جست‌وجوی استاد و پژوهشگر

• Information Theory
• Cryptography
• Lattice Theory
• +۵ مورد دیگر

Cong Ling is a Professor of Information Theory and Cryptography at Imperial College London's Department of Electrical and Electronic Engineering, within the Faculty of Engineering. His research focuses on lattice theory and its applications in coding, cryptography, quantum information, and number theory. Key affiliations include the Academic Centre of Excellence in Cyber Security Research and the Engineering Secure Software Systems group. Education details are not explicitly provided in the text, but his professional experience indicates advanced qualifications in electrical engineering and mathematics. Research interests span lattice-based cryptography, post-quantum security, algebraic coding theory, and quantum-resistant algorithms. His work bridges information theory and number theory, with contributions to MIMO systems, secure communication protocols, and cryptographic protocol design. Recent publications emphasize lattice reduction techniques, quantum algorithms for the shortest vector problem, and advancements in polar codes. Notable trends include exploration of non-commutative algebras for cryptography, Gaussian sampling optimizations, and hybrid quantum-classical approaches to hard integer problems. Over 50+ articles published since 2018 reflect his leadership in lattice-based research and quantum-safe technologies. Awards: None explicitly listed in the text. Grants/Advising: No specific grants or student advisees mentioned; focus remains on collaborative research outputs. Labs/Teams: Associated with Imperial's Cyber Security Research groups and quantum engineering initiatives.

Zhou Zhi-Huaمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۳ مورد دیگر

Zhou Zhi-Hua is a Professor at Nanjing University's Department of Computer Science & Technology, serving as Standing Deputy Director of the National Key Lab for Novel Software Technology and Founding Director of LAMDA (Institute of Machine Learning and Data Mining). He holds simultaneous fellowships from ACM, AAAI, AAAS, IEEE, IAPR, IET/IEE, and CCF, reflecting his exceptional contributions to computational intelligence. His educational background includes: B.Sc. in Computer Science from Nanjing University (1996) M.Sc. in Computer Science from Nanjing University (1998) Ph.D. in Computer Science from Nanjing University (2000) Zhou's research pioneers fundamental advances in machine learning theory and applications. His seminal work on ensemble methods established new frameworks for classifier combination, while innovations in multi-label learning and anomaly detection addressed critical challenges in complex data analysis. His research bridges theoretical rigor with practical implementations across diverse domains including biometrics, data mining, and computer vision, resulting in over 150 publications and 18 patents. His textbooks "Ensemble Methods" (2012) and "Machine Learning" (2016) have become standard references in the field. Analysis of his publication trajectory reveals sustained leadership in core machine learning challenges: evolving from neural network ensembles (2002) through semi-supervised learning breakthroughs (2005) to foundational work on multi-instance learning (2012) and theoretical margin analysis (2013). His recent focus demonstrates increasing sophistication in handling complex data structures while maintaining theoretical soundness. His scientific excellence is recognized through: National Natural Science Award of China (2013) PAKDD Distinguished Contribution Award (2016) IEEE ICDM Outstanding Service Award (2016) IEEE CIS Outstanding Early Career Award (2013) Microsoft Professorship Award (2006) Simultaneous fellowships from 7 major international societies Zhou provides extraordinary service to the academic community as Executive Editor-in-Chief of Frontiers of Computer Science and Associate Editor-in-Chief of Science China Information Science. He founded the ACML conference and has chaired premier events including ICDM'16 and PAKDD'14. His leadership extends to serving as General Chair for ICDM'16, Program Chair for IJCAI'15 Machine Learning Track, and Area Chair for multiple top conferences. The available text does not specify student advising details or research grants. He directs LAMDA research group at Nanjing University, which has established itself as a global powerhouse in machine learning research, and contributes significantly to the National Key Lab for Novel Software Technology's mission of developing next-generation intelligent systems.

