Find Academic Supervisors

• Theoretical Computer Science
• Algorithms
• Complexity Theory
• +8 more

Anish Mukherjee is a Lecturer in the Department of Computer Science. He has held postdoctoral positions at the University of Warwick, University of Warsaw / IDEAS-NCBR, and Charles University in Prague. He earned his Ph.D. from the Chennai Mathematical Institute in India. Ph.D. in Computer Science (Chennai Mathematical Institute) Postdoctoral Experience: Warwick, Warsaw/IDEAS-NCBR, Charles University His research focuses on theoretical computer science, particularly algorithms and complexity theory for dynamic, parallel, and distributed computation. Additional interests include streaming algorithms, graph algorithms, string algorithms, circuit complexity, and exact exponential-time algorithms for NP-hard problems. He has received the TCS Scholarship during his Ph.D. studies. Recent publications examine problems in semi-streaming matchings, network design parameterization, and dynamic query maintenance. Recipient of TCS Scholarship He coordinates the Cyber Security (COMP232) module. His research outputs span conferences like FOCS, ACM SPAA, SIAM, and journals such as the Journal of Computer and System Sciences.

• Computational Science
• Scientific Visualisation
• GPU Computing
• +5 more

Dr. Robert (Rob) G. Belleman is a Lecturer and researcher in the Computational Science Lab at the Informatics Institute, Faculty of Science, University of Amsterdam. He concurrently serves as Director of the university-wide College of Informatics (CoI), manager of the Visualisation Lab at Science Park, and head of the Education Group, consolidating teaching, research, and infrastructure leadership roles. Education details are not disclosed in the supplied text. His research integrates large-scale scientific computing with immersive visualisation. Core themes encompass GPU-accelerated simulations—from gravitational N-body dynamics to flood modelling—interactive visual analytics for criminal and biological networks, 3-D morphological analysis of marine organisms, sign-language lexicon technologies, and in-silico oncology platforms that fuse multiscale cancer models with clinical IT workflows. Recent articles (2022-2024) emphasise interdisciplinary visual analytics: uncovering cocaine-trafficking scenarios, discriminating coral species via 3-D quantification, and constructing 3-D sign-language lexicons. Earlier work advanced GPU computing for astrophysics and MD simulations, hybrid socio-genetic network modelling of HIV spread, and grid-enabled oncosimulation for nephroblastoma therapy planning. No scientific awards are listed in the provided source. Belleman leads educational innovation through the College of Informatics and the Education Group, while infrastructural oversight of the Visualisation Lab supports numerous UvA research teams. No explicit grant amounts or named PhD advisees appear in the text. He heads the Visualisation Lab facility at Science Park and coordinates the Education Group within the Computational Science Lab, fostering cross-disciplinary collaboration among computer scientists, life scientists, and physicists.

Kuan-Hsun ChenView profile

Assistant Professor

• Computer Architecture
• Embedded Systems
• Real-Time Systems
• +5 more

Kuan-Hsun Chen is an Assistant Professor specializing in Computer Architecture Design for Embedded Systems Real-Time Systems Non-Volatile Memory (NVM) Optimization Machine Learning and Edge AI His research focuses on bridging hardware-software gaps in real-time embedded applications through innovative architectural solutions. Research highlights include: Decision Tree Optimization for Real-Time Inference Wear-Leveling Techniques in NVM Probabilistic Timing Guarantees GPU-Accelerated Graph Algorithms Security-Enhanced NVM Designs He explores theoretical foundations while emphasizing practical implementations in autonomous systems and cyber-physical platforms. Scientific achievements: Best Paper Award (2023) Outstanding Paper Award (2023) Outstanding Reviewer Award (2024) His work spans algorithm design, hardware-software co-optimization, and rigorous system validation through fault injection and simulation frameworks.

Rob A. RutenbarView profile

Professor

• Integrated Circuit Design Tools
• Nanoscale Statistics in Chip Design
• Hardware Accelerators for AI
• +3 more

