Past Meetup

March Meetup

This Meetup is past

89 people went



• Lightning Talks (40 min)

• Discussions and Networking over Tea sponsored by Clay Labs ( (30 min)

• Lightning Talks (40 min)

• Discussions (20 min)

Lightning Talks

Python-powered brain simulations

Speaker: Ashutosh Mohan (

This talk will have two parts. 1. The specific: how I used python for my doctoral research on the dynamics of neurons, their connections, and neuronal networks. 2. the general: how python, with it's simplicity, power, and openness is becoming the de facto language for computational neuroscience research.

Bitten By Python

Speaker: Vijay Kumar (

Python puts lots of power in the hands of the developer. It takes lot of discipline to wield it, without hurting oneself. Through this talk I would like to convey my experiences, the techniques I have learnt and hope to inspire others to adopt them.


My Python-BCI journey.

Speaker: Kannan (

If GUI is WYSIWYG (what you see is what you get) then BCI could become WYTIWYG (what you think is what you get). This talk would be an intro on BCI, use of python in creating brain controlled apps and live demos.

Python scripting in Android using SL4A

Speaker: Sivasubramanyam (

SL4A allows to run scripts in android using most APIs that are available to native apps except that the process is simpler. It can be used to build anything from home automation systems to high altitude ballooning projects.


How Emacs + org-mode replaced a few apps in my workflow

Speaker: Kiran Gangadharan (

In this talk, I'll present a short demo about using org-mode in Emacs and how it has replaced the functionality of a few apps in my workflow.

Up and running with PySpark

Speaker: Krishna Sangeeth (

Pyspark is the python binding available for Apache spark. Spark is now a really popular project under ASF and through the talk we can look at some basic spark concepts and using PySpark.


Sentiment Analysis in Simple Steps

Speaker: Sharmila Gopirajan (

This talk is an exploration of the natural language processing capabilities of python through a simple implementation of Sentiment Analysis using a naive bayes approach.