ABOUT ACADEMICS PROJECTS
Ph.D. Applied Scientist, Microsoft (MSAI)
Reshmi Ghosh
I’m Reshmi, Applied Scientist at Microsoft’s Search and Intelligent Assistance (MSAI) team, focusing on developing state-of-the-art Responsible AI and Trustworthy evaluation mechanisms for LLM applications. I have focused my efforts in the past year on identifying, evaluating, and mitigating security vulnerabilities (prompt injections) in M365 Chat CoPilot, extending to Office 365 CoPilots (PowerPoint, Excel, Word, Outlook, Teams, etc.). Shortly before that, I was also responsible for leading the efforts to architect and integrate Generative AI capabilities in Microsoft’s Productivity applications (M365 CoPilots, see Satya Nadella’s announcement of these products here (patent filed).

Before joining MSAI, I was selected to be part of the highly coveted AI rotational program at Microsoft’s New England Research and Development Center (NERD) (currently under the Office of CTO, previously under Jaime Teevan’s Office of Applied Research). I was one of the 17 individuals from 10K applicants (~0.2% acceptance rate) to this esteemed program. During my time in MAIDAP, I spent time bringing ML capabilities to detect anomalies in Azure services, serviced intelligent commanding features to Microsoft Office using seq2seq language modeling as well as researching user preferences in UX positioning by developing a novel loss function. I furthermore worked with the Viva Topics team to develop new probabilistic graphical methods to measure time saved to find new knowledge and developed an LLM application and novel evaluation framework for generating rich-text formatting within a client application.

Prior to joining Microsoft, I graduated with a Ph.D. from Carnegie Mellon University in 2021. I have always been passionate about machine learning, deep learning, economics, climate change, and renewable energy. I have 5+ years of working with data-driven techniques in python to discover underlying patterns in large datasets. I love leveraging data science techniques to derive key actionable insights about business and societal problems, and translating these insights into business strategies.

My doctoral research reflects the blend of of my interest areas in a public policy setting for energy systems. I am analyzing the future reliability of power systems for the United States by developing probabilistic mathematical models using terrabytes of reanalysis datasets to measure the resource adequacy metrics. Additionally, I am also working to reconstruct decades of missing electricity demand data using large seq2seq models for detecting anomalous behavior (vanilla and with Attention). I am also attempting to use causal inference methods to identify underlying patterns in multi-decadal electricity load data, and understand it’s implication on grid system.

Ph.D. Dissertation Resume LinkedIn Github Twitter

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