Rob A. Rutenbar is the Senior Vice Chancellor for Research at the University of Pittsburgh and holds Distinguished Professorships in Computer Science (School of Computing and Information) and Electrical and Computer Engineering (Swanson School of Engineering). He also serves as Adjunct Professor at UIUC and CMU. With nearly 40 years in academia and industry, his research focuses on integrated circuit tools, nanoscale chip design statistics, and AI hardware accelerators. He led the University of Illinois' CS department (ranked #5 nationally) and spent 25 years at CMU, where he pioneered analog CAD tools and founded Neolinear (acquired by Cadence) and Voci Technologies (acquired by Medallia). Education: PhD (Computer Engineering, University of Michigan, 1984), MS (Computer Engineering, University of Michigan, 1979), BS (Electrical Engineering, Wayne State University, 1978). Research interests include analog circuit synthesis, statistical methods for nanoscale ICs, and hardware architectures for AI. Notable contributions include the first MOOC on chip design tools (60k+ learners) and the Phil Kaufman Award for lifetime contributions to EDA. His work spans over 200 publications and 14 patents, with funding from DARPA, NSF, and industry partners like Google and IBM. Awards include ACM SIGDA Pioneering Achievement Award (2021), AAAS Fellowship (2019), and NAI Fellow (2019). He has advised over 50 PhD students, many of whom lead industry and academia (e.g., Amith Singhee at IBM, Saurabh Tiwary at Google). Rutenbar's leadership initiatives at Pitt include the Momentum Funds for team science, LifeX Labs for life sciences commercialization, and the Office of Industry Partnerships to align research with industry needs.

Chris Callison-BurchView profile

Professor

• Natural Language Processing
• Machine Translation
• Paraphrasing
• +5 more

Chris Callison-Burch is a Professor of Computer and Information Science at the University of Pennsylvania , where he leads research in Natural Language Processing , Large Language Models , and Multimodal Learning . Previously, he worked at Johns Hopkins University’s Center for Language and Speech Processing for six years. Research Areas Automated paraphrasing and natural language understanding Machine translation without bilingual parallel corpora Crowdsourcing for NLP and social justice applications Generative AI and vision-language models Recent work focuses on LLM soundness guarantees, multimodal reasoning, AI-generated text detection, and ethical applications of language models. His research has been cited over 25,000 times, and he testified before Congress in 2023 on generative AI and copyright law. Awards & Funding Sloan Research Fellow Faculty awards from Google, Microsoft, Amazon, Facebook, and Roblox Grants from DARPA, IARPA, and NSF He chairs major NLP conferences (ACL 2017, EMNLP 2015) and contributes to editorial boards of TACL and Computational Linguistics .

Edwin CarlinetView profile

Assistant Professor

• Mathematical Morphology
• Image Processing
• High Performance Computing
• +1 more

Edwin Carlinet is an Assistant Professor at EPITA Research Laboratory (LRE), specializing in mathematical morphology, hierarchical image representations, and high-performance computing. His research develops efficient algorithms for image processing with applications in document analysis and historical map vectorization. Key projects include the SoDuCo initiative extracting data from 19th-century Parisian trade directories and parallel implementations of morphology algorithms. He leads development of the Pylene image processing library, focusing on generic and efficient implementations. Teaching covers parallel programming, data compression, C++, and GPU programming at EPITA, alongside bioinformatics courses at Sup'Biotech. Research supervision includes PhD candidates working on modern implementations of mathematical morphology algorithms and noise estimation techniques. Recent publications focus on optimizing computational workflows for historical document processing, including noise impact analysis on morphological structures and GPU acceleration of max-tree computation.

Chua Tat SengView profile

Professor

• Multimedia Information Retrieval
• Social Media Analytics
• Unstructured Multimodal Data Analytics
• +4 more

Professor Chua Tat Seng is a distinguished academic at the National University of Singapore's School of Computing, serving as KITHCT Chair Professor and Director of the NUS-Tsinghua Extreme Search Center (NExT). He also holds Distinguished Visiting Professorships at Tsinghua and Zhejiang Universities in China. PhD in Computer Science (University of Leeds, 1983) Founding Dean of School of Computing (1998-2000) Co-founded ViSenze and 6Estates technology startups His research focuses on unstructured multimodal data analytics, with particular emphasis on multimedia information retrieval, social media analytics, recommendation systems, and trustworthy AI. He has pioneered work in computational wellness and fintech applications, establishing the Lab for Media Search and leading NExT++ research initiatives. Over 300 publications in leading venues (CVPR, SIGIR, WWW, AAAI) Recipient of ACM SIGMM Technical Achievement Award (2015) Supervised 37 PhD students since 2004 Editorial leadership in ACM Transactions and IEEE Multimedia Recent work explores multimodal LLMs, knowledge editing techniques (AlphaEdit), and 3D generation frameworks, reflecting his commitment to advancing web intelligence and user empowerment.

