Oliver (Yifeng) Tang
Graduate Student
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About Oliver
Born and raised in Nanjing, a beautiful city in southeast China, Oliver obtained a BS degree in Chemistry at Shanghai Tech University, where he graduated with a President’s Award. He conducted his undergrad research in the organic synthesis of chiral fatty acids, advised by Dr. Zhi Li, during which he gradually discovered his strong passion for combining chemistry and programming.
After an attempted dive into computational chemistry, including quantum methods and molecular simulations, he found machine learning to be a significantly powerful tool in making effective and efficient breakthroughs in chemistry as well as chemical biology.
Officially starting research in 2020 and being co-advised by Prof. Esser-Kahn and Prof. Andrew Ferguson, he is determined to apply deep learning and active learning techniques to drug discovery, making it easier to find promising drug precursors.
Outside the lab, Oliver spends time in the gym lifting weights; he is also interested in music, specifically pop and EDMs, loving to record and arrange tracks in his off time, and he managed to publish original songs on mainstream music platforms such as Spotify and Apple Music. You can hear his music by clicking his social links above!
Education
Bachelor of Science, Chemistry, ShanghaiTech University, 2015-2019
Title of thesis: Organic Synthesis of Chiral Artificial Fatty Acids
Advisor: Dr. Zhi Li
Favorite Quote
“The future depends on some graduate student who is deeply suspicious of everything I have said.”
— Geoffrey Hinton
Scientific Hero
Alan Turing
Favorite Paper
WATSON, J., CRICK, F. Molecular Structure of Nucleic Acids: A Structure for Deoxyribose Nucleic Acid. Nature 171, 737–738 (1953).
If you could be a piece of lab equipment, what would you be?
"A top-notch workstation that can do high-level and efficient data processing and analysis."
Oliver's Research
With the development of high-throughput experimentation techniques, a large amount of data can be readily produced, but the key is to use the right computing resources and algorithm tools to extract intelligent insights from the data. Oliver works on integrating machine learning, an array of emerging algorithms specialized in handling big data, into immunological research, to design better immunomodulators, that can help mediate the adverse side-effects of vaccination or boost the efficacy of cancer immunotherapy. With the help of machine learning, he seeks to understand the embedded pattern in the experimental data and guide the direction of the next-step experimental design. By doing many of the experiments on his own, he strives to leverage the experience in the wet lab to inform the design of machine learning models, and to combine computation and experimentation in an inseparable way.