N. Karystinos is a Professor at the School of Electrical and Computer Engineering , Technical University of Crete , where he has served since 2005. Since 2021, he also holds the position of Dean of the School. Previously, he was an Assistant Professor at Wright State University (2003–2005). Education: Ph.D. in Electrical Engineering, State University of New York at Buffalo , 2003 Diploma in Computer Engineering and Science, University of Patras , 1997 Additional studies in Piano, National Conservatory of Athens and State University of New York at Buffalo Research Interests: His research spans telecommunications theory and systems , coding theory , adaptive signal processing , wireless communications , MIMO systems , neural networks , and optimization with limited data . He is particularly known for contributions to L1-norm PCA , noncoherent detection , RFID systems , and code design for CDMA . Scientific Awards: IEEE Transactions on Neural Networks Outstanding Paper Award (2003) IEEE ICT Best Paper Award (2001) IEEE ISWCS Best Paper Award (2013) IEEE ICASSP Best Student Paper Award (2015) IEEE RFID-TA Second Best Student Paper Award (2011) IEEE MOCAST Best Student Paper Award (2018) Laboratory and Teaching: He leads research at the Telecommunications Laboratory and teaches courses such as Signals and Systems , Information and Code Theory , and Probability and Random Process Theory . Grants and Projects: While specific grant details are not listed, his extensive publication record and award history indicate active participation in funded research projects, particularly in wireless communications and signal processing.

Huang FangView profile

Researcher

• Optimization
• Learning theory
• Data mining
• +3 more

Huang Fang is a Research Scientist at Baidu Research's Cognitive Computing Lab, specializing in optimization theory, machine learning, and data mining. With a strong academic background from top institutions, Huang has established themselves as a prolific researcher in the field of machine learning, particularly in federated learning, privacy-preserving algorithms, and optimization methods. Ph.D. in Computer Science, 2021, University of British Columbia M.S. in Statistics and Computer Science, 2017, University of California, Davis B.Sc. in Financial Mathematics, 2015, Central University of Finance and Economics (China) Huang Fang's research spans multiple critical areas in machine learning, with a particular focus on optimization theory and its applications. Their work bridges theoretical foundations with practical implementations, especially in federated learning systems where privacy and efficiency are paramount. The research portfolio demonstrates expertise in developing novel algorithms for large-scale learning problems, with particular emphasis on convergence analysis, sparse optimization, and scalable methods for knowledge representation. Huang has made significant contributions to understanding the theoretical properties of optimization algorithms under various constraints and settings. The publication record reveals a strong trajectory in advancing machine learning methodologies, with recent work focusing on differentiable neuro-symbolic reasoning, efficient Markov logic networks, and improved convergence guarantees for privacy-preserving stochastic optimization. The research shows a consistent pattern of addressing fundamental challenges in distributed and large-scale machine learning, particularly in the areas of federated learning fairness, efficient coordinate descent methods, and matrix completion techniques. Huang Fang has demonstrated exceptional productivity in high-impact venues including NeurIPS, ICML, ICLR, WWW, and other top-tier machine learning conferences. The work consistently combines theoretical rigor with practical applicability, making significant contributions to both the foundational understanding of optimization methods and their implementation in real-world systems.

• Medical Data Analysis
• Functional Data Analysis
• Deep Neural Networks
• +4 more

