جست‌وجوی استاد و پژوهشگر

Eric Chownمشاهده پروفایل

استاد

• Artificial Intelligence
• Cognitive Science
• Robotics
• +۷ مورد دیگر

Eric Chown is the Sarah and James Bowdoin Professor of Digital and Computational Studies at Bowdoin College, where he has been a faculty member since 1998. He previously served as Chair of the Computer Science Department and is currently active in teaching, research, and program leadership. His work bridges artificial intelligence, cognitive science, and robotics, with a growing interest in the societal and philosophical implications of digital technology. Bachelor of Arts, Northwestern University (1985) Master of Science, Northwestern University (1987) PhD, University of Michigan (1994) Chown’s research focuses on artificial intelligence, cognitive modeling, robotics, and digital metaphors . He has made significant contributions to spatial reasoning, emotional modeling in agents, and humanoid robot control. His 2023 book, Meaningful Technology , co-authored with Fernando Nascimento, explores how digital metaphors shape human cognition and behavior. He is also known for leading the Northern Bites RoboCup team to a world championship in 2007. His recent publications reflect a strong trajectory in AI and robotics, particularly in team coordination, locomotion control, and hybrid cognitive architectures. The articles span from foundational work in connectionist models of memory and perception to applied robotics in competitive environments. Keywords across his work include machine learning, spatial reasoning, emotion modeling, and human-robot interaction. Scientific awards and honors include: NSF CAREER Award (2001–2006) NSF RUI Grant (2010–2013) RoboCup Federation Research Grant (2014) NCH and Google Responsible AI Curriculum Grant (2022) Walter P. Murphy Fellowship Nissan Cognitive Science Fellowship Chown has advised numerous undergraduate researchers, many of whom appear as co-authors on his papers. He has led major research initiatives, including the development of the AiboConnect programming environment for educational robotics and the design of locomotion algorithms for humanoid robots. He is also involved in curriculum innovation, particularly in ethics in AI. He leads the Northern Bites robotics team and has been instrumental in organizing and publishing team reports for RoboCup competitions. He is affiliated with the Digital and Computational Studies program at Bowdoin College and has previously contributed to the Computer Science Department. His research lab, embedded within the Northern Bites team, fosters interdisciplinary collaboration between computer science, cognitive science, and engineering students.

• Knowledge Representation and Reasoning
• Quantum Computation
• Artificial Intelligence
• +۳ مورد دیگر

Professor Sanjiang Li is affiliated with the University of Technology, Sydney (UTS) as a Professor in the Faculty of Engineering and Information Technology , specifically within the Centre for Quantum Software and Information . With a PhD in Mathematics from Sichuan University and a BSc from Shaanxi Normal University, his research spans quantum computation , knowledge representation and reasoning , and formal verification of quantum systems . His recent work focuses on quantum circuit transformation , including novel methods like adaptive divide-and-conquer and Monte Carlo Tree Search frameworks. He has advanced symbolic verification techniques using tensor decision diagrams and explored classical-quantum hybrid algorithms for resource-efficient training. Dr. Li has received prestigious awards such as the ARC Future Fellowship and Alexander von Humboldt Research Fellowship . He supervises PhD students including Calum Holker and Guangxi Li , and contributes to quantum software development through funded projects like the Sydney Quantum Academy and ARC Discovery Projects .

Stacey Jefferyمشاهده پروفایل

استاد

• Quantum Algorithms
• Quantum Cryptography
• Quantum Computation Models
• +۳ مورد دیگر

