Find Academic Supervisors

• Distributed Systems Security
• Distributed Algorithms
• Algorithms in Networking
• +4 more

Yu Haifeng is a Dean's Chair Associate Professor at the Department of Computer Science, National University of Singapore (NUS) School of Computing. He is a member of the Faculty Teaching Excellence Committee (FTEC) and has made significant contributions to distributed systems, security, and algorithms. Ph.D. (Computer Science, Duke University, 2002) M.S. (Computer Science, Duke University, 1999) B.E. (Computer Science, Shanghai Jiao Tong University, 1997) His research focuses on Distributed Systems Security , particularly blockchains and Sybil attacks, Distributed Algorithms , and Algorithms in Networking . He explores fundamental challenges in dynamic networks, including bandwidth constraints, diameter estimation, and adversarial robustness. Recent publication trends highlight theoretical advancements in blockchain scalability, Byzantine fault tolerance, and dynamic network analysis. His work bridges theoretical computing with practical applications like vehicular communication and disaster recovery systems. Best Paper in ACM SPAA, 2020 Best Paper in ACM SIGCOMM, 2010 Best Paper in ACM/IEEE IPSN, 2009 Best Paper in ACM NSDI, 2006 He has advised students such as Yuda Zhao and Irvan Jahja on projects addressing network diameter estimation and dynamic network challenges. His work has been published in top venues including JACM, Distributed Computing, PODC, and Oakland.

Anthony RichardsonView profile

Professor

• Marine Spatial Planning
• Ecosystem Modeling
• Climate Change Impacts on Marine Systems
• +6 more

Professor Anthony Richardson is a mathematical ecologist at the University of Queensland , holding a joint position with CSIRO Environment since 2005. He leads the Mathematical Marine Ecology Lab , focusing on marine conservation through mathematical, statistical, and computational modeling. School of the Environment, Faculty of Science, University of Queensland Affiliate, Centre for Marine Science Affiliate, Centre for Biodiversity and Conservation Science Research Interests span marine spatial planning (climate-smart protected areas, 3D ocean zoning), marine ecosystem modeling (lower trophic levels, plankton-dynamics), and climate change impacts (carbon sequestration, fisheries productivity, and marine diseases). His work has been cited in IPCC Assessment Reports and emphasizes international collaboration through projects like the Global Working Group on Climate Change Synthesis . Recent Publications focus on Zooplankton's role in carbon cycling Climate-smart marine protected area design Ecosystem modeling under climate uncertainty Marine megafauna connectivity patterns Supervision is offered for >250 peer-reviewed papers and advanced projects in climate-smart conservation planning, with established funding from ARC Discovery Projects and Norwegian Polar Institute .

• Electrical and Electronic Engineering
• Computer Hardware
• Computer Software
• +7 more

Professor Peter Y. K. Cheung is a Professor of Digital Systems at Imperial College London, holding dual affiliations within the Department of Electrical and Electronic Engineering and the Dyson School of Design Engineering. His work focuses on reconfigurable systems, FPGA architectures, and high-level synthesis tools. He co-founded one of the UK's leading FPGA research groups with Professor Wayne Luk, addressing challenges in variability mitigation, reliability, and application-specific FPGA deployments. His research spans Field-Programmable Gate Arrays (FPGAs) Reconfigurable computing Neural network acceleration Cryptographic protocols Embedded systems He has pioneered techniques such as logic shrinkage for FPGA-based neural networks and developed frameworks like LUTNet for efficient inference. His contributions also include fault-tolerant FPGA designs and methodologies for distributed computation protocols. Key collaborations include work with the Department of Computing on FPGA-based AI acceleration and cybersecurity applications. His recent work explores edge computing, secure decentralized systems, and pandemic modeling using adaptive control strategies. Notable projects include the DSCS protocol for secure distributed computation, acceleration of gravitational wave detection algorithms, and energy-efficient CNN implementations. His research bridges hardware-software co-design with real-world applications in healthcare, finance, and aerospace.

