PyData Global 2022

Lightning Talks
12-03, 12:00–13:30 (UTC), Talk Track II

Lightning Talks are short 5-10 minute sessions presented by community members on a variety of interesting topics.

Order of Presentations
1. Utilizing Word Embeddings and Gradient Boosting to identify, analyze, prevent and predict machine errors in the Computer Aided Manufacturing Industry by Aadit Kapoor
2. Think Inside the Box(es): Excel-Hosted Dashboards With Python Graphics by Ted Conway
3. Introducing Tilburg Science Hub: An Open Source Platform for Computational Social Science by Shrabastee Banerjee, Roshini Sudhaharan
4. Tour of UnionML: An Open Source framework for Building Machine Learning Microservices by Shivay Lamba
5. Robyn: An asynchronous Python web framework with a Rust runtime by Shivay Lamba
6. Bessel's Correction: Effects of (n-1) as the denominator in Standard deviation by SARADINDU SENGUPTA
7. Python for the Unsolvable: Machine Learning Applications in Chaos Theory by Srivatsa Kundurthy
8. Dask Powering lower end PC by Kefentse Mothusi
9. How to format strings for logging in Python by Lutz Ostkamp
10. Use pandas in tidy style by Srikanth

Prior Knowledge Expected

No previous knowledge expected

Shivay Lamba is a software developer specializing in DevOps, Machine Learning and Full Stack Development.

He is an Open Source Enthusiast and has been part of various programs like Google Code In and Google Summer of Code as a Mentor and has also been a MLH Fellow. He has also interned at organizations like EY, Genpact.
He is actively involved in community work as well. He is a TensorflowJS SIG member, Mentor in OpenMined and CNCF Service Mesh Community, SODA Foundation and has given talks at various conferences like Github Satellite, Voice Global, Fossasia Tech Summit, TensorflowJS Show & Tell.

Welcome! I am Srikanth Komala Sheshachala

    Interpretable machine learning
    Causal inference
    Setting up, running and infering from AB tests and multi-armed bandits
    Optimization (Operations Research)
    Graph theoretic approaches to problems in data science
    Spatio-temporal analysis
    Multivariate statistics

Day Job: Staff Data Scientist at Walmart Gobal Tech, India.

Data enthusiast with a touch of development

I'm an Assistant Professor of Marketing at the Tilburg School of Economics and Management. I am broadly interested in online marketplaces and e-commerce. Particularly, I aim to look at how consumers make use of various cues in an e-commerce setting, and how these cues might have an impact on decision making. Examples include user-generated content such as reviews/ratings, non-focal prices advertised by a platform on their product page, and recommender systems. The primary methodologies I use are causal inference, experiments/quasi experiments and applied machine learning. In a separate stream of projects, I am also interested in applications of digitization for equity and development.

I received my PhD in Marketing from Boston University, where I was also a Rafik Hariri Graduate Fellow. Prior to that, I did my B.Sc (Calcutta University) and M.Sc (Warwick University, as a Commonwealth scholar) in Economics.

Research Master student in Marketing at Tilburg University | RA at Tilburg Science Hub

Ted Conway is currently a Data Analyst working with Big Data in the financial sector. Ted studied Computer Science at the University of Illinois (UIUC, BS LAS) and DePaul University (MS CS).

Data Engineer at Flixbus

I am an ML Engineer at Nunam where I build learning systems to keep li-ion batteries in EVs safe, sustainable and performant.

This speaker also appears in:

Srivatsa Kundurthy is a student based in the Greater New York City Area. As a Python practitioner, his projects include Open Source Intelligence tools for extracting public data and Python notebooks for explaining and simulating chaotic dynamical systems. His work in machine learning includes studying computer vision applications and researching neural networks for predicting states of chaotic dynamical systems. Additionally, he is working with the LAION Research Group and has co-authored LAION-5B, the world's largest open-source image-text dataset and the source dataset for Stable Diffusion. Apart from Machine Learning Research, Srivatsa is greatly interested in technology policy and community-related issues, particularly those extending to the accessibility of programming education. On the side, Srivatsa enjoys science communication and stargazing.

I am Aadit Kapoor. I love to think of new innovative ideas and most importantly try hard to make them a reality. In addition to that, I am extremely curious.
I am interested in solving technically difficult, real world AI problems. I enjoy solving applied A.I problems that are value-driven and can fundamentally change the way humans live. Additionaly, I love reading and analyzing businesses and companies.
My research areas of interests include: Artificial Intelligence , Machine Learning (A.I/M.L in Healthcare), Data Science, Biomedical Informatics/NLP.
I believe Data Science allows me to express my curiosity in ways I'd never imagine. The coolest thing in Data Science is that I see data not as numbers but as an opportunity (business problem), insights(predictive modelling\stats and data wrangling), and improvement (metrics).