جست‌وجوی استاد و پژوهشگر

• Computational Design and Fabrication
• Design for Additive Manufacturing
• Adaptive Process Planning
• +۶ مورد دیگر

Dr. Xiong Yi is an Assistant Professor at the School of System Design and Intelligent Manufacturing (SDIM) at Southern University of Science and Technology (SUSTech) in Shenzhen, China. He leads the Computational Design and Fabrication (CoDeFab) research group, focusing on the integration of computational design methods with advanced manufacturing technologies, particularly in the field of additive manufacturing. Dr. Xiong has established himself as a leading researcher in computational design for additive manufacturing, with a strong international research background spanning Europe and Asia. Dr. Xiong's educational journey includes: Doctor of Science (DSc) in Engineering Design and Production from Aalto University, Finland (2012-2016) Master of Science (MSc) in Machine Automation from Tampere University of Technology, Finland (2010-2012) Bachelor of Engineering (BEng) in Mechanical Engineering from Hubei University of Technology, China (2006-2010) Dr. Xiong's research primarily focuses on computational design and fabrication methodologies, with particular emphasis on design for additive manufacturing (DfAM), intelligent manufacturing systems, and smart materials. His work bridges the gap between theoretical design principles and practical manufacturing constraints, developing novel approaches for the production of complex engineered products. He has pioneered research in continuous fiber-reinforced composite additive manufacturing, developing innovative process planning and optimization techniques that enable the production of high-performance structural components. His research in electrothermally controlled origami and 4D printing of smart materials represents cutting-edge work at the intersection of materials science, mechanical engineering, and computational design. Dr. Xiong's recent publications reveal a strong focus on continuous fiber-reinforced composites, with significant contributions to 4D printing, metamaterials, and intelligent process planning. His work integrates computational design with manufacturing constraints, creating novel approaches for topology optimization, toolpath planning, and structural design that consider both performance requirements and manufacturability limitations. The research demonstrates increasing sophistication in materials science applications, particularly in programmable materials and multi-functional structures. Dr. Xiong has received multiple prestigious awards for his research contributions, including: Best Presentation Award at the 24th Chinese Conference on Mechanisms and Machine Science (IFToMM CCMMS2024) Best Presentation Award at the International Conference on Frontiers of Additive Manufacturing Research (RAAM 2024) Best Paper Award at the International Conference on Design for 3D Printing (ICD3DP 2023) PhD Scholarship from Aalto University (2016) Research Travel Grant from the International Association for Vehicle System Dynamics (IAVSD) (2013) National Scholarship from the Ministry of Education (2008) As a dedicated educator and mentor, Dr. Xiong serves as a PhD supervisor at SUSTech and has successfully guided students who have gone on to pursue advanced studies and careers at prestigious institutions including Hong Kong Polytechnic University, Beihang University, DJI Innovations, and Singapore's A*STAR research institute. His research is supported by multiple competitive grants, including key projects from the National Key R&D Program of China, the National Natural Science Foundation of China, and provincial and municipal funding agencies. Dr. Xiong also serves on the editorial board of the Journal of Engineering Design and as a guest editor for Composites Communications, contributing to the advancement of his field through scholarly service. Dr. Xiong leads the CoDeFab research group, which maintains a strong collaborative culture focused on 'design leading manufacturing, manufacturing driving design, and digital-intelligent integration.' The group has developed several advanced manufacturing platforms, including multi-axis continuous fiber-reinforced composite additive manufacturing systems, smart composite additive manufacturing platforms, and multifunctional soft matter open manufacturing platforms. With a focus on practical applications and innovation, the CoDeFab group actively collaborates with industry partners and has established a joint laboratory to bridge academic research with industrial implementation.

