جستجوی استاد راهنمای دانشگاهی

Hong Huaqingمشاهده پروفایل

استاد

• Corpus Linguistics
• Computer-Assisted Instruction
• Machine Translation
• +۳ مورد دیگر

Hong Huaqing is a Professor and doctoral supervisor at the Corpus Research Institute of Shanghai International Studies University. He holds roles as honorary director of the Chinese Corpus Linguistics Research Association and international expert at Peking University's Education Development Center. Formerly, he worked at Nanyang Technological University (Singapore) in roles such as researcher at the Learning Research and Development Center and director of the e-Learning Center of the Lee Kong Chian School of Medicine. His research spans machine translation, natural language processing, corpus linguistics, and educational technology. He supervises master's and doctoral students, co-supervises postdoctoral researchers, and focuses on smart education driven by big data analysis and innovative learning ecosystems. Research emphasizes corpus-based methods applied to language education, including computational frameworks for student engagement, wearable sensors in learning analytics, and cross-linguistic rhetoric studies. His work bridges technological innovation (e.g., AI-driven tutorial systems) with pedagogical practice, addressing challenges in non-English language education and teacher training.

• Human-Computer Interaction
• Design
• Interactive Systems
• +۴ مورد دیگر

Pengcheng An is an Assistant Professor at the School of Innovation and Creative Design at Southern University of Science and Technology (SUSTech) in Shenzhen, China. His research sits at the intersection of Design and Human-Computer Interaction (HCI), focusing on the design and implications of intelligent interactive systems in everyday contexts such as smart classrooms, offices, and other learning environments. Education: Ph.D. in Industrial Design, Eindhoven University of Technology, Netherlands Postdoctoral Research, Eindhoven University of Technology Postdoctoral Research Fellow and Sessional Lecturer, School of Computer Science, University of Waterloo, Canada An's research interests center on creating interactive systems that enhance learning, education, and support for children, families, and underprivileged groups. His work explores how emerging technologies like IoT, intelligent systems, and multimodal interfaces can be seamlessly integrated into real-world settings to improve user experiences without disrupting natural workflows. He particularly focuses on peripheral and unobtrusive interaction techniques that support busy practitioners like teachers and healthcare workers. His publication record demonstrates consistent contributions to top-tier HCI venues, with a focus on educational technology applications, assistive systems, and novel interaction techniques. The research trajectory shows increasing sophistication in understanding real-world deployment challenges and developing solutions that respect existing workflows while providing meaningful augmentation. Scientific Awards: Best Paper Honorable Mention Award (ACM CHI) Reviewers' Choice Award (INTERACT) Red Dot Design Award: Best of the Best (twice) iF Design Talent Award An serves as an active member of the HCI community, having been Associate Chair for the "Learning, Education, and Family" track at ACM CHI since 2021. He has secured research funding including a National Social Science Fund Youth Project exploring AI technology in art therapy for special children. His service extends to reviewing for major conferences (CHI, DIS, TEI, IDC) and journals (Behavior & Information Technology, Experimental Results). His laboratory work focuses on real-world deployments of interactive systems, particularly in educational settings, where he collaborates with teachers and students to develop and evaluate technologies that support learning without disrupting classroom dynamics.

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).

• Artificial General Intelligence (AGI)
• Data Mining
• Social Networks
• +۲ مورد دیگر

Jie Tang is a Professor at the Department of Computer Science, Tsinghua University , and a Fellow of ACM, AAAI, IEEE . His research focuses on Artificial General Intelligence (AGI) , with significant contributions to large pre-trained models like GLM-130B, ChatGLM, CogView, CogVideo, and CodeGeex. Research Trends : Jie Tang's work spans AGI development with human-like reasoning Graph Neural Networks for network representation Social network mining and influence modeling Academic knowledge graph construction (AMiner system) Advancing foundation models for cross-modal tasks Scientific Awards : SIGKDD Test-of-Time Award SIGKDD Service Award NSFC Distinguished Young Scholar 2nd National Award for Science & Technology Advising and Grants : He mentors highly-motivated students and postdocs in AGI research. His work has received extensive funding and recognition, including over 400 publications in top conferences (IJCAI, AAAI, NeurIPS, KDD) and journals (TPAMI, TKDE).

• Analog IC Design
• Power IC Design
• GaN Power Devices
• +۵ مورد دیگر

Dr. GAO Yuan serves as an Assistant Professor at the School of Microelectronics, Southern University of Science and Technology (SUSTech) since November 2019. His academic journey includes a Ph.D. in Electronic and Computer Engineering from the Hong Kong University of Science and Technology (2017), preceded by M.Eng. and B.Eng. degrees from Xi'an Jiaotong University (2012 and 2009 respectively). Prior to his current position, he completed a Post-Doctoral Fellowship at HKUST (2017-2019). Education Background: Ph.D. in Electronic and Computer Engineering, HKUST (2012-2017) M.Eng. in Electronic Science and Technology, Xi'an Jiaotong University (2009-2012) B.Eng. in Microelectronics, Xi'an Jiaotong University (2005-2009) Dr. Gao's research focuses on analog and power integrated circuits design, with particular expertise in high-voltage applications. His work spans GaN power device drivers and controllers, efficient LED/LD driver ICs, and high-performance DC-DC/AC-DC power converters. His publication record demonstrates consistent innovation in power management circuits, with recent work exploring applications in photoacoustic imaging systems and advanced battery conversion technologies. His research output shows a clear trajectory toward increasingly sophisticated power management solutions, with recent publications (2023-2025) emphasizing bidirectional power transfer, high-efficiency battery-to-LED conversion, and specialized drivers for medical imaging applications. The consistent presence in top-tier venues (IEEE JSSC, ISSCC, VLSI Symp., CICC) reflects the high impact of his work in the integrated circuits community. Scientific Recognition: Best Paper Award at IEEE ISCAS 2023 (top 3 among 1,400 submissions) Best Student Paper Finalist at IEEE CICC 2023 Shenzhen Overseas High-Caliber Personnel C ("Peacock Plan") IEEE Solid-State Circuits Society STGA for ISSCC 2017 1st Place, Synopsys International Microelectronics Olympiad 2014 Dr. Gao actively contributes to the academic community through service on review boards for major international journals and conferences. His teaching portfolio includes undergraduate courses in analog circuit fundamentals and power electronics, as well as graduate-level instruction in advanced power converter design. His research group appears to focus on practical implementations of power management solutions with applications spanning consumer electronics, medical devices, and energy-efficient lighting systems.