Michela Becchiمشاهده پروفایل

دانشیار

• Computer Architecture
• Systems Software
• Heterogeneous Computing
• +۵ مورد دیگر

Michela Becchi is an Associate Professor in the Department of Electrical and Computer Engineering at North Carolina State University. She specializes in computer architecture, systems software, and applications, with a focus on heterogeneous systems, parallel algorithms, and acceleration techniques for bioinformatics, pattern recognition, and quantum computing. Her work spans multi-core CPUs, GPUs, FPGAs, and distributed clusters, emphasizing the boundary between hardware and software design. Dr. Becchi holds a Ph.D. and Master’s degree in Computer Engineering from Washington University in St. Louis (2009) and a Bachelor’s degree in Computer Engineering from Politecnico di Milano, Italy (2000). Her research has been recognized with prestigious awards, including the NSF CAREER Award (2015) and the University of Missouri System President Award for Early Career Excellence (2016). Her research interests include compiler and runtime techniques for heterogeneous systems, acceleration of bioinformatics algorithms, and high-speed networking applications. She has pioneered frameworks for efficient data transformation, GPU-accelerated compression, and memory-efficient graph algorithms for quantum computing. Her work also explores thread coarsening, mixed-precision auto-tuning, and secure multi-core processor design. Key contributions include the PILOT runtime system for GPU memory management, the GPU-FPtuner auto-tuner for floating-point applications, and innovative approaches to automata processors for genomic analysis. Her publications emphasize reproducible accuracy in scientific simulations and the optimization of irregular applications on many-core platforms.

Sherry Tongshuang Wuمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Natural Language Processing
• Artificial Intelligence
• +۶ مورد دیگر

Sherry Tongshuang Wu is an Assistant Professor at Carnegie Mellon University's School of Computer Science, with primary appointments in the Human-Computer Interaction Institute (HCII) and secondary affiliation with the Language Technology Institute (LTI) . Trained at the University of Washington under Jeffrey Heer and Dan Weld, she bridges HCI and NLP to study human interactions with AI systems across diverse user groups. Her educational background includes a Ph.D. (2016-22) and M.S. (2016-18) in Computer Science and Engineering from the University of Washington, and a B.Eng. (2012-16) from Hong Kong University of Science and Technology. Industry experience includes research internships at Google Brain, Microsoft Research, and Apple. Wu's research focuses on three interconnected pillars: Real-world AI Evaluation (developing frameworks like SPHERE for systematic assessment), Task-specific AI Test & Distill (optimizing general-purpose models for specific use cases), and Human-AI Task Delegation (designing optimal collaboration between humans and AI). Her work emphasizes practical deployment, user-specific net gains, and error recovery mechanisms. Analysis of her 15 most recent publications reveals a strong trend toward evaluation frameworks (35%), human-AI collaboration systems (30%), and specialized model distillation (25%), with growing emphasis on educational applications (10%). Key methodological themes include checklist-based evaluation, perspective-aware retrieval, and structural analysis of AI outputs. Google Academic Research Award (2024) Amazon Research Awards (2024) AIED 2024 Best Paper Award ACL 2020 Best Paper Award Rising Stars in EECS Workshop (2020) Wu actively mentors 19 students across PhD, Master's, and undergraduate levels, with notable projects including synthetic data generation, LLM literacy tools, and retrieval system optimization. She leads significant grant-funded work through Amazon Research Awards and Google Academic Research Awards, focusing on deployable model generation and human-AI collaboration frameworks. Her lab develops practical tools like Promp2Model and SPHERE that bridge theoretical research with industry applications.