Łukasz Kidziński is a Research Fellow at Stanford University's Clark Center, specializing in medical data analysis for high-dimensional sparse clinical observations. His methodological expertise spans functional data analysis, inverse problems in high dimensions, and deep neural networks, with recent applications in biomechanics and cancer immunology. Previously, he developed adaptive learning systems for educational environments. His academic background includes: Ph.D. in Mathematical Statistics, Université libre de Bruxelles (2014) M.S. in Mathematics, University of Warsaw (2011) M.S. in Computer Science, University of Warsaw (2010) B.S. in Mathematics & Computer Science, University of Warsaw (2009) Dr. Kidziński's research bridges statistical theory and clinical practice, focusing on translating complex data into actionable medical insights. Current projects emphasize smartphone-based human movement analysis (OpenCap system), spatial mapping of immune cells in cancer, and AI-driven diagnostics for osteoarthritis. His work uniquely integrates computational statistics with real-world healthcare challenges, particularly in decentralized clinical trials and biomechanical interventions. Analysis of his 15 most recent publications reveals three dominant trajectories: (1) smartphone-based biomechanics (6 articles, 2021-2023) advancing OpenCap for 3D motion capture; (2) spatial cancer immunology (4 articles, 2022-2024) mapping macrophage niches in colon/breast tumors; and (3) clinical AI applications (5 articles, 2021-2022) for osteoarthritis assessment and decentralized trials. These intersect statistics, computer vision, and translational medicine. His accolades include: Mobilize Center Distinguished Postdoctoral Fellow (2016) Swiss National Science Foundation grant (2015) ULB-UC Berkeley research exchange grant (2014) PASCAL Group Fellowship (2011) University of Warsaw Distinguished Students Fellowship (2007) As a Mobilize Center fellow, Dr. Kidziński leads NIH-funded projects on AI-enhanced clinical trials and biomechanical modeling. His collaborations span Stanford Bio-X, UC Berkeley, and European institutions, with open-source contributions like OpenCap enabling community-driven research. While no formal advisees are documented, his educational data mining work indicates student mentorship in adaptive learning systems. Active Grants: Mobilize Center Distinguished Fellowship, Swiss NSF Key Collaborators: Pierre Dillenbourg (EPFL), Scott Delp (Stanford), David Cohen (EPFL) Based in Stanford's Clark Center, he co-develops the Mobilize Center's mobile health ecosystem, contributing to open-source tools for biomechanical analysis (OpenCap) and knee cartilage assessment. His work leverages Bio-X's interdisciplinary infrastructure to bridge engineering, medicine, and data science for real-world clinical impact.

Haim SompolinskyView profile

Professor

• Computational Neuroscience
• Theoretical Neuroscience
• Statistical Physics of Neural Networks
• +3 more

Haim Sompolinsky is a Professor at Harvard University, holding appointments in both the Department of Molecular and Cellular Biology and the Department of Physics. He serves as Director of Harvard’s Swartz Program in Theoretical and Computational Neuroscience and is an inaugural associate faculty member at the Kempner Institute for the Study of Natural and Artificial Intelligence. His research bridges statistical physics and neuroscience to uncover principles of brain computation. University: Harvard University Departments: Molecular and Cellular Biology, Physics Programs: Swartz Program in Theoretical Neuroscience, Kempner Institute His work spans theoretical neuroscience, computational modeling of neural circuits, and the intersection of physics and machine learning. Recent publications focus on neural manifolds, relational reasoning, and statistical mechanics of deep networks. Notable awards include the 2022 Gruber Foundation Neuroscience Prize and The Brain Prize. His lab offers graduate and postdoctoral positions, emphasizing interdisciplinary collaboration and adherence to Harvard’s non-discrimination policies.

Peter ManoharView profile

Researcher

• Theoretical Computer Science
• Algorithms
• Coding Theory
• +4 more

Peter Manohar is a postdoctoral researcher in the Computer Science and Discrete Math group at the Institute for Advanced Study , focusing on Theoretical Computer Science with emphasis on algorithms, coding theory, and cryptography. His work explores spectral algorithms for semirandom and smoothed instances of NP-hard constraint satisfaction problems, linking these methods to coding theory, extremal combinatorics, and cryptography. Education: PhD in Computer Science from Carnegie Mellon University, advised by Venkatesan Guruswami and Pravesh K. Kothari B.S. in EECS from UC Berkeley, advised by Alessandro Chiesa and Ren Ng Research Trends: His recent publications highlight advancements in spectral refutation techniques, locally decodable/correctable codes, and connections between complexity theory and coding. Articles span venues like FOCS, STOC, APPROX, and arXiv, reflecting his interdisciplinary approach. Awards: He has received prestigious NSF and Cylab Presidential Fellowships, along with ARCS scholarships during his PhD. His work on quantum proofs (TCC 2019) and constraint satisfaction problems has been recognized in invited journal special issues. Teaching & Collaboration: Peter has taught courses at Carnegie Mellon, including Quantum Computing and Computer Graphics. He interned at TTIC in Summer 2023 and co-organized CMU's Theory Club, demonstrating active engagement in academic communities.

