Find Academic Supervisors

Thomas G. AndersonView profile

Assistant Professor

• Numerical Analysis
• Spectral Methods
• Scientific Computing
• +9 more

Thomas G. Anderson is an Assistant Professor in the Department of Computational Applied Mathematics and Operations Research at Rice University. His research focuses on numerical analysis, spectral methods, and scientific computing, with applications in wave propagation and fluid dynamics. He joined Rice in 2023, following postdoctoral work at the University of Michigan and a PhD in applied and computational mathematics from Caltech. Education: PhD in Applied and Computational Mathematics, Caltech (Department of Computing+Mathematical Sciences) MS in Applied Mathematics, New Jersey Institute of Technology Bachelor’s in Applied Mathematics, New Jersey Institute of Technology Research Interests: Development of high-order numerical methods for partial differential equations Fast algorithms for singular integral operators in potential theory Parallelizable algorithms for long-time wave propagation simulations Volumetric discretization techniques for complex geometries Articles Trends: His recent work emphasizes efficient computational methods for wave and fluid dynamics, including hybrid frequency-time analysis, fast evaluation of volume potentials, and stabilization of viscous liquid layers. He explores both theoretical foundations and practical algorithm design. Grants and Advising: No specific grants or advisees listed in the provided text. Labs/Teams: Active collaboration with computational mathematics groups at Rice and prior institutions, including work at Lawrence Livermore National Laboratory.

Charles GillanView profile

Senior Lecturer

• High Performance Computing (HPC)
• Artificial Intelligence (AI) in Healthcare
• Quantum Computing
• +4 more

Charles Gillan is a Senior Lecturer at Queen's University Belfast's School of Electronics, Electrical Engineering and Computer Science, affiliated with the High Performance and Distributed Computing department and the Institute of Electronics, Communications & Information Technology. His research bridges HPC systems, AI applications in healthcare, and computational physics. Key projects include managing ICU patient care via neural networks, exascale-ready mathematical packages, and edge computing architectures. Research interests focus on high-performance computing (HPC), quantum computing, real-time data analytics, and electron-molecule scattering simulations. Notable contributions include developing microserver architectures for edge analytics and advancing AI-driven clinical decision support systems. Gillan has collaborated on interdisciplinary projects like food authenticity testing using spectroscopy and improving ventilator management in intensive care units. Publications span AI in healthcare, HPC system design, and computational methods for physics problems. He has secured funding for initiatives such as the KTP partnership with Foods Connected Ltd and the HANDHELD olfactory detection project. Gillan's work emphasizes practical applications of advanced computing across healthcare, engineering, and cybersecurity domains.

Thomas LudwigView profile

Professor

• High-Performance Computing (HPC)
• Data Storage Systems
• Energy Efficiency in Computing
• +3 more

Prof. Dr. Thomas Ludwig is the Director of the German Climate Computing Center (DKRZ) and a Professor at the Universität Hamburg. He holds a doctoral degree and habilitation from the Technische Universität München, with expertise in High-Performance Computing (HPC), energy efficiency, and data storage systems. His research focuses on optimizing parallel systems, storage technologies, and computational efficiency for climate science applications. He leads projects like AIMES and PeCoH, advancing HPC storage and energy-aware computing. Education: Doctoral degree and habilitation from TU München (1988–2001). Chair in Parallel Computing at Universität Heidelberg (2001–2009). Research Interests: HPC, data reduction techniques, energy-efficient systems, parallel I/O optimization, and climate modeling infrastructure. Recent Research Trends: His work emphasizes storage system efficiency, machine learning in HPC, and convergence between HPC and Big Data. Key contributions include frameworks for portability (Vecpar), automated performance tools, and energy-aware storage solutions. Awards: Some publications received recognition, e.g., a Best Paper award in 2014 for work on energy efficiency. However, no personal awards are explicitly listed. Advising & Grants: Supervised numerous theses in HPC, I/O optimization, and energy efficiency. Leads major projects funded by national and international initiatives. Labs/Teams: Heads the DKRZ team providing supercomputing and data management for climate research, collaborating with global institutions like the University of Hamburg and European research networks.