Stacey Jeffery serves as Professor of Quantum Information at the University of Amsterdam's Korteweg-de Vries Institute for Mathematics since 2023 and Senior Researcher at Centrum Wiskunde & Informatica (CWI) and QuSoft since 2017. Her foundational work bridges theoretical computer science and quantum information processing, with emphasis on algorithmic frameworks and cryptographic security in quantum systems. Her educational trajectory includes a PhD in Computer Science from the University of Waterloo (2014), where she was affiliated with the Institute for Quantum Computing, following prior research at Caltech as an IQIM Postdoctoral Fellow. Jeffery's research centers on quantum algorithms and quantum cryptography, with pioneering contributions to quantum walk frameworks that enable quadratic speedups for graph problems. She has developed critical frameworks for secure delegation of quantum computation and advanced models for quantum error correction. Her work on multidimensional quantum walks and subroutine composition establishes new paradigms for quantum algorithm design, while investigations into space-time tradeoffs challenge fundamental limits of quantum computation. These theoretical advances directly enable practical quantum cryptographic protocols and error mitigation strategies. Analysis of her 15 most recent publications reveals a cohesive evolution toward unified frameworks for quantum algorithm composition, particularly through quantum walk methodologies. Her 2023-2025 works demonstrate increasing sophistication in error reduction techniques and space-efficient quantum computation, while maintaining strong connections to cryptographic applications like software leasing and multi-party computation. The consistent focus on foundational complexity questions—span programs, query complexity, and graph connectivity—underscores her commitment to advancing theoretical underpinnings of quantum computing. Her scientific recognition includes: NWO WISE Fellowship NWO Veni Grant ERC Starting Grant QDNL Award (for co-founding WIQD) CIFAR Fellow in Quantum Information Science Jeffery leads significant research initiatives including an ERC Starting Grant and NWO-funded projects. Her academic mentorship extends through QuSoft's collaborative environment, though specific advisee names aren't publicly cataloged. Professional service includes co-chairing QIP 2022, chairing TQC's steering committee (2021), and directing the Lorentz Center Informatics Advisory board. She actively shapes community standards through roles in QCrypt and ACM SIGACT. As a core QuSoft researcher and co-founder of WIQD (Women in Quantum Development), Jeffery drives both technical innovation and diversity initiatives. Her laboratory focuses on quantum algorithm frameworks with real-world cryptographic applications, while WIQD—awarded the inaugural QDNL Award—creates vital networking and development opportunities for women across quantum technology sectors. Current efforts target quantum subroutine composition and space-efficient error correction for near-term quantum devices.

Luong Nguyenمشاهده پروفایل

دانشیار

• Robotics
• Robot Operating System (ROS)
• Manipulator Kinematics and Dynamics
• +۴ مورد دیگر

Dr. Luong Nguyen is an Associate Professor of Computer Engineering at the University of Houston-Clear Lake (UHCL), part of the College of Science and Engineering. He holds a Ph.D. in Electrical and Computer Engineering from Rice University, with additional master's degrees from University of Houston-Clear Lake and the University of Washington. With over 20 years of experience, he specializes in space robotics, contributing to NASA's Space Shuttle and Space Station Remote Manipulator Systems at Johnson Space Center. He currently serves as Vice Chair of the Robotics and Automation Section of the IEEE Galveston Bay Chapter. Dr. Nguyen has led UHCL student teams in prestigious competitions like the NASA Swarmathon Robotics Competition (2016-2019) and the NASA Space Robotics Challenge (2019-2020), winning multiple awards. His research focuses on robotics and automation, including inverse kinematics, swarm robotics, computer vision, and parallel processing algorithms. He teaches courses such as Robotics and ROS (CENG 5435) and Foundations of Robotics (CENG 6533) at the graduate level. His work bridges academic research and industry applications, emphasizing collaborative robotics and space exploration technologies. His recent publications explore particle swarm optimization for robot kinematics and multibody dynamics in space systems.

Alex Nicolauمشاهده پروفایل

استاد

• Parallelizing Compilers
• High-Performance Java
• Power-aware Computing
• +۳ مورد دیگر

Alex Nicolau is a Professor of Computer Science at the University of California, Irvine within the Donald Bren School of Information and Computer Sciences and an IEEE Fellow. He serves as Editor for PeerJ Computer Science and leads research at the Center for Embedded Computer Systems, focusing on high-performance computing systems and compiler-driven hardware optimization. His research spans Parallelizing Compilers , High-Performance Java , Power-aware Computing , and Reconfigurable Computing , with current projects including Julius C (divide-and-conquer algorithm modeling), EXPRESS (retargetable compiler framework), FORGE (distributed embedded systems optimization), SPARK (C-to-VHDL synthesis), and CoReComp (reconfigurable architecture compilers). These initiatives address critical challenges in energy efficiency, hardware-software co-design, and performance optimization for embedded platforms. Recent publications (2003-2004) reveal concentrated efforts on mobile energy efficiency and reconfigurable systems, with recurring themes of power management in multimedia streaming, task partitioning for watermarking algorithms, and network topology exploration in mesh architectures. His work demonstrates a cohesive vision bridging theoretical compiler advances with practical hardware implementations. His professional recognition includes: IEEE Fellow Professor Nicolau has mentored over 20 Ph.D. students to completion, including current advisees Weiyu Tang and Radu Cornea, and alumni like Sumit Gupta and Joseph Hummel who now lead industry and academic research. His editorial leadership as Editor-in-Chief of the International Journal of Parallel Programming and program committee roles for ICS and LCTES conferences reflect his significant contributions to the field. Based at UC Irvine's Center for Embedded Computer Systems (CECS 204), he directs a research ecosystem focused on compiler-driven hardware adaptation, with ongoing exploration into dynamic resource management and energy-aware system design for next-generation computing platforms.

