gowriaddepalli Goto Github PK

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Name: Gowri Addepalli

Type: User

Company: New York University, Courant Institute of Mathematical Sciences.

Bio: Me(Today) += 0.01Me(Yesterday)

Location: New York

Blog: https://www.linkedin.com/in/sgaddep/

Hi there 👋 This is Sree Gowri Addepalli.

📫 I Graduated from MS in CS with Focus in ML from New York University's Courant Institute of Mathematical Sciences.
🤔 My current focus is Computer Vision, Natural Language Processing and Machine Learning Systems.
👯 I am a community builder and a program manager at Indian Women in Computing, a chapter associated with AnitaB.org. Focussed on building a stronger community for Women in AI.
💬 You can connect with me on Linkedin and Medium.
🌱 "I believe as the universe is infinite, their is space for everyone who are trying to carve their own niche".
⚡ Fun fact: "I have a twin sister LakshmiAdepalli and we spent whole of our lives (28 years) together attending all schools/workplaces straight from birth till masters, traversing states, countries and continents and a pandemic. She is my complementary in all aspects of life.
Languages and Frameworks:

Java, Python, C++ (basic), Scala (basic), Pytorch, Spark (basic), bash.
Interests: Computer Vision, NLP and Search/Recommendations, Engineering Impact driven AI/ML products.
Some of the problem statements that i worked in:
1. Self-supervision (Contrastive learning)
2. Weak-supervision
3. Object Detection and Segmentation (2D Computer Vision)
4. Neural Radiance Field (3D Computer Vision)
5. Vision Transformers

I'm best reached via email([email protected]). I'm always open to interesting conversations and collaboration.

Gowri Addepalli's Projects

3d-machine-learning

A resource repository for 3D machine learning

3dv_tutorial

An Invitation to 3D Vision: A Tutorial for Everyone

a-pytorch-tutorial-to-image-captioning

Show, Attend, and Tell | a PyTorch Tutorial to Image Captioning

ai-expert-roadmap

Roadmap to becoming an Artificial Intelligence Expert in 2021

aif360

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

This is a source of annotated papers from my end each week focussed on the area of computer vision, deep learning, Machine Learning Systems, Search/Ranking & Information Retrieval Systems, Recommendations