جست‌وجوی استاد و پژوهشگر

Shoudong Huangمشاهده پروفایل

استاد

• Mobile robot navigation
• Simultaneous localization and mapping (SLAM)
• Underwater robotics
• +۴ مورد دیگر

Shoudong Huang is a Professor at the School of Mechanical and Mechatronic Engineering , University of Technology Sydney, and Deputy Director of the UTS Robotics Institute. His research focuses on mobile robot navigation , SLAM , nonlinear state estimation , and surgical robotics . He has published over 200 papers and is recognized as one of the 100 Most Influential Scholars in Robotics (Aminer, 2018). PhD in Automatic Control, Northeastern University (China) Postdoctoral Research Fellow, University of Hong Kong (1998-2000) Research Fellow, Australian National University (2001-2003) Full-time academic roles at UTS since 2004 His work addresses challenges in robot localization across extreme environments (underwater, underground mining, surgical settings) and develops globally optimal SLAM algorithms with guaranteed performance. He has secured over $4 million AUD in external funding, including ARC Discovery grants and industry partnerships. Recent publications emphasize cross-modal calibration (camera-LiDAR), interval analysis for bounded noise , and template-based deformable surface reconstruction . These span applications in autonomous driving, surgical navigation, and UAV guidance. Chancellor’s Medal for Research Excellence (2020) Supervisor of the Year (2023) Best Paper Award (2016 ICARCV) Huang serves as Associate Editor for IEEE Transactions on Robotics and International Journal of Robotics Research , and has held leadership roles in top robotics conferences like IROS and RSS. His collaborations span MIT, USC, Zhejiang University, and industry partners including PMSW Research Pty Ltd and Multiplex Constructions Pty Ltd.

Shantanu Duttمشاهده پروفایل

استاد

• VLSI CAD
• Discrete Optimization
• FPGA Testing and Trusted Design
• +۲ مورد دیگر

Shantanu Dutt is a full Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Chicago , located in Chicago, IL. His office is in 930 SEO at 851 S. Morgan St, and he can be reached at dutt@uic.edu . Education Ph.D. in Computer Science and Engineering, University of Michigan, Ann Arbor (1990) M.Tech. in Computer Engineering, Indian Institute of Technology Kharagpur (1984) B.E. in Electronics and Communication Engineering, Maharaja Sayajirao University of Baroda (1983) Research Interests Professor Dutt’s research agenda centers on VLSI Computer-Aided Design (CAD) , with particular emphasis on physical design and incremental synthesis targeting both ASICs and FPGAs. He explores discrete optimization techniques to solve placement, routing, and partitioning problems. Another major thrust is FPGA built-in self-test (BIST) and trusted design , ensuring provable diagnosability and security against hardware Trojans. He also investigates fault-tolerant computing at both chip and system levels, and develops parallel and distributed computing algorithms for scalable performance. Scientific Awards 1996 Best Paper Award , ACM/IEEE Design Automation Conference, for “A probability-based approach to VLSI circuit partitioning” 1995 Most Influential Paper Award , Fault-Tolerant Computing Symposium, for “Design and reconfiguration strategies for near-optimal k-fault-tolerant tree architectures” Research Impact and Funding Professor Dutt has published extensively in top-tier journals (IEEE TVLSI, ACM TRETS, IEEE TCAD) and premier conferences (ICCAD, DAC, DATE, FTCS). His work has shaped incremental placement, timing-driven routing, FPGA security, and fault-tolerant multiprocessor architectures. While specific grant numbers are not listed, the breadth and longevity of his publication record indicate sustained funding from NSF, industry, and other agencies. Laboratory and Collaborations Although no formal laboratory name is provided, Professor Dutt’s research is conducted within the ECE department’s VLSI CAD and Fault-Tolerance groups, collaborating with graduate students and colleagues across the US and internationally.

