ABE 591: Machine Learning & High-Performance Computing for Digital Ag & Biological Engineering; Part 1: Algorithms, resilient data lakes, & analytics at the edge

Dr. Somali Chaterji (Assistant Professor, Department of Ag and Biological Engineering, Purdue University) will be offering a one-credit data science & data engineering course for non-computer science majors titled: ABE 591: “Machine Learning & High-Performance Computing for Digital Ag & Biological Engineering; Part 1: Algorithms, resilient data lakes, & analytics at the edge”.

This course is about data stories, data lakes, and algorithms that will provide the conceptual and foundational bases for machine learning (ML) applied to genomics, digital agriculture, and IoT, to name a few domains. These are some of the domains that are generating terabytes of data, both diverse and available in large volumes. These data sets can be used for “deciphering the rules of life” or to extract actionable information from the ubiquitous IoT sensors. Overall, this course is meant for both advanced undergraduate or graduate students and does not assume any prior knowledge of ML algorithms. This course is part of a Purdue initiative that aims to deliver stackable one-credit courses to create a custom data-science curriculum.

Course webpage:;

Offering: Spring 2020 [starting week of Mar 9th, 2020]; 1 lecture/week for 2 hours (Wed evenings). Prerequisite: STAT 30100 OR ABE 20500 OR CHE 32000 OR Graduate Standing; a more advanced statistics course is also acceptable as long as the student has taken the course for credit.

Keywords: High-performance computing (HPC), resilient data lakes, noSQL databases, edge computing, deep learning, reward learning, distributed computing, supercomputers, graphics processing units (GPU).


Undergraduate Research Assistant Positions at Bumsoo Han’s Research Group

We are looking for motivated students who are interested in research in biomechanical engineering and biomaterials. The groups research aims to develop new biomaterials and tissue models using microfluidics and 3D printing technologies. These models are used to quantitatively measure cellular processes, discover new drugs, and test drug formulations for effective delivery.

For Spring 2020, we are looking for students for following projects:

  1. Development of new hybrid hydrogel-elastomer materials
  2. 3D printed tumor-on-chip microfluidic platforms for high throughput drug screening

Strong background in mechanical engineering disciplines are desired. These include thermal and materials sciences.  Knowledge on biology and medical sciences are preferred but not required. Most importantly, willingness to learn new disciplines is essential. Interested individuals can email their interests to Professor Han at