Find Academic Supervisors

Hu DingView profile

Professor

• Algorithms
• Computational Geometry
• Machine Learning
• +4 more

Hu Ding is a pre-tenure Professor in the School of Computer Science and Engineering at the University of Science and Technology of China (USTC), where he directs the Data Intelligence, Algorithms, and Geometry (DIAG) research group. He previously held positions as a tenure-track Assistant Professor at Michigan State University (2016-2018) and a Simons-Berkeley Research Fellow jointly at Tsinghua University and UC Berkeley (2015-2016). Education: • Ph.D. in Computer Science, State University of New York at Buffalo (2015) • B.S. in Mathematics, Sun Yat-Sen University (2009) Research Interests: Hu Ding's research focuses on developing efficient algorithms for geometric optimization problems with applications in machine learning, big data, and biomedical imaging. His work bridges theoretical computer science (especially computational geometry) with practical challenges in distributed systems, outlier detection, and high-dimensional data analysis. Key areas include constrained clustering, truth discovery in crowdsourced data, and geometric methods for biomedical image analysis. Publication Trends: His recent publications demonstrate a strong focus on scalable algorithms for high-dimensional geometric optimization, particularly in distributed environments with noisy data. A consistent theme is developing theoretically-grounded solutions with practical efficiency, evidenced by work on sublinear-time algorithms, coreset constructions, and approximation frameworks for problems like k-center clustering and SVM optimization with outliers. Awards and Honors: Young Investigator Award, Ministry of Science and Technology (2021) Simons-Berkeley Research Fellowship (2015-2016) CCF Committee Member for Theoretical CS and Big Data (2021) Grants and Projects: USTC Innovation Group Grant: 'Toward Electronic Design Automation: Theories and Algorithms from AI' (2021) MOST Young Investigator Grant: 'Optimal Transportation in Medical Imaging' (3M RMB, 2021) Research Group: Leads the DIAG group with focus on geometric algorithms for data intelligence. Current team includes 6 PhD students and 15 Master's students working on problems in clustering, distributed optimization, and biomedical applications. Former students hold positions at Alibaba, ByteDance, and academic institutions.

Sourya RoyView profile

Assistant Professor

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

Sourya Roy is an Assistant Professor in the Department of Computer Science at the University of Iowa. Prior to his academic career, he worked as a Data Scientist at Foursquare. He earned his PhD in Computer Science from the University of California, Riverside in 2022, advised by Silas Richelson and Amey Bhangale. Education: PhD in Computer Science (University of California, Riverside, 2022) His research spans theoretical and applied domains, focusing on pseudorandomness, coding theory, and cryptography in theoretical computer science, while also contributing to computer vision and machine learning applications. Recent publications emphasize expander graph theory, coding algorithms, and spatio-temporal modeling. Dr. Roy is actively seeking PhD students to collaborate on theoretical computer science projects. His publications highlight expertise in pseudorandomness, expander graphs, and scalable unsupervised learning techniques. Key conferences include FOCS, ECCV, and IEEE Transactions on Information Theory.

• Image Processing and Analysis
• Signal Processing
• Pattern Recognition
• +7 more

Michel Ménard is a Teacher-Researcher at the University of La Rochelle, affiliated with the Mathematics and Computer Science departments. His research focuses on image and signal processing, particularly in cardiovascular imaging, dynamic texture analysis, and UWB radar applications for through-wall imaging. Key projects: ANR DIAMS, FISC consortium, A.Gaugue project Applications: Cardiovascular imaging, environmental monitoring, mobile application programming Research Interests Ménard's work centers on modeling information ambiguity, imprecision, and uncertainty in image analysis, pattern recognition, and information fusion. He has developed generalized fuzzy coalescence methods, non-parametric Bayesian approaches for trajectory analysis, and variational formulations for image filtering inspired by quantum physics. His team focuses on: Dynamic texture modeling via spatio-temporal decomposition Low-level image processing with information theory Through-wall imaging systems using UWB radar Information fusion techniques with minimal a priori assumptions Applications in coastal environment monitoring and biomedical imaging Publications Ménard's publications reflect his expertise in advanced image processing techniques applied to diverse domains. Notable contributions include: Theoretical works on total variation and sublinear functionals Algorithm developments for multistatic radar systems Applications in 3D bee tracking and cardiovascular flow analysis Extensions of Chambolle's algorithm to color images Decomposition methods for dynamic textures Integration of quantum physics concepts in image filtering Collaborations He collaborates with: Laboratoires: L3i, MIA, CLDG/BQR, IRPHE CNRS, ETIS, LASIE Institutions: University Hospitals of Poitiers and Angers, ONERA, LEAT, Tronico Researchers: Abdallah El-Hamidi, Alain Gaugue, Damien Coisne, Gilles Aubert Teaching Ménard teaches across eight departments/programs including: Electronics and Industrial Computing Automation Network Security and Cryptography Video Game Programming Smartphone Programming Digital Media Distribution He has developed new educational initiatives in mobile application programming since 2010.

