Research Interests

Here is the vision of my lab, which straddles digital agriculture and computational biology using applied data analytics:

ICAN

Innovatory for Cells and Neural Machines

Center affiliations: CRISP and OATS

Cells and Machines Innovatory logo

My areas of interest and expertise

The primary focus of my research is applied data analytics and data engineering directed at the domains of digital agriculture and precision health.

On the digital agriculture front, our focus is on the data engineering side especially designing algorithms for lightweight in-sensor analytics, partitioning algorithms across different platforms of computation (sensor → edge → cloud), and designing robust backend databases for high-performance data lakes for IoT in digital agriculture.

On the precision health front, our focus is on the computational genomics and synthetic biology sub-domains.

Research Updates

Here I will provide new dimensions and storylines from the work that we do at ICAN. In general, I will write this so that it is accessible to a general technical audience.

Summer 2020: Work inspired by funding from the Lilly Endowment and Army Research Laboratories [ARL].

My lab [https://schaterji.io] called Innovatory for Cells and Machines, abbreviated ICAN, got started in 2018 and since then we have been branching out in two distinct, albeit inter-related technical areas, first: Internet-of-Things (IoT) and edge computing for digital agriculture and other related IoT application domains, and second: genome engineering. They are related in the sense that my lab develops machine learning-based algorithms for efficient data analytics on the IoT side and for decoding the genome on the genome engineering side. Let me describe our novel approach in these two areas.

IoT and Edge Computing for Digital Agriculture: We are developing algorithms for handling large volumes of IoT data and to derive actionable insights from them. This is relevant in digital agriculture with data being generated by ground sensors and aerial drones. Our novel approach allows the data to be stored and processed close to the source of the data rather than being ferried all the way to a data center. This is important to ensure the privacy of the IoT data (e.g., it can be made to stay completely within the premises) and to ensure that the scarce wireless bandwidth and constrained energy resources are not spent carrying raw and irrelevant data to the backend. As part of our solution approach, we have developed:

These three thrusts have come together to create an integrated system that we have applied to WHIN data. The software is functional, robust to various failure modes and physical environment variations, and is providing actionable insights from the deployed sensors.

Genome engineering: Genome reads are still rather error prone, especially as we look toward newer technologies, like nanopore sequencing. ICAN considers these reads as words of a language and just like Natural Language Processing (NLP) can correct errors in language, we are optimizing technologies from there to correct genomic reads. Our approach enables the huge flourishing of the work in deep neural networks (DNNs) to be applied to understanding the “language” of the genome, so that we can derive actionable insights from them, such as, developing precise genome editing technologies.


Thank you to our sponsors:

National Science
Foundation

National Institutes
of Health

Army Research
Laboratory

Purdue ABE

Purdue Ag

Purdue Engineering

American Heart Association