A lineup of our speakers

Keynote Speakers

Alexander Hultner

Alexander Hultnér

Anand Pillai

Anand Pillai

Prof. Sudarshan Iyengar

Prof. Sudarshan Iyengar

Dr. T. Venkatesh

Dr. T. Venkatesh

Invited Speakers

Dr. Shrinivas Mangalwede

Dr. Shrinivas Mangalwede

Dr. Shrirang Kulkarni

Dr. Shrirang Kulkarni

Talks

Renaldi Gondosubroto Renaldi Gondosubroto
The Many Faces of Mutability
Despite mutability being a common concept in the Python community, many tend to see it as something to avoid more often than building alongside it. This talk will discuss the don’ts about mutability before then discussing how we can use it for positive contribution based on best practices.
Aman Sharma Aman Sharma
Data data everywhere, No time to think
A talk to break common misconception with data science projects. Understand data projects with much simpler approach and gain huge gains from it.
Bhanu Kamapantula Jaidev Deshpande Bhanu Kamapantula & Jaidev Deshpande
Building a charts classifier with deep learning
With Design2Code, one can transform screenshots to JavaScript or Python charts using AI. You can seamlessly convert designs to charts, convert charts from any applications (Tableau, PowerPoint etc.) to web-based charts.
Ankit Mahato Ankit Mahato
Ready, Set, Go with Numba
Ease of learning, usability & a vast package ecosystem are some reasons for the wide adoption of Python. But, often we encounter scenarios leaving us scratching our heads in search of ways to speed up our Python code. In this talk we will explore how Numba can speed-up our Python code.
Gagandeep Reehal Gagandeep Reehal
Building Self Driving Cars in India
Understand the technology behind Self-Driving Cars in simplest way possible, balanced between concepts and mathematical techniques, along with approaches behind the work being done in Tesla, Minus Zero, etc. outlining the major challenges faced and how they are being tackled to build it in India.
Sohan Maheshwar Sohan Maheshwar
Build Cloud Infrastructure using Python
Infrastructure as code (IaC) is the process of managing and provisioning computer data centers through machine-readable definition files, rather than physical hardware configuration or interactive configuration tools. This talk will show how you can build cloud infrastructure using Python
Kalyan Prasad Kalyan Prasad
Resampling Time Series With Python & Pandas
You may have observations at the wrong frequency. Maybe they are too granular or not granular enough. The Pandas library in Python provides the capability to change the frequency of your time series data.
Mariano Mugana Mariano Mugana
Accessibility matters on Django Projects
Web is essential many parts of our lives. Making accessible sites and apps provides a good experience for all users, the talk aims to bring a understanding of what web accessibility is all about. How can we implements Accessibility on our Django Projects? How and why do we have to guarantee that all users can use our site with ease?
Eyal Trabelsi Eyal Trabelsi
Practical Optimisation for Pandas
Writing performant pandas code is not an easy task, in this talk, I will explain how to find the bottlenecks and how to write proper code with computational efficiency, and memory optimization in mind.
Reuven Lerner Reuven Lerner
How to sort anything
Python’s builtin “sorted” function seems quite simple. But it turns out that it hides an amazing amount of sophisticated complexity. In this talk, I’ll show you how you can take advantage of this complexity to sort many different types of data structures.
Rob Richardson Rob Richardson
Level-up Your DevOps with GitHub Actions and Kubernetes
Are you looking to rapidly deploy your content? Are Docker containers in your future? Come for this demo-only presentation where we start from scratch, build up a DevOps pipeline with GitHub Actions, and deploy to Kubernetes. Once setup, commit, and watch the magic flow into place.
Jaimin Khanderia Jaimin Khanderia
Streamlit - Build interactive data dashboards quickly
Streamlit is a framework that allows you to build interactive analytical apps very rapidly. It is gaining popularity among data scientists and analysts around the world as it helps them make a data dashboard in Python (you heard it right, no JS required :D) that is elegant and aesthetically pleasing.
Jatin Gupta Jatin Gupta
Ethical Considerations in AI and Cloud Computing Systems
With the growing amount of data being produced and reproduced every second in AI applications and Cloud platforms such as AWS, Azure, etc. talking about the importance and implementation of ethics is essential - privacy and security, compliance, transparency, explainability, accountability, etc.
Ayon Roy Ayon Roy
PySpark : Combining Machine Learning & Big Data
How about using the potential of big data libraries with support in Python to deal with this huge amount of data for deriving business insights using ML techniques?
Let’s use the power of pyspark ( Spark’s ML library ) for combining Big Data technologies to supplement ML.
Harsh Bardhan Mishra Harsh Bardhan Mishra
Build Serverless Python Applications using AWS Chalice
AWS Chalice is a Micro-Web Framework that allows for the development of Serverless Python Applications. With a unified CLI, it allows us to develop and deploy Python applications almost instantaneously. The talk will introduce you to Chalice and how to develop RESTful APIs and deploy them flexibly.
Arnab Biswas Arnab Biswas
Automatic Feature Engineering on Large Scale Time Series Data using tsfresh & Dask
  1. Why feature engineering is super important for Analytics using Time Series data?
  2. Challenges of feature engineering with Time Series Data.
  3. How to automatically engineer & select features using tsfresh?
  4. How to use local and distributed computing if the size of the data is large?
Ashita Prasad Ashita Prasad
