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 at the Trustworthy Information Systems Lab (TISL) and Mila – Quebec AI Institute, under the supervision of Prof. Ulrich Aïvodji.

You can follow my updates about the project at aca.meghanabhange.com.

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).

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

Survey on AI Ethics: A Socio-technical Perspective
An overview of fairness, accountability, and privacy in AI systems.
HinglishNLP: Fine-tuned Language Models for Hinglish Sentiment Detection
Workshop on Semantic Evaluation 2020.

Top Essay – Kaggle AI Report 2023, AI Ethics Landscape
Best All-Female Team – UN PET Lab Hackathon 2022
2nd Place – Toptal Hackathon: AI-assisted Cards Against Humanity
ÉTS Tuition Fee Exemption, 2024

Django #13458 – Improved error messages in formsets for ticket #32042.
Wagtail Release 2.11 – Contributed to document validation and StreamField customization.
FashionMNIST – Added one of 3-layer CNN benchmark to the official leaderboard.

🌐 Connect with Me

References

Hardt, Moritz, Eric Mazumdar, Celestine Mendler-Dunner, and Tijana Zrnic. 2023. “Algorithmic Collective Action in Machine Learning.” In International Conference on Machine Learning. https://api.semanticscholar.org/CorpusID:256662616.

Kulynych, Bogdan, Rebekah Overdorf, Carmela Troncoso, and Seda F. Gürses. 2018. “POTs: Protective Optimization Technologies.” Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. https://api.semanticscholar.org/CorpusID:52135036.

Vincent, Nicholas, Hanlin Li, Nicole Tilly, Stevie Chancellor, and Brent J. Hecht. 2020. “Data Leverage: A Framework for Empowering the Public in Its Relationship with Technology Companies.” Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. https://api.semanticscholar.org/CorpusID:229331815.