Carlos Andujar GranView profile

Associate Professor

• Computer Graphics
• 3D Modeling and Animation
• Virtual and Augmented Reality
• +5 more

Carlos Andújar Gran is an Associate Professor in the Computer Science Department at the Polytechnic University of Catalonia . He is a key member of the ViRVIG Research Center for Visualization, Virtual Reality, and Graphics Interaction, as well as the Eurographics Association . His work bridges technical innovation with cultural and educational applications. Primary affiliation: Universitat Politècnica de Catalunya Research center: ViRVIG - Research Center for Visualization, Virtual Reality and Graphics Interaction Professional network: Eurographics Association His research interests focus on advanced topics in computer graphics and virtual reality : 3D Modeling - Digital reconstruction of complex geometries Animation Systems - Real-time motion capture and pose estimation Cultural Heritage - Digital restoration techniques for medieval monuments Human-Computer Interaction - 3D user interface design Sports Analytics - Player position tracking through computer vision Medical Education - VR nursing training platforms His technical publications demonstrate a consistent focus on solving practical problems through innovative algorithms: Developed DragPoser for motion reconstruction with sparse sensors Created PADELVIC dataset for sports analytics Advanced automated color restitution methods for mural paintings Improved normal estimation in point cloud processing Optimized terrain super-resolution techniques using convolutional networks Designed interactive cultural heritage systems for museum exhibitions

Laurent PoirrierView profile

Adjunct Professor

• Discrete Optimization
• Computational Optimization
• Mathematical Programming
• +3 more

Laurent Poirrier is an Adjunct Professor in the Department of Computing Sciences at Bocconi University in Milan, Italy. Previously, he was affiliated with the Combinatorics and Optimization department at the University of Waterloo. His academic work spans both theoretical and applied aspects of optimization, with a focus on computational methods. Dr. Poirrier's research interests center on the computational aspects of discrete optimization, with particular emphasis on the implementation of fast optimization algorithms. His work encompasses linear algebra, combinatorics, and numerical analysis as they relate to optimization problems. He has made significant contributions to mathematical programming, particularly in areas such as cutting planes, integer programming, and polyhedral combinatorics. His publication record demonstrates consistent contributions to the field of optimization over the past two decades, with a focus on theoretical foundations of optimization algorithms and practical implementations. His work spans pure optimization theory, computational methods, and applications in areas like vehicle routing. The progression of his research shows increasing sophistication in handling multi-dimensional optimization problems and developing numerically stable algorithms. At Bocconi University, Dr. Poirrier teaches Software Engineering (course 20875) and has previously taught courses in computer science. His teaching reflects his research expertise, covering topics from low-level computing concepts to software engineering practices.

Arthur de JongView profile

Assistant Professor

• Single-molecule biosensing
• Medical diagnostics
• Biosensors
• +7 more

Arthur de Jong is an Assistant Professor at Eindhoven University of Technology (TU/e) working in the Department of Applied Physics and Science Education. He leads research in the Molecular Biosensing for Medical Diagnostics group, a multidisciplinary team within the Institute for Complex Molecular Systems (ICMS). Develops next-generation single-molecule biosensors Focuses on continuous patient monitoring applications Specializes in colloidal particle-based sensing systems His research combines colloid chemistry , molecular engineering , and microfluidics to create non-fouling biosensor surfaces. Recent work explores: Stochastic heterogeneity in single-molecule sensors DNA-PAINT for surface density analysis Machine learning-enhanced biomedical signal processing Antibody fragment-based competition assays Current educational contributions include courses in optics, subatomic physics, and molecular biosensing technologies.