Adam Johansenمشاهده پروفایل

استاد

• Monte Carlo methodology
• Sequential Monte Carlo algorithms
• Bayesian statistics
• +۳ مورد دیگر

Adam Johansen is a Professor of Statistics at the University of Warwick, specializing in computational statistics, sequential Monte Carlo algorithms, Bayesian methodology, and gradient flows. His research spans theoretical advancements and practical applications in simulation-based inference. Current projects: OCEAN (ERC Synergy Grant for next-gen ML algorithms), CoSinES, and APTS (co-director until 2023) Research interests focus on Monte Carlo methodology , Bayesian statistics , and sequential inference . Recent work explores scalability in decentralized systems, gradient flows for sampling, and robust algorithmic frameworks. His 15 most recent publications emphasize particle methods, convergence analysis, and mathematical statistics. He supervises a research group with PhD students including Rocco Caprio, Shu Huang, and Usman Ladan, while contributing to editorial roles at the Journal of the Royal Statistical Society: Series B and London Mathematical Society Newsletter . He developed the SMCTC and RcppSMC libraries for sequential Monte Carlo applications.

• Robotics
• Machine Learning
• Reinforcement Learning
• +۴ مورد دیگر

Nicolas Perrin-Gilbert is a CNRS Research Fellow at the Institute of Intelligent Systems and Robotics (ISIR), which is part of Sorbonne University in Paris, France. He has been working at ISIR since 2013 as part of the MLIA (Machine Learning and Artificial Intelligence) team, where he conducts research at the intersection of robotics and machine learning. His office is located at ISIR, Campus Pierre et Marie Curie, 4 place Jussieu, BC173, 75005 Paris. Dr. Perrin-Gilbert's research focuses on developing advanced algorithms for robot locomotion, control, and learning. His work bridges theoretical machine learning with practical robotics applications, particularly in reinforcement learning, motion planning, and humanoid robotics. He has made significant contributions to quality-diversity algorithms, state representation learning, and controller synthesis for dynamic multi-agent systems. His approach often combines insights from control theory with modern machine learning techniques to solve complex robotics challenges. His publication record shows consistent research output since 2010, with a notable acceleration in recent years. His work spans both theoretical contributions (such as unifying frameworks for motion planning and diversity search) and practical implementations (including humanoid robot control systems). A key trend in his research is the integration of motion planning techniques with reinforcement learning, creating more efficient and robust learning systems for robotics applications. Dr. Perrin-Gilbert is the primary maintainer of xpag , a modular reinforcement learning library with JAX agents that supports standard reinforcement learning and goal-conditioned reinforcement learning. This open-source project demonstrates his commitment to developing practical tools for the research community. The library has gained significant traction with 27 GitHub stars and 6 forks, and is used by other researchers in the field. His collaborations span multiple institutions and research groups, with frequent co-authorship with researchers from ISIR including Olivier Sigaud, Alexandre Chenu, and Stéphane Doncieux. He has also collaborated with researchers from other institutions on projects involving humanoid robots like COMAN and Cassie, demonstrating the applied nature of his work.

Aaron Stumpمشاهده پروفایل

استاد

• type theory
• proof assistants
• dependent types
• +۱ مورد دیگر

Aaron Stump holds the John R. and Pamela Egan Chair of Computer Science at Boston College, specializing in programming languages and formal verification. He develops the Cedille programming language and proof assistant for dependently typed programming. Research focuses on: Dependent type theory implementations Zero-cost program reuse methodologies Recursive type encodings Formal verification of functional programs Leads research on lambda encodings and formal methods, contributing to proof assistant technologies and type-based program verification.