Shi Liمشاهده پروفایل

استاد

• Theoretical Computer Science
• Design and Analysis of Algorithms
• Combinatorial Optimization
• +۱۱ مورد دیگر

Shi Li is a Professor in the Theory Group at the Department of Computer Science and Technology, School of Computer Science, Nanjing University. He previously held faculty positions at the University at Buffalo (2015–2023) as Assistant and Associate Professor, and was a Research Assistant Professor at Toyota Technological Institute at Chicago (2013–2015). He earned his Ph.D. from Princeton University in 2014 under Moses Charikar and completed his B.S. in Computer Science and Technology at Tsinghua University, where he was part of Andrew Chi-Chih Yao’s Special Pilot Class. His research lies at the intersection of theoretical computer science and combinatorial optimization, with a focus on the design and analysis of algorithms for problems in clustering, scheduling, network design, facility location, and online algorithms. He also explores learning-augmented algorithms and differential privacy in algorithmic contexts. His work combines deep theoretical insights with practical algorithmic frameworks, often leveraging linear programming relaxations, iterative rounding, and randomized techniques. The recent publications highlight a consistent trend in approximation algorithms, particularly in clustering (e.g., correlation clustering, fair k-set selection), scheduling (e.g., unrelated machine scheduling, load balancing), and robust optimization. His work frequently appears in top-tier theoretical venues such as STOC, FOCS, SODA, and ICALP, with increasing emphasis on fairness, privacy, and efficiency in algorithm design. Best Paper Award of Track A, ICALP 2011 Co-winner of Best Paper Award, FOCS 2012 Invited to Special Issue of SICOMP (FOCS 2017 paper) Best Paper Award, COCOON 2018 Invited to Special Issue of SICOMP (STOC 2019 paper) Best Paper Award of Track A, ICALP 2024 Outstanding Paper Award, SPAA 2024 Shi Li has advised several PhD and master’s students, including Yuda Feng, Han Dai, Zihao Liang, and Jia Ye, and has mentored postdoctoral researcher Ruilong Zhang. He has served on numerous program committees (e.g., STOC, SODA, ICALP) and is an Editorial Board Member of ACM Transactions on Algorithms . He teaches core algorithm courses such as Design and Analysis of Algorithms and Advanced Algorithms , and actively collaborates with researchers worldwide. His lab focuses on theoretical foundations of efficient and fair algorithm design, with applications in large-scale data analysis and distributed systems.

Cao Haishanمشاهده پروفایل

دانشیار

• Cryogenic Cooling
• Micro Cryocoolers
• Joule-Thomson Cooling
• +۱۲ مورد دیگر

Cao Haishan is an Associate Professor at Tsinghua University, affiliated with the Department of Energy and Power Engineering in the School of Mechanical Engineering. His research focuses on cryogenic cooling systems, high heat flux thermal management, and the physics of amorphous ice formation and phase transitions. He leads a research group supported by the National Natural Science Foundation of China and industry partners including Huawei, Midea, and Lenovo. Ph.D., Mechanical Engineering, University of Twente, 2013 M.Sc., Chemical Engineering, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 2009 B.Sc., Chemical Engineering, Zhejiang University, 2006 Dr. Cao's research spans three major areas: cryogenic cooling (including micro cryocoolers and sorption systems), high heat flux electronic cooling (especially with non-condensable gases), and the formation and transformation of amorphous water ice. His work combines theoretical modeling, computational simulation, and experimental validation, often at micro and nano scales. He applies principles from thermodynamics, fluid dynamics, and materials science to solve engineering challenges in refrigeration and thermal control. The recent publications reflect a strong trend toward interdisciplinary research, integrating machine learning for heat transfer prediction, computational screening of MOFs for cryogenic switches, and fundamental studies of ice nucleation on various substrates. The articles span journals in physics, engineering, materials, and applied thermal sciences, indicating broad impact across multiple domains. Notable scientific awards include: Gustav and Ingrid Klipping Award (2016) Cryogenics Best Paper Award (2017) Annual Teaching Excellence Award, Tsinghua University (2023) Excellent Supervisor Award, Tsinghua University (2024) Multiple First Prize Advisor awards in national student contests on energy saving Dr. Cao has been principal investigator on several grants, including projects funded by the National Natural Science Foundation of China on amorphous ice lifetime and micro-cryocooling for semiconductor chips. He has also led industry-university collaborations with Huawei, Midea, and Lenovo. He advises graduate students and leads a research team focused on next-generation cooling technologies. He serves on editorial boards for Journal of Refrigeration , Vacuum and Cryogenics , and Energies , and has chaired sessions at major international conferences such as ICEC-ICMC and ACTS. His research group operates within the Institute of Thermophysics at Tsinghua University, leveraging facilities in the Lee Shau Kee Science and Technology Building. The team collaborates with national laboratories and international institutions, particularly maintaining ties with the University of Twente. Current efforts are directed toward ultra-low vibration cooling, efficient separation of non-condensable gases, and extending the stability of amorphous ice for cryobiological applications.