Applied Machine Learning in Python using scikit-learn, mlxtend and pandas
We will take a deep dive into some of the most widely used traditional machine learning algorithms and apply them on real life use cases. This will benefit students and professionals who aspire to pursue data science as a career, but are still wondering how they can start their journey.
Deepak Kulkarni Deepak Kulkarni
Application of Machine learning algorithms for "House rent prediction"
House rent in any area depends upon number of factors to name few are city, area, number of rooms etc. The objective of the proposed work is to predict the house rent by applying different machine learning algorithms.
Anustup Mukherjee Anustup Mukherjee
Lifting Up: Deep Learning for implementing anti-hunger and anti-poverty programs- Keras Python Library
Wondered how to look into satellite imagery and Understand the land properly with out much efforts? Or what about a social reformer or NGO looking to plot the economic or poverty status of countries and help them to get proper findings? Then this deep learning approach in AI for well being is what you want.
Rishab Kattimani Rishab Kattimani
Flask : Hands on introduction to Python based micro web framework
Introduction to Flask, a Python based micro web framework to create dynamic web pages. In this video we will review its comparison to other webservers, usecases, along with a hands on demo.
Shvay Lamba Shivay Lamba
Tensorflow For the Web : Converting Python Machine Learning Models to Javascript using TFJS Converter
Introducing machine learning models for web developers specializing in JavaScript can be a daunting task with scripts to be written in Python. Tensorflow.js enables the models to be deployed in Javascript and using Tensorflow.js converter complex python models can be converted to Javascript models.
Ella Sheer Ella Sheer
The Art of Debugging
We don’t talk enough about debugging. We should. Also, we should learn practices and get better at it. How did I learn? From my work on real-time multimedia systems, where I met some crazy, super frustrating, how-the-hell-did-you-solved-this bugs.
Anmol Jindal Anmol Jindal
Concurrency Patterns in Python
Concurrency in programming is when two or more events or computations are occurring or being executing at the same time. Understanding how to use concurrency can be a great tool for anyone looking to speed up their executions or a way to make their code execution non-sequential.
Yiwen Li Yiwen Li
How to perform data science analysis using Elyra?
Have you ever wanted to run multiple data science notebooks in sequential and parallel order with one click? If so, come join our Intro to Elyra - an AI centric extension for JupyterLab session to learn how you can get set up and running with Elyra!
Cheuk Ting Ho Cheuk Ting Ho
Why AI Industry needs Revision Control Graph Database
Through use cases, Cheuk will show you how a graph database with revision control would revolutionize the AI industry by providing a logical way of storing data and providing data ops - branch, merge and rollback.
Laisha Wadhwa Laisha Wadhwa
Towards a more transparent AI - Decrypting ML models using LIME
With wide number of libraries available for building ML models it has become a blackbox these days. Model explainability is vital. But it is hard to define a model’s decision boundary in simple terms. With LIME it is easy to produce faithful explanations & decrypt any ML model. Learn how LIME & SHAP does it.
Noah Alorwu Noah Alorwu
Contributing to the Tech Ecosystem as a Newbie
Many newbies (novice) including experts don’t know that they can contribute to the tech ecosystem in big ways. I said big because it’s really worth contributing to the tech field regardless of your skill level. In the tech field, there’s no such thing as too small when it comes to contribution.
Rohit Sanjay Rohit Sanjay
Unit Testing in Python using pytest
This workshop will introduce you to unit testing in Python using the pytest library. By the end of the workshop, you will be able to confidently look at any (well written) code and be able to confidently write unit tests for it.
Manoj Gudi Manoj Gudi
Yet Another Language Using Python
There are a lot of programming languages in the world already, however we wish sometimes a programming language could understand some of our own terminology, and was simple enough for us to express clearly. So why not create our own small language for our own application (aka Domain Specific Language)? In this talk, I will introduce briefly what DSLs (domain specific languages) are and why they are useful. I will also run through how to implement one using Python, and hopefully show how we use it in our field.
Reuven Lerner Reuven Lerner
Working with dates and times in Pandas
Pandas is popular and powerful, allowing us to import, analyze, and export data in all sorts of ways. But Pandas offers particularly powerful functionality for working with dates and times. In this talk, I’ll introduce these features, and show how they allow new types of analysis.
Gaurav Bhasme Gaurav Bhasme
Detection of Predatory Journal Using Machine Learning and Django
Recently there are lots of piracy events happening in various areas. Predatory Journals take advantage of authors by asking them to publish for a fee without providing peer-review or editing services. To tackle this we have created a solution using the concept of machine learning and Django.
Rob Richardson Rob Richardson
Containerized Deployments for your SPA and API
You’ve built a SPA and API backend, now how to deploy? Docker is the natural fit, but where do we begin? We’ll use the Vue and dotnet CLIs to start, then craft Dockerfiles to deploy these in various configurations: together or scale different pieces separately. Docker can empower you at cloud scale.