جستجوی استاد راهنمای دانشگاهی

Yuanzhen Chen is an Associate Professor in the Department of Physics at Southern University of Science and Technology (SUSTech), where he has been working since 2015. His research focuses on experimental investigation of superconducting quantum computation and quantum simulation, aiming to advance quantum information processing using superconducting circuits. Chen's work contributes to the development of quantum technologies that leverage quantum superposition and entanglement for computational advantages over classical systems. His educational background includes: Ph.D. in Physics from University of Maryland (1998-2005) M.S. from Chinese Academy of Sciences, Institute of Semiconductors (1995-1998) B.S. from University of Electronic Science and Technology of China (1991-1995) Chen's research interests center on superconducting quantum systems. His team focuses on two main areas: developing superconducting quantum chips using Josephson-junction-based devices to build qubits and measurement apparatus, and implementing quantum computation and simulation on these chips. They explore geometric quantum computation, quantum automata, quantum few-body systems, and simulation of condensed matter physics models. Their work aims to address fundamental challenges in quantum computing, including error correction and scalability. Analysis of Chen's recent publications reveals a consistent focus on advancing superconducting quantum circuit technology. His research has progressed from fundamental quantum gate implementations toward more complex quantum systems including multi-qubit architectures and topological quantum phenomena. A notable trend is the increasing sophistication of quantum control techniques, with recent work exploring hybrid digital-analog approaches and noise-resilient quantum gates. Chen collaborates extensively with theoretical researchers, particularly with Man-Hong Yung, to bridge theory and experiment in quantum computing. His laboratory at SUSTech has developed capabilities in fabricating and measuring superconducting quantum devices, with research outputs published in high-impact journals including Physical Review Letters and npj Quantum Information. The research team operates within SUSTech's Department of Physics, part of the Faculty of Science. They maintain experimental facilities for superconducting quantum device fabrication and characterization, with current work focusing on advancing toward practical quantum computing applications and exploring fundamental quantum phenomena using controllable superconducting systems.

• Superconducting quantum computation
• Quantum simulation
• Quantum information processing
• +۷ مورد دیگر

HUANG Li is an Associate Professor in the Department of Physics at Southern University of Science and Technology (SUSTech) , where she has been since September 2019. She joined SUSTech in February 2013 as an Assistant Professor, advancing to her current rank. Her research focuses on quantum materials , novel energy materials , nonequilibrium phase selectivity , and surface/interfacial phenomena using first-principles computational methods and theoretical models . Educational Background: Ph.D. in Physics, Fudan University (2006) M.Sc. in Physics, Xiangtan University (2002) B.A. in Physics, Xiangtan University (1999) Research Interests span quantum materials (e.g., Moire superconductors , topological insulators ), novel energy materials (e.g., thermoelectrics , supercapacitors ), and surface/interfacial dynamics. Her recent work emphasizes two-dimensional systems , spin-valley-layer coupling , and nonequilibrium phase transitions in van der Waals heterostructures and metal-organic frameworks . Publications highlight her expertise in ab initio molecular dynamics , density functional theory , and quantum transport . Key trends include Moiré engineering in twisted bilayers, strain-tunable magnetism , and ferroelectricity in layered materials. Scientific Awards and Grants include leadership roles in projects funded by the National Natural Science Foundation of China and Shenzhen Science & Technology Commission . Notable grants address quantum state control in Moiré superlattices , nonlinear Hall effects , and thermoelectric performance enhancement . Professional Experience: Postdoctoral Research Associate at Ames Laboratory (2008–2012) Joint Postdoc at Georgia Institute of Technology & Ames Laboratory (2006–2008) Visiting Scholar at Harvard University & Oak Ridge National Laboratory (2004–2006)

• Quantum Materials
• Novel Energy Materials
• Phase Selectivity under Nonequilibrium Conditions
• +۷ مورد دیگر