Mitchell L. NeilsenView profile

Professor

• Cyber-Physical Systems
• Distributed Systems
• Scientific Computing
• +7 more

Mitchell L. Neilsen is a Professor in the Department of Computer Science at Kansas State University's College of Engineering, where he also serves as the graduate program director. He holds the Warren and Gisela Kennedy - Carl and Mary Ice Keystone Research Scholar position and maintains an active research program with multiple ongoing projects. His educational background includes a Ph.D. in Computer Science (1992), M.S. in Computer Science (1989), and M.S. in Mathematics (1987), all from Kansas State University, plus a B.S. in Mathematics Education from the University of Nebraska-Kearney (1982). After beginning his career as an assistant professor at Oklahoma State University, he returned to K-State in 1996. Research Interests: Cyber-Physical Systems: Design, Analysis, Verification of systems integrating computing, networking, and physical processes Distributed Systems: Algorithms, design, and analysis of distributed computing systems Scientific Computing: Computational Fluid Dynamics, Finite Element Analysis, High Performance Computing, and Simulation Application Areas: Agriculture technology, Dam safety analysis, Mobile applications, Natural resources management, and Real-time Embedded Systems His research program shows clear evolution toward agricultural technology applications, particularly high-throughput phenotyping, while maintaining strong foundations in cyber-physical systems and scientific computing. Recent publications indicate increasing integration of machine learning and computer vision techniques into traditional research areas. Research Funding: National Science Foundation U.S. Department of Agriculture Sandia National Laboratories Department of Homeland Security Private industry partners Dr. Neilsen has mentored numerous graduate students through their M.S. and Ph.D. programs, with recent advisees focusing on applications in agricultural technology, dam safety, and embedded systems. His advising approach emphasizes practical applications of theoretical computer science concepts. Current Teaching (Fall 2024): CIS 450 - Computer Architecture and Operations CIS 625 - Concurrent Software Systems CIS 720 - Advanced Operating Systems

Paolo TrunfioView profile

Professor

• Computer Engineering
• Big Data Analysis
• Machine Learning
• +5 more

Paolo Trunfio is a Professor of Computer Engineering at the University of Calabria, Italy, and co-founder of DtoK Lab S.r.l., an academic spin-off focused on data analysis and distributed systems. He holds a Ph.D. and is affiliated with the DIMES Department, specializing in big data, cloud computing, and high-performance computing (HPC). His research emphasizes scalable data analysis frameworks, edge-cloud continuum solutions, and machine learning applications for social media and disaster monitoring. Trunfio serves as an Associate Editor for ACM Computing Surveys and Journal of Big Data , and is on the editorial boards of several journals including Future Generation Computer Systems . He has authored four influential books, including Programming Big Data Applications (2024) and Data Analysis in the Cloud (2015). His work spans distributed systems, IoT-based smart objects, and exascale computing. Notable projects include the EU-funded eFlows4HPC and ASPIDE initiatives, which focus on HPC workflows and exascale programming models. Trunfio’s publications (over 200 papers) address topics like social media analytics, energy-efficient P2P networks, and parallel data mining. He leads research in urgent computing for disaster response, edge-cloud integration for urban mobility, and AI-driven data analysis. His contributions to cloud frameworks (e.g., JS4Cloud, ParSoDA) and HPC libraries (e.g., DCEx) highlight his expertise in bridging theory and practice in distributed computing ecosystems.

Hari SundarView profile

Associate Professor

• Parallel Algorithms
• High Performance Computing
• Computational Sciences
• +3 more

Hari Sundar is an Associate Professor in the Department of Computer Science at Tufts University, holding the Ada Lovelace Associate Professorship. Previously, he served as an Associate Professor at the Kahlert School of Computing, University of Utah. His research focuses on developing parallel algorithms for computational sciences and high-performance computing, addressing challenges in biosciences, geophysics, computational fluid dynamics, and computational relativity. He leads efforts in adaptive mesh refinement, geometric multigrid methods, and scalable scientific computing frameworks like Dendro-GR for numerical relativity. Education: Ph.D. in Computer Science from the University of Pennsylvania (2009), and a Bachelor of Engineering from the University of Delhi (2000). Postdoctoral work at the Oden Institute, University of Texas at Austin. Research Interests: Parallel algorithms, high-performance computing architectures, computational relativity (binary black hole simulations), multiphase flow modeling, and domain-specific languages for scientific computing. His work emphasizes scalability and efficiency on modern supercomputers. Key Contributions: Development of the Dendro-GR platform for gravitational wave simulations, scalable PDE solvers, and GPU-optimized algorithms for phonon transport and genomic sequence alignment. His recent work includes advancements in gravitational waveform modeling for LISA space missions and thermodynamically consistent two-phase flow simulations. Grants & Collaborations: Active in NSF-funded projects on computational relativity, multiphase flow algorithms, and scalable PDE solvers. Collaborates across disciplines in astrophysics, materials science, and bioinformatics.

