Handan Liu
Teaching Professor,
Information and Software Engineering
Contact
- h.liu@northeastern.edu
- 360 Huntington Ave
Boston, MA 02115
Office
- 500 DA
- 617.373.5424
Research Focus
My research focuses on scalable AI systems and efficient large language models through high-performance computing and distributed machine learning.
About
Dr. Liu is an experienced educator and a former software engineer, as well as a successful entrepreneur. She has served as a member of the teaching faculty in Computer Science at Shanghai University for seven years and, at the same time, successfully ran her own business as a developer and CEO for cutting-edge software products and services. Dr. Liu earned a Ph.D. from Shanghai Jiao Tong University and worked extensively with heterogeneous supercomputing to develop large high performance parallel algorithms. She then worked as a software engineer on several projects for NASA and the US Air Force on research algorithms and developing software to solve flight problems at supersonic speeds. Furthermore, she was a research faculty member at Virginia Tech and worked for the US Department of Energy to develop parallel algorithms for the large-scale software MFIX on national supercomputers. Before she became a full-time teaching professor, Dr. Liu led and participated in building the HPC clusters at Northeastern University from 2015. Since her appointment as teaching professor, Dr. Liu developed original and unique courses to combine the HPC parallel computing into data science and AI.
With more than 20 years of industrial, private industry and teaching experience, Dr. Liu is very familiar with the stresses and difficulties involved with student learning and, as a result, she strives to effectively integrate market-needed technologies into teaching and student research projects.
Research Overview
My research focuses on scalable AI systems and efficient large language models through high-performance computing and distributed machine learning.
- High-Performance Computing (HPC) for AI
- Scalable and Distributed Machine Learning
- Efficient Large Language Models (LLMs)
- LLM Optimization: Quantization, Parameter-Efficient Fine-Tuning (PEFT)
- LLM Training and Alignment: SFT, DPO, RLHF
- Efficient LLM Architectures: KVFormer
- Agentic AI Systems
- Multi-GPU Parallel and Distributed Computing
Selected Publications
- Ramshankar Bhuvaneswaran. Master’s Thesis: “ModelOpt: Research Framework for Zero-Shot Computer Vision Model Optimization with Tree Search and Federated Knowledge Sharing”. Advisor: Handan Liu. Thesis defensed on December 3, 2025.
- Ramshankar Bhuvaneswaran, Handan Liu*. “BitSkip: An Empirical Analysis of Quantization and Early Exit Composition”. October 27, 2025. https://arxiv.org/abs/2510.23766
- Liu, Handan. (June, 2025), A Unique Course Designed for Graduate Students: Integrating High-Performance Parallel Computing into Machine Learning and Artificial Intelligence. Paper presented at 2025 ASEE Annual Conference & Exposition, June 22-25, 2025. Montreal, Quebec, Canada. https://peer.asee.org/55408
- Handan Liu, Danesh K. Tafti, Tingwen Li, Hybrid parallelism in MFIX CFD-DEM using OpenMP, Powder Technology, Volume 259, 2014. https://doi.org/10.1016/j.powtec.2014.03.047
Oct 16, 2025
Combining Skills and Experience to Build Meaningful Solutions
Aditi Ashutosh Deodhar, MS’25, information systems, through her academic journey at Northeastern, has been able to grow her skill set and experiences through impactful projects, an incredible co-op opportunity and participating in vigorous hackathons.