Victor LagerkvistView profile

Associate Professor

• Computational Complexity
• Constraint Satisfaction Problems (CSP)
• Theoretical Computer Science
• +3 more

Victor Lagerkvist is an Associate Professor at the Department of Computer Science (IDA) , Linköping University, Sweden. He is affiliated with the Theoretical Computer Science Laboratory (TCSLAB) and the Artificial Intelligence and Integrated Computing Systems (AIICS) division. His research focuses on the algebraic method for analyzing computational complexity , particularly in constraint satisfaction problems (CSPs), SAT, and graph homomorphism problems. PhD in Computer Science (2016, Linköping University) Habilitation (2020, Linköping University) His recent work investigates fine-grained complexity , twin-width , and universal algebra to improve algorithms for NP-hard problems. Publications span topics like propositional abduction, Allen's interval algebra, and semiring-based dynamic programming. His scientific awards include the Swedish Research Council Starting Grant (2020) and the 2017 Young Researcher Prize from the Ruth and Nils-Erik Stenbäck Foundation. He supervises PhD students such as Leif Eriksson and serves as a secondary supervisor for others at Linköping University and Université de Lorraine.

• Computer Science
• Data Structures and Algorithms
• Theoretical Computer Science
• +1 more

Assistant Professor at the Department of Agricultural Economics and Rural Development, Agricultural University of Athens. Holds a PhD, MSc, and BSc in Computer Engineering and Informatics from the University of Patras. Research focuses on dynamic graph problems, query optimization, and theoretical data structures with sublinear space. Teaches courses like Business Intelligence Systems and Informatics , emphasizing computer systems, databases, and data processing. His work bridges computer science theory with agricultural informatics applications. Educated at the University of Patras, he transitioned from the Department of Science (2004-2013) to his current role. Course content highlights his expertise in ETL processes, operating systems, and collaborative learning technologies.

Daniel WichsView profile

Professor

• Cryptography
• Computer Security
• Algorithms
• +7 more

Daniel Wichs is a tenured Professor at the Khoury College of Computer Sciences at Northeastern University in Boston. He is a prominent researcher in the field of cryptography and serves as a Senior Scientist at NTT Research. His academic journey includes a PhD from New York University under Yevgeniy Dodis, a postdoctoral fellowship at IBM Research T.J. Watson, and degrees from Stanford University. Current Position: Professor at Northeastern University Prior Positions: IBM Josef Raviv Memorial Postdoctoral Fellow (2011-2013) Education: PhD NYU (2011), BS/MS Stanford (2005) Professor Wichs specializes in cryptography with research interests spanning computing on encrypted data, program obfuscation, lattice-based cryptography, information-theoretic cryptography, and foundational aspects of cryptography. He also maintains broad interests in computer security, algorithms, complexity theory, coding theory, and information theory. His research group includes current PhD students Manu Kondapaneni, LaKyah Tyner, and Ethan Mook, along with numerous successful alumni who have become professors at institutions worldwide. His recent publications demonstrate a strong focus on advanced cryptographic primitives including homomorphic encryption, zero-knowledge proofs, private information retrieval, and functional encryption. These works frequently appear at top-tier conferences like STOC, FOCS, CRYPTO, and EUROCRYPT, with several receiving best paper awards. His research often bridges theoretical foundations with practical applications in privacy-preserving computation. Best Paper at STOC 2023 Alfred P. Sloan Research Fellowship (2018) NSF CAREER Award (2018) J.P. Morgan Faculty Research Award (2022) IBM Postdoctoral Fellowship (2011-2013) Professor Wichs actively contributes to the academic community through service as Area Chair for CRYPTO 2025 and EUROCRYPT 2024, Program Chair for ITC 2020, General Chair for STOC 2016, and membership on numerous program committees. He teaches advanced courses including Foundations of Cryptography (CS 7810) and Theory of Computation (CS 3800), and has organized events like the Charles River Crypto Day and the Simons Summer Program in Cryptography 2025. His research group operates at the forefront of cryptographic theory, addressing fundamental questions about secure computation on encrypted data while developing practical cryptographic tools for real-world applications.