Position: Machine Learning Engineer, Data Scientist
Category: Data Science
English level: fluent
Years of experience: 3


Research Fellow, Boston University
• Applied Machine Learning techniques towards intricate problems in biophysics and complex systems
• Theoretically and computationally investigated Phase Transitions in Complex Systems, studied Liquid-liquid phase
transitions, as well as out of out-of-equilibrium dynamics with and without external source
• Performed multiple parallel simulations in C on the scientific cluster using Bash, analyzing vast amount of data via Python
and applying numerous statistical techniques to verify the  theory of the models.

Deep Learning Specialization by Andrew Ng, DeepLearning.Ai:
Neural Networks and Deep Learning; Improving Deep Neural Networks: Hyperparameter, Tuning, Regularization and Optimization; Structuring Machine: Learning Projects; Convolutional Neural Networks; Sequence Models.

MAIN PROJECTS
• Identification of Phase transitions of Ising Model
- Classified phases of 2D Ising Model via Logistic regression, Random forests, NN in TensorFlow
- Classified phases of 2D Ising Model via CNN using PyTorch and TensorFlow
- Learned effective low-dimensional representations of equilibrium samples via Variational Autoencoders with Keras
- Employed Generative Models (Generative Boltzmann Machine) to learn a variational approximation to the probability distribution used to draw thermal spin configurations in the 2D Ising model
• Prediction of Stock Market Prices of S&P 500 with machine learning
- Collected data from WRDS including monthly fundamental and return data since 1980-1-31 to 2017-12-29 for 17324 companies using SAS code and cleaned the data using Python
- Predicted monthly returns using machine learning techniques, such as LASSO, Random Forests and k-nearest neighbors
- Fine-tuned the models to increase the accuracy of the prediction models to maximize the returns of chosen portfolios