Kedar Dabhadkar
I am a process engineer and data scientist with over six years of experience in statistical data analysis, engineering, and machine learning. I’m a Microsoft-certified data scientist and a Databricks-certified data engineer. I work in the Equipment Intelligence group at Lam Research, independently research LLM-based applications and machine learning for environmental monitoring.
I built and maintain Fast Dash, an open-source Python library that transforms Python functions into interactive web applications.
Sessions
Work not shown is work lost. Many excellent scientists and engineers are not always adept at showcasing their work. This results in many interesting scientific ideas that have never been seen in the light of day.
However, using today's tools, one no longer has to leave the Python ecosystem to create classy, complete prototypes using modern data visualization and web development tools. With over five years of experience building and presenting data solutions at huge science companies, we show it doesn't have to be challenging. We give a walkthrough of the primary web application frameworks and showcase Fast Dash, an open-source Python library we built to solve specific prototyping needs.
This tutorial is meant for all data professionals who find value in quickly turning their science code into web applications. Participants will learn about the leading frameworks, their strengths and limitations, and a framework for picking the best one for a given task. We will go through some day-to-day applications and hands-on tutorials in the final section.
Algal blooms threaten human health and aquatic ecosystems, making monitoring essential. While Chlorophyll a (Chl-a) effectively indicates algal presence, laboratory analysis is complex. This study utilizes satellite imagery as an alternative, addressing previous research limitations caused by scarce lab data. By combining the extensive Water Quality Portal dataset with Landsat satellite imagery, these models estimate Chl-a levels in New York's inland waters. Training with eight years of data demonstrated a strong correlation between satellite-derived and actual measurements (MAPE: 0.96%; RMSE: 3.2 μg/L), enabling improved spatial and temporal monitoring capabilities.