• Journalism
• Communication
• Computational Communication
• +۵ مورد دیگر

Xu Xiang is a Tenured Professor and Vice Dean at Tongji University School of Art and Media , where he serves as Director of the Big Data and Computational Communication Research Center. He is also Editor-in-Chief of Computing, Intelligence, and Communication , reviewer for CSSCI journals, and former Deputy Secretary-General of Beijing Society of Literature and Art. PhD in Literary Theory (Beijing Normal University, 2009) M.A. in Chinese Language and Literature (Tsinghua University, 2006) B.A. in Sociology (Nanjing University, 2004) His research focuses on computational communication, particularly analyzing social media networks, user behavior, and information dissemination. He established key theoretical frameworks including 'Opinion Paradigm', 'Social Echo Chamber', 'Influence Circle', 'Idol Conformity', 'Emotional Setting', and 'Silent Public Opinion', with extensive publications on social media emotional dynamics, information narrowing, and cultural communication mechanisms. His 15 most recent publications (2017-2022) demonstrate consistent exploration across computational communication theory, algorithmic platform analysis, and social media's role in cultural dissemination. Key research trends include information narrowing mechanisms, emotional preference patterns, and structural transformations in social media ecosystems. Fourth China 'News Communication Academic Award' (2018) Youth New Media Academic Research 'Qi Hao Award' (2018) National News Academic Youth Scholar Award (2012) Beijing-Taiwan Research Award (2013) Beijing Outstanding Talent Funding (2011) Tongji University '13th Five-Year Plan' Scientific Research Advanced Individual (2020) As an award-winning mentor, he guided student teams to first, second, and third prizes in multiple national competitions including China Data Journalism Competition and National Undergraduate New Media Creative Competition. His research bridges academic theory with policy practice, with findings receiving Central Politburo member instructions and extensive national media coverage.

Yiguo Xueمشاهده پروفایل

استاد

• Geotechnical Engineering
• Structural Engineering
• Tunnel Engineering
• +۲ مورد دیگر

Yiguo Xue is a Professor and PhD Tutor at the Geotechnical and Structural Engineering Center, School of Civil Engineering, Shandong University. He has contributed extensively to tunnel engineering, subsea infrastructure, and slope stability analysis through advanced prediction models and numerical simulations. Research Interests: New tunnel geological condition prediction technologies Subsea tunnel and underground energy storage systems High slope stability evaluation Engineering exploration methods His recent work focuses on tunnel safety, subsea structural mechanics, and AI-driven hazard prediction models. Publications highlight his expertise in rockburst analysis, water inrush risk, and excavation optimization using machine learning and numerical frameworks. Scientific Awards: Recipient of China's Top 100 Most Influential Domestic Academic Paper Award (2008 paper on tunnel geological hazard forecasting) He has secured multiple national invention patents for tunnel monitoring devices (e.g., vibration sensors, collapse prediction systems) and developed specialized software for displacement prediction and rock classification.

Wang Chunمشاهده پروفایل

دانشیار

• Operations Management
• Stochastic Control
• Decision-Making under Uncertainty
• +۹ مورد دیگر

Wang Chun is a tenured Associate Professor in the Department of Management Science and Engineering at the School of Economics and Management, Tsinghua University. He currently serves as Deputy Director of the department and has held academic positions at Tsinghua since 2017. His work bridges stochastic control, reinforcement learning, and machine learning applications in operations management and financial technology. Education: Bachelor's Degree from Tsinghua University (2005) Master's Degree from the University of Wisconsin-Madison (2008) Doctorate in Operations Research from Columbia University (2014) Research Interests: His research focuses on stochastic control and decision-making under uncertainty, particularly in inventory management, revenue management, financial engineering, and sports analysis. He also investigates reinforcement learning algorithms for dynamic optimization and applies machine learning to business data analytics and financial technology. Published Work Trends: Wang’s publications span operations research, financial mathematics, and computational management science. Recent studies address seasonal optimization patterns, robust inventory models, and game-theoretic approaches to sports analytics. His work integrates theoretical advancements in dynamic programming with practical applications in financial engineering and market microstructure. Career History: 2023–Present: Associate Professor, Tsinghua University 2020–2023: Associate Professor, Tsinghua University 2017–2020: Assistant Professor, Tsinghua University Researcher at KCG (New York), leading studies on quantitative investment and asset optimization

