Find Academic Supervisors

• Geometry processing
• Massive model rendering
• Procedural generation
• +4 more

Antonio Chica is an Associate Professor at the Department of Computer Science, Universitat Politècnica de Catalunya (UPC), specializing in geometry processing, real-time rendering, and virtual reality applications. His research focuses on 3D reconstruction, procedural landscape generation, and LiDAR data optimization. Teaching at Terrassa School of Engineering and Barcelona School of Informatics Member of the Modeling, Visualization, Interaction and Virtual Reality Group Key research areas include: Geometry processing techniques for signed distance fields Procedural generation of 3D landscapes and vegetation Game development frameworks and VR training systems Efficient algorithms for massive point cloud rendering His recent publications emphasize Bayesian reconstruction methods, adaptive SDF approximations, and optimized VR training tools. He actively collaborates on LiDAR data calibration, terrain modeling, and cultural heritage visualization projects. Antonio Chica's work integrates advanced graphics algorithms with practical applications in urban modeling, medical training, and historical preservation. He develops open-source tools like MeshPipe to simplify geometry processing workflows.

• Signal Processing
• Machine Learning
• Graph Theory
• +5 more

Rohan Thekkemarickal Money is a Postdoctoral Fellow at Simula, affiliated with the Department of Signal and Information Processing for Intelligent Systems within the Simula Metropolitan Centre. His research focuses on advanced signal processing, graph theory, and machine learning applications to dynamic networks and topological data analysis. Recent research trends include developing simplicial vector autoregressive models for network dynamics, online topology identification for higher-order systems, and privacy-aware learning frameworks . His work addresses challenges in time-series forecasting, edge flow backcasting, and capacity estimation for lithium-ion batteries using graph-based methodologies. Rohan collaborates with researchers like Dr. B. Beferull-Lozano and Dr. E. Isufi, contributing to interdisciplinary projects at the intersection of signal processing , network science , and topological data analysis . His publications demonstrate expertise in handling nonlinear systems with sparse data and designing scalable algorithms for real-time applications.

• Information Theory
• Coding Theory
• Statistical Learning Theory
• +3 more

Shunsuke Horii is an Associate Professor at the Center for Data Science, Waseda University. His research spans information theory, coding theory, statistical learning theory, and data science applications. He actively collaborates with industry through initiatives like the Waseda Data Science Consortium. Education: Ph.D. in Science and Engineering from Waseda University (2009), Master's from Waseda University Graduate School of Science and Engineering (2004). Research Focus: Addresses causal effect estimation in data science using Bayesian decision theory, sparse modeling, and optimization techniques like ADMM and variational inference. Develops efficient algorithms for multiuser communication, matrix completion, and privacy-preserving distributed computing. Teaching: Instructs courses on statistics literacy, data science, and programming with Python/R across multiple academic quarters. Grants: Leads projects funded by Japan Society for the Promotion of Science, including causal inference frameworks, product recommendation systems, and business analytics. Publications: 21 papers with 61 Scopus citations, focusing on LP decoding, Bayesian hierarchical models, and statistical causal analysis.

• Statistical Science
• Earthquake Prediction
• Actuarial Science
• +5 more

Shunichi Nomura is an Associate Professor at the Faculty of Commerce, Graduate School of Accountancy, with a PhD in Statistical Science from The Graduate University for Advanced Studies (Sokendai). His research bridges statistical methodology with applications in earthquake forecasting and actuarial science. Education: PhD in Statistical Science (Sokendai). He develops Bayesian space-time models for earthquake recurrence prediction and applies data mining techniques to insurance risk and healthcare data. His work integrates mathematical statistics with seismological observations to improve probabilistic hazard assessments. Recent publications focus on nonstationary renewal processes, slip rate inversion, and sparse estimation methods like group fused lasso. These studies span geophysics, actuarial mathematics, and machine learning applications. Scientific Awards: Actuarial Excellent Paper, The Japanese Actuarial Society (2017). He leads JSPS-funded research projects on spatiotemporal earthquake modeling and Bayesian prediction frameworks. His teaching includes advanced statistics and data science courses for actuarial students.