Wei Chenمشاهده پروفایل

استاد

• Parallel and distributed computing
• Cooperative communication and cognitive radio networks
• Wireless ad-hoc/sensor/mobile networks
• +۵ مورد دیگر

Dr. Wei Chen is a Professor in the Department of Computer Science at Tennessee State University's College of Engineering. He holds a Ph.D. in Information and Computer Science from Osaka University (1994), an M.E. from the same institution (1991), and a B.A. in Mathematics from Shanghai Marine Institute (1982). His research focuses on: Wireless networks including cooperative communication and cognitive radio networks Distributed/parallel computing architectures Bioinformatics and DNA computing applications Network security and cloud computing His recent publications demonstrate strong emphasis on wireless network optimization, MIMO technologies, and cross-layer designs, with emerging interest in DNA-based computing paradigms. Research trends show increasing focus on security aspects in cooperative networks. Significant grants include multiple U.S. Air Force awards on cognitive networks ($1.5M+) and NSF funding for bioinformatics education programs. Current projects investigate cooperative communication paradigms and cloud computing resource allocation. Dr. Chen teaches both undergraduate and graduate courses including Discrete Mathematics, Cryptography, Parallel Algorithms, and Computer Theory. He maintains active research collaborations with Japanese institutions and regularly serves on program committees for distributed systems conferences.

Sarah Miracleمشاهده پروفایل

دانشیار

• Randomized Algorithms
• Sampling
• Markov Chains

Sarah Miracle is an Associate Professor in the Department of Computer & Information Science at the University of St. Thomas, College of Arts and Sciences. Her research specializes in randomized algorithms , sampling techniques , and Markov chain applications within theoretical computer science. Teaches undergraduate courses including Introduction to Programming & Problem Solving (CISC 131) and Algorithms (CISC 380). Coursework emphasizes foundational programming logic, algorithm design paradigms (divide-and-conquer, graph algorithms, dynamic programming), and computational complexity including NP-completeness. No information is available regarding research grants, student advising, or scientific awards. Laboratory or team affiliations are not mentioned in available records.

Daochen Wangمشاهده پروفایل

استادیار

• Quantum Computing
• Quantum Algorithms
• Quantum Cryptography
• +۳ مورد دیگر

Daochen Wang is an Assistant Professor of Computer Science at the University of British Columbia (UBC), affiliated with the Faculty of Science. His research focuses on quantum computation, quantum algorithms, and quantum cryptography, with an emphasis on understanding quantum speed-ups and their applications. Education: Ph.D. in Computer Science from the University of Maryland (advisors: Andrew Childs and Carl Miller), BA and MMath in Mathematics from the University of Cambridge. Research interests include quantum algorithm design, post-quantum cryptography, and the theoretical foundations of quantum computing. His work spans quantum graph algorithms, lattice-based security, and variational quantum eigensolvers. Teaching includes undergraduate (CPSC 436Q) and graduate (CPSC 536W) courses on quantum computation. He advises PhD student Xingyu Zhou and undergrad Rain Zimin Yang. Key research contributions include quantum algorithms for reinforcement learning, cryptographic security analysis of post-quantum schemes (e.g., CRYSTALS-Dilithium), and foundational studies on quantum speedups via symmetry analysis.

Daren Wangمشاهده پروفایل

استادیار

• Scientific computing in high dimensions
• High-dimensional linear algebra
• Nonparametric estimation
• +۵ مورد دیگر

Daren Wang is an Assistant Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame, within the College of Science. He holds a Ph.D. in Statistics from Carnegie Mellon University (2018), an M.S. in Mathematics from the University of Michigan (2011), and a B.S. in Mathematics from the University of Virginia (2010). His research focuses on high-dimensional statistical methods, including scientific computing, nonparametric estimation, and change point detection in functional and temporal-spatial models. He has developed R packages such as 'changepoints' for change point localization and 'functional_regression' for mixed predictor models. His work bridges theoretical statistics, machine learning, and computational methods. Research interests include high-dimensional linear algebra applications to machine learning, nonparametric estimation in complex systems, and the development of efficient algorithms for change point detection in dynamic networks and time series. He advises Ph.D. students from diverse backgrounds, including applied mathematics and computational physics. His contributions include advancements in density estimation via tensor decomposition and variance-reduced sketching techniques.

