Jennifer Marsman
Microsoft, Principal Engineer for the CTO
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Jennifer Marsman is a Principal Engineer in the Office of the CTO at Microsoft, focused on generative AI with OpenAI models. She has worked for Microsoft for over 20 years; previous teams include Azure Cognitive Search, AI for Earth, CSE, Developer Evangelism, the Assistance Platform, and the Natural User Interface Platform (reorged from the Natural Language Group). Jennifer is a frequent speaker at software development conferences around the world, including the United Nations AI for Good Summit. Since 2016, Jennifer was recognized as one of the “top 100 most influential individuals in artificial intelligence and machine learning” by Onalytica, reaching the #2 slot in 2018. In Dec 2017, Jennifer was named one of the “10 Influential Female Developers to Know and Love on StackOverflow” by Traackr. She has been featured in Bloomberg for her work using EEG and machine learning to perform lie detection. In 2009, Jennifer was chosen as "Techie whose innovation will have the biggest impact" by X-OLOGY for her work with GiveCamps, a weekend-long event where developers code for charity. She has also received many honors from Microsoft, including the “Best in Role” award for Technical Evangelism, Central Region Top Contributor Award, Heartland District Top Contributor Award, DPE Community Evangelist Award, CPE Champion Award, MSUS Diversity & Inclusion Award, Gold Club, and Platinum Club. Previously at Microsoft, Jennifer was the Principal Software Engineer Lead of the Grok AI group on the Azure Cognitive Search team, where she focused on applying machine learning to unstructured data. Prior to that, Jennifer was the Principal Engineer of the AI for Earth team, where she used data science, machine learning, and artificial intelligence to aid with clean water, agriculture, biodiversity, and climate change. