Multi-Agent Collaboration in Incident Response with Large Language Models
Zefang Liu
arXiv preprint arXiv:2412.00652, 2024
[Paper] [Code]
About Me
Hello! I’m a Data Scientist at JPMorganChase, specializing in machine learning, natural language processing, and large language models. With a Ph.D. in Nuclear Engineering and a M.S. in Computational Science and Engineering from Georgia Tech, I bring interdisciplinary approaches to solving complex problems. My work focuses on applying advanced machine learning techniques across various domains, including cybersecurity, supply chains, and plasma physics.
Work Experience
- Applied AI ML Associate Sr, JPMorganChase, 2023 - Present
- Quantitative Research Associate, JPMorganChase, 2022 - 2023
- Applied AI & Machine Learning Summer Associate, JPMorganChase, 2021
Education
- Ph.D. in Nuclear Engineering, Georgia Institute of Technology, 2018 - 2022
- M.S. in Computational Science and Engineering, Georgia Institute of Technology, 2018 - 2021
- M.S. in Aerospace Engineering, Georgia Institute of Technology, 2016 - 2018
- B.E. in Energy and Power Engineering, Shandong University, 2012 - 2016
Publications
Efficient Retrieval of Temporal Event Sequences from Textual Descriptions
Zefang Liu, Yinzhu Quan
arXiv preprint arXiv:2410.14043, 2024
[Paper] [Code] [Data]TPP-LLM: Modeling Temporal Point Processes by Efficiently Fine-Tuning Large Language Models
Zefang Liu, Yinzhu Quan
arXiv preprint arXiv:2410.02062, 2024
[Paper] [Code] [Data]Application of Neural Ordinary Differential Equations for ITER Burning Plasma Dynamics
Zefang Liu, Weston M. Stacey
AAAI 2025 Workshop on AI to Accelerate Science and Engineering (AI2ASE), 2024
[Paper] [Code]InvAgent: A Large Language Model based Multi-Agent System for Inventory Management in Supply Chains
Yinzhu Quan, Zefang Liu
arXiv preprint arXiv:2407.11384, 2024
[Paper] [Code]EconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential Reasoning
Yinzhu Quan, Zefang Liu
2024 Conference on Empirical Methods in Natural Language Processing (EMNLP) Findings, 2024
[Paper] [Poster] [Data]AdaMoLE: Fine-Tuning Large Language Models with Adaptive Mixture of Low-Rank Adaptation Experts
Zefang Liu, Jiahua Luo
2024 Conference on Language Modeling (COLM), 2024
[Paper] [Poster] [Code]Application of Neural Ordinary Differential Equations for Tokamak Plasma Dynamics Analysis
Zefang Liu, Weston M. Stacey
ICLR 2024 Workshop on AI4DifferentialEquations in Science (AI4DiffEqtnsInSci), 2024
[Paper] [Poster] [Code]A Review of Advancements and Applications of Pre-trained Language Models in Cybersecurity
Zefang Liu
2024 International Symposium on Digital Forensics and Security (ISDFS), 2024
[Paper]CyberBench: A Multi-Task Benchmark for Evaluating Large Language Models in Cybersecurity
Zefang Liu, Jialei Shi, John F. Buford
AAAI 2024 Workshop on Artificial Intelligence for Cyber Security (AICS), 2024
[Paper] [Slides] [Code] [Data]SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security
Zefang Liu
arXiv preprint arXiv:2312.15838, 2023
[Paper] [Code] [Data]Anomaly Detection of Command Shell Sessions based on DistilBERT: Unsupervised and Supervised Approaches
Zefang Liu, John F. Buford
2023 Conference on Applied Machine Learning in Information Security (CAMLIS), 2023
[Paper] [Slides]A Multi-Region Multi-Timescale Burning Plasma Dynamics Model for Tokamaks
Zefang Liu, Weston M. Stacey
64th Annual Meeting of the APS Division of Plasma Physics, 2022
[Paper] [Slides] [Code]Deep Reinforcement Learning based Group Recommender System
Zefang Liu, Shuran Wen, Yinzhu Quan
arXiv preprint arXiv:2106.06900, 2021
[Paper] [Slides] [Code]Yelp Review Rating Prediction: Machine Learning and Deep Learning Models
Zefang Liu
arXiv preprint arXiv:2012.06690, 2020
[Paper] [Code]