Burak Himmetoglu

Burak Himmetoglu

Computational Physicist, Data Scientist, HPC Specialist, and Research Associate at UC, Santa Barbara

Bio: As an aspiring data scientist, I analyze large amounts of data, search for patterns, and solve interesting problems spanning a wide range of areas. I also work on applications of machine learning methods to predict electronic properties of molecules for discovering new compounds computationally. Currently, I am a staff member at UCSB as a computational physicist and High Performance Computing (HPC) specialist. I support to faculty and researchers in obtaining high performance computing resources, provide computational research consultation and manage supercomoputing allocations of UCSB. I help researchers port their codes, usually from Python, R and Matlab to C/C++ for high performance computing environments, I am enthusiastic about applying my skills to solve difficult business problems and develop new products using data science methods.

An example of web scraping with R: Online Food Blogs

An example of web scraping with R: Online Food Blogs

In this blog post I will discuss web scraping using R. As an example, I will consider scraping data from online food blogs to construct a data set of recipes. This data set contains ingredients, a short description, nutritional information and user ratings. Then, I will provide a simple exploratory analysis which provides some interesting […]

Yet another introduction to Neural Networks

Yet another introduction to Neural Networks

In this notebook, I will explain how to implement a neural network from scratch and use the version of MNIST dataset that is provided within Scikit-Learn for testing. I will specificallty illustrate the use of Python classes to define layers in the network as objects. Each layer object has forward and backward propagation methods which […]

Time series classification with Tensorflow

Time series classification with Tensorflow

Time-series data arise in many fields including finance, signal processing, speech recognition and medicine. A standard approach to time-series problems usually requires manual engineering of features which can then be fed into a machine learning algorithm. Engineering of features generally requires some  domain knowledge of the discipline where the data has originated from. For example, […]

Machine Learning Meets Quantum Mechanics

Machine Learning Meets Quantum Mechanics

Recently, I have published an article on Journal of Chemical Physics, entitled Tree based machine learning framework for predicting ground state energies of molecules (link to article and preprint). The article discusses in detail, the application of machine learning algorithms to predict ground state energies of molecules. Current standard of computationally efficient electronic structure simulations is unarguably based on Density Functional […]

Feature Engineering with Tidyverse

Feature Engineering with Tidyverse

In this blog post, I will discuss feature engineering using the Tidyverse collection of libraries. Feature engineering is crucial for a variety of reasons, and it requires some care to produce any useful outcome. In this post, I will consider a dataset that contains description of crimes in San Francisco between years 2003-2015. The data can be […]

Stacking models for improved predictions

Stacking models for improved predictions

If you have ever competed in a Kaggle competition, you are probably familiar with the use of combining different predictive models for improved accuracy which will creep your score up in the leader board. While it is widely used, there are only a few resources that I am aware of where a clear description is available […]

Deciphering the Neural Language Model

Deciphering the Neural Language Model

Recently, I have been working on the Neural Networks for Machine Learning course offered by Coursera and taught by Geoffrey Hinton. Overall, it is a nice course and provides an introduction to some of the modern topics in deep learning. However, there are instances where the student has to do lots of extra work in order […]