• Second Language Acquisition
• Curriculum Development
• College English Teaching
• +۲ مورد دیگر

Yuxiu Hu serves as an English Instructor at the Center for Language Education, Southern University of Science and Technology (SUSTech) in Shenzhen, China. She has held the position of Lecturer at SUSTech since 2013, specializing in English language instruction for science and engineering students in a non-English speaking environment. Education: PhD in Linguistics, The University of Hong Kong (2007-2011) MA in Linguistics, The University of Hong Kong (2005-2006) BA in English Education, Harbin Normal University (1999-2003) Dr. Hu's research focuses on Second Language Acquisition, Curriculum Development, and College English Teaching. Her work examines how Mandarin influences English acquisition among Chinese students and develops specialized English curricula for STEM fields. She has conducted extensive research on technology-enhanced language learning, particularly exploring web-based instruction, MOOCs, and the flipped classroom model for engineering English education. Her longitudinal studies track language acquisition patterns and the effectiveness of various teaching methodologies in non-English speaking contexts. Scientific Recognition: Excellent Teaching Award, Southern University of Science and Technology (2018) The University of Hong Kong Research Student Scholarship (2007-2011) The University of Hong Kong Oversea Conference Grant (2008) Dr. Hu has led significant research initiatives including "The Development of STEM-based English Curriculum in Higher Education" (2017-2019) as Principal Investigator, and has contributed as Co-investigator to multiple projects involving MOOC development for engineering English, flipped classroom models for public speaking instruction, and web-based learning systems. She serves the academic community as a Proposal Reviewer for TESOL International Convention and previously as Associate Editor for Ubiquitous Learning: An International Journal. Her teaching portfolio encompasses College English Reading & Writing, Academic English, TOEFL preparation, Academic Writing, Engineering English, and English Reading Strategies courses.

Xiao ZHANGمشاهده پروفایل

مربی

• Robotics
• Teleoperation
• Machine Learning
• +۳ مورد دیگر

Xiao ZHANG is a Faculty Tutor at the School of Civil Engineering, Shandong University, specializing in advanced robotics, teleoperation systems, and machine learning applications in manufacturing. Their work bridges human-robot interaction, surgical robotics, and materials engineering. Research focuses on improving precision in telemanipulation, optimizing additive manufacturing processes, and developing context-aware robotic systems for healthcare and industrial automation. Key research interests include gaze-based control mechanisms, intent recognition in human-robot collaboration, and AI-driven quality assessment in manufacturing. Xiao ZHANG has extensively explored applications of neural networks and multi-agent systems to enhance robotic dexterity and safety, particularly in scenarios requiring real-time adaptation like laparoscopic surgery and autonomous vehicles. Recent Trends: Recent publications emphasize integrating machine learning with traditional manufacturing processes (e.g., laser additive manufacturing) and advancing human-centric robotics through shared control frameworks. Awards: No specific awards listed, but contributions span over two decades of peer-reviewed work. Grants/Advising: No explicit grants or student advisees mentioned in the provided text. Labs/Teams: Affiliated with robotics and manufacturing research groups at Shandong University, focusing on experimental and computational methods.

Guanhua Chenمشاهده پروفایل

استادیار

• Natural Language Processing (NLP)
• Large Language Models (LLMs)
• Applied Machine Learning
• +۴ مورد دیگر

Dr. Guanhua Chen is an Assistant Professor in the Department of Statistics and Data Science at Southern University of Science and Technology (SUSTech). His research focuses on Natural Language Processing (NLP), Large Language Models (LLMs), and Multimodal AI, with applications in drug discovery and urban planning. He previously earned his Ph.D. from the University of Hong Kong and B.S./M.S. from Tsinghua University, with internships at Microsoft Research Asia and Huawei Noah’s Ark Lab. Education: Ph.D. in Computer Science (University of Hong Kong, 2022), B.S. and M.S. in Computer Science (Tsinghua University, 2012 & 2014) Dr. Chen's research explores LLMs, data synthesis, multimodal systems, and model compression. He leads a lab equipped with 16 RTX 4090 GPUs, 16 L40 GPUs, and 4 A100 GPUs, providing resources for students to experiment with open-source and proprietary LLM APIs. His recent publications highlight innovations in LLM jailbreaking, parameter-efficient tuning (MiLoRA), cross-lingual transfer (mCLIP), and evaluation frameworks for robustness testing. Notably, he was awarded the Microsoft Research Asia StarTrack Scholar in 2025. Key Publications: mCLIP (ACL 2023), ImPart (ACL 2025), MiLoRA (NAACL 2025) Dr. Chen actively seeks motivated PostDoc, PhD, and Master's students. He serves as an Area Chair for ACL and EMNLP and contributes to advancing AI through open-source projects like mCLIP on GitHub.