Meghana Bhange
Researcher in Algorithmic Collective Action | Machine Learning Engineer
Hi! I’m Meghana Bhange — a researcher and engineer working at the intersection of machine learning, privacy, and social impact.
My current research investigates Algorithmic Collective Action as a lens for designing socio-technical systems that empower communities — not just individuals — to resist harm and reclaim agency.
I pursue this direction as a PhD researcher under the co-supervision of Prof. Ulrich Aïvodji and Prof. Elliot Creager. I am currently affiliated with Trustworthy Information Systems Lab (TISL) and Mila – Quebec AI Institute as a Student Researcher.This work builds on ideas from Algorithmic Collective Action (Hardt et al. 2023), Protective Optimization Technologies (Kulynych et al. 2018), and Data Leverage (Vincent et al. 2020).
You can follow updates about the project at aca.meghanabhange.com.
I completed my Master’s in Information Technology Engineering at ÉTS Montréal. My thesis focused on building privacy-preserving infrastructure for collective complaint systems, co-supervised by Prof. Ulrich Aïvodji and Prof. Jean-Marc Robert.
Previously, I have worked as a Machine Learning Engineer and also currently do freelance consultancy at Toptal in my free time, where I’ve contributed to AI and NLP-driven projects like building inclusive hiring pipelines (using SpaCy, GPT-3, FastAPI) and an automated trading platform integrating technical indicator based strategies that can trade live with platforms such as Interactive Brokers. I enjoy collaborating with clients from different domains as it helps me sharpen my practical problem-solving.
✨ Quick Overview ✨
- Algorithmic Collective Action
- Privacy-Preserving Systems
- Complaint Resolution Systems
- AI Ethics & Governance
- Natural Language Processing
Journal Publications
- Mbiazi, D., Bhange, M., Babaei, M., Sheth, I., Kenfack, P., & Kahou, S. E. (2025). Survey on AI Ethics: A Socio‐Technical Perspective. Computational Intelligence, 41(6), e70149.
Workshop
- Bhange, M., & Kasliwal, N. (2020). HinglishNLP at SemEval-2020 Task 9: Fine-tuned Language Models for Hinglish Sentiment Detection. In A. Herbelot, X. Zhu, A. Palmer, N. Schneider, J. May, & E. Shutova (Eds.), Proceedings of the Fourteenth Workshop on Semantic Evaluation (pp. 934–939). International Committee for Computational Linguistics.
- Solanki, R., Bhange, M., Aïvodji, U., & Creager, E. (2025). Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacy. In Algorithmic Collective Action Workshop (ACA@NeurIPS 2025). Accepted (Poster).
- Libon, L., Bhange, M., Solanki, R., Creager, E., & Aïvodji, U. (2025). Conscious Data Contribution via Community-Driven Chain-of-Thought Distillation. In Algorithmic Collective Action Workshop (ACA@NeurIPS 2025). Accepted (Oral).
- Top Essay – Kaggle AI Report 2023, AI Ethics Landscape
- Best All-Female Team – UN PET Lab Hackathon 2022
- ÉTS Scholarship, 2025 – $1,000 award recognizing academic performance (Bourses, Apr 9 2025)
- 2nd Place – *Toptal Hackathon: AI-assisted Cards Against
AI for Working with the machines: Ways to Combat and Resist Algorithmic Harms with Collective Action
IVADO Digital Futures 2025
Oct 22, 2025 | Montréal, QCActions collectives algorithmiques : vers une gouvernance participative de l’IA
92e Congrès de l’Acfas 2025 | Colloque 428 Nouvelles perspectives sur les stratégies individuelles et collectives de résistance et de résilience mises en place par des groupes minorisés
May 8, 2025 | Montréal, QCExtracting names from multi-lingual conversation
PyData Bangalore Meetup #2
July 13, 2019 | Bangalore, India
Django #13458 – Improved error messages in formsetsfor ticket #32042.
Wagtail Release 2.11 – Contributed to document validation and StreamFieldcustomization.
FashionMNIST – Added one of 3-layer CNN benchmark to the official leaderboard.