B. Aditya PrakashView profile

Associate Professor

• Data Science
• Machine Learning
• Artificial Intelligence
• +6 more

Aditya Prakash is an Associate Professor and Associate Chair for Academic Affairs in the School of Computational Science and Engineering at Georgia Tech. He holds a PhD from Carnegie Mellon University (2012) and a B.Tech from IIT Bombay (2007). His research focuses on data science, machine learning, and AI applied to epidemiology, healthcare, security, and urban computing. His work has led to impactful tools used by organizations like CDC, ORNL, and Walmart. Notable awards include the NSF CAREER Award (2018) and IEEE's 'AI Ten to Watch' (2017). Education: PhD (Computer Science, CMU, 2012), B.Tech (IIT Bombay, 2007) Research Interests: Epidemic forecasting, network analysis, healthcare informatics, and large-scale data-driven solutions for societal challenges. He has authored over 80 papers and holds two patents. His lab develops methods for disease modeling, urban infrastructure analysis, and cybersecurity. Key projects include the NSF-funded BEHIVE initiative for pandemic prediction and collaborations with MIDAS network for infectious disease modeling. Awards: NSF CAREER, Facebook Faculty Award, IEEE AI Recognition, and multiple best-paper awards. His group advises students across PhD, MS, and undergraduate levels, with notable alumni in academia and industry. Labs & Affiliations: Core faculty at ML@GT (Machine Learning Center) and IDEaS (Institute for Data Engineering and Science). Active in organizing conferences like AAAI, KDD, and SIGMOD.

Juan GarayView profile

Professor

• Cryptography
• Information Security
• Cryptographic Protocols
• +7 more

Juan Garay is a Professor in the Department of Computer Science & Engineering at Texas A&M University, affiliated with the College of Engineering. His research focuses on cryptography, information security, and distributed systems, with notable contributions to cryptographic protocols, blockchain technologies, and consensus mechanisms. He holds a leadership role in advancing theoretical and applied aspects of secure computation and network security. Research Interests: Cryptography and Information Security Secure Multiparty Computation Cryptocurrencies and Blockchain Protocols Consensus Algorithms Distributed Computing Game Theory in Cryptography Publications highlight his work on the Bitcoin Backbone Protocol, secure multiparty computation, and post-quantum cryptographic systems. He actively contributes to conferences and workshops in cryptography and distributed systems. He advises graduate students in computer science and engineering, though specific advisee names are not listed. His work is supported by grants from the National Science Foundation (NSF) and other institutions, focusing on secure protocols and distributed systems. Office: Peterson Building (PETR 429) Contact: garay@cse.tamu.edu

• Security
• Cryptography
• Theoretical Computer Science
• +3 more

Yan Huang is an Associate Professor in the Department of Computer Science at Indiana University Bloomington, focusing on security and cryptography. His work bridges theoretical foundations with practical systems, emphasizing cryptographic protocols with strong security guarantees for generic computation. University: Indiana University Bloomington School: College of Arts and Sciences Department: Computer Science Academic Rank: Associate Professor His research combines theoretical computer science , program analysis , artificial intelligence , and software engineering to address real-world security problems. Key areas include secure computation , zero-knowledge proofs , and privacy-preserving technologies . Recent publications, such as Phecda (SP'25) and Dubhe (USENIX Security'23), highlight advancements in post-quantum cryptography , AES verification , and privacy-preserving deep packet inspection , reflecting his focus on scalable and practical cryptographic solutions. He has advised notable students including Changchang Ding (PhD, Computer Science) and Ruiyu Zhu (PhD, now at Facebook), and served on program committees for top conferences like ACM CCS and CRYPTO .