• Neuroscience
• Molecular Biology
• Neurodevelopmental Disorders
• +3 more

Hong Li is an Associate Research Scholar in the Department of Neuroscience at Yale University's Yale School of Medicine. Their research focuses on molecular mechanisms of cortical developmental malformations in neurodevelopmental and psychiatric disorders, employing animal models and integrative methodologies including molecular biology, genetics, morphology, and transcriptomics. Dr. Li holds a DPhil from the University of Helsinki (2007) and an MD from Anhui University (1997). Their work aims to advance understanding of neuropsychiatric diseases and develop therapeutic strategies. Educational Background: DPhil in Neuroscience, University of Helsinki (2007) MD, Anhui University (1997) Research Interests: Investigating molecular pathways underlying cortical development abnormalities, with emphasis on translational research to bridge basic science and clinical applications. Techniques include advanced genetic engineering, transcriptomic profiling, and cross-disciplinary approaches combining neuroscience and molecular biology. Advising & Grants: No specific grants or trainees listed in available materials, though their publications suggest collaborative research in quantum computing, embedded systems, and telecommunication networks. Recent work includes cross-domain contributions to quantum error correction systems and LLM-based robotic applications. Professional Contributions: Active in systems engineering, publishing on operating systems design, FPGA-based quantum decoders, and secure memory management. Their work bridges biomedical research with computational and hardware innovations.

Cayden CodelView profile

Researcher

• Programming Languages
• Formal Verification
• Satisfiability (SAT) Solvers
• +3 more

Cayden Codel is a Researcher at Carnegie Mellon University's Computer Science Department , focusing on programming languages and formal verification. His work bridges theoretical logic with practical applications in automated reasoning and constraint solving. Research Interests: Programming Languages, Formal Verification, Satisfiability (SAT) Solvers, Satisfiability Modulo Theories (SMT), Automated Theorem Proving, Machine Learning Institution: Carnegie Mellon University (CMU) His publications emphasize formal verification for logical systems, SMT/SAT solvers , and reinforcement learning applications. Articles from 2024-2019 reveal a trajectory from foundational logic to real-world dataset design (e.g., Minecraft-based AI research). Thesis Advisors: Marijn Heule, Jeremy Avigad Contact: ccodel@andrew.cmu.edu

Brian RailingView profile

Teaching Professor

• Computer Science Education
• Computer Architecture
• Systems Programming
• +3 more

Brian Railing is an Associate Teaching Professor in the Computer Science Department at Carnegie Mellon University, part of the School of Computer Science. He specializes in educational technologies and systems programming, with a focus on improving teaching methods and student engagement in computer science courses. His recent courses include 15213, 15346, and 15513 across multiple semesters. His research spans electronic examination analysis, computer architecture education tools like CADSS, and optimizing parallel computing systems through Contech and LLVM instrumentation. His publications highlight advancements in student-centered learning, such as enhancing office hours and analyzing exam-taking behaviors. He also contributes to compiler optimization and memory systems evaluation. Despite extensive teaching and research, no awards or grants are explicitly mentioned. Brian’s work emphasizes bridging theory and practice in systems education, with ongoing efforts to develop scalable tools for parallel program analysis and student-driven learning environments.

Huaming ChenView profile

Senior Lecturer

• Software Engineering
• AI
• Cybersecurity
• +3 more

Huaming Chen is a Senior Lecturer at the School of Electrical and Computer Engineering, The University of Sydney (equivalent to Associate Professor in the US system). He specializes in trustworthy AI, software security, and computational biology. His research focuses on integrating AI techniques into software engineering and scientific domains to enhance system reliability and ethical compliance. Currently, he advises PhD and master's students on projects involving AI in intelligent building systems, blockchain-driven governance frameworks, and open-source AI security. He holds roles as Area Chair for ACM MM, PC member for venues like ACM CCS and IJCAI, and Guest Editor for Computers & Security . His service extends to IEEE/ACM memberships and discipline expertise in Software Engineering and Electrical & Computer Systems. Awards include the 2020 IEEE CIS Student Grant and 2017 travel fellowships for bioinformatics and services computing conferences. Research interests span adversarial machine learning, AI ethics, computational biology applications, and secure AI system design. Key contributions include work on federated learning security, adversarial attacks on vision transformers, and privacy-aware STI/HIV risk prediction models. He actively promotes responsible AI through workshops and interdisciplinary collaborations.

• Distributed Computing
• Algorithms
• Parallel Processing
• +3 more

Dr. Manuela Fischer is a Lecturer at the Department of Computer Science at ETH Zürich. Her research focuses on distributed computing, algorithms, and parallel processing, with a particular emphasis on graph theory and massively parallel computation. She has contributed to advancements in algorithms for sparse graphs, approximation techniques in semi-streaming models, and distributed systems optimization. Her work often explores theoretical computer science challenges, including algorithm design and computational complexity. Dr. Fischer’s publications span topics such as exponential speedups in MPC frameworks, deterministic symmetry breaking in graphs, and efficient connectivity algorithms in distributed environments. These contributions highlight her expertise in leveraging parallel and distributed computing paradigms to solve complex computational problems. No scientific awards or grants are explicitly mentioned in the provided information. She is affiliated with ETH Zürich and contributes to teaching and research in computer science, though specific educational qualifications or lab affiliations are not detailed in the text.