Robert StrzodkaView profile

Professor

• Parallel algorithms on parallel hardware (GPUs, many-core CPUs, FPGAs)
• Numerical methods (AMG, Krylov, preconditioners, FMM, multilevel methods, SVD)
• Graph algorithms (partitioning, coloring, decomposition, BFS, MST)
• +7 more

Robert Strzodka is a full professor at Heidelberg University, holding the chair on Application Specific Computing since 2015. He is renowned for pioneering work in GPU-based scientific computing, focusing on optimizing interactions between mathematics, algorithms, and parallel hardware architectures. Current affiliation: Institute of Computer Engineering (ZITI), Heidelberg University Prior roles: NVIDIA (AmgX group), Max Planck Institute (Integrative Scientific Computing), Stanford University (visiting assistant professor) PhD: Numerical mathematics from University of Duisburg-Essen (2004) His research spans parallel algorithms (GPUs, FPGAs), numerical methods (AMG, Krylov, preconditioners), and graph algorithms (partitioning, MST). Publications emphasize GPU acceleration , cache optimization , and mixed precision computing across domains like astrophysics, fluid dynamics, and finite element methods. The 15 most recent articles (2024-2008) demonstrate consistent innovation in parallel computing , GPU algorithms , and numerical methods . Key subfields include stochastic modeling , tridiagonal solvers , level set visualization , and cache-aware algorithms .

• Network Science
• Applied Dynamical Systems
• Mathematical Biology
• +4 more

Joel Nishimura is an Associate Professor at Arizona State University's School of Mathematical and Natural Sciences. His research spans several interconnected fields including network science, dynamical systems, and mathematical biology. He focuses on how simple rules can create complex behaviors and structures, with applications in biological systems, network algorithms, and engineered protocols. Dr. Nishimura received his Ph.D. from Cornell University in 2013. His dissertation explored decentralized coordination and how actors with limited information can coordinate their timing. His research interests include Network Science, Applied Dynamical Systems, Mathematical Biology, Mathematical Modeling, and Game Theory. A unifying feature of his work is examining how simple rules can create complex behaviors and structures. In biological settings, these simple models capture key qualitative features of observed behaviors, suggesting generalizable principles. In network settings, simple rules form the basis for predictive algorithms, while in engineered settings, they serve as backbones for robust decentralized protocols. Analysis of Dr. Nishimura's recent publications reveals a strong focus on network science and its applications across multiple domains. His work spans theoretical foundations of graph theory and network models, applications to social and biological systems, and connections to decision-making processes and game theory. A notable trend is his consistent exploration of how simple rules or interactions can produce complex emergent phenomena, whether in network structures, biological behaviors, or decision-making processes. Dr. Nishimura has supervised several undergraduate and graduate students, as indicated by the asterisks and other symbols in his publication record. His teaching includes courses such as Applied Linear Algebra (MAT 343), Individualized Instruction (MAT 499), Honors Directed Study (MAT 492), and Honors Thesis (MAT 493). His research has been supported by various grants, though specific details are not provided in the available information. His GitHub repository for the "Double Edge Swap MCMC Graph Sampler" suggests involvement in computational tools development for network science research.

