Mingqian Ma (马鸣谦)

I am a senior undergraduate student at the Computer Science and Engineering department at the University of Michigan, Ann Arbor. I'm also in the dual degree program with Shanghai Jiao Tong University, where I major in Electrical and Computer Engineering.

My research Interest focus at training Foundation Models in Science and General domains. I'm especially interested in pretraining foundation models in Genomics and Optics. More generally, I'm interested in using Machine Learning Methods in Applicative Scenario. I've been advised by Dr. Guoqing Liu @MSR, Prof. L. Jay Guo @UMich EECS, and Prof. Xiaofeng Gao @SJTU SEIEE.

I'm finding a PhD position in the field of AI for Science and NLP. If you are interested in my research, please feel free to contact me.

Email  /  GitHub  /  Google Scholar  /  LinkedIn

News

[October-2024] Our paper was accepted by Foundation Model for Sciecne Workshop at NeurIPS 2024. See you in Vancouver this December!

[September-2024] Our survey paper in Multilayer Thin Film Design is under review. Check it out on Arxiv!

[May-2024] I've joined Microsoft Research AI4Science team as a research intern advised by Dr. Guoqing Liu. I'm working on pretraining large-scale foundation models in Genomics.

[March-2024] Our paper on travel route recommendation was accepted by IEEE Transactions on Service Computing!

[October-2023] Our paper on ridesharing route planning was accepted by COCOON 2023. I'll be presenting it in Hawaii in December!

Research

I'm interested in building Foundation Models in AI for Science fields, especially in Genomics and Optics. Also I'm interested in general domain LLM pretraining.

Solving Out-of-Distribution Challenges in Optical Foundation Models using Self-Improving Data Augmentation

Optical multilayer thin film structures are widely used in many photonic applications. The important part to enable these applications is inverse design, which seeks to identify a suitable structure that satisfy desired optical responses. We propose a self-improving data augmentation technique by leveraging neural networks’ extrapolation ability. Using this method, we show significant improvement in various real-applicative design tasks with minimum fine-tuning, which can also be potentially generalized to inverse scientific foundation models.

Optical Multilayer Thin Film Structure Inverse Design: From Optimization to Deep Learning

A survey paper of optical multilayer thin film structure inverse design. The survey convers all aspects from the traditional optimization-based methods to state-of-the-art deep learning-enabled inverse design algorithms.

Encoder-Decoder Based Route Generation Model for Flexible Travel Recommendation

A ML4CO framework to recommend suitable routes for tourists while satisfying explicit constraints like must-visit points, unavailable hours, etc.

Algorithms for Shortest Path Tour Problem in Large-Scale Road Network

A paper on planning the ridesharing route planning problem as the Shortest Path Tour Problem (SPTP) and use a Stage Dijkstra algorithm to plan it efficiently in polynomial time.

Design and source code from Jon Barron's website