Guglielmo ScovazziView profile

Professor

• Finite element methods
• Computational fluid mechanics
• Computational solid mechanics
• +9 more

Guglielmo Scovazzi is a Professor at Duke University with appointments across multiple departments including the Department of Civil and Environmental Engineering, the Thomas Lord Department of Mechanical Engineering and Materials Science, and as Professor of Mathematics. His interdisciplinary research bridges computational mechanics, scientific computing, and engineering applications. Dr. Scovazzi earned his B.S/M.S. in aerospace engineering (summa cum laude) from Politecnico di Torino (Italy), followed by an M.S. and Ph.D. in mechanical engineering from Stanford University. Prior to joining Duke, he was a Senior Member of the Technical Staff at Sandia National Laboratories' Computer Science Research Institute. His research focuses on developing advanced numerical methods for computational mechanics, particularly finite element methods for fluid and solid mechanics. Key areas include multiphase porous media flows, computational methods for materials under extreme conditions, turbulent flow computations, and instability phenomena. His work emphasizes creating accurate computational approaches that reduce design/analysis costs for complex engineering problems involving fluid-structure interactions and transient phenomena in complex geometries. Dr. Scovazzi's most significant recent contribution is the development of the Shifted Boundary Method, an innovative computational framework that enables efficient simulations on complex geometries without requiring boundary-fitted meshes. This method has found applications in geomechanics, energy systems, and resilient infrastructure design. Kavli Fellow, National Academy of Sciences & Kavli Foundation (2018) Presidential Early Career Award for Scientists and Engineers (PECASE), White House (2017) Early Career Award, U.S. Department of Energy, Advanced Scientific Computing Research Program (2014) Dr. Scovazzi teaches multiple courses in computational mechanics including Nonlinear Finite Element Analysis and Introduction to the Finite Element Method. His research has been supported by substantial federal funding, and he actively collaborates across disciplines to address challenging problems in energy, environment, and infrastructure resilience through advanced computational methods.

Jignesh M. PatelView profile

Professor

• Data Management
• Database Systems
• Machine Learning
• +3 more

Jignesh M. Patel is a Professor in the Computer Science Department at Carnegie Mellon University , focusing on Data Management , System Efficiency (e.g., Scalable Data Platforms), and Human Efficiency (e.g., LLM-Based Query Interfaces). His work bridges Database Systems , Machine Learning , and Human-Computer Interaction . Co-founder of four startups: Paradise (1997), Locomatix (2007), Quickstep (2015), and DataChat (2017). Member of SIGMOD 2025 (AE) , CIDR 2024 (Co-Chair) , and other program committees. Research Interests include efficient data analysis algorithms , LLM-based data interaction , and systems security . His group develops platforms combining scalability and user productivity . Scientific Awards include Best Paper Awards at SIGMOD and VLDB, and Fellowships from AAAS, ACM, and IEEE. He also received Teaching Awards at CMU. Professional Activities feature co-founding startups , serving on program committees , and teaching courses like Database Systems and Advanced Database Systems at CMU.

Jee ChoiView profile

Assistant Professor

• High Performance Computing
• Parallel and Scalable Algorithm Design
• Performance Analysis Optimization
• +2 more

Jee Choi is an Assistant Professor in the Department of Computer and Information Science within the College of Arts and Sciences at the University of Oregon. His research focuses on developing high-performance algorithms for big data analytics, particularly in tensor decomposition and parallel computing systems. Education: PhD in Electrical and Computer Engineering, Georgia Institute of Technology, 2015 MS in Electrical and Computer Engineering, Georgia Institute of Technology, 2004 BS in Electrical and Computer Engineering, Georgia Institute of Technology, 2000 Research Interests: Dr. Choi specializes in High Performance Computing with expertise in parallel algorithm design, performance modeling, and energy efficiency. His work targets Tensor Decomposition & Data Mining for big data, developing scalable solutions for sparse and dense tensor computations. He advocates leveraging HPC systems to transform massive datasets into societal benefits through efficient data mining techniques. Publication Trends: Choi's recent publications (2021-2025) demonstrate consistent innovation in tensor decomposition methodologies, with increasing focus on adaptive storage (Alto), streaming data factorization, and energy-aware autotuning. His work spans GPU clusters, distributed systems, and emerging architectures, maintaining strong connections to real-world big data challenges while advancing theoretical algorithm design. Professional Background: After completing his PhD under Richard Vuduc at Georgia Tech (notable for SpMV GPU autotuning and Energy Roofline Model), Choi conducted DoD-funded research on tensor decomposition at IBM Watson Research Center (2015-2018) before joining the University of Oregon faculty.