David Harris-Birtillمشاهده پروفایل

مدرس ارشد

• Medical technology
• Image analysis
• Signal processing
• +۱۱ مورد دیگر

Dr. David Harris-Birtill is a Senior Lecturer in the School of Computer Science at the University of St Andrews, where he conducts interdisciplinary research in medical technology and artificial intelligence. He is affiliated with the Centre for Research into Ecological & Environmental Modelling and actively supervises postgraduate research students. Research Interests: His work spans medical imaging, signal processing, ambient sensing, remote pulse oximetry, photoacoustics, photothermal therapy, and machine learning—particularly deep learning applications in digital pathology and healthcare diagnostics. His research contributes to UN Sustainable Development Goals related to health and well-being. Recent Research Trends: His recent publications (2023–2024) focus on AI-driven medical diagnostics, including automated cervical biopsy reporting, colorectal cancer biomarker detection using foundation models, and remote photoplethysmography for non-invasive vital sign monitoring. These works demonstrate a strong emphasis on applying deep learning to small and complex biomedical datasets. Scientific Awards: Best Paper Award, LABELS @ MICCAI 2020 Winner, Converge Challenge KickStart Award, 2016 Grants and Projects: He has led or co-investigated multiple funded projects, including I-CAIRD (Industrial Centre for AI Research in Digital Diagnostics), EPSRC Global Challenge Research Fund initiatives, and collaborations on millimetre wave radar sensing. He has also contributed to open datasets and software tools in medical imaging and human motion tracking. Labs and Teams: He is involved in AI and medical imaging research groups at St Andrews, collaborating with experts in digital pathology and clinical medicine, including Professor David Harrison. His work is highly collaborative, involving interdisciplinary teams across computer science and medicine.

• Automated Reasoning
• Software Verification
• Concurrency
• +۳ مورد دیگر

Azadeh Farzan is a Professor and Vice Chair in the Department of Computer Science at the University of Toronto, specializing in automated reasoning and software verification. She leads research in concurrency, program synthesis, and probabilistic systems with significant contributions to formal methods. Her research interests focus on automated verification of concurrent programs , where she develops novel techniques for detecting data races, proving termination, and synthesizing correct-by-construction code. Key areas include commutativity-based reasoning, hypersafety properties, and probabilistic program analysis, addressing challenges in scalability and modularity for real-world systems. Farzan has received the 2023 Amazon Research Award for her work on data race detection predictability. She serves on prestigious conference committees including CAV (2025 Area Chair) and POPL (2025 Program Committee), having previously chaired CAV 2016 and POPL 2021. She advises doctoral students such as Victor Nicolet and Emanuele D’Osualdo, with research funded through collaborations like the Amazon Scholars program. Her work bridges theoretical foundations and practical tools for software reliability, impacting both academia and industry.

• Computer Architecture
• Programming Languages and Compilers
• Scientific Computing

Siddhartha Chatterjee serves as a Professor of Instruction in the Department of Computer Science at the University of Texas at Austin. With 25+ years of cross-sector experience spanning major corporations, government research laboratories, and academic institutions, he leverages a distinctive fusion of technical expertise, business acumen, and executive leadership capabilities. His faculty role emphasizes translating complex technological concepts for non-specialist audiences while maintaining active research engagement. His scholarly work centers on three interconnected domains: computer architecture (particularly cache-efficient systems and SIMD acceleration), programming languages and compilers (focusing on array expression optimization), and scientific computing (with emphasis on parallel algorithms). Key contributions include foundational research on recursive memory layouts for matrix operations, theoretical frameworks for cache-conscious algorithms, and hardware-software co-design for supercomputing platforms like IBM BlueGene/L. His publications consistently address performance bottlenecks in high-performance computing through algorithmic innovation and architectural insights. Chatterjee's recent publications reveal an evolving trajectory toward hardware-aware software design, where compiler techniques increasingly intersect with processor microarchitecture. His work demonstrates consistent focus on bridging theoretical computer science with practical engineering constraints, particularly in memory hierarchy optimization and parallel execution models. While maintaining core expertise in classical architecture domains, his research shows growing relevance to emerging challenges in exascale computing and energy-efficient design.