Joab WinklerView profile

Academic

• Computer-Aided Design (CAD)
• Polynomials in Bernstein Basis
• Resultant Matrices
• +9 more

Dr. Joab Winkler is a Reader in the Department of Computer Science at the University of Sheffield , where he has been since 1998. He obtained his undergraduate and PhD degrees from Imperial College London and University College London , respectively, and previously held an EPSRC Advanced Research Fellowship at Brunel University. His research focuses on algebraic and numerical methods in computer-aided design (CAD), with a particular emphasis on resultant matrices, polynomial deconvolution, and robust computations in geometric modeling. Education: BSc (Eng) in Electrical Engineering, Imperial College London (1977-1980) PhD in Computer Science, University College London (1981-1984) Research interests include the numerical stability of polynomial operations in CAD systems, blind image deconvolution, optical flow algorithms, and neural networks. His work often bridges theoretical algebraic geometry with practical engineering problems, such as data exchange between CAD systems and the numerical conditioning of geometric singularities. Notable scientific awards include an EPSRC Advanced Research Fellowship and a Global Research Award from The Royal Academy of Engineering. He has led grants like Sparse Linear Models: Their Existence and Stability (2022-2023) and Robust Computations in Geometric Modelling (2005-2008). Professional activities include organizing workshops on polynomial equations and structured matrix methods, co-organizing the Sheffield Machine Learning Workshop, and collaborating on EPSRC-funded projects. He also contributes to teaching as Course Director for MSc Artificial Intelligence and through lecture notes on Pattern Processing and Network Architectures .

Haesun ParkView profile

Professor

• Data analysis
• Visual analytics
• Numerical computing
• +4 more

Dr. Haesun Park is a Regents' Professor and Chair in the School of Computational Science and Engineering at Georgia Institute of Technology, Atlanta, Georgia. She has held this position since August 2020, after becoming a Regents' Professor in July 2019 and a Professor since July 2005. Prior to joining Georgia Tech, she served as a Professor in the Department of Computer Science and Engineering at the University of Minnesota from 1987 to 2005 (Assistant Professor 1987-1993, Associate Professor 1993-1998, Professor 1998-2005) and as a Program Director at the National Science Foundation from 2003-2005. Education: Ph.D. in Computer Science (minor: Mathematics), Cornell University, 1987 M.S. in Computer Science, Cornell University, 1985 B.S. (Summa Cum Laude) in Mathematics, Seoul National University, 1981 (with Presidential Medal for top graduate) Dr. Park's research spans multiple domains within computational science, with particular focus on data analysis, visual analytics, numerical computing, text mining, social media mining, parallel computing, and bioinformatics. Her work integrates mathematical foundations with practical applications across various fields, developing innovative algorithms for large-scale data analysis. She has made significant contributions to nonnegative matrix factorization techniques and their applications in diverse domains including healthcare, social media analysis, and scientific computing. Her recent publication trends show a strong focus on parallel and distributed implementations of matrix factorization algorithms, applications in healthcare data analysis (particularly patient profiling and clustering), and development of scalable frameworks for handling large-scale datasets. The work demonstrates increasing interdisciplinary collaboration, especially with medical researchers, and a growing emphasis on practical implementations that can handle real-world data challenges. Scientific Awards: SIAM Fellow (2013) IEEE Fellow (2016) ACM Fellow (2020) Dr. Park has held significant leadership roles including Executive Director of the Center for Data Analytics (2013-2015) and Director of the NSF/DHS FODAVA-Lead Center (2008-2014). She has served as conference co-chair for the SIAM International Conference on Data Mining in 2008 and 2009, and as an editorial board member for leading journals including IEEE Transactions on Pattern Analysis and Machine Intelligence, SIAM Journal on Matrix Analysis and Applications, and SIAM Journal on Scientific Computing. She has been a plenary keynote speaker at major international conferences including SIAM Conference on Applied Linear Algebra in 1997 and 2015, and SIAM International Conference on Data Mining in 2011. Dr. Park is affiliated with multiple research centers at Georgia Tech including the Algorithms and Randomness Center (ARC) and the Institute for Data Engineering and Science (IDEaS). She has maintained a long-term collaboration with the Korea Institute for Advanced Study (KIAS) as a KIAS Scholar from 2008-2018, demonstrating her international research impact.