Andrew FriendView profile

Researcher

• Carbon Cycle
• Ecosystem Ecology
• Climate Science
• +5 more

Andrew Friend is a Researcher at the University of Cambridge , affiliated with the Department of Geography . His work focuses on terrestrial ecosystem dynamics, particularly the interplay between vegetation, carbon fluxes, and climate systems. He develops process-based computer models to simulate vegetation growth, competition, and carbon cycle responses, while also conducting experimental and field studies in collaboration with institutions like the Sainsbury Laboratory, NIAB, and Forestry England. Friend’s research integrates plant physiological knowledge with global vegetation modeling . Key interests include source-sink carbon interactions, physiological diversity in ecosystems, and the impact of climate extremes on forest resilience. His models address challenges such as drought, temperature changes, and deforestation, with applications in predicting future carbon balances and informing climate mitigation strategies. The 15 most recent articles highlight his contributions to understanding Amazon deforestation tipping points , wood formation mechanisms , and fire/disturbance impacts on ecosystems . These works span climate science , forest ecology , and biogeochemical cycles , emphasizing multi-scale approaches from cellular processes to global systems. Friend collaborates with institutions like Forestry England (Thetford Forest studies), Sainsbury Laboratory , and the C-CLEAR Doctoral Training Partnership . His work bridges computational modeling, experimental validation, and field data to advance terrestrial carbon cycle science.

Leo SchwinnView profile

Lecturer

• Robust Machine Learning
• Large Language Models Security
• Data-Efficient Learning
• +5 more

Leo Schwinn is a Lecturer at the Technical University of Munich (TUM) within the Department of Computer Science (I26), working in the Data Analytics and Machine Learning group supervised by Prof. Stephan Günnemann at the TUM School of Computation, Information and Technology. His research focuses on robust machine learning with particular emphasis on data-efficient learning and robustness vulnerabilities of Large Language Models (LLMs). Dr. Schwinn's research interests span multiple critical areas in contemporary machine learning including: Robustness against adversarial attacks in LLMs Embedding space vulnerabilities and defenses Model unlearning and privacy preservation Efficient training methodologies for large models Time-series forecasting with probabilistic frameworks Graph-based machine learning approaches His work bridges theoretical understanding with practical security implications of modern AI systems. Analysis of his recent publications (2023-2025) reveals a strong focus on LLM security, with multiple papers accepted at premier conferences including ICML, CVPR, ICLR, and NeurIPS. His research demonstrates consistent innovation in identifying novel attack vectors while developing practical defense mechanisms, particularly through embedding space manipulation techniques. The work shows increasing sophistication in handling both theoretical aspects of model robustness and practical deployment concerns. His notable scientific achievements include: Receiving the ATE dissertation price for his PhD work at FAU Securing an oral presentation at ICLR 2025 Organizing the ICLR BlogPost Track Becoming a member of ELLIS (European Laboratory for Learning and Intelligent Systems) Dr. Schwinn has served as review process chair for the 2024 Conference on Lifelong Learning Agents (CoLLAs) and actively collaborates with researchers at Mila Quebec AI Institute. His research group at TUM focuses on addressing fundamental challenges in machine learning robustness, particularly as they apply to real-world deployment scenarios where security and reliability are paramount. He maintains active GitHub repositories related to LLM security research, including circuit-breakers-eval and LLM_Embedding_Attack, demonstrating his commitment to open science and reproducible research in the field of AI security.