Liang Kong is a Researcher at the Southern University of Science and Technology (SUSTech) . He previously held academic positions at Tsinghua University (Associate Professor), Harvard University (Research Associate), University of New Hampshire (Lecturer), and several postdoctoral institutes including the Max Planck Institute, IHES, and California Institute of Technology. Education : PhD in Mathematics (2005, Rutgers University), Master (1997) and Bachelor (1994) in Physics (University of Houston and University of Science & Technology of China). Research Interests : His work bridges mathematical physics , topological field theories , and category theory , focusing on 2D conformal field theories , topological phases of matter , and enriched monoidal categories . He explores connections between quantum field theories and algebraic structures like tensor categories and braided fusion categories, with applications to quantum computing and condensed matter physics . Publications : His research spans topological quantum field theory , conformal field theory , and quantum lattice models , with notable contributions to Levin-Wen models , Cardy algebras , and anyon condensation . Collaborative works with Xiao-Gang Wen and Ingo Runkel underpin his impact in topological order classification and gapless edge states . Positions : Current role at the Shenzhen Institutes of Quantum Science and Engineering (2018–present) follows appointments at Harvard, Tsinghua, and the University of New Hampshire.

• Mathematical Physics
• Topological Field Theories
• 2D Conformal Field Theories
• +۳ مورد دیگر

Fang Kong is an Assistant Professor in the Department of Statistics and Data Science at the Southern University of Science and Technology (SUSTech). He earned his PhD in Computer Science from Shanghai Jiao Tong University under the supervision of Prof. Shuai Li and received his Bachelor's degree in Software Engineering from Shandong University. Education: PhD in Computer Science, Shanghai Jiao Tong University (2020-2024) Bachelor's Degree in Software Engineering, Shandong University (2016-2020) Dr. Kong is broadly interested in developing theoretically guaranteed algorithms for sequential decision-making problems, with particular focus on multi-armed bandits and reinforcement learning, as well as their applications in online experimentation and recommendation systems. His research spans theoretical foundations of bandit algorithms, matching markets, influence maximization, and online learning under various feedback structures. He has made significant contributions to the understanding of best-of-both-worlds algorithms that perform well in both stochastic and adversarial environments. His publication record shows a strong trajectory of high-impact work in top-tier conferences including NeurIPS, ICML, ICLR, AAAI, WWW, and AAMAS. His research demonstrates expertise in theoretical machine learning with a focus on bandit algorithms, particularly in matching markets and sequential decision-making problems. His work often bridges theoretical guarantees with practical applications in recommendation systems and online experimentation. Scientific Awards: CCF Doctoral Dissertation Award in Agent and Multi-Agent Systems (2025) Baidu Scholarship (2024) National Scholarship for PhD students (2023, 2022) AAMAS Student Scholarship (2023) Microsoft Research Asia Excellence Award (2022) Dr. Kong actively mentors students at various levels, including PhD and Master's students at SUSTech, visiting students from other institutions, and undergraduate researchers. He serves as a reviewer for top machine learning conferences (ICLR, NeurIPS, ICML, WWW) and journals (IEEE PAMI, TMLR). His teaching includes graduate Machine Learning and undergraduate Artificial Intelligence courses at SUSTech.

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

Dr. Li Lei is an Associate Professor and doctoral supervisor in the Department of Materials Science and Engineering at the Southern University of Science and Technology (SUSTech). He holds a BS from the University of Science and Technology of China and a PhD in Chemistry (2016) from the University of Nebraska-Lincoln. From 2016-2020, he conducted postdoctoral research with Prof. Graeme Henkelman at the University of Texas at Austin, where he developed the machine-learning software package PyAMFF. Education : BS (Materials Science, USTC), PhD (Chemistry, UNL) Affiliation : Southern University of Science and Technology, Shenzhen Peacock Plan Category B Talent Research Focus : His work bridges computational method development and catalytic mechanism discovery through: Machine learning algorithms for force field training Long-timescale dynamics simulations of nanomaterials Saddle-point searching methods for reaction pathways Monte Carlo-based catalyst optimization Development of catalytic reaction databases His recent publications in JACS , Nat. Commun. , and ACS Catalysis demonstrate expertise in: Machine learning-enhanced molecular dynamics Electrochemical catalyst design (ORR/OER) in situ reaction mechanism analysis Atomic-scale electrostatic modeling Scientific Contributions : - Led development of PyAMFF machine learning potential framework - Published 27+ SCI papers in top-tier journals - Collaborated with leading computational chemists (Henkelman, Zeng, Francisco) Awards : Shenzhen Peacock Plan Category B Talent Contact : Office: 5th Floor, North Building, College of Engineering, SUSTech Phone: 0755-88015040 Email: lil33@sustech.edu.cn | lei.li@sustech.edu.cn