W. Eric WongView profile

Professor

• Software Engineering
• Software Testing
• Software Reliability
• +6 more

W. Eric Wong is a Professor of Computer Science at The University of Texas at Dallas (UTD), affiliated with the Erik Jonsson School of Engineering and Computer Science. He holds a Ph.D. in Computer Science from Purdue University (1993), following earlier degrees from Purdue and Eastern Michigan University. His research focuses on reducing software production costs while enhancing reliability, safety, and quality through program-based and architecture/design-based testing methodologies. Key areas include automated test generation, fault localization, debugging, and software safety analysis. Professional Background: Tenured Professor at UTD since 2002 Prior roles include Senior Scientist at Telcordia Technologies (1995–2002) and Consultant at Texas Instruments (2004–2005) Active in industry partnerships, such as projects with Motorola, Avaya Labs, and Raytheon Research Interests: Software Testing & Debugging Dependable Software Development Security Requirements Engineering Fault Localization Techniques Model-Based Testing Software Reliability Modeling Awards & Recognition: 2007 IEEE COMPSAC Best Paper Award 1997 NASA Quality Assurance Special Achievement Award Recipient of a $404,772 NSF grant for software safety and reliability research (2021) Professional Activities: Editorial roles for journals like Journal of Systems and Software and International Journal of Software Engineering Program Chair for ISSRE 2012, COMPSAC 2010, and multiple ACM SAC conferences Member of IEEE Reliability Society Administrative Committee (2008–2013) IBM System z Curriculum Advisory Panel member Labs & Teams: Leads the Software Engineering Group at UTD, collaborating on projects like eXVanatge (dependable software solutions) and Fault-Prone Module Identification in telecommunications systems. Engages in cross-disciplinary efforts with industry and international institutions.

Kevin W. HamlenView profile

Professor

• Cybersecurity
• Software Security
• Formal Methods
• +6 more

Kevin W. Hamlen is the Louis A. Beecherl, Jr. Distinguished Professor in the Department of Computer Science at the University of Texas at Dallas. He serves as Executive Director of UT Dallas' Cyber Security Research and Education Institute. His research focuses on language-based security , binary software hardening , cyberdeception , and formal program verification . He has received multiple grants from agencies like AFOSR, NSF, DARPA, and industry partners including Lockheed Martin and Intel. PhD and MS from Cornell University BS from Carnegie Mellon University His research explores automated approaches to software security through techniques like binary disassembly , control-flow integrity , and honey-patching . He has pioneered methods for malware defense and cloud/web/mobile security . Recent work examines adaptive cyberdeception and GPU-based security frameworks . His publications span binary code manipulation , malware mitigation , and blockchain security . Key awards include the NSF IUCRC Technology Breakthrough Award and two CSAW Best Paper 2nd Prizes . He advises numerous PhD students, many of whom now work at Google, IBM, and Microsoft. His book Autonomous Cyber Deception (Springer, 2019) with Ehab Al-Shaer and Cliff Wang provides comprehensive coverage of adaptive cyberdeception strategies.

Marc Sánchez ArtigasView profile

Associate Professor

• Distributed Computing
• Cloud Computing
• Big Data
• +6 more

Marc Sánchez Artigas is an Associate Professor at Rovira i Virgili University, Department of Computer Engineering and Mathematics. He holds a PhD from Pompeu Fabra University (2009) and conducted postdoctoral research at EPFL (Switzerland). His research focuses on distributed computing, cloud storage systems, and serverless architectures. He leads the CloudLab research group and coordinates major EU projects like Horizon Europe's CloudSkin and H2020's IOStack. Education: PhD in Computer Science (2009), Pompeu Fabra University MSc in Computer Engineering (2004), Universitat Rovira i Virgili BSc in Computer Engineering (2002), Universitat Rovira i Virgili Research Interests: Distributed systems, cloud computing, software-defined storage, serverless computing, and privacy-preserving storage solutions. His work emphasizes scalable architectures, data management in heterogeneous environments, and optimizing cloud storage efficiency through novel algorithms and frameworks. Awards: Best Paper (IEEE LCN 2007), Best Dataset (ACM IMC 2015), Serra-Hunter Excellence Professorship, and multiple grants from EU and Spanish funding bodies. Grants & Projects: Coordinated over €5 million in projects including H2020 CloudButton (serverless analytics), FP7 CloudSpaces (personal clouds), and national initiatives like Software-Defined Edge Clouds. Active in coordinating IPCEI-CIS for cloud infrastructure. Teaching: Courses on distributed systems, parallel architectures, and cloud computing. Taught at Universitat Rovira i Virgili and Universitat Oberta de Catalunya.