Olivier Sigaudمشاهده پروفایل

استاد

• Reinforcement Learning
• Robotics
• Decision Modeling
• +۳ مورد دیگر

Olivier Sigaud is a Full Professor at Sorbonne University, affiliated with the ISIR (Intelligent Systems and Robotics Institute) and the Machine Learning and Intelligent Autonomous Systems (MLIA) team. He holds an engineering degree from ISEN and dual PhDs in Computer Science (University of Paris XI, 1996) and Philosophy (University of Paris I, 2004). Previously employed at Dassault Aviation (1995–2001), he transitioned to academia as a Lecturer and later a Professor at LIP6 and ISIR. His research focuses on reinforcement learning, robotics, computational neuroscience of decision-making in animals, and human-robot interaction. Key contributions include advances in goal-conditioned reinforcement learning, intrinsically motivated agents, and human-in-the-loop systems. He has co-authored over 100 publications in top-tier conferences (NeurIPS, ICML) and journals, with recent work exploring large language model grounding, open-ended learning frameworks, and motor skill acquisition through interactive curricula. Notable projects include the CURIOUS framework for modular multi-goal RL and the DREAM architecture for open-ended robotic learning. His work bridges theoretical AI with practical robotics applications, emphasizing interdisciplinary collaboration between computer science and neuroscience.

• Computational Statistics
• Machine Learning
• Monte Carlo Methods
• +۴ مورد دیگر

Alain Durmus is a Professor at École Polytechnique, affiliated with the applied mathematics department (CMAP). His research focuses on computational statistics, machine learning, and stochastic methods, including Monte Carlo algorithms, Bayesian inference, and optimization. He explores topics such as Markov chain Monte Carlo (MCMC), stochastic approximation, and generative models. His work emphasizes theoretical guarantees for algorithms like Langevin Monte Carlo and Hamiltonian Monte Carlo, with applications to high-dimensional Bayesian inference and inverse problems. Key contributions include hypocoercivity analysis of piecewise deterministic MCMC processes, convergence guarantees for stochastic gradient methods, and the development of efficient sampling techniques. He has also contributed to Bayesian imaging and federated learning through works like the QLSD algorithm. Awarded the Best Student Paper Award at ICASSP 2020 for his work on the Sliced-Wasserstein distance. His teaching spans mathematical statistics, stochastic methods, and probability at École Polytechnique and ENS Paris-Saclay. He has also contributed to conferences and workshops on topics ranging from MCMC convergence to optimization in machine learning.

Yingfei Xiongمشاهده پروفایل

دانشیار

• Software Engineering
• Programming Languages

Yingfei Xiong is an active Associate Professor at Peking University, China, specializing in software engineering and programming languages. With a consistent research trajectory from 2013 through 2026, Xiong has established themselves as a prominent figure in the software engineering research community, regularly contributing to top-tier conferences including SPLASH, ICSE, ASE, and PLDI. Dr. Xiong's research primarily focuses on program synthesis, automated program repair, and software analysis techniques. Their work bridges theoretical programming language concepts with practical software engineering applications, particularly in developing novel approaches for code generation, bug fixing, and program optimization. The research demonstrates strong interdisciplinary connections between traditional software engineering, programming languages theory, and emerging AI techniques. Analysis of Xiong's publication trends reveals a clear evolution in research focus, beginning with foundational work in API transformations and program adaptation around 2013-2016, shifting toward program repair techniques from 2017-2020, and most recently incorporating machine learning and neural approaches into program synthesis and repair (2021-2026). The work consistently addresses practical challenges in software development while maintaining theoretical rigor, with increasing integration of AI techniques in recent years. Dr. Xiong has served in various leadership roles across the software engineering conference ecosystem, including program committee membership and session chair positions at major conferences. Their extensive service demonstrates recognition by peers as a subject matter expert in software engineering and programming languages research. While specific grant information isn't detailed in the provided text, the sustained publication record suggests successful research funding.

Tom Bootمشاهده پروفایل

دانشیار

• Macroeconomic Forecasting
• High-Dimensional Data Analysis
• Econometric Theory
• +۴ مورد دیگر

Dr. Tom Boot is an Associate Professor at the Department of Economics, Econometrics & Finance at the University of Groningen. He holds a PhD in Econometrics from Erasmus University Rotterdam (2017) and an MSc in Econometrics from the same institution (2012), along with an MSc in Physics from the University of Groningen (2010). His research focuses on econometric theory applied to macroeconomic forecasting, high-dimensional data analysis, and causal inference. He has been recognized with the Veni grant (2021–2024) for his work on forecasting methodologies. Boot’s research interests include improving forecast accuracy through methods like subspace projections, structural break modeling, and privacy-aware marketing analytics. His recent work explores privacy-utility trade-offs in data-driven marketing and unbiased estimation techniques for clustered errors. He has supervised PhD students including Jhordano Aguilar Loyo and Gilian Ponte, whose theses addressed panel data heterogeneity and differential privacy applications. Boot is also a program director for the MSc Econometrics, Operations Research, and Actuarial Studies (since 2024). His contributions to econometrics span over a dozen peer-reviewed publications, with a focus on advanced statistical techniques for economic forecasting and policy analysis. Collaborations include work with institutions like Harvard/MIT and the organization of workshops on causal inference and machine learning.