• Materials Science
• Computational Method Development
• Catalytic Mechanism Study
• +۳ مورد دیگر

Zhang Jin is a Professor at the Department of Mathematics in the College of Science at Southern University of Science and Technology (SUSTech) since December 2024. He also serves as the Associate Vice Director of the Shenzhen National Applied Mathematics Center since February 2023. Education: Ph.D. in Applied Mathematics (2014, University of Victoria); M.Sc. in Operational Research (2010, Dalian University of Technology); B.Art in Journalism (2007, Dalian University of Technology) Research Interests span optimization theory , variational analysis , bilevel programming , and their applications in machine learning , economics , and data science . His work includes convergence analysis of first-order methods , stochastic/robust optimization , and error bound conditions . Recent Publications focus on nonconvex bilevel optimization , gradient-based algorithms , and stochastic programming , with papers in IEEE TPAMI , SIAM Journal on Optimization , and conferences like ICML , NeurIPS , and ICLR . Scientific Awards include the Youth Science and Technology Innovation Award from Guangdong Province (2022) , Youth Science and Technology Award from the Operations Research Society of China (2020) , and Junior Research Award from SUSTech's Faculty of Science (2020) . Students: Supervises Ph.D. candidates like Yixia Song , Peixuan Yang , and Qichao Cao , along with Master's students Yixuan Zhang , Kaiqi Sun , and Feifan Wang . Grants: Leads projects such as the National Key R&D Program (3.2M RMB, 2024-2028) and National Science Fund for Distinguished Young Scholars (2M RMB, 2023-2025) .

• Optimization Theory
• Variational Analysis
• Bilevel Programming
• +۶ مورد دیگر

Xue Hang is a Lecturer in the Animation Department at Tongji University's School of Art and Media, where he has been teaching since 2002. He holds a Master of Science degree in Optics from Fudan University (1993) and has expanded his expertise through international exchanges at Hungary's Academy of Art and Design (1998-1999) and Germany's Bauhaus University (2010). His multidisciplinary career spans physics, visual design, and new media arts. Research interests include: Film and television animation Creative photography Art history Visual aesthetics in digital media Key research projects involve: Teaching reform for high-definition film/TV development Multimedia documentation of historical narratives (e.g., 'Jinggangshan' music-dance epic) Cultural value preservation through modern creative industries Scientific awards highlight: 2021 Special Prize in Chinese Poetry Competition Multiple teaching excellence awards (2008-2014) 1996 Li Guohao Young Faculty Award 2009 Shanghai Teaching Achievement Award (Team member) His 15 most recent publications demonstrate expertise in multimedia production, cultural adaptation, and digital art education, with projects spanning both theoretical and practical domains of new media arts. Notable collaborations include international academic exchanges with Germany and Hungary.

• Film and Television Animation
• Creative Photography
• Art History
• +۲ مورد دیگر

CHEN Xin is an Associate Professor in the Department of Statistics and Data Science at Southern University of Science and Technology (SUSTech), where he has been employed since January 2019. He initially joined the Department of Mathematics before transitioning to his current department in August 2019. Prior to SUSTech, he held academic positions as Assistant Professor at the National University of Singapore (2011-2019) and Syracuse University (2010-2011). Education History: PhD in Statistics from University of Minnesota, USA (2005-2010) MS in Statistics and Applied Probability from National University of Singapore (2001-2003) BS in Mathematics from Nankai University, China (1994-1999) Research focuses on developing statistical methodologies for high-dimensional and complex data analysis. Primary research domains include: sufficient dimension reduction techniques for efficient data representation; variable selection methods for high-dimensional models; robust statistical approaches for complex datasets; and optimization of computational efficiency in statistical estimation. His work consistently addresses theoretical foundations while developing practical implementations for modern data challenges. Publications demonstrate sustained focus on statistical methodology development, particularly in dimension reduction and high-dimensional analysis. Recent works emphasize robust methods for ultrahigh-dimensional data, efficient computation algorithms, and model diagnostics. Research consistently appears in top-tier statistics journals including Biometrika, Journal of the American Statistical Association, and Annals of Statistics. No scientific awards, student advising relationships, or research grants are mentioned in the source materials. Similarly, there is no information available regarding laboratory affiliations or research team composition.

