Ziqi Ma

2023.9.14 - Moved to Pasadena to start my PhD at Caltech as a Kortschak Scholar!
2023.9.5 - Check out this new article I wrote on Data Science@Microsoft medium blog: Many facets of un-truth: LLM hallucination 101. This is a 101 on hallucination that I wish I had when getting started with LLMs. Hope it could be helpful to you too!
2023.4.25 - Check out this new article I wrote on Data Science@Microsoft medium blog: Reasoning about change: Lessons learned from building a near real-time system for Azure pricing. The pricing projects led me to read up on the development of various industrial streaming systems in the past decade. Such a journey from POC to industry standard - quite inspiring!
2023.3.7 - Check out this new article I wrote on Data Science@Microsoft medium blog: Speeding up “Reverse ETL”.
2021.12.21 - Check out this new article I wrote on Data Science@Microsoft medium blog: What do we talk about when we talk about ML robustness. Distribution shift is only part of the story!
2021.8.24 - I got to write about one of my favorite topics - “how/when research becomes useful in practice”, based on interviews with ML practitioners and organization leaders at Microsoft! It’s published on Data Science@Microsoft medium blog: Navigating the (long and winding) road from innovation to production
2021.6.8 - Check out this article I wrote with my co-authors on Data Science@Microsoft medium blog: Data science perspectives on averages and related areas
2021.6.2 - The fair ML compression project I initiated got featured by PureAI: Researchers Explore Intelligent Sampling of Huge ML Datasets to Reduce Costs and Maintain Model Fairness
2020.10.15 - Presented work on circuit learning for quantum sensing at IEEE QCE20 Practical Quantum Sensing Workshop

Ziqi Ma, Pranav Gokhale, Tian-Xing Zheng, Sisi Zhou, Xiaofei Yu, Liang Jiang, Peter Maurer, Frederic T. Chong
In Proceedings of IEEE International Conference on Quantum Computing and Engineering (QCE)
Virtual, Oct 2021

I initiated a collaboration with Microsoft Research on coreset compression to speed up ML training (MSR AI School invited project, 8% acceptance rate), but during implementation realized existing techniques do not consider fairness, which motivated this research as side project.
In the press: Researchers Explore Intelligent Sampling of Huge ML Datasets to Reduce Costs and Maintain Model Fairness

I graduated early to lead this collaboration project between members of CS and Molecular Engineering Departments - formulated sensing protocol design as a learning problem by constructing parametrized encoder/decoder circuits; enabled on-device learning from limited, noisy measurements via robust gradient estimation

This is an ioT project for environment monitoring, including a soil sensor network deployed in UChicago campus and a water-body sensing system deployed in India. I used machine learning (tree-based models, neural networks, active learning) for water quality (BOD, COD) prediction, and set up the LoRa system (embedded, wireless, backend) for soil sensing.

Designed online visual block-based CS-learning games for k-12 education, and analyzed performance of students from Kenwood Academy

Built virtual mentor to give disabled students targeted job-seeking advice (rule-based recommendation, running on Django), as member of TechTeam at UChicago’s Institute of Politics