Tomaso A. Poggioمشاهده پروفایل

استاد

• Computational Neuroscience
• Machine Learning
• Computer Vision
• +۵ مورد دیگر

Tomaso A. Poggio is the Eugene McDermott Professor in the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology , an investigator at the McGovern Institute for Brain Research , a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) , and the director of both the MIT Center for Biological and Computational Learning (CBCL) and the multi-institutional Center for Brains, Minds and Machines (CBMM) . Research Interests Poggio’s research is fundamentally interdisciplinary, sitting at the intersection of computational neuroscience , machine learning , and computer vision . His work is driven by the conviction that learning is the core gateway to both biological intelligence and artificial systems. Current themes include: Mathematical foundations of statistical learning theory Engineering applications in computer vision, graphics, bioinformatics, and intelligent search Computational neuroscience of visual object recognition and the ventral stream of the visual cortex Scientific Awards & Honors Eugene McDermott Professorship, MIT Advising & Grants Over three decades, Poggio has advised a large cohort of doctoral and master’s students whose theses span machine learning, computer vision, neuroscience, and bioinformatics. Representative graduates include H. Jhuang, Stanley Bileschi, Jacob Bouvrie, Jennifer Louie, M. Kouh, Sayan Mukherjee, Ryan Rifkin, Alexander Rakhlin, Thomas Serre, Gene Yeo, and many others. His research has been continuously supported by major federal and private funding initiatives, most recently through the multi-institutional Center for Brains, Minds and Machines (CBMM) headquartered at the McGovern Institute since 2013. Laboratories & Teams Poggio directs the Poggio Lab (CBCL) at MIT, an interdisciplinary group comprising neuroscientists, computer scientists, mathematicians, and engineers. The lab collaborates closely with experimental neuroscientists to develop predictive computational theories of visual cortex function and to translate those insights into practical algorithms for computer vision and machine learning.

Pavel Panchekhaمشاهده پروفایل

استادیار

• Programming Languages
• Program Synthesis
• Verification
• +۴ مورد دیگر

Pavel Panchekha is an Assistant Professor in the School of Computing at the University of Utah, where he holds the Warnock Chair for Junior Faculty. His research spans programming languages, web browsers, and numerical analysis, with a focus on developing programming language techniques to address challenges across computer science. Dr. Panchekha received his educational training at prestigious institutions: PhD in Computer Science from the Paul G. Allen School for Computer Science and Engineering at the University of Washington, advised by Michael D. Ernst and Zachary Tatlock BS in Mathematics from MIT Panchekha's research program has two major thrusts. First, he works on web browser internals , with projects including fuzzing layout invalidation, multi-tenant garbage collection, and optimizing 2D graphics. He is also authoring a textbook on web browsers that informs much of this research. Second, he focuses on automatic numerical analysis , with projects such as automatic accuracy improvement, synthesis via term rewriting, scalable static accuracy analysis, and math library implementation. He leads the FPBench and Herbie projects, which are major deployments of his research. His scholarly output demonstrates consistent contributions across programming languages, verification, and numerical methods. Recent work shows a growing emphasis on bidirectional typing systems, layout invalidation in browsers, and robust floating-point error analysis. His publications reveal a trajectory from foundational work on floating-point accuracy (notably the Herbie tool that won a Distinguished Paper Award at PLDI 2015) toward more comprehensive systems for program synthesis, verification, and browser optimization. Panchekha has received significant recognition for his research contributions: NSF Fellowship ARCS Foundation Fellowship Adobe Research Fellowship Wissner-Slivka Foundation Fellowship 2015 PLDI Distinguished Paper Award for work on the Herbie numerical analysis and repair tool As an advisor, Panchekha mentors a substantial group of students across multiple levels. He currently advises six students: Marisa Kirisame (PhD), Bhargav Kulkarni (PhD), Yumeng He (PhD), Artem Yadrov (MS), Jesus Ponce (BS), and Jonas Regehr (BS). Previously, he has advised over twenty students including PhD candidates like Ian Briggs and numerous MS and BS students. His advising spans theoretical topics in programming languages and practical applications in web browsers and numerical computing. Panchekha leads research groups focused on programming languages applications to web browsers and numerical analysis. His work on the Herbie tool for floating-point accuracy improvement has become influential in the programming languages community, and his more recent work on browser internals is shaping how researchers understand and optimize modern web rendering engines. He is currently developing a textbook on web browsers that aims to synthesize knowledge about browser architecture and implementation.

