Ph.D. Candidate in Computer Science | AI Research & Distributed Systems Expert
Hangzhou, CN.
About
Highly accomplished Ph.D. Candidate in Computer Science with extensive expertise in large-scale AI infrastructure, federated learning, and distributed optimization. Proven ability to develop cutting-edge algorithms for heterogeneous environments and privacy-preserving machine learning, demonstrated through significant contributions to over 20 peer-reviewed publications. Driven to build democratized and efficient AI systems, leveraging strong research and development skills to advance the state-of-the-art in LLM training and deployment.
Work
Alibaba Group
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Research Intern
Hangzhou, Zhejiang, China
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Summary
As a Research Intern at Alibaba Group, Tao Shen collaborated with industry researchers to develop algorithms for large-scale AI infrastructure projects in heterogeneous distributed learning environments.
Highlights
Collaborated with leading industry researchers on large-scale AI infrastructure projects, advancing solutions for distributed computing environments.
Developed and optimized algorithms for heterogeneous distributed learning environments, improving efficiency and scalability of AI model training.
Zhejiang Lab
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Research Intern
Hangzhou, Zhejiang, China
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Summary
As a Research Intern at Zhejiang Lab, Tao Shen focused on researching advanced machine learning algorithms and distributed systems, with a particular emphasis on federated learning and privacy-preserving techniques.
Highlights
Conducted in-depth research into advanced machine learning algorithms and distributed systems, contributing to novel theoretical and practical advancements.
Implemented and evaluated federated learning and privacy-preserving machine learning techniques, enhancing data security and model robustness for distributed AI applications.
Zhejiang University
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Research Assistant
Hangzhou, Zhejiang, China
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Summary
As a Research Assistant at Zhejiang University, Tao Shen supported research on distributed optimization and control systems, developing numerical algorithms for critical power system analysis.
Highlights
Contributed to foundational research on distributed optimization and control systems, advancing understanding of complex networked systems.
Developed and refined numerical algorithms for power flow analysis and optimization, enhancing the efficiency and reliability of power system operations.
Education
Zhejiang University
Hangzhou, Zhejiang, China
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Ph.D.Computer ScienceGrade:
Courses
Federated Learning
Distributed Optimization
Trustworthy AI
Zhejiang University
Hangzhou, Zhejiang, China
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M.S.Control Science and EngineeringGrade:
Courses
Power Systems
Numerical Methods
Optimization
China University of Petroleum
Qingdao, Shandong, China
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B.S.AutomationGrade: Top-tier Class
Courses
Neural Network
Control Systems
Awards
Graduate Academic Excellence Scholarship
Zhejiang University
Recognized for outstanding academic performance and research contributions at the graduate level.
National Innovation Program Fellowship
China University of Petroleum
Awarded for exceptional potential in innovation and research during undergraduate studies.
Research Innovation Scholarship
College of Control Science & Engineering
Received for demonstrating significant aptitude and contributions to research within the college.
Publications
Watch Wider and Think Deeper: Collaborative Cross-modal Chain-of-Thought for Complex Visual Reasoning
arXiv
Knowledge-empowered, Collaborative, and Co-evolving AI Models: The Post-LLM Roadmap
Engineering
Remedy: Recipe Merging Dynamics in Large Vision-Language Models
Proc. ICLR
Improving Model Fusion by Training-time Neuron Alignment with Fixed Neuron Anchors
IEEE Trans. Pattern Anal. Mach. Intell.
Each Rank Could Be an Expert: Single-ranked Mixture of Experts LoRA for Multi-task Learning
arXiv
FedCFA: Alleviating Simpson's Paradox in Model Aggregation with Counterfactual Federated Learning
Proc. AAAI
FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Models
arXiv
FedGuCci: Making Local Models More Connected in Landscape for Federated Learning
Proc. ACM SIGKDD Conf. Knowl. Discovery Data Mining
Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices
arXiv
Fedeve: On Bridging the Client Drift and Period Drift for Cross-device Federated Learning
arXiv
An Adaptive Aggregation Method for Federated Learning via Meta Controller
Proc. ACM Int. Conf. Multimedia Asia
Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large Language Models
arXiv
Retrieval-augmented Mixture of LoRA Experts for Uploadable Machine Learning