• Numerical Linear Algebra
• Fast Algorithms
• Structured Matrices
• +۴ مورد دیگر

Jianlin Xia is a Professor of Mathematics at Purdue University, with a courtesy appointment in the Department of Computer Science. He joined the university in 2014. Xia holds a Ph.D. in Applied Mathematics from the University of California, Berkeley (2006). His research focuses on numerical linear algebra, fast algorithms for structured matrices, and their applications in computational science and engineering. His work addresses challenges in solving large-scale linear systems, eigenvalue problems, and partial differential equations (PDEs) using innovative methods like fast multipole techniques, hierarchical structures, and randomized algorithms. Key areas of research include: Design and analysis of fast algorithms for structured matrices (e.g., hierarchical, semiseparable, Cauchy matrices) Efficient direct and iterative solvers for PDEs, especially Helmholtz equations in seismic modeling Stability and robustness of numerical methods in high-performance computing Applications in wave propagation, inverse problems, and machine learning Xia’s contributions include advancements in low-rank approximations, divide-and-conquer eigenvalue decomposition, and scalable preconditioning techniques. His work emphasizes both theoretical analysis and practical implementation, often leveraging parallel computing architectures. Contact: xiaj@purdue.edu .

Dora Erdosمشاهده پروفایل

مدرس ارشد

• Algorithm Design
• Data Mining
• Combinatorial Optimization
• +۳ مورد دیگر

Dora Erdos is a Senior Lecturer and Director of Undergraduate Studies in the Department of Computer Science at Boston University. She specializes in algorithmic challenges, data mining, and combinatorial optimization with a focus on network-based problems. Her work bridges theoretical computer science and practical applications in education technology and network analysis. Education: PhD in Computer Science, Boston University (2015) MSc in Pure Mathematics, Eotvos University (Advisor: Andras Frank) Postdoctoral Research at Brown University's Raphael Lab (Advisor: Ben Raphael) Research Interests: Erdos investigates algorithms for network analysis, including centrality measures, graph reconstruction, and optimization problems in educational systems. She develops scalable methods for tensor factorization and content placement in navigational networks. Her work often integrates combinatorial approaches with real-world applications. Professional Roles: As Director of Undergraduate Studies, Erdos oversees academic advising and curriculum development. She emphasizes student accessibility, maintaining office hours and encouraging direct communication via email (edori@bu.edu). Recent Research Trends: Her publications (2011–2017) focus on network-centric problems such as centrality evaluation frameworks, boolean tensor decomposition, and team formation algorithms for educational scheduling. These contributions highlight her dual expertise in theoretical algorithm design and applied educational technology.

Grey Ballardمشاهده پروفایل

دانشیار

• Numerical Linear Algebra
• High Performance Computing
• Computational Science
• +۶ مورد دیگر

Dr. Grey Ballard is an Associate Professor in the Department of Computer Science at Wake Forest University . He earned a B.S. in Math and Computer Science (2006), M.A. in Math (2008) from Wake Forest, and PhD in Computer Science (2013) from the University of California, Berkeley. He was a Truman Fellow at Sandia National Laboratories before joining Wake Forest. Research Focus: Ballard develops communication-optimal algorithms for high-performance computing , particularly in tensor decompositions , symmetric matrix computations , and nonnegative matrix factorization . His work combines numerical linear algebra with parallel algorithm design to reduce data movement costs in distributed systems. Publications demonstrate expertise in communication lower bounds , randomized tensor rounding , and visualization tools for parallel algorithms. He has contributed software packages such as TuckerMPI , GentenMPI , and PLANC for large-scale data compression and clustering. Scientific Awards: Wake Forest Excellence in Research Award NSF CAREER Award SIAM Linear Algebra Prize Three Conference Best Paper Awards (SPAA, IPDPS, ICDM) C.V. Ramamoorthy Distinguished Research Award (UC Berkeley) ACM Doctoral Dissertation Award – Honorable Mention Teaching: Courses include Introduction to Computer Science , Numerical Linear Algebra , and Parallel Algorithms . He has developed educational tools using the Thread-Safe Graphics Library to visualize parallel dynamic programming and collective communication.

