Divya Ramesh Email & Phone Number

I am an empirical computer scientist, whose research contributes to human-centered AI methodologies for AI governance. I combine human-computer interaction methods and critical social science theories to assess the risks of AI systems on vulnerable populations. I view problems through sociotechnical systems lenses, and use a combination of qualitative case studies, quantitative, and computational (i.e., machine learning, reinforcement learning, crowdsourcing) methods.My research has been recognized through several forums. I have published in top human-computer interaction and AI venues such as ACM CHI, FAccT, DIS, TOCHI and AAMAS. I received a Best Paper nomination and Pragnesh Jay Modi Best Student Paper award at AAMAS 2020. I have contributed to public conversations via CNBC TV18 and Techcrunch Perceptron, one of which influenced policy outcomes for FinTech Apps on the Google Playstore in 2023. I was recognized as a Quad Fellow by the governments of Australia, India, Japan, and the US for 2023-24, and I was named a Rackham Barbour Scholar by the University of Michigan for 2024-25. I will graduate with a PhD in Computer Science and a certificate in Science, Technology, and Public Policy from the University of Michigan in 2024-25. My dissertation develops a socio-politically inclusive framework for algorithmic accountability by drawing on the experiences of financially vulnerable communities and economic inclusion technologies in India and the US. This framework will help reimagine context-specific design and policy mechanisms for algorithmic accountability, ensuring effective AI governance.I have over a decade of expertise in AI. I hold an MS in EE, with a focus on Computer Vision and Machine Learning from the University of Southern California. Between 2014-18, I was a computer vision and deep learning research engineer at CloudSight Inc. At Cloudsight, I led the R&D efforts and architected the company's first human-AI interaction pipeline for analyzing visual content in real-time. I have also had short stints at Microsoft Research and Google Research during my PhD.

Listed skills include Deep Learning, Computer Vision, Machine Learning, C, and 18 others.