Laura Collinsمشاهده پروفایل

دانشیار

• Applied Linguistics
• Bilingualism
• Language Acquisition
• +۳ مورد دیگر

Laura Collins is an Associate Professor in the Department of Education at Concordia University, specializing in applied linguistics. Her research focuses on input processing in second language acquisition, pedagogical grammar, and cross-linguistic influence among bilingual speakers. She has extensive international teaching experience across diverse age groups and serves as the 2nd VP of the American Association for Applied Linguistics and Chair of the IRIS digital repository advisory board. PhD in Humanities, Concordia University MEd in Second Language Teaching, University of Ottawa BEd in Second Language Teaching, University of Toronto BA in History, York University Collins' current projects examine the use of speech technologies to extend second language classrooms and document longitudinal grammatical development in school programs. Funded by SSHRC Insight Grants, her work bridges cognitive approaches with practical pedagogical applications. Recent publications highlight trends in classroom input optimization, frequency-based phonological learning, and intensive language program efficacy, with subfields spanning morpheme perception, vocabulary distribution, and task-based interaction dynamics. Her academic leadership includes editorial roles in The Modern Language Journal and contributions to major handbooks. She teaches courses ranging from classroom-based SLA research to pedagogical grammar, emphasizing practical applications of theoretical insights.

Ken Duffyمشاهده پروفایل

استاد

• Network Coding
• Algorithms
• Probability and Statistics
• +۳ مورد دیگر

Ken Duffy is a Professor and Chair of the Department of Mathematics at Northeastern University, with a joint appointment in the Department of Electrical and Computer Engineering. He joined Northeastern in 2023 and previously served as Interim Chair of the latter department. Previously, he was a professor at the National University of Ireland, Maynooth, where he directed the Hamilton Institute (2016–2022) and co-directed the Science Foundation Ireland Centre for Research Training in Foundations of Data Science. He earned a PhD in Mathematics from Trinity College Dublin. His research focuses on collaborative, multi-disciplinary algorithm design using probability and statistics, with applications in digital circuits, DNA, and network coding. Notable contributions include the Royal Statistical Society’s Applied Probability Section (co-founded in 2011) and numerous award-winning papers in IEEE conferences and journals. Recent work emphasizes decoding algorithms like GRAND (Guessing Random Additive Noise Decoding), applied to error correction, wireless systems, and biomedical imaging. His articles address topics like soft-output decoding, interference mitigation, and cellular lineage tracing. Awards: Best Paper Awards (IEEE ICC 2015, IEEE TNSE 2019), COMSNETS Best Demo (2022–2023), and the IEEE Ellersick Award (2024). Advising: The SFI Centre he co-directed funded over 120 PhD students. Labs/Teams: Hamilton Institute, Royal Statistical Society’s Applied Probability Section, and collaborative projects in cellular dynamics and secure communication.

Gabriele Ligaمشاهده پروفایل

استادیار

• Digital Communications
• Information Theory
• Optical Communications
• +۵ مورد دیگر

Gabriele Liga is an Assistant Professor at the Department of Electrical Engineering, Eindhoven University of Technology (TU/e), affiliated with the Signal Processing Systems (SPS) Group. He holds a Marie Curie Eurotech Fellowship focusing on signal shaping techniques for nonlinear optical fiber channels. His academic journey includes a Ph.D. in optical communications from University College London, followed by postdoctoral research in digital signal processing and nonlinearity compensation. Education: B.Sc. in Telecommunications Engineering from Università degli Studi di Palermo (2005), M.Sc. in Telecommunications Engineering from Politecnico di Milano (2011), and a Ph.D. in Optical Communications from University College London (2017). Research Interests: Digital communications, information theory, fiber-optic systems, nonlinearity compensation, channel coding, and multi-user optical communication theory. His work emphasizes achieving transmission limits through signal shaping and advanced signal processing techniques. Projects: Active roles in NESTOR (Next-gen optical networks), QuNEST (quantum communication security), Fun-NOTCH (nonlinear optical channel fundamentals), and SSTOC (signal shaping tailored to optical channels). Collaborations span institutions globally, focusing on optical fiber communication challenges. Awards: 2023 ACP/POEM Best Student Paper Award and 2019 OECC Best Paper Award. Serves as a reviewer for IEEE journals and OSA publications. Labs/Teams: Core member of the SPS Group and involved in interdisciplinary projects blending theory and experimental validation.