Yang Zhishuمشاهده پروفایل

استاد

• Financial Market Microstructure
• Behavioral Finance
• Banks and Financial Institutions
• +۳ مورد دیگر

Prof. Zhishu Yang is a full-time faculty member at the School of Economics and Management, Tsinghua University, serving as Professor of Finance since 2009. He holds a Ph.D. in Economics from Tsinghua University (1997-2001) and a Bachelor of Engineering from Harbin Institute of Technology (1984-1988). Specializes in Financial Market Microstructure, Behavioral Finance, and Banks and Financial Institutions Has taught courses including Financial Market Microstructure, Securities Analysis, and Investment Maintains active collaborations with international institutions like MIT Sloan and HKUST His research explores Chinese financial markets, government regulation effects, investor behavior, and policy impacts. Publications include studies in Management Science , Journal of Finance , and Review of Financial Studies . Recent master's students have been admitted to top global PhD programs including Harvard, Stanford, and Yale. Contact: Room B312, Lihua Building | yangzhsh@sem.tsinghua.edu.cn

• Nanoplasmonics
• Nano-optics
• Biochips
• +۷ مورد دیگر

Li Yi is a Tenured Associate Professor at the School of Microelectronics, Southern University of Science and Technology (SUSTech) , with expertise in nanoplasmonics, nano-optics, and biochips. His career spans institutions including KU Leuven, Imperial College London, and Ludwig-Maximilians-Universität München. Education: Ph.D. (2015, KU Leuven & IMEC), M.S. (2010) and B.S. (2007) in Biomedical Engineering from Zhejiang University. Career: Associate Professor (2021–present) and Assistant Professor (2019–2020) at SUSTech; LMU Research Fellow (2018–2019); Research Associate (2015–2018) at Imperial College London. Research Interests focus on single-molecule biochips, DNA data storage, and nanopore sequencing. His work bridges nanotechnology with biomedical applications, including: Integrated circuit-compatible optical/electrical devices Computational lithography and nanoscale imaging Neural network training for biosensing Publications include >60 peer-reviewed articles with 25 H-index. His recent work emphasizes DNA-based data encryption, nanopore engineering, and portable biosensing systems. Key journals: Nature Communications, Nano Letters, ACS Nano. Scientific Awards : LMU Research Fellowship (Marie Curie COFUND) imec Excellent Scientific Award Shenzhen 'Peacock Plan' Category B Talent Outstanding Poster Award at Surface Plasmon Photonics 2017 Participation in Lindau Nobel Laureates Meeting (2019) Grants include Ministry of Science and Technology Key R&D Youth Program, NSFC General Program, and Guangdong Provincial Key Program. His lab develops: Fluidic nanopores for single-molecule sensing Optical antennas for enhanced detection Training programs in biochip design and nano-optics

Daohong Qiuمشاهده پروفایل

دانشیار

• Geotechnical Engineering
• Underground Engineering
• Structural Health Monitoring
• +۲ مورد دیگر

Daohong Qiu is an Associate Professor and Master Tutor at the Geotechnical and Structural Engineering Center, School of Civil Engineering, Shandong University. His research focuses on advanced geological prediction in tunnels, surrounding rock stability, structural health monitoring for urban rail transit, and TBM tunneling performance optimization. Position: Associate Professor Affiliation: School of Civil Engineering, Shandong University Email: qiudh@sdu.edu.cn Research Interests: He specializes in geotechnical engineering challenges related to underground construction, including rock burst prediction , disaster control , and machine learning applications in geological modeling. His work emphasizes integrating advanced computational methods like quantum genetic algorithms and RBF neural networks with field data to improve tunnel safety and efficiency. Publication Trends: His recent 2019 studies address subsea tunnel risk assessment , rock burst prediction in underground caverns, and machine learning-driven surrounding rock classification . Earlier works (2014-2015) explore SVM/GA-SVM for geological disaster forecasting, while pre-2010 papers focus on optimization theory and stress field analysis. Patent Contributions: He holds multiple invention patents for geological prediction devices, including three-dimensional geological network modeling , seismic signal detection , and concrete elevation control systems .