• Sufficient Dimension Reduction
• Variable Selection
• High Dimensional Analysis
• +۱ مورد دیگر

Dr. QUAN Chen is an Associate Professor at the School of Microelectronics, Southern University of Science and Technology (SUSTech), holding this position since May 2025 after serving as Assistant Professor (2019-2025) and Research Assistant Professor at the University of Hong Kong (2012-2018). A Shenzhen high-level overseas talent, he earned his PhD from the University of Hong Kong and conducts cutting-edge research in electronic design automation. His academic credentials include: Ph.D. from The University of Hong Kong (2010) Master's degree from The University of Hong Kong (2007) Bachelor's degree from Sun Yat-Sen University (2005) Dr. Chen's research pioneers advanced EDA algorithms for large-scale analog/RF circuit simulation, post-Moore multi-physics analysis, and AI-assisted design technologies. His work addresses critical challenges in nanodevice modeling and quantum computing circuits, resulting in over 50 publications in top venues like IEEE TCAD and DAC, plus four Chinese patents. Analysis of his recent publications reveals dominant trends in exponential integrator methods for transient simulation, model order reduction techniques, and physics-informed machine learning for reliability analysis. His work bridges numerical mathematics with practical EDA applications across analog circuits, quantum hardware, and emerging memory technologies. Key recognitions include: Wu Wenjun Artificial Intelligence Science and Technology Award, Second Prize (2020) ICCAD Best Paper Award Nomination (2012) Dr. Chen actively recruits Postdoctoral Fellows, Research Assistants, and Graduate Students while leading major funded projects including NSFC key/general programs and Guangdong Provincial R&D initiatives. His industry partnerships with Huawei, Empyrean, and Guowei Group translate theoretical advances into real-world EDA solutions. He directs a specialized research group at SUSTech focused on computational methods for next-generation circuit design, fostering innovation in simulation algorithms and multi-physics analysis through academic-industry collaboration.

• Electronic Design Automation (EDA)
• Analog/RF Circuit Simulation
• Multi-physics Analysis
• +۲ مورد دیگر

Qihang Liu serves as Professor in the Department of Physics at Southern University of Science and Technology (SUSTech), where he leads a research group focused on theoretical condensed matter physics and computational materials science. Previously Associate Professor at SUSTech (2018-2023), he maintains active collaborations with Peking University and international institutions including University of Colorado Boulder and Northwestern University. Education: Ph.D. in Condensed Matter Physics, Peking University (2007-2012) B.S. in Physics, Peking University (2003-2007) Research Focus: His work centers on symmetry-driven phenomena in quantum materials, particularly spin-group theory for magnetic systems and topological effects induced by spin-orbit coupling. Key contributions include: Development of FINDSPINGROUP program for spin symmetry identification Prediction of unconventional magnetism in antiferromagnets Design principles for quantum anomalous Hall effect in superlattice structures Computational exploration of spin-orbitronics materials Publication Trends: Recent publications (2019-2022) demonstrate consistent innovation in symmetry applications to magnetic topological materials, with emphasis on antiferromagnetic spintronics and computational design of novel quantum states. His work bridges theoretical predictions with experimental realizability in systems like Mn-Bi-Te compounds. Scientific Recognition: SUSTech Young Researcher Award (2021) SUSTech Young Professor Award (2022) Shenzhen Jieqing Talent Project (2022) Stanford Top 2% Scientists (2021, 2022) Grants & Leadership: Principal Investigator for China's National Key R&D Program "Quantum Control and Quantum Information" (2020), securing major funding for quantum materials research. His group actively mentors graduate students while developing next-generation spintronic materials and symmetry-based computational tools. Research Infrastructure: The Liu Research Group maintains specialized computational facilities for first-principles calculations and collaborates with experimental teams to validate theoretical predictions on spin-orbit coupling effects and topological phenomena in low-dimensional systems.