• intelligent systems
• high performance distributed computing
• resource management
• +8 more

Professor Omer Rana serves as Professor of Performance Engineering and International Dean for the Middle East at Cardiff University's School of Computer Science and Informatics. He also holds the prestigious position of Cross-Council Research Director for the UK National Edge AI Hub, demonstrating his leadership in national research initiatives. Previously, he led the Complex Systems research group and served as Dean of International for the Physical Sciences and Engineering College at Cardiff University. Professor Rana is a Fellow of both the Learned Society of Wales and the Higher Education Academy, and serves on the Advisory Board of the Welsh Ethnic Minority Professors Initiative (WEMPI). His research expertise centers on the intersection of intelligent systems and high performance distributed computing, with particular focus on applying intelligent techniques to resource management in distributed systems. His scholarly contributions span edge computing, cloud computing, Internet of Things (IoT), artificial intelligence, cybersecurity, federated learning, privacy-preserving systems, and sustainable computing. Professor Rana has published extensively in top-tier journals and conferences, with recent work emphasizing practical applications in industrial automation, smart buildings, and sustainable computing solutions. Professor Rana's publication record demonstrates consistent leadership in edge computing and distributed systems research, with a growing emphasis on practical implementations across diverse application domains. His work bridges theoretical computer science with real-world technological challenges, particularly in industrial automation, smart environments, and sustainable infrastructure. Fellow of the Learned Society of Wales Fellow of the Higher Education Academy Professor Rana actively supervises postgraduate students and has secured significant research funding for projects related to edge computing, IoT, and distributed systems. His international collaborations span Europe, Asia, and the Middle East, reflecting his global influence in the field. He serves as a sought-after keynote speaker, workshop chair, and panel moderator at major international conferences including IEEE/ACM Utility and Cloud Computing (UCC) and IEEE Edge Computing. He leads multiple research initiatives, most notably as Cross-Council Research Director for the UK National Edge AI Hub, where he shapes national research directions in edge computing and AI. His work has practical applications across industrial automation, smart building management, electric vehicle infrastructure, and sustainable computing solutions.

• Neurosurgery
• Functional Neurosurgery
• Neuromodulation
• +3 more

Zion Zibly, MD, MBA is an Associate Professor in the Department of Neurosurgery at Yale School of Medicine . He holds multiple leadership roles including Director of the Center of Neuromodulation , Director of the Center of Neurosurgical Cancer Pain , and Head of Stereotactic & Functional Neurosurgery and the Focused Ultrasound Institute . Previously served as Chair of Neurosurgery at Sheba Medical Center after graduating from Technion’s Faculty of Medicine (MD) and Coller School of Management (MBA). Research Interests: Specializes in Neuromodulation for movement disorders (Parkinson’s, tremors, dystonia), Deep Brain Stimulation , Gene Therapy for pediatric neurodegenerative conditions, Oncological Neurosurgery , and Neurological Pain Management . Combines Functional Neurosurgery with Focused Ultrasound technology. Scientific Contributions: Participated in pioneering Alzheimer’s brain stimulator procedures and Gene Therapy applications. Active member of the North American Association of Functional Neurosurgery and Israeli Neurosurgical Society . Clinical Expertise: Implantation of electrostimulators for Parkinson’s and essential tremor, treatment of Benign/Malignant CNS Tumors , and management of Neurological Pain Conditions . Affiliated with Yale Cancer Center and Center for Brain & Mind Health .