Samir BhattView profile

Professor

• Statistics
• Machine Learning
• Public Health
• +5 more

Samir Bhatt is a Professor of Machine Learning and Public Health at the University of Copenhagen's Faculty of Health and Medical Sciences, Department of Public Health, Section for Health Data Science and AI. He also holds a position as Professor of Statistics and Public Health at Imperial College London since 2016. His work focuses on developing mathematical, statistical, and computer science tools to address critical questions in human health. His educational background includes a DPhil in Statistical Genetics from the University of Oxford (2010), an MPhil in Computational Biology from the University of Cambridge (2006), and a BEng in Chemical and Bioprocess Engineering from the University of Bath (2005). Professor Bhatt's research spans the intersection of statistics, machine learning, and public health with particular emphasis on infectious disease modeling. His primary research areas include Bayesian inference, genomic epidemiology, and kernel methods applied to health data. His work bridges theoretical statistical approaches with practical public health applications, particularly in disease surveillance and outbreak response. The integration of AI with traditional epidemiological methods represents a key innovation in his research program. Analysis of his recent publications reveals a strong focus on applying advanced computational methods to pressing public health challenges. His work demonstrates consistent innovation in developing AI-driven approaches for infectious disease modeling, genomic surveillance, and survival analysis. Notable themes include the application of graph neural networks to epidemiological data, development of interpretable AI tools for public health decision-making, and sophisticated modeling of disease transmission dynamics across multiple pathogens including malaria, cholera, and respiratory viruses. Professor Bhatt has published extensively with over 107 research outputs to date. His work has received significant attention, with multiple publications covered by news outlets, referenced on social media platforms, and read by researchers on academic platforms like Mendeley. His research on AI for infectious disease modeling published in Nature demonstrates the high impact of his work in both academic and policy spheres. His research group at the University of Copenhagen appears to focus on developing computational tools for health data science, with particular emphasis on creating analytical frameworks that can be rapidly deployed during disease outbreaks. The development of GRAPEVNE (Graphical Analytical Pipeline Development Environment for Infectious Diseases) represents one such effort to create accessible tools for public health practitioners.

Horst BischofView profile

Associate Professor

• Computer Vision
• Medical Imaging
• Pattern Recognition
• +3 more

Ao.Univ.Prof. Dipl.-Ing. Dr.techn. Horst Bischof is an Associate Professor at Vienna University of Technology (TU Wien). His work focuses on computer vision, medical imaging, and pattern recognition. He has contributed to projects in automated medical diagnosis (e.g., rheumatoid arthritis assessment), 3D reconstruction, and cultural heritage preservation through advanced segmentation techniques. He has supervised numerous students including S. Veigl, A. Belbachir, and G. Langs. Bischof has edited proceedings like the 2005 Computer Vision Winter Workshop and published extensively on topics like Markov Random Fields, Active Appearance Models, and Canonical Correlation Analysis. His research bridges computer science and biomedical applications, with notable contributions to anatomical structure localization and robust model learning. He collaborates with institutions like the European Society of Musculoskeletal Radiology and has developed systems such as the RheumaCoachCAD for automated scoring of bone erosions. Education: Dipl.-Ing. (Master of Engineering) and Dr.techn. (Doctor of Technical Sciences) from TU Wien. Key Projects: CAD systems for rheumatoid arthritis, 3D rock-art segmentation, and MRF-based medical image analysis. Research interests emphasize integrating computational methods into clinical diagnostics and heritage conservation. His work on sparse MRF models and group-wise learning has advanced automated medical image processing techniques.

Saman AmarasingheView profile

Professor

• Artificial Intelligence
• Machine Learning
• Computer Architecture
• +5 more

Professor Saman Amarasinghe is a leading academic in the MIT Electrical Engineering and Computer Science Department, specializing in compiler design, high-performance computing, and programming languages. His research bridges theoretical computer science with practical applications, focusing on optimizing compilers for modern hardware architectures. He holds a professorship within the School of Engineering at MIT. His research interests span artificial intelligence, machine learning integration into compilers, sparse tensor algebra optimizations, and domain-specific languages (DSLs) for high-performance computing. He has pioneered frameworks like TACO (Tensor Algebra Compiler) and GraphIt, which enable efficient handling of complex data structures across diverse computational domains. Recent work emphasizes compiler-driven optimizations for sparse data formats, machine learning models for code optimization, and unified interfaces for large language models. His projects often address scalability challenges in exascale computing and bioinformatics applications. Publications highlight advancements in compiler techniques for sparse tensor operations, graph analytics, and memory-efficient algorithms. His contributions to Halide, Simit, and Weld further demonstrate his impact on DSLs for image processing and physical simulation. Awards and recognitions are integral to his career, though specific personal accolades are not detailed here. His work is characterized by a strong focus on practical compiler solutions for emerging computational challenges.