• Theoretical Condensed Matter Physics
• Computational Materials Science
• Spin-Orbit Coupling
• +۵ مورد دیگر

Prof. Junhao Lin is a tenured Full Professor at the Department of Physics, Southern University of Science and Technology (SUSTech). With a dual background in experimental and theoretical physics, he specializes in atomic-scale characterization of 2D materials, combining aberration-corrected STEM, EELS spectroscopy, and density functional theory calculations to study defect-structure-property relationships. Ph.D. (2015) from Vanderbilt University Postdoctoral work at National Advanced Institute of Science and Technology (Japan) under Kazu Suenaga Research Interests : Atomic-scale defect engineering in 2D materials In-situ high-resolution electron microscopy under external stimuli High-energy-resolution EELS for optical property characterization Quantum material structure-property correlations Publication Trends : Over 130 high-impact publications (Nature, Science, PRL) focusing on 2D material defects, phase transitions, and atomic engineering. Recent works explore nickelate superconductivity, magnetic phenomena in topological materials, and energy storage applications. Scientific Recognition : MIT Technology Review TR35 China (2021, Visionary category) Elsevier 2024 Highly Cited Researcher (China) Multiple international conference awards 8+ million RMB investment in glove-box interconnected lab systems Lab Overview : The Advanced Low-Dimensional Materials Laboratory at SUSTech (10+ members) integrates atomic-resolution STEM, in-situ TEM, and computational modeling. Collaborations span Oak Ridge National Lab, Peking University, and Singaporean/Japanese institutions.

• Two-Dimensional Materials
• Defect Structure-Property Correlations
• Scanning Transmission Electron Microscopy (STEM)
• +۴ مورد دیگر

Dr. Xiang Li serves as a Research Assistant Professor in the Department of Mechanics and Aerospace Engineering at the College of Engineering, Southern University of Science and Technology (SUSTech). He earned his Ph.D. in Mechanics from Harbin Institute of Technology in 2022 and previously held the position of SUSTech Presidential Postdoctoral Fellow. His academic journey includes: Ph.D. in Mechanics, Harbin Institute of Technology (2018-2022) Master in Mechanical Engineering, Shantou University (2015-2018) Bachelor in Mechanical Engineering, Shenyang University of Chemical Technology (2010-2014) Dr. Li's research specializes in experimental and computational fluid dynamics of complex multiphase systems, with particular focus on magnetic fluids. His work integrates theoretical modeling, numerical simulation techniques, and practical applications in microfluidics and advanced manufacturing. He has developed novel lattice Boltzmann methods for simulating ferrofluid behavior under magnetic fields, contributing significantly to the understanding of multiphase interface phenomena. His scholarly impact is evidenced by 19 research papers (including 4 ESI highly cited papers and 1 ESI Hot paper), 58 filed national patents in China (26 granted), and 3 PCT patents. His publications demonstrate a consistent focus on computational methods for magnetic multiphase flows, with recent work extending into biomedical applications such as COVID-19 detection systems. Professional recognitions include: Young Research Award of Guangdong Mechanics Society (2023) Excellent Postdoc of Shenzhen (2023) PoF Young Researcher Award (2017) National Scholarship at both Master's and PhD levels Outstanding Doctoral Graduate and Dissertation awards from SUSTech (2022) Dr. Li has secured five research projects totaling 930,000 yuan in funding, including the National Natural Science Foundation of China Youth Project. He actively contributes to the scholarly community as a reviewer for multiple journals, maintaining Star Reviewer status for Physics of Fluids from 2019-2023.

• Multiphase flows
• Ferrofluids
• Microfluidics
• +۴ مورد دیگر

Shan Chen is an Assistant Professor at the Southern University of Science and Technology (SUSTech) , Shenzhen, China, with research expertise in Cryptography and Information Security , particularly focused on real-world applications and secure protocols. Ph.D. in Computer Science , Georgia Institute of Technology (2020) MEng in Computer Science and Technology , Tsinghua University (2014) BSc in Mathematics and Applied Mathematics , Beijing Normal University (2011) His research addresses practical challenges in cryptographic protocol design, including security analysis of TLS 1.3 and QUIC, causality preservation in secure messaging, and committing authenticated encryption. Key trends include improving protocol efficiency while maintaining provable security against real-world adversaries. He leads the Applied Cryptography Group and teaches courses like Algorithm Design and Analysis and Discrete Mathematics . Contact: chens3@sustech.edu.cn