Husnu YenigunView profile

Professor

• Automata Theory
• Concurrency Theory
• Formal Methods
• +5 more

Husnu Yenigun is a Professor at Sabanci University's Computer Science and Engineering Program , Faculty of Engineering and Natural Sciences (Istanbul, Turkey). He earned his BSc, MSc, and PhD in Electrical and Electronics Engineering from Middle East Technical University (Ankara) in 1992, 1995, and 2000 respectively. His professional career includes roles at TUBITAK (1992-1997), Bell Laboratories (1997-1998), and as a consultant at Bell Labs (1999-2000). Research interests: Dr. Yenigun specializes in automata and concurrency theory, formal methods, software quality assurance, model checking, and complexity relief techniques for software verification. His work bridges theoretical automata analysis with practical testing frameworks. Key publication trends: His recent work spans automata synchronization (2018-2021), matrix optimization (2017-2018), and adaptive testing sequences (2016-2018), with applications in Wireless positioning systems Finite state machine verification Parallel computing Formal method implementations Professional activities: He serves on technical program committees for major conferences like MODELSWARD, QRS, and ICTSS (2016-2025). He chaired the programming committee for ICTSS 2013 and 2017, and co-chaired UYMS 2018. He also acted as guest editor for the International Journal on Software Tools for Technology Transfer (2016). Contact: yenigun@sabanciuniv.edu | Office: FENS 2054, Sabanci University

Yue SongView profile

Researcher

• Structured Representation Learning
• Deep Learning
• Machine Learning
• +5 more

Yue Song is a postdoctoral researcher at the California Institute of Technology (Caltech) in the Department of Computing and Mathematical Sciences, working under the supervision of Yisong Yue, Pietro Perona, and Max Welling. Previously, they completed their PhD at the European Laboratory for Learning and Intelligent Systems (ELLIS), affiliated with the Multimedia and Human Understanding Group (MHUG) at University of Trento (Italy) and Amsterdam Machine Learning Lab (AMLab) at University of Amsterdam (Netherlands), advised by Nicu Sebe and Max Welling. Education: B.Sc. (cum laude) from KU Leuven (Belgium) Joint M.Sc. (summa cum laude) from University of Trento (Italy) and KTH Royal Institute of Technology (Sweden) Innovation & Entrepreneurship minor from European Institute of Innovation and Technology (EIT Digital) Research focuses on structured representation learning at the intersection of AI and science. Key themes include: Exploiting geometric, temporal, and topological structure in scientific data Integrating inductive biases from physics/chemistry/biology into ML models (Science4AI) Using AI to uncover scientific insights (AI4Science) Generalizable, interpretable, and data-efficient methods Recent publication trends show work across: Latent space analysis for GANs/diffusion models (ICCV 2023, CVPR 2023) Geometric and Lie group-based ML (ICLR 2024, ICLR 2025) Theoretical foundations of matrix operations in deep learning (TPAMI 2022, TPAMI 2024) Scientific applications in chemistry, neuroscience, and physics (NeurIPS 2024) Major contributions include: 2025 : Co-authored book Structured Representation Learning (Springer Nature) 2024 : Area Chair for NeurIPS 2025 Collaborations span institutions: Caltech, University of Trento, University of Amsterdam, KU Leuven, KTH. Codebases available on GitHub demonstrate applications in: Fast differentiable matrix operations (ICLR 2022, TPAMI 2022) Householder projectors for GANs/diffusion models (ICCV 2023) Latent flow dynamics (ICML 2023)