• GPU Architecture
• Hardware Accelerators
• Machine Learning
• +3 more

José L. Abellán is a Ramón y Cajal Fellow (Tenure-Track Associate Professor) and European R3 researcher at the University of Murcia's Department of Computer Engineering and Technology. He leads the EcoArTech research group and holds a Ph.D. in Computer Science from the University of Murcia (2012). His career includes postdoctoral positions at Boston University and previous faculty roles at Universidad Católica de Murcia. Dr. Abellán's research focuses on architectural enhancements for GPU systems and customized accelerators targeting machine learning and fully homomorphic encryption applications. His work spans hardware/software co-design, microarchitectural extensions for privacy-preserving computation, and efficient parallel processing. His extensive publication record demonstrates consistent contributions to GPU architecture, processing-in-memory, hardware acceleration for cryptography, and graph neural networks. Recent work has appeared in top computer architecture venues including MICRO, ASPLOS, and HPCA. Awards and Honors HiPEAC Paper Awards (2019, 2020, 2023, 2024) Best Paper Award at IPDPS 2011 Top Picks in Hardware and Embedded Security 2024 European R3 Certificate (2024) Dr. Abellán leads the EcoArTech research team and serves as Associate Editor for ACM Transactions on Architecture and Code Optimization (TACO) and Frontiers in Electronics. He is a Senior Member of IEEE and active in the HiPEAC European network.

Paul PangView profile

Associate Professor

• Cyber Security
• Digital Health
• Cognitive Cyber Security Intelligence
• +6 more

Dr. Paul Pang is an Associate Professor of Cyber Security at Federation University Australia's Institute of Innovation, Science and Sustainability (IISS). Previously, he held a Professorship in Data Analytics and directed the Center for Computational Intelligence for Cybersecurity at Unitec Institute of Technology, New Zealand. His research focuses on Cognitive Cyber Security Intelligence, Cyber Resilience, and Applied Data Analytics for Digital Health, leveraging over NZD$3.5M in grants from organizations like MBIE and HRC. He has authored over 100 refereed articles and holds 1 patent with 3 pending applications. Education: PhD in Engineering from Shanghai Jiao Tong University (2000). Research Interests: Pang's work spans cybersecurity, digital health, and big data analytics. He develops intelligent systems for cyber threat detection, resilience frameworks, and healthcare applications. His methods include machine learning for data streams, incremental learning algorithms, and multi-agent cooperative systems. Publications Trends: His articles emphasize cybersecurity (e.g., darknet traffic analysis, malware grouping), healthcare analytics (e.g., schizophrenia prediction), and foundational machine learning (e.g., SVMs, LDA algorithms). Collaborations span institutions in Japan, China, and the U.S. Awards: IEEE Best Paper Awards (2003, 2008), Unitec Excellence Awards (2011-2015), ARC Assessor (Australia). Advising & Grants: Supervised 10+ PhD/Masters students; led 13 grants totaling NZD$3.5M. Notable students include Snjezana Soltic (Pest Survival Analysis) and Simon Dacey (Land Use Management). Professional Roles: IEEE Senior Member, Vice President of APNNS, Global AI Summit Judge (2018). Labs/Teams: Active in Federation University's Cybersecurity and Digital Health research groups. Collaborates with industry partners like NICT Japan and Telecom NZ.

• Distributed Systems
• Network Coding
• Information Theory
• +3 more

MALAK Derya is an Assistant Professor at EURECOM's Communication Systems department, specializing in networked distributed computing systems. Her research focuses on optimizing communication and computation trade-offs in networks, with a strong emphasis on network coding, information theory, and wireless communication systems. She holds a dual role in advancing theoretical foundations and practical implementations of distributed computing frameworks. Dr. Malak has received prestigious recognitions, including the Best Paper Awards at WiOpt 2023 and WIOPT 2022 for her work on content distribution optimization and random access protocols. Her research explores cutting-edge topics such as non-linear function computation, structured coding for matrix operations, and congestion-aware job scheduling in real-time systems. Key Research Areas: Distributed Algorithms, Network Coding, Machine Learning Systems, Wireless HetNets, and Multi-Server Architectures Awards: WiOpt 2023 Best Paper, WIOPT 2022 Best Paper Professional Activities: Invited speaker at major conferences and workshops, including the National Center on Networks and Systems for Digital Transformation Her work bridges theoretical insights with practical engineering solutions, addressing critical challenges in modern distributed and networked systems.