• Telecommunications
• Wireless Sensor Networks
• Air Quality Monitoring
• +۷ مورد دیگر

Alexandre Manuel de Castro Passos de Almeida is an Assistant Professor in the Department of Information Science and Technology at the University Institute of Lisbon (ISCTE-IUL), where he also contributes to the School of Technology and Architecture. He is an Associate Researcher at the Institute of Telecommunications - IUL, actively involved in the Radio Systems Group, focusing on telecommunications and sensor-based environmental monitoring. PhD in Telecommunications, ISCTE-IUL, 2012 His research interests span telecommunications, wireless sensor networks, air quality monitoring, computer architecture, and robotics. He has led and contributed to innovative projects such as ExpoLis, which uses mobile sensor networks on public buses to map urban air pollution. His work bridges engineering and environmental science, aiming to influence urban policy and public health through real-time data systems. The most recent publications highlight a strong trend toward deploying low-cost, mobile sensor networks for environmental monitoring, particularly in urban settings. His work integrates computer systems, signal processing, and sustainable technology, with applications in smart cities, public health, and robotics. Topics like air quality mapping, energy harvesting, and noise-aware robot navigation reflect a multidisciplinary approach to solving real-world problems. He has advised seven Master’s students at ISCTE-IUL, guiding research in sensor networks, air pollution, database performance, robotic navigation, and personal photography assistants. While no specific scientific awards are listed, his contributions to funded research projects and consistent scholarly output indicate recognition in his field. He has held leadership roles in academic governance, including Vice President of the Pedagogical Council, underscoring his institutional engagement. His teaching portfolio includes core courses such as Fundamentals of Computer Architecture, Operating Systems, and Big Data Processing, delivered across multiple undergraduate and postgraduate programs. He is involved in research projects that combine academic innovation with practical urban applications, particularly through the deployment of scalable, open-source environmental sensing systems.

O-Jong Kimمشاهده پروفایل

استادیار

• Nano-satellite development and operation
• Alternative positioning navigation and timing
• Indoor precise positioning
• +۵ مورد دیگر

O-Jong Kim is an Assistant Professor in the Department of Aerospace Engineering at Sejong University, specializing in satellite navigation systems and precise positioning technologies. His research bridges theoretical advancements with practical applications in aerospace engineering, particularly focusing on CubeSat development and GNSS technologies. His educational background includes: B.A. from Seoul National University (2011) M.A. from Seoul National University (2013) Ph.D. from Seoul National University (2018) Dr. Kim's research interests center on nano-satellite development and operation, alternative positioning navigation and timing systems, indoor precise positioning, and GNSS receiver technology. His work addresses critical challenges in navigation systems, particularly in urban and indoor environments where traditional GNSS signals face significant limitations due to signal obstructions and multipath effects. Analysis of his recent publications reveals a strong focus on practical navigation solutions, with particular emphasis on multipath mitigation techniques, CubeSat-based navigation systems, and alternative positioning approaches for emerging applications like Urban Air Mobility. His research demonstrates consistent progression from theoretical algorithms to field-tested implementations, with publications showing increasing citation impact. Dr. Kim has established productive research collaborations, particularly with Seoul National University colleagues, evidenced by his extensive co-authorship network. His research output shows steady growth, with significant publication activity in 2020, 2023, and 2024, indicating active research momentum and sustained scholarly productivity. His major achievements include the design, development, launch, and in-orbit operation of the SNUGLITE cubesat, and the development of single-transmitter-based positioning systems for alternative navigation and indoor navigation applications. These accomplishments demonstrate his ability to translate theoretical research into operational space systems.

