Hi, I'm Ruchira Ray
(she/her)

I am a second-year master's student in the Department of Computer Science at UT Austin advised by Dr. Amir Zamir and Dr. Philipp Krähenbühl where I am working on controlled data generation. I am also a member of the Robot Interactive Intelligence Lab, where I am researching the social effects of household robots as a part of the UT Austin Good Systems.

Previously, as a Samsung PRISM Student Researcher, I have worked on encoder-decoder models for audio anti-spoofing. I have also worked on mathematical equation recognition (as a MITACS Globalink Intern), under the supervision of Dr. Jeffrey Davis, and on autoencoder models for recommender systems, under the supervision of Dr. Bidyut Kumar Patra.

Click here to know more!

Email  /  CV  /  Github  /  LinkedIn  /  Google Scholar  /  Twitter

profile photo
Research

I am broadly interested in machine learning models that facilitate collaboration between humans and machines. I'm interested in multimodal learning for visual perception and robotics. I'm also interested in incorporating them for social good.
I find probabilistic and variational ML models to be very interesting.

Updates
  • June 2023: Won Best Paper Award at ACM FAccT23 for Queer In AI: A Case Study in Community-Led Participatory AI
  • December 2022: Organised Queer in AI Affinity Workshop Neurips 2022.
  • March 2022: Major Project "Cost-efficient Gait Recognition using 3D CNN" selected as one of the top 10 in the Computer Science and Engineering Department.
  • December 2021: Organised Queer in AI Affinity Workshop Neurips 2021.
  • July 2021: Presented our work "Feature Genuinization based Residual Squeeze-and-Excitation for Audio Anti-Spoofing in Sound AI" at 12th ICCCNT, 2021. Presented poster on the same at the "Queer in AI" workshop, ICML'21
  • July 2021: Attended Eastern European Machine Learning Summer School
  • January 2021: Selected to attend Mediterranean Machine Learning (M2L) Summer School and presented our poster DBVAE: Deep Belief Variational Autoencoder for top-N Recommender Systems
  • February 2019: Won a silver medal at SRM University Research Day for my paper presentation on music genre classification
  • June 2018: Won SRMJEE Merit Scholarship from SRM University that waives 50 percent of my tuition
Publications & Posters
Queer In AI: A Case Study in Community-Led Participatory AI
(Video)
Organizers Of Queer in AI (Ruchira Ray)
ACM FAccT, 2023 (Best Paper)

Queer in AI as a case study for community-led participatory design in AI

Feature Genuinization based Residual Squeeze-and-Excitation for Audio Anti-Spoofing in Sound AI
(Poster)
Ruchira Ray, Sanka Karthik, Vinayak Mathur, Prashant Kumar, Maragatham G, Sourabh Tiwari, Rashmi T Shankarappa
ICCCNT, 2021
Queer in AI, ICML, 2021

Squeeze-and-Excitation encoder decoder model for fake/spoofed audio detection

DBVAE: Deep Belief Variational Autoencoder for top-N Recommender Systems
Rabi Shaw, Ruchira Ray, Sharmistha Mandal, Bidyut Kr. Patra,
M2L School, 2021

Deep Belief Network and Variational Autoencoder hybrid architecture for collaborative filtering

Clubs & Societies
Queer in Robotics Organiser, 2023

Foster awareness of LGBTQ+ issues, cultivate a dynamic community of LGBTQ+ researchers and illuminate the remarkable contributions of LGBTQ+ scientists in the field of robotics. Currently organising Welcome Social for IROS’23.

Queer in AI Organiser, 2021

Actively working towards increasing the participation of LGBTQ+ individuals in AI and spreading awareness of diversity in STEM. Currently organising QAI Workshop for Neurips’23.

Chairperson and ML Lead, 2020
Member, 2019

Spearhead a team of 45+ students to organize events, increase outreach and supervise projects. Outreach and number of events tripled in the year 2020-2021.

Next Tech Lab Undergraduate Artificial Intelligence Researcher, 2019

Our lab won the Silver prize for Student-led Innovation award (2018) by QS Reimagine Education and the Wharton School of Business.

Working on multidisciplinary pet-projects, incharge of mentoring associates, conducting interviews for new recruits , managing events etc.


Adapted from Jon Barron