Changcheng HuangView profile

Professor

• Machine Learning
• Network Architecture
• Optical Networks
• +3 more

Changcheng Huang is a Professor at Carleton University's Department of Systems and Computer Engineering, part of the Faculty of Engineering and Design. He holds a Ph.D. from Carleton University and is licensed as P.Eng. His research focuses on Machine Learning, Network Architecture, and Optical Networks, emphasizing resource optimization and protocol design. Dr. Huang leads the Advanced Optical Network Laboratory (AONL), funded by CFI and OIT, which explores optical network technologies and interworking with electronic networks. His lab includes state-of-the-art equipment like Nortel switches and photonic switches. Recently, he advised PhD students Qiao Lu, Khoa Nguyen, and others, and completed postdoc Eslam G. AbdAllah. RA positions are available at both master's and PhD levels. His work spans publications in journals like IEEE Transactions and conferences such as Globecom and ICC. Research areas include intelligent network control mechanisms, wireless networks, and network protocol implementation. Education: Ph.D. (Carleton University). Research interests also include modeling/simulation techniques and reliability mechanisms for optical networks. He teaches courses like SYSC 5108 (Deep Learning) and SYSC 4602 (Computer Communications). Grants funded by CFI and OIT support his lab's optical networking projects. Over 150+ publications highlight his contributions to virtual network embedding, edge computing, and optical data center networks. Lab facilities include OMM photonic switches, Nortel routers, and Dell servers. Collaborative projects involve industry and academic partnerships, advancing interworking technologies between optical and electronic networks. His work bridges theoretical research with practical implementations, addressing challenges in network scalability, energy efficiency, and reliability.

Md ArifuzzamanView profile

Assistant Professor

• High-Performance Networking
• Quantum Networking
• Distributed Systems
• +3 more

Dr. Md Arifuzzaman is an Assistant Professor in the Department of Computer Science at Missouri University of Science and Technology (Missouri S&T). He specializes in High-Performance Systems, Quantum Networking, and Distributed Systems, focusing on optimizing large-scale system performance and scalability. His work addresses challenges in next-generation networks and storage systems, with publications in top venues like IEEE TPDS and ACM Supercomputing. Education: Ph.D. in Computer Science and Engineering, University of Nevada, Reno (2023) B.S. in Computer Science and Engineering, Bangladesh University of Engineering and Technology (2016) Research Interests: Dr. Arifuzzaman's work spans cutting-edge topics including quantum entanglement routing, reinforcement learning for network optimization, and high-speed file transfer protocols. His research emphasizes practical solutions for emerging technologies like terabit networks and quantum communication systems. Publications: Recent work focuses on quantum network protocols, machine learning-driven network probing, and storage reliability. His articles highlight advancements in both theoretical frameworks and real-world system implementations. Awards: No specific awards mentioned in the provided information. Advising/Grants: Details regarding student advising and grant activities are not explicitly stated in the text.

• Quantum Physics
• Quantum Computing
• Quantum Error Correction
• +3 more

Dr. Dominic Williamson is a theoretical quantum physicist and DECRA Research Fellow at the School of Physics, University of Sydney. He specializes in quantum phases of matter and their applications to quantum error correction and computing. His work bridges condensed matter theory and quantum information science, focusing on fracton topological phases and fault-tolerant quantum architectures. Education: PhD in Physics from the University of Vienna (2017); Postdoctoral research at Yale University, Stanford University, and IBM Quantum. Current roles include faculty membership at the University of Sydney’s Quantum Science Group and prior industry experience at IBM and PsiQuantum. Research interests: Topological phases of matter, quantum error correction codes (e.g., fracton codes, QLDPC systems), fault-tolerant quantum computing architectures, and non-Abelian anyon systems. Recent breakthroughs include low-overhead quantum architectures and novel approaches to parallelized logical measurements. Grants: 2022 ARC Discovery Early Career Researcher Award for topological phases in quantum computation. Collaborations include projects on gauging logical operators and quantum code surgery. Professional activities: Editor for Quantum , frequent speaker at international conferences, and mentor for students at all levels (undergraduate to postdoctoral). Active in open-source research and public engagement through platforms like arXiv and Google Scholar.

