Meghana
Bhange
I work at the intersection of machine learning, privacy, and social impact — building socio-technical systems that let communities (not just individuals) resist algorithmic harm and reclaim agency.
About / Research
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 the Trustworthy Information Systems Lab (TISL) and Mila — Québec AI Institute as a Student Researcher.
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. Aïvodji and Prof. Jean-Marc Robert.
Previously, I worked as a Machine Learning Engineer and still consult via Toptal — contributing to NLP-driven projects like inclusive hiring pipelines (SpaCy, GPT-3, FastAPI) and an automated trading platform integrating technical-indicator strategies.
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).
→ Follow the project at meghanabhange.com/posts/aca-research-blog
Research Interests
Selected Publications
meghana.history
Let's connect.
Open to collaborations on algorithmic collective action, privacy-preserving ML, and socio-technical systems. Always happy to chat.
References
- Hardt, M., Mazumdar, E., Mendler-Dünner, C., & Zrnic, T. (2023). Algorithmic Collective Action in Machine Learning. ICML.
- Kulynych, B., Overdorf, R., Troncoso, C., & Gürses, S. F. (2018). POTs: Protective Optimization Technologies. FAccT 2020.
- Vincent, N., Li, H., Tilly, N., Chancellor, S., & Hecht, B. J. (2020). Data Leverage: A Framework for Empowering the Public. FAccT 2021.