Qiang Wuمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Data Mining
• +۳ مورد دیگر

Qiang Wu serves as a Professor in the Department of Mathematics at the University of Tennessee, Knoxville, with his office located in Ayres Hall 215. His contact email is qiang.wu@utk.edu. His research centers on the mathematical underpinnings of machine learning and data science, with significant contributions including the development of efficient algorithms for genetic association studies, theoretical frameworks for information theoretic learning, advancements in kernel methods and support vector machines, innovations in distributed learning through divide-and-conquer strategies, and novel dimension reduction techniques. Applications span agroecosystems, biomedical research, stylometry analysis, and actuarial science. Dr. Wu has secured research funding from major agencies including the National Science Foundation (NSF), Simons Foundation, and USDA, supporting his extensive publication record of over 80 journal and conference papers. He actively mentors graduate students within the mathematics department, though specific advisees are not listed in available materials.

Diana Marinمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Computational Geometry
• Point Cloud Processing
• +۳ مورد دیگر

Diana Marin is a PostDoc Researcher at TU Wien's Institute of Visual Computing & Human-Centered Technology. She holds a BSc, MEng, and Dr.techn. (PhD) in technical fields. Her research focuses on computational geometry, point cloud processing, and distributed computing for large-scale datasets. She has contributed to projects like Distributed Surface Reconstruction and RE:STOCK INDUSTRY. Education: BSc, MEng, Dr.techn. (PhD) Her work emphasizes curve and surface reconstruction from unorganized point clouds, leveraging proximity graphs and distributed computational methods. Key projects include optimizing surface reconstruction for massive datasets and developing parameter-free algorithms for connectivity analysis. Her publications span topics like SING neighborhood graphs, Riemannian manifold curve reconstruction, and distributed processing techniques. She collaborates on projects such as PostDisaster and Mixed Reality Lab.

• Computational Statistics
• Probabilistic Machine Learning
• Markov Chain Monte Carlo
• +۷ مورد دیگر

Professor Christopher Nemeth of Lancaster University's School Of Mathematical Sciences is a leading researcher in computational statistics and probabilistic machine learning. His work focuses on Markov chain Monte Carlo (MCMC), sequential Monte Carlo (SMC), Gaussian processes, and approximate Bayesian computation, with applications in environmental science, target tracking, and econometrics. He currently holds a UKRI Turing AI Acceleration Fellowship and leads the ProbAI research hub. Research Interests: Development of probabilistic AI algorithms for large-scale learning, state-space modeling, and intersections between sampling and optimization algorithms. Grants: £9M UKRI-EPSRC ProbAI hub (2024-2029), £1.1M Turing AI Acceleration Fellowship (2021-2026), and multiple NERC grants. Academic Roles: Turing University Academic Liaison (2023-present), Associate Editor for ACM Transactions on Probabilistic Machine Learning (2023-present), and leadership roles in the Royal Statistical Society. Supervision: Completed supervision of 7 PhD students with projects on scalable Gaussian processes, Monte Carlo methods, and network modeling.

• Algorithms
• Randomized Data Structures
• Divide-and-Conquer Algorithms
• +۵ مورد دیگر

Salvador Roura Ferret is a faculty member at the Universitat Politècnica de Catalunya (UPC), affiliated with the Faculty of Informatics of Barcelona (FIB) and the Department of Computer Sciences. He is an active researcher in the ALBCOM group, focusing on algorithmics, bioinformatics, complexity, and formal methods. His research interests span algorithms , randomized data structures , divide-and-conquer methods , and computational complexity . His work often bridges theoretical computer science with practical applications in voting systems, game theory, and educational technologies. The recent publications (2013–2025) reflect a strong trend in theoretical computer science, particularly in algorithm design, data structures (e.g., K-d trees, binary search trees), and game-theoretic models. There is a notable emphasis on mathematical rigor, complexity analysis, and formal modeling of computational problems. Best student paper award Distinció Jaume Vicenç Vives a la qualitat docent universitària (modalitat individual) 18è Premi UPC a la Qualitat en la Docència Universitària Roura has contributed to teaching innovation through projects like Eines per a la Transformació de la Docència en Programació and the development of Jutge.org , an online programming judge platform. He has participated in numerous competitive R+D+i projects, often in collaboration with researchers such as Conrado Martinez, Xavier Molinero, and Maria Serna. His work has been supported by national and regional funding programs. While no direct mention of grants or student advising is made, his extensive publication and project history suggest significant research leadership.