Nicola CalabrettaView profile

Professor

• Optical Communication Systems
• Photonics
• Telecommunications Engineering
• +6 more

Nicola Calabretta is a Full Professor in Electro-Optical Communication Systems and Senior Research Fellow at Eindhoven University of Technology (TU/e). His work focuses on smart optical networks, high-speed electronics, FPGA implementations for scheduling algorithms, and photonic integrated circuits. He holds a PhD from TU/e (2004) and previously conducted research at DTU Fotonik and the Sant'Anna School of Advanced Studies. His expertise spans optical signal processing, multi-level modulation formats, and applications in data center and metro networks. Key research areas include optical switching architectures (e.g., SOA-based switches), WDM systems, and low-latency interconnect networks. He has led projects like ADAPTOR (resource optimization), SmartTWO (future telecom technologies), and 5G-MOBIX (cross-border mobility). His courses include 'Optical Fibre Communication Technology' and 'Optical Interconnection Networks.' Collaborations involve institutions globally, with recent work emphasizing photonic integration for neural networks, ultra-fast switching, and edge computing. His contributions align with UN SDGs through sustainable telecom infrastructure advancements.

Do Young EunView profile

Professor

• Distributed Optimization
• Machine Learning
• Network Modeling
• +5 more

Do Young Eun is a Professor in the Department of Electrical and Computer Engineering at North Carolina State University (NC State), with affiliations in Computer Science and Operations Research. He holds a Ph.D. from Purdue University and M.S./B.S. degrees from KAIST, Korea. His research focuses on distributed optimization for machine learning, network modeling, and algorithms for social/wireless networks, with applications in epidemic analysis and graph analytics. Education: Ph.D. in Electrical and Computer Engineering, Purdue University (2003) M.S. in Electrical Engineering, KAIST (1997) B.S. in Electrical Engineering, KAIST (1995) Research Interests: Distributed optimization and machine learning Network modeling and performance analysis Epidemic modeling and control Graph analytics and social network analysis Stochastic processes and algorithms Highlighted Awards: NSF CAREER Award (2006) Outstanding Paper Award, ICML 2023 Best Paper Awards at IEEE ICCCN (2005), IPCCC (2006), NetSciCom (2015) Best Student Paper Award, ACM MobiCom 2007 Advising & Grants: Current advisees include Jie Hu, Yi-Ting Ma, and Feiya Xiang NSF Grant (2024–2027): 'Toward Maximally Efficient Sampling and Optimization for Decentralized Learning' Supervised over 10 Ph.D. students, many in academic or industry leadership roles Labs/Teams: His research group focuses on interdisciplinary projects at the intersection of networking, machine learning, and stochastic systems, with collaborations in academia and industry.

Chen XuView profile

Associate Professor

• Sparse Modeling
• Statistical Learning
• Feature Selection
• +5 more

Chen Xu is an Associate Professor in the Department of Mathematics and Statistics at the University of Ottawa. He holds an M.A. from York University and a PhD from the University of British Columbia. His research focuses on sparse modeling, statistical learning, and big data processing, with an emphasis on both theoretical and computational advancements. Dr. Xu is affiliated with the Faculty of Science and contributes to editorial roles for journals such as the Journal of the American Statistical Association and Electronic Journal of Statistics. Education: M.A., York University PhD, University of British Columbia Research interests include feature selection, regularization methods, kernel methods, and high-dimensional regression. His work addresses computational challenges in big data, proposing efficient algorithms for tasks like singular value decomposition, clustering, and distributed feature screening. Recent publications highlight advancements in multiview PCA, low-tubal-rank tensor recovery, and model-free regression techniques. Publications span prestigious journals like the Journal of the American Statistical Association and IEEE Transactions series, focusing on statistical methodology, machine learning applications, and scalable computational solutions for complex data problems. Editorial Service: Associate Editor, Journal of American Statistical Association-T&M (2023–present) Associate Editor, Electronic Journal of Statistics (2023–present) Former Associate Editor, The Canadian Journal of Statistics (2019–2021) His research groups are Statistics and Biostatistics, and Data Science, Machine Learning, and Artificial Intelligence. He has no listed awards but maintains active editorial and academic collaborations in computational statistics and machine learning.