Jianke Zhuمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• 3D Reconstruction
• +۳ مورد دیگر

Jianke Zhu is a Professor at the College of Computer Science and Technology of Zhejiang University . He obtained his Ph.D. in Computer Science and Engineering from The Chinese University of Hong Kong and conducted postdoctoral research at the BIWI Computer Vision Lab, ETH Zurich . His research focuses on Computer Vision and Machine Learning , with a particular emphasis on 3D scene understanding, LiDAR-based mapping, and neural rendering. Dr. Zhu’s research spans several subfields, including 3D Reconstruction , Semantic Segmentation , Multimodal Learning , and Autonomous Driving . His work integrates Neural Networks , LiDAR Processing , and Uncertainty Quantification to address challenges in real-time and adverse conditions. Selected Recent Trends: 2025 publications highlight his work in Hexagonal Mesh-based Neural Rendering , Instance-aware 3D Scene Understanding , and Efficient Visual Projectors for Multimodal LLMs . Earlier works include Box2Mask for Instance Segmentation (2024) and Token Selection for Point Cloud Learning (2025). Scientific Awards : Senior member of the IEEE Advising and Grants : As a Doctoral Supervisor , he mentors students in advanced topics like LiDAR Odometry and Multi-view Stereo Recovery . His projects have attracted funding for autonomous driving , 3D scene modeling , and neural rendering .

Xuehai Qianمشاهده پروفایل

استاد

• Computer Architecture
• Computer Systems
• Hardware Security
• +۳ مورد دیگر

Xuehai Qian is a Tenured Full Professor in the Department of Computer Science at Tsinghua University since July 2024. Prior to this, he served as an Associate Professor at Purdue University (2022-2024), Assistant Professor at the University of Southern California (2015-2022), and Postdoctoral Researcher at the University of California Berkeley (2013-2015). Ph.D. in Computer Science, University of Illinois at Urbana-Champaign, USA (2013) ME in Computer Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, China (2007) BE in Computer Science and Technology, Beihang University, China (2004) His research focuses on parallel computer architecture , hardware/software co-designed domain-specific architectures for graph analytics and machine learning , hardware security , and quantum computer architecture . He has pioneered scalable cache coherence protocols for atomic block execution, hardware sequential consistency violation detection, and distributed frameworks for graph processing leveraging emerging memory technologies. Xuehai Qian’s publications span computer architecture , graph analytics , machine learning systems , and quantum computing , with a strong emphasis on distributed systems , accelerators , and memory optimization . His work includes novel architectures for graph processing, decentralized training protocols, and ReRAM-based accelerators for deep learning. NSF CAREER Award (2018) ACSIC (American Chinese Scholar In Computing) Rising Star Award (2019) IEEE Senior Member (2019) Hall of Fame inductions: ASPLOS (2018), HPCA (2019), ISCA (2021), MICRO (2021) W.J. Poppelbaum Memorial Award (2013) He has advised students who have received Microsoft Research Lovelace Fellowships , Ph.D. Fellowships , and Facebook Fellowships . His research has been supported by grants such as the NSF SPX project on FPGA-based machine learning platforms and smaller NSF grants. Notably, he has served as an Associate Editor for Science China (Information Sciences) since 2023 and as a Guest Editor for IEEE Transactions on Parallel and Distributed Systems (2019).

Lei Buمشاهده پروفایل

استاد

• Model Checking
• Bounded Model Checking
• Formal Methods
• +۷ مورد دیگر

Lei Bu is a Professor and Vice Dean at the Software Institute , Nanjing University . He leads research in formal verification, cyber-physical systems, and software engineering, with a focus on bounded model checking and hybrid system analysis. B.Sc. and Ph.D. in Computer Science from Nanjing University (2004, 2010) Visiting student at Carnegie Mellon University and University of Texas at Dallas His research integrates formal methods and machine learning for verifying complex systems like IoT and software with real-time constraints. Key projects include BACH Toolset and BRICK for reachability analysis. Recent publications demonstrate expertise in hybrid system verification , cache side-channel detection , and parallel code analysis frameworks . His work bridges theoretical advancements with practical applications in safety-critical systems. Zhongchuang Software Talent Award (2023) CCF-IEEE CS Young Computer Scientist Award (2022) High-Tech Software Innovation Awards (2019-2023) As Principal Investigator, he leads major projects funded by National Science Foundation of China and Jiangsu Natural Science Foundation (2020-2027). Current tools include BACH for hybrid systems and MLB for Java symbolic execution.