Mohammad Poursinaمشاهده پروفایل

دانشیار

• Dynamic Modeling and Simulation of Multibody Systems
• Robotics and Robotic Rehabilitation
• Artificial Intelligence and Control Systems
• +۴ مورد دیگر

Mohammad Poursina is an Associate Professor at the Department of Engineering Sciences , University of Agder, Norway. His research spans multibody dynamics , robotics , and celestial mechanics , with a focus on dynamic modeling, control systems, and biomechatronics. He has held prior academic roles at the University of Arizona and Rensselaer Polytechnic Institute. Ph.D. in Mechanical Engineering, Rensselaer Polytechnic Institute (2011) M.Sc. and B.Sc. in Mechanical Engineering, University of Tehran (2006, 2003) His work addresses complex systems through interdisciplinary approaches, including robotic rehabilitation , flexible manipulators , and molecular dynamics . Recent publications highlight advancements in trajectory optimization , VR/AR surgical training , and asteroid system modeling . He serves on the editorial boards of Multibody System Dynamics and Journal of Computational and Nonlinear Dynamics , and has organized ASME conference sessions. Key research groups: Artificial Intelligence, Biomechatronics, and Collaborative Robotics Projects: Reality-Connected Machine Simulation of Heavy Machinery (RealSim)

Zhenjiang Huمشاهده پروفایل

استاد

• Programming Languages
• Software Engineering
• Functional Programming
• +۷ مورد دیگر

Zhenjiang Hu is a Chair Professor and Dean of the School of Computer Science at Peking University. He serves as Director of the Programming Languages Laboratory and has held significant academic positions including Professor at the National Institute of Informatics and University of Tokyo. BS and MS from Shanghai Jiaotong University (1988, 1991) PhD from University of Tokyo (1996) Lecturer/Assistant Professor at University of Tokyo (1997) Associate Professor at University of Tokyo (2000) Full Professor at National Institute of Informatics (2008) Full Professor at University of Tokyo (2018-2019) Professor Hu's research primarily focuses on programming languages and software engineering, with special emphasis on functional programming, bidirectional transformation, and software adaptation. His work explores transformational programming approaches for automatic program optimization, systematic parallelization of sequential programs, efficient manipulation of structured documents, and bidirectional model transformation for software development. His research has significantly advanced the field of bidirectional programming, developing foundational theories and practical applications that enable more reliable and maintainable software systems. His recent publications demonstrate a strong trajectory in bidirectional programming, program synthesis, and graph processing. The research shows increasing sophistication in handling program transformations, with growing emphasis on practical applications in software engineering contexts. His work increasingly integrates formal methods with practical programming language design, creating systems that maintain theoretical soundness while addressing real-world software development challenges. The research spans multiple venues including top conferences like PLDI, POPL, ICFP, and OOPSLA, reflecting its broad impact across programming language research. Fellow of JFES (Japan Federation of Engineering Society, 2016) ACM Distinguished Scientist (2016) Member of Academia Europaea (2019) IEEE Fellow (2020) Member of Engineering Academy of Japan (2020) Professor Hu actively mentors students and has welcomed excellent candidates to join his group through Peking University's International Elite PhD Program and Boya Postdoctoral Fellowship Program. He serves on numerous program committees for major conferences including PLDI, POPL, ICFP, and OOPSLA, and holds editorial positions for prestigious journals such as Journal of Functional Programming and Science of Computer Programming. His leadership extends to conference organization, having served as PC Chair for CNCC 2024 and General Co-Chair for SoICT 2019. As Director of the Programming Languages Laboratory at Peking University, Professor Hu leads a research team focused on advancing programming language theory and practice. His lab has developed influential frameworks like BiGUL for bidirectional programming and Fregel for graph processing. The laboratory maintains strong international collaborations and contributes to both theoretical foundations and practical implementations in programming languages and software engineering.