Josie Clowneyمشاهده پروفایل

دانشیار

• Neural Circuits
• Developmental Neuroscience
• Chemosensory Systems
• +۳ مورد دیگر

Josie Clowney is an Associate Professor in the Department of Molecular, Cellular, and Developmental Biology at the University of Michigan, where she has held faculty position since 2017. Her research investigates the genomic algorithms that construct neural circuits during development, using Drosophila as a model to study how chemosensory systems drive both instinctual behaviors and learning. She teaches Bio 172 and an upper-level seminar on cellular diversity and scientific writing, and mentors graduate students through MCDB, CMB, NGP, and BIOINF PhD programs. Her educational background includes: Ph.D. in Biomedical Sciences (2012) from the University of California, San Francisco B.S. in Cellular and Molecular Biology (2005) from the University of Michigan, where she conducted research with Cunming Duan Clowney's research centers on understanding how definitive neuronal parameters are encoded in genomic information and translated into cellular architectures. Her lab hypothesizes that developmental algorithms for learning circuits versus instinctual circuits differ fundamentally in their genomic requirements, with chemosensory circuits serving as key models. Using fruit flies for their tractable brain organization, her work bridges computational principles and biological implementation to uncover universal brain organization rules. Analysis of her 15 most recent publications (2016-2025) reveals consistent focus on Drosophila mushroom body development, neural sexual differentiation, and spatial constraints in circuit formation. Key themes include non-deterministic mechanisms diversifying cell surface expression, chromatin dynamics in circadian regulation, and how input density tunes sensory responses. Her work integrates genomics, neuroanatomy, and behavior to model how compact genomic information generates complex neural architectures. No scientific awards were mentioned in the provided text. Dr. Clowney advises graduate students through multiple PhD programs at the University of Michigan, though specific student names and grant details are not provided in the source material. Her teaching includes foundational undergraduate coursework and advanced seminars emphasizing scientific writing. The active publication record spanning 2016-2025 indicates sustained research funding supporting her lab's investigations into neural circuit development. The Clowney Lab, housed in the Biological Sciences Building (4218 BSB), employs Drosophila genetics and neuroanatomical techniques to dissect developmental algorithms of brain wiring. Current projects explore how spatial constraints structure learning circuits, mechanisms of neural sexual differentiation, and the genomic encoding of circuit diversity. The lab collaborates within Michigan's neuroscience community through the Program in Biology and participates in interdisciplinary initiatives studying brain evolution and function.

• Statistical Mechanics
• Statistical Physics
• Error-Correcting Codes
• +۱۲ مورد دیگر

David Saad is a Professor at Aston University, affiliated with the College of Engineering and Physical Sciences, the School of Computer Science and Digital Technologies, and the Applied Mathematics & Data Science group. He also leads the Aston Centre for Artificial Intelligence Research and Application and holds the 50th Anniversary Chair of Complexity Physics. His academic appointments include Professor of Information Mathematics since 1999 and former Head of the Mathematics Group (2006–2012, 2015–2019). His educational background includes: PhD in Engineering, Tel Aviv University (1993) MSc in Physics, Tel Aviv University (1987) BA in Physics and BSc in Electrical Engineering, Technion, Israel (1982) David Saad's research focuses on the application of statistical physics to complex computational and engineering problems. His primary interests include statistical mechanics of disordered systems, error-correcting codes, message-passing algorithms, learning in neural networks, and distributed optimization in networks such as smart grids and optical communication systems. He combines theoretical rigor with practical applications in machine learning, communications, and energy systems. His recent publications (2024–2025) demonstrate a strong trend in interdisciplinary research, bridging physics, computer science, and engineering. Key themes include neuromorphic computing, network inference from data, viral load estimation, and cascading failure modeling, indicating a continued focus on inference, optimization, and resilience in complex systems. His scientific recognition includes: Fellow of the Institute of Mathematics and its Applications Senior Fellow of the Higher Education Academy David Saad has supervised several research students, including H. F. Po, E. Harrison, M. Sheikh, B. Li, E. Manuylovich, and R. Sadeghi, many of whom appear as co-authors on recent publications. He has also been involved in research grants and projects related to smart grids, optical networks, and neuronal network analysis. His work involves collaborations with institutions across the UK and internationally, as evidenced by his research council membership and co-authored outputs. He leads research in the Aston Centre for Artificial Intelligence, focusing on theoretical and applied aspects of machine learning and complex systems. His team applies message-passing techniques to problems in energy distribution, communication networks, and biological data analysis, contributing to both foundational theory and real-world applications.