• Cryptography
• Information Security
• Computer Science
• +۲ مورد دیگر

DAI Jiansheng is a Chair Professor at Southern University of Science and Technology (SUSTech) and Director of the Robotics Research Institute. He is a Fellow of the Royal Academy of Engineering (FREng), Fellow of the Academia Europaea, and holds multiple fellowships including IEEE, ASME, RSA, and IMechE. As Editor-in-Chief of the international journal Robotica, he has established himself as a leading authority in mechanisms and robotics research. His educational background includes a PhD from the University of Salford (1989-1993), a Master's degree from Shanghai Jiao Tong University (1982-1984), and a Bachelor's degree from the same institution (1978-1982). His academic journey has taken him from postdoctoral work at Salford University to research positions at Unilever Liverpool Research Centre before becoming a faculty member at the University of Sunderland and ultimately King's College London, where he served as Reader and then Chair Professor from 2007 until his current position at SUSTech. Professor Dai's research spans theoretical kinematics, screw theory, Lie algebra, and their applications to metamorphic and reconfigurable mechanisms. His pioneering work bridges the gap between versatile but expensive robots and efficient but non-flexible machines. His research interests include origami-inspired robotics, rehabilitation robotics for ankle treatment, soft robotics, and industrial applications in packaging and manufacturing. His theoretical framework has enabled significant advances in reconfigurable parallel mechanisms and metamorphic robotics. His extensive publication record shows a clear progression from fundamental theoretical work on screw algebra and Lie groups to practical applications in rehabilitation, manufacturing, and soft robotics. Recent publications demonstrate increasing focus on soft robotics, variable stiffness actuators, and continuum robots with Shape Memory Alloy applications, while maintaining strong theoretical foundations in screw theory and kinematic analysis of metamorphic mechanisms. ASME Mechanisms and Robotics Award (2015) ASME Machine Design Award (2020) IFToMM Excellence Award (2023) Tianjin Municipal Natural Science First Prize (2021) Crossley Award (2018) AT Yang Award in Theoretical Kinematics (2019) Professor Dai has supervised over 50 PhD students who now hold faculty positions at world-leading universities including University College London, Queen Mary University London, Purdue University, and Wollongong University. His research has been supported by numerous grants enabling the establishment of advanced robotics laboratories and international collaborations. He founded the IEEE Triennial International Conference on Reconfigurable Mechanisms and Robots (ReMAR), creating a major platform for international scholarly exchange in this specialized field. His research group maintains strong industry partnerships with companies including Cambridge Consultants, Goldman Sachs, and Amazon. The Robotics Research Institute he directs at SUSTech serves as a hub for interdisciplinary research, bringing together experts in mechanical engineering, computer science, biomedical engineering, and materials science. The institute focuses on both fundamental theoretical advances in mechanism design and practical applications in healthcare, manufacturing, and service robotics, with particular emphasis on metamorphic and reconfigurable systems that can adapt to multiple tasks.

• Theoretical Kinematics
• Screw Theory
• Lie Algebra
• +۷ مورد دیگر

Dr. Lili Wang serves as an Associate Professor at Southern University of Science and Technology (SUSTech), joining in May 2024 as an independent Principal Investigator and PhD advisor. Her research focuses on theoretical and applied control systems for intelligent sensor networks through interdisciplinary integration of control theory, optimization, and machine learning. Her academic foundation includes: PhD in Engineering and Applied Science from Yale University (2014-2020) Master's in Control Theory and Control Engineering from Zhejiang University (2011-2014) Bachelor's in Automation from Zhejiang University (2007-2011) Dr. Wang's research spans social dynamics , distributed estimation , distributed control , formation control for multi-agent systems , and distributed learning applications . Her work develops novel algorithms for networked control systems with applications in intelligent sensor networks and multi-robot coordination, emphasizing theoretical rigor and practical implementation. Analysis of her publications reveals consistent advancement in distributed observer design, formation control strategies, and consensus algorithms. Key trends include innovative applications of graph theory and spectral methods to multi-agent systems, with growing integration of learning techniques for adaptive control in uncertain environments. Her distinguished recognition includes: IEEE Senior Member (2024) Lillian Gilbreth Fellowships at Purdue University (2023) CPS Rising Star Award (2023) Henry Prentiss Becton Graduate Prize (2019) Zhang Siying Outstanding Paper Award (2012) As a PhD advisor at SUSTech, Dr. Wang mentors graduate researchers in control theory and its applications. She is currently founding COL-LABORATORY, a dedicated research team advancing control systems for intelligent sensor networks through interdisciplinary collaboration, with ongoing projects in distributed learning frameworks and robust multi-agent coordination.

• Social dynamics
• distributed estimation and calculation
• distributed control
• +۲ مورد دیگر