Fang Kongمشاهده پروفایل

استادیار

• Online Learning
• Reinforcement Learning
• Machine Learning
• +۴ 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.

• Digital Economy
• Social Welfare
• Consumer Behavior
• +۶ مورد دیگر

Dr. Shaobo (Kevin) Li is a Tenured Associate Professor at the School of Business, Southern University of Science and Technology (SUSTech), with secondary appointment in the Department of Information Systems & Management Engineering. He holds a PhD in Business Administration from Nanyang Technological University (2019), Master of Business Administration (University of Virginia, 2014), Master of Finance (West Virginia University, 2012), and Bachelor of Business Administration (Lanzhou University, 2011). National High-Level Youth Talent Program awardee Overseas High-Caliber Personnel in Shenzhen City Recipient of 2018 China Scholarship Council’s Excellent Self-Funded Student Scholarship His research focuses on the intersection of digital economy, social welfare, and technology-driven marketing, employing experimental methods, empirical models, and neuroscience. He serves as Associate Editor for the Journal of Business Research and has published in top UTD-24 journals including Journal of Consumer Research, Information Systems Research, and Production and Operations Management. His work examines platform economics, brand management during crises, and consumer behavior related to AI, sustainability, and health decisions. He has led 10+ research projects (National Natural Science Foundation, Ministry of Science and Technology 2030 Program) and received multiple research awards, including Academy of Marketing Science Best Doctoral Dissertation Proposal Award (2019) and Best Paper Awards at China Marketing Science (2020,2024), China Marketing International Conference (2023), and CMAU Annual Conference (2023,2024). Teaching accolades include SUSTech’s Premier Teaching Award, Top 10 Most Popular Professor among undergraduates, and Nanyang Business School’s Best Graduate Teaching Award (2017). Teaches Financial Marketing (undergraduate) Teaches Frontiers of Management & Research Methods (PhD) Actively recruiting research assistants, postdocs, and joint PhD candidates with Hong Kong Polytechnic University

• Gene Transcription
• Epigenetic Regulation
• RNA Regulation
• +۱ مورد دیگر

Huanhuan CUI is a Research Associate Professor in the Department of Biology within the School of Life Sciences at Southern University of Science and Technology (SUSTech) in Shenzhen, China. She joined SUSTech in December 2018 and serves as a master's supervisor, contributing to both research and graduate education in molecular biology and genetics. Educational Background: PhD in Biology, Free University of Berlin (2011.09-2016.04) MS in Animal Genetics, Northwest A&F University (2008.09-2010.06) BE in Bioengineering, Northwest A&F University (2004.09-2008.06) Dr. CUI's research focuses on the mechanisms and method development of gene transcription and epigenetic regulation, with particular emphasis on RNA translation control. Her work spans multiple biological systems including cancer biology, developmental biology, and cardiovascular research, demonstrating a strong interdisciplinary approach that integrates molecular biology, genomics, and bioinformatics techniques. She has made significant contributions to understanding chromatin remodeling, transcriptional regulation, and RNA processing mechanisms. Dr. CUI's publication record shows a consistent trajectory of high-impact research, with publications in prestigious journals including Nature Communications, Nucleic Acids Research, and Cellular & Molecular Immunology. Her work demonstrates expertise in CRISPR-based technologies, epigenetic regulation, and multi-omics approaches to studying complex biological processes. Before joining SUSTech, Dr. CUI held positions as a Clinical Research Manager at BGI Genomics (2017.09-2018.11), a PostDoc at Charite Universitaetsmedizin Berlin (2016.05-2017.06), and a Research Assistant at Humboldt University of Berlin (2010.09-2011.08), building a diverse research background that bridges basic science and clinical applications.