ABOUT BLOG TEACHING MENTORSHIP

Reshmi is a Senior Applied Scientist at Microsoft’s Intelligent Search team (MSAI), currently involved in R&D and productionization of several natural language and multimodal solutions for securing Copilot reasoning and enterprise RAG from adversarial users. Most recently, she also contributed towards the post-training of AI-systems for aligning with human-preferences while ensuring secured search experiences experiences. Reshmi loves working in fast-paced teams and has a rich experience of delivering high-impact features/products for Fortune 500 companies and end users. She also loves prototyping and working on incubation projects, a desire stemming from her past experience of finishing a forward-looking Ph.D. from Carnegie Mellon University in the intersection of deep learning, natural language processing, and renewable energy policies.

Overview:

[2024 Fall - now ] - Working on Agents research and applications.

[2023- Fall 2024] In the past 2 years of AI revolution, she has been focused on developing state-of-the-art Responsible AI and Trustworthy evaluation mechanisms for LLM and Multimodal model applications. Additionally, in 2023, Reshmi contributed to the development and shipped M365Copilot (Business Chat), followed by Azure Prompt Shileds in 2024. Shortly before that, she was tech leading efforts to architect and integrate first-ever Generative AI capabilities in Microsoft’s Productivity applications (M365 CoPilots, see Satya Nadella’s announcement of these products here).

[<2023] Before joining MSAI, Reshmi was selected to be part of the highly coveted (~0.1% acceptance rate) AI incubation program at Microsoft’s New England Research and Development Center (NERD) (currently under the Office of the CTO, previously under Jaime Teevan’s Office of Applied Research). During this time, she worked on several incubation projects, notably:

Developed and implemented anomaly detection methods for SLA in Azure core services
Developed and deployed in production, intelligent commanding features leveraging seq2seq language model to Microsoft Office. Also prototyped a novel loss function that can predict as well as researching user preferences in UX positioning by developing a novel loss function.
Developed probablistic graphical models to measure time saved in finding new information by knowledge workers, aiding in productivity increases through Viva Topics (see product announcement here!)
Shipped a LLM application and novel evaluation framework for generating rich-text formatting within a client application.

Reshmi holds a Ph.D. from Carnegie Mellon University. She has always been passionate about applying machine learning, deep learning, economics to solving socio-technical problems, such as in the intersection of climate change and renewable energy integration. With over 8+ years of innovating, leading, and strategizing AI solutions for nuanced user problems, Reshmi hopes to continue working with automation tools that can benefit the scientific community, and uers equally.

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Products/Software Highlights

2025- it's becoming all about Agents
- Working with alignment issues of facts for a global audience and reduce over-refusal
- Contributed to safe release of Agentic workflows in M365 Copilots
- Learn about Agents in Copilot
2024 - Year 2 of bringing AI into search and productivity
- Working on novel post-training of AI-systems to prevent security breaches
- Azure Prompt Shields now available for third party usage. Try it now!
- Safeguarding automated search workflows from security breaches/prompt injections

Research Highlights

Search and Safety for AI
- Read our latest paper on detecting and mitigating prompt injections hidden in images here
- Hiring a PhD Research Intern to work with me on cutting egde multimodal safety issues in Agents - apply here
Human-AI Alignment
- Highlight 3
- Highlight 4
Interpreting and Explaining Black-box Language Models
- Highlight 3
- Highlight 4

Updates

[June 2024]	Paper from IEEE Secure and Trustworthy ML (SatML), LLM Capture-The-Flag competition now live. Read research paper here here. We discovered that [...]
[March 2024]	Came out as silver medal winners at the IEEE Secure and Trustworthy ML (SatML), LLM Capture-The-Flag competition
[October 2023]	Paper on "Surgical Fine-Tuning" for resource efficient task-adaptation for Language encoders accepted to EMNLP 2023. Read research paper here here
[April 2023]	Invited Technical Keynote Speaker at Voices of Data Science Conferenc, University of Massachusetts, Amherst </a>
[Feb 2023]	Paper: Topic Segmentation for Unstructured Conversations accepted to IntelliSys Sept 2023. Read the Springer 'Intelligent Systems and Applications' book chapter here
[Jan 2023]	ICLR 2023, AISTATS 2023, NIPS 2022, ICCV 2022 Reviewer
[October 2022]	Paper: Topic Segmentation in the Wild: Towards Segmentation of Semi-structured & Unstructured Chats accepted to NeurIPS 2022. Read here
[Aug 2022]	NLP + Reinforcement Learning lead reviewer @ Microsoft Machine Learning, AI and Data Science Conference, Fall 2022
[July 2022]	Research paper on topic segmentation for noisy conversational datasets (from joint effort by University of Massachusetts at Amherst & Microsoft Research) under review at COLING 2022
[July 2022]	AACL 2022 and AAAI 2023 reviewer
[July 2022]	Invited Speaker at WiMLDS Bay Area to present a talk about Deep Reinforcement Learning
[May 2022]	Selected as a reviewer for evaluating research paper submitted to Microsoft's Machine Learning, AI, and Data Science journal
[April 2022]	Invited Speaker at ODSC East 2022 to present a talk about Quantization methods for large sequential models
[April 2022]	Guest Lecturer at Carnegie Mellon University, Language Technologies Institute to present a talk about Contextual Bandits & Deep Reinforcement Learning
[February 2022]	Semi-finalist at Open AI Codex challenge (selected project: Integrating Open AI to databases to accelerate database querying process for non-technical users)
[Oct 2021]	Defended Ph.D. thesis on the use of deep learning and stochastic methods to detect anomalous checkpoints in sequential electricity demand profiles.
[Jun 2021]	Selected to be part of the high coveted (0.1% acceptance rate) 2-year Applied ML research rotational program: Microsoft AI Development Acceleration Program here
[Jun 2021]	Research paper on using deep learning methods to back-forecast electricity demand accepted at ICML 2021
[Jan 2021]	Invited to be a part of the Jane Street Ph.D. Symposium and present doctoral research related to large-scale optimization of electricity grids
[Jun 2020]	Presented research poster at WindEurope Technology Workshop 2020, Naples (virtual owing to Covid restrictions) on stochastic methods in energy systems
[May 2020]	Graduated with second Master of Science degree from Carnegie Mellon University in Engineering and Public Policy
[Apr 2020]	Inducted as a part of research team at Asset Lab, University of Michigan, Ann Arbor to use stochastic methods to assess future grid reliability and also leverage advanced machine learning methods
[May 2019]	Selected to present first year doctoral work at the prestigious American Wind Energy Association (AWEA) Clean Power Expo and Conference
[Mar 2019]	Finalist at national level Yale Graduate Consulting Case competition.
[Aug 2018]	Started serving as the Vice President of CMU Graduate Consulting Club
[Mar 2018]	Elected to receive Dean's fellowship to support doctoral studies.
[Jan 2018]	Awarded doctoral fellowship by CMU Portugal, to work on the +Atlantic project - a consortium of 8 cross-functional research and academic institutions
[Dec 2017]	Graduated with a Master of Science degree in Civil and Environmental Engineering, and started working as a Research Intern with Prof. H. Scott Matthews on economic analysis and risk analysis of energy systems
[Mar 2017]	Received Merit Scholarship from Carnegie Mellon to support master's studies in the academic year 2017-2018.
[Sep 2016]	Started serving as CMU Graduate Student Advisory Committee Representative - an initiative to help bridge the gap between graduate students and the school administration.
[Jan 2016]	Received Civil and Environmental Engineering department scholarship to support master's studies for the academic year 2016-2017. Scholarship resulted in 25% tuition waiver.