• Plant Nutrition
• Soil Fertility
• Soil Spectroscopy
• +3 more

Du Changwen is a Researcher (Professor) at the Nanjing Institute of Soil Science, Chinese Academy of Sciences, serving as Deputy Director of the National Engineering Laboratory for Soil Nutrient Management. He supervises doctoral and master's students in soil science and agricultural technology development. His academic journey includes: Bachelor's degree from Huazhong Agricultural University's College of Resources and Environmental Science (1997) Master's degree from Huazhong Agricultural University's Trace Element Laboratory (2000) PhD from Nanjing Institute of Soil Science, Chinese Academy of Sciences (joint program with Technion - Israel Institute of Technology) (2003) Dr. Du's pioneering research focuses on precision fertilization technologies, particularly polymer-coated controlled-release fertilizers developed through model membrane and water-based reaction film-forming techniques. His work integrates Fourier Transform Infrared spectroscopy (ATR and PAS modes) with engineering mathematics to monitor nutrient release dynamics, soil chemistry processes, and plant nutrition in real-time. This interdisciplinary approach bridges agricultural chemistry, materials science, and environmental engineering to optimize fertilizer efficiency while minimizing ecological impact. Analysis of his 2015-2017 publications reveals a consistent emphasis on spectroscopic methods for soil-plant system analysis, with dominant themes in controlled-release fertilizer development, soil organic matter characterization, and in-situ nutrient monitoring. His work demonstrates strong cross-disciplinary integration between agricultural technology, analytical chemistry, and environmental science. His scientific recognition includes: Special Award of the First China Agricultural Science and Technology Innovation and Entrepreneurship Competition First Prize of Jiangsu Science and Technology Award First Jiangsu Youth Entrepreneurship Award Second Prize of Chinese Academy of Sciences Science and Technology Contribution Award First Prize of China Agricultural Science and Technology Award Dr. Du has secured major research funding including National Natural Science Foundation projects (key, general, youth), National '973' Basic Research Program, '13th Five-Year' R&D Plan sub-projects, '863' High-tech Program sub-projects, and Jiangsu Provincial Science and Technology Support Plan initiatives. His leadership in the National Engineering Laboratory for Soil Nutrient Management drives innovation in fertilizer technology, with significant outputs including 198 academic papers (89 SCI, 35 EI), 6 monographs, 1 international patent, 8 national patents, and 2 software copyrights. His laboratory specializes in advanced spectral analysis of soil-plant systems, utilizing FTIR-ATR and FTIR-PAS technologies for real-time monitoring of nutrient dynamics and polymer membrane reactions. Current research focuses on next-generation controlled-release fertilizers, machine learning-enhanced spectral analysis, and precision nutrient management systems for sustainable agriculture.

• Software Engineering
• Computer Systems
• Software Security
• +11 more

Cristian Cadar is a Professor in the Department of Computing at Imperial College London, leading the Software Reliability Group . His research focuses on improving software reliability and security through practical techniques in software engineering, computer systems, and program analysis. Education: Ph.D. in Computer Science, Stanford University M.Eng. in Computer Science, MIT B.S. in Computer Science and Mathematics, MIT His research interests center on software engineering and software security , particularly symbolic execution , dynamic symbolic execution (DSE) , and multi-version execution . Current work explores techniques for scalability, constraint solving, and runtime security in software systems. Key trends in his recent articles include optimizing symbolic execution for testing, addressing path explosion in constraint-based test generation, and advancing multi-version execution for dynamic software updates. His publications also cover program analysis , automated testing , and formal methods for software reliability. Awarded prestigious honors such as the Humboldt Research Award (2024) , ERC Consolidator Grant (2018) , and IEEE New Directions Award (2022) . Other accolades include the BCS Roger Needham Award (2019) and SIGOPS Hall of Fame (2018) . Cadar supervises PhD and postdoctoral researchers in software reliability and security. His group has secured grants from the ERC and EPSRC , including a Consolidator Grant (2018) and Early-Career Fellowship (2013) . He actively contributes to conference organizing committees and editorial boards. The Software Reliability Group at Imperial College, led by Cadar, specializes in techniques like KLEE and EXE for automated testing. Their work has been adopted by industry partners such as Fujitsu, IBM, and Microsoft, particularly in runtime security tools like WIT .