Mingkai Chen

Email  /  CV  /  Google Scholar  /  Github

I am currently a first-year doctoral student at Department of Computer Engineering, Rochester Institute of Technology. Prior to that, I graduated from Stony Brook University with Bachelor of Science in Computer Science.

Under the supervision of Prof. Dongfang Liu, I am actively conducting research in the field of Artificial Inteligence. My research interests cover a wide range of topics in this field, such as AI for Science, Large Language Models, and Multi-modal Models, among others.

profile photo
Research

My research interests lie in the field of Artificial Inteligence. The most of my current works focused on AI for Science , Large Language Models and Multi-modal Models .

Inertial Confinement Fusion Forecasting via LLMs
Mingkai Chen, Taowen Wang, James Chenhao Liang, Chuan Liu, Chunshu Wu, Qifan Wang, Ying Nian Wu, Michael Huang, Chuang Ren, Ang Li, Tong Geng, Dongfang Liu
arXiv, Under review by NeurIPS 2024 
We developed Fusion-LLM, a novel integration of Large Language Models (LLMs) with reservoir computing paradigms, tailored for Inertial Confinement Fusion (ICF). The approach includes an LLM-anchored Reservoir for accurate forecasting of hot electron dynamics, Signal-Digesting Channels for detailed temporal and spatial analysis of laser intensity, and a Confidence Scanner to quantify prediction confidence. Demonstrated superior performance in predicting Hard X-ray (HXR) energies, achieving state-of-the-art results. Introduced Fusion4AI, the first ICF benchmark based on physical experiments, to foster advancements in AI-driven plasma physics research.
CoCoT: Contrastive Chain-of-Thought Prompting for Large Multimodal Models with Multiple Image Inputs
Daoan Zhang*, Junming Yang*, Hanjia Lyu*, Zijian Jin, Yuan Yao, Mingkai Chen, Jiebo Luo
ICPR 2024 
We investigated Large Multimodal Models' (LMMs) ability to process multiple image inputs, focusing on fine-grained perception and information blending. Our research involved image-to-image matching and multi-image-to-text matching assessments, using models like GPT-4V and Gemini. We developed a Contrastive Chain-of-Thought (CoCoT) prompting method to improve LMMs' multi-image understanding, significantly enhancing model performance in our evaluations.
Aggregation of Disentanglement: Reconsidering Domain Variations in Domain Generalization
Daoan Zhang*, Mingkai Chen*, Chenming Li, Lingyun Huang, Jianguo Zhang
arXiv, Under review by IJCV 
We proposed a new perspective to utilize class-aware domain variant features in training, and in the inference period, our model effectively maps target domains into the latent space where the known domains lie. We also designed a contrastive learning based paradigm to calculate the weights for unseen domains.
* equal contribution.
Services
SUSTech Student Assistant, Department of Computer Science, Stony Brook University





credits