About Me
I am currently a fifth-year PhD candidate at Carnegie Mellon University, actively pursuing opportunities as a Research Scientist, Applied Scientist, Machine Learning Engineer, or Data Scientist, and am available to commence work as early as October 2023.
My research endeavors are deeply rooted in AI4Science, specifically in harnessing machine learning for data-driven tasks spanning chemistry, drug discovery, biophysics, material discovery, and optics. Throughout my doctoral studies, I’ve amassed comprehensive experience in deciphering mechanisms within enzymatic and synthetic catalyst systems. I’ve also had the privilege of interning with the AI4Science team at Microsoft Research, where I delved into the subject of Machine Learning Force Fields(MLFF).
Technically, I am adept at a plethora of machine learning methodologies, encompassing deep learning, natural language processing, and computer vision. Furthermore, I am proficient with tools like Git and platforms related to cloud computing. I’ve honed skills in an array of programming languages and platforms, including but not limited to Python, C, TensorFlow, PyTorch, Scikit Learn, Pandas, AWS, GCP, Azure, and Git.
- AI4science
- Generative Models
- Natural Language Processing
- Computational Modelling
- Advanced Spectroscopy
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PhD in Chemistry, 2023-08 (expected)
Carnegie Mellon University
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M.S in Machine Learning, 2022-12
Carnegie Mellon University
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B.S. in Chemistry, 2018-06
Nanjing University
Experience
Responsibilities include:
- Discovered new enzyme-substrate complex structures of metalloenzymes with Alphafold2, AutoDock, and investigated substrate binding modes with molecular dynamics (MD) simulation.
- Collaborated with several synthetic groups to elucidate magnetic and electronic structures of model complexes by utilizing Density Functional Theory(DFT) calculations and advanced spectroscopic methods(Mössbauer, EPR, NRVS).
Responsibilities include:
- Constructed multifunctional oriented nanoprobes with good stability and excellent optical properties by combining noble metal nanoparticles with asymmetric modification, and applied them to dark field imaging technology.
- Designed a nano-sized biosensor for single-cell electrochemical analyses and investigated giant magnetic field effects on the electrochemiluminescence(ECL) of bipolar electrodes.
Responsibilities include:
- Developed collagen phantoms with structural properties in different regions as a representation of tissue and studied functional imaging using fiber-optic needle probes on Raman spectroscopy.
- Designed a new protein storage container for collecting Raman signals with a 3D printer and Solidworks software.