Klaus Ingemann Pedersenمشاهده پروفایل

استاد

• Wireless Communication Networks
• Dependable Wireless Communications
• 5G-Advanced Technology
• +۱۰ مورد دیگر

Klaus Ingemann Pedersen is a Professor in the Department of Electronic Systems at the Technical Faculty of IT and Design, Aalborg University, Denmark. His research focuses on wireless communication networks and dependable wireless communications with substantial contributions to 5G-Advanced and 6G technologies. His research interests include: Wireless Communication Networks Dependable Wireless Communications 5G-Advanced Technology 6G Networks Radio System-Level Simulations Ultra-Reliable Low Latency Communication Heterogeneous Networks Professor Pedersen's recent work demonstrates cutting-edge research in next-generation wireless systems, with particular emphasis on deep reinforcement learning applications, link adaptation techniques, and system-level simulations. His publications show consistent high-impact output with 152 total publications spanning from 2012 to 2025, reflecting ongoing productivity and relevance in the rapidly evolving field of wireless communications. His notable achievements include: 5G-prisen (The 5G Award) received on October 23, 2019, shared with E. Mogensen and I.R. Larrad Professor Pedersen has supervised 8 PhD students according to institutional records. His research has practical applications in wireless communication, as evidenced by his involvement in patented technology 'Method and apparatus for wireless communication' which delivers social impact and quality of life improvements. His work shows strong international collaboration across multiple countries as indicated by the research network visualization.

David Loمشاهده پروفایل

استاد

• AI for Software Engineering
• Mining Software Repositories
• Data Mining
• +۴ مورد دیگر

David Lo is the OUB Chair Professor of Computer Science and the founding Director of the Center for Research in Intelligent Software Engineering (RISE) at Singapore Management University. He has held significant leadership roles including General Chair of MSR'22 and ASE'16, and Program Committee Co-Chair for ASE'20, FSE'24, and ICSE'25. Lo has championed the field of AI for Software Engineering (AI4SE) since the mid-2000s, demonstrating how data mining, machine learning, information retrieval, natural language processing, and search-based algorithms can transform software engineering data into actionable insights and automation. His research spans Mining Software Repositories (MSR), large language models for code, software testing, smart contract analysis, and developer tooling. His recent publications reveal a strong focus on the intersection of large language models and software engineering, with particular attention to code generation, evaluation, documentation, and the practical implications of AI tools for developers. His work increasingly addresses economic efficiency, privacy concerns, and human factors in AI-assisted development. Two Test-of-Time awards Eleven ACM SIGSOFT/IEEE TCSE Distinguished Paper awards ACM Fellow IEEE Fellow ASE Fellow National Research Foundation Investigator (Senior Fellow) Lo has supervised numerous students and collaborated extensively across the software engineering community. His work on Mining Software Repositories has led to practical tools and insights that have shaped the field. He regularly contributes to major conferences and has served in leadership roles across ASE, ICSE, and FSE communities. As founding Director of the Center for Research in Intelligent Software Engineering (RISE) at SMU, Lo leads a research group focused on advancing AI techniques for software engineering problems, with emphasis on practical applications that address real developer pain points.