Jimmy LinView profile

Professor

• Information Retrieval
• Natural Language Processing
• Data Management
• +2 more

Jimmy Lin is a Professor and David R. Cheriton Chair at the University of Waterloo's David R. Cheriton School of Computer Science. His research focuses on information retrieval, natural language processing, and data management, with applications in large-scale data analysis and AI systems. Previously at the University of Maryland, he earned his PhD from MIT in 2004. PhD in Electrical Engineering and Computer Science, MIT (2004) MS, Mechanical Engineering, Penn State University BSc, Mechanical Engineering, Addis Ababa University His research integrates information retrieval, NLP, and data management to build tools for understanding large datasets. Current interests include neural retrieval models, cross-lingual systems, and efficient search algorithms. Recent publications focus on retrieval-augmented generation (RAG), model efficiency, multilingual NLP, and evaluation methodologies. Trends include hybrid dense-sparse retrieval systems, robustness improvements, and practical toolkits like Anserini and Tevatron. ACM Fellow SIGIR Academy ACL Fellow SAE Ralph R. Teetor Educational Award (2014) NSF CAREER Award (2011) He advises 36+ PhD/Master's students and leads multiple industry collaborations (Primal, RSVP, Twitter). His lab develops open-source tools for retrieval and NLP, focusing on scalable and multilingual applications.

Raphael PhanView profile

Professor

• Security
• Cryptography
• Malicious AI
• +3 more

Raphaël Phan is a Professor at the Malaysia School of Information Technology, Monash University, specializing in security, cryptography, and malicious AI. His research focuses on areas including privacy, emotion recognition, motion analysis, and generative AI, with a particular interest in adversarial behavior. He has published over 200 papers and secured research funding exceeding RM3 million from government and industry sources. Phan led projects such as the privacy-preserving data mining initiative funded by the UK government and Ministry of Defence, and co-designed the hash function BLAKE, a finalist in NIST’s SHA-3 competition. He currently supervises 18 PhD students and has graduated 13, focusing on topics like AI security, generative models, and neurological disease prediction using AI. Recent research contributions include advancements in adversarial AI, brain disorder identification via graph deep learning, and post-quantum cryptography. He actively serves on technical committees for major conferences (e.g., AAAI 2024, Eurocrypt 2024) and has an h-index of 49 with an Erdős number of 2. Key collaborations include projects on Parkinson’s disease tremor analysis, brain network prediction using signal decomposition, and Indo-Pacific post-quantum cryptography initiatives. His work aligns with UN Sustainable Development Goals addressing health and technological innovation.

Qing ZhouView profile

Professor

• Causal inference and graphical models
• High-dimensional statistics
• Monte Carlo methods
• +5 more

Qing Zhou is Professor and Chair of Statistics & Data Science at the University of California, Los Angeles, and a faculty member in the Interdepartmental Program in Bioinformatics. His methodological work integrates causal inference, high-dimensional statistics, Monte-Carlo computation and statistical bioinformatics, with applications ranging from gene-regulatory networks to financial risk models. Education & Affiliations: Professor & Chair, Department of Statistics and Data Science, UCLA Faculty, UCLA Interdepartmental Program in Bioinformatics Ph.D. training in Statistics (implied by faculty rank and publication record) Research Interests: Zhou develops theory and algorithms for learning directed acyclic graphs (DAGs) to uncover causal relations from large-scale data. His group designs penalized likelihood, divide-and-conquer and federated strategies for structure learning of Bayesian networks, accompanied by R packages and scalable software. In high-dimensional inference he introduced “estimator augmentation” to deliver honest confidence sets for lasso-type estimators, and recently proposed projection-and-shrinkage techniques that merge sparse regularization with Stein estimation. Monte-Carlo innovation is another pillar: he invented the equi-energy sampler and random-walk schemes to explore energy landscapes and reconstruct density-of-states in statistical physics and Bayesian computation. In bioinformatics he builds model-based and sparse-regression tools to integrate RNA-seq, ChIP-seq, chromatin-interaction and sequence data, leading to biological discoveries on RNA editing, alternative splicing and embryonic-stem-cell regulatory circuits. Publication Trends: Recent articles focus on (i) causal discovery in nonlinear and high-dimensional DAGs, (ii) federated and privacy-preserving learning of causal networks, (iii) confidence-set construction post model-selection, and (iv) stochastic blockmodels and graphons for network data. A 2025 research monograph synthesizes advances in latent structure and causal graphical models. Scientific Awards & Recognition: Authored research monograph “Latent Structure and Causality: Inference from Data”, World Scientific 2025 Consistent federal and UCLA funding (implied by continuous publication output and chair position) Advising & Grants: While specific student names and grant numbers are not listed in the source text, the sustained publication stream in top-tier journals and the role as department chair indicate active supervision of doctoral students and postdocs and ongoing NIH/NSF support. Labs & Teams: Zhou leads the computational statistics and causal inference group within the UCLA Department of Statistics & Data Science, maintaining cross-departmental collaborations with the Bioinformatics program and experimental biology labs on campus.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +4 more

Chen Lin is affiliated with Xiamen University, School of Informatics , China. Their research spans Computer Science , Artificial Intelligence , and Bioinformatics , with a focus on Recommender Systems , Machine Learning , and Natural Language Processing . Chen Lin has published extensively on topics such as language models , index advisors , and adversarial attacks . Their work includes optimizing multi-modal recommendation systems , knowledge graphs , and drug-target interaction prediction . Recent publications in 2024 explore hypergraph pre-training , robustness in index advisors , and anonymized LLM annotation techniques . Key collaborations include researchers like Yeyun Gong , Zhenghao Lin , and Guoliang Li . While no formal awards are listed, their contributions to deep learning , data mining , and security are evident through their high-impact publications in venues such as NeurIPS , ICML , and KDD .

• Applied Signal Processing
• Inverse Problems
• Greedy Algorithms
• +5 more

Florian Bossmann is an Associate Professor at the College of Mathematics, Harbin Institute of Technology (HIT), specializing in Applied Signal Processing with a focus on algorithms and applications. His research encompasses inverse problems, greedy algorithms, sparsity, and compressed sensing, with applications in seismic exploration, ptychography, and video processing. He holds a PhD in Mathematics from the University of Göttingen (2013) and a Diplom in Mathematics from the University of Duisburg-Essen (2009). Current projects include NSFC-funded seismic data interpolation and a start-up grant for applied signal processing research. He teaches courses like 'Signal and Image Processing' and 'Calculus for Civil Engineering'. Supervising Master/PhD students in signal processing, he emphasizes collaboration and innovation. His lab, the ASP Group, explores cutting-edge methods in multidimensional data reconstruction and algorithm design. Prominent publications focus on impedance inversion, neural network applications in geoscience, and object reconstruction techniques. He has led collaborative research initiatives with institutions like the Fraunhofer Institute and the Helmholtz Center for Environmental Health.

Mohamad AssaadView profile

Professor

• 5G/6G Systems
• Age of Information
• Resource Optimization
• +7 more

Mohamad Assaad is a Professor at CentraleSupélec and researcher at L2S (CNRS). He holds the 5G Chair and has authored over 160 publications. His research focuses on 5G/6G systems, Age of Information, resource optimization, and Machine Learning in wireless networks. He has led major projects funded by Qualcomm, Orange, Thales, and Nokia. Assaad is an Editor for IEEE Wireless Communications Letters and has served as TPC co-chair for IEEE conferences. His work spans RIS-aided networks, distributed optimization, and semantic communications. Education: M.Sc. and PhD in Telecommunications from Telecom ParisTech (2002, 2006) Research Interests: 5G/6G systems, Reconfigurable Intelligent Surfaces (RIS), Federated Learning, Massive MIMO, Networked Control Awards: Best paper finalist at IEEE WiOPT 2019, Guest co-editor roles, TPC leadership Grants: €250K/year 5G Chair (2017–2021), Qualcomm €180K (2023–2024), RTE Smart Grids project Labs/Teams: IPHYCOM Team (Intelligent Physical Layers), ILOCOS Team (Optimization & Learning), and collaborations with Orange Labs, Huawei, and Nokia Bell Labs.