Python · C++ · C · Mathematica · Matlab · SQL
Framework & Packages
Pytorch · Tensorflow · PyData ecosystem (NumPy, SciPy, scikit-learn, pandas, matplotlib, etc.)
Docker · slurm · Linux · git · HTML
regression & classification · Bayesian statistics · neural network
I am finishing my PhD in physics at Duke, expected to graduate in December 2022. I am also pursuing a concurrent computer science master degree. I am actively looking for an internship in summer 2022 and a full-time job afterwards. My current research focuses on applying Bayesian techniques and machine learning tools in nuclear physics.
As a theoretical nuclear physicist, I work as a big data researcher. We build computational models to simulate high energy particle collisions. By running those physics models millions of times, a huge amount of data are generated using high-throughput computation. We then apply statistical tools to analyze the generated data. You can find the details about we applying Bayesian analysis to quantify model parameters in Projects and Publications.
Now I am trying to make a transition from academia to industry. I performed several projects about recommendation system and image recognition supervised by Computer Science professors with details in Projects. I have also taken classes about numerical analysis, statistics, algorithms, machine learning and deep learning. I have been programming for 15 years. I use python and C++ everyday. Now I am learning SQL. Through the solid academic training as a phD student, I am good at summarizing my research outcomes as nice presentations and well-written papers. With the experience of presenting my research in various international conferences, I am skilled at communicating to different audience. I am always looking forward to an opportunity to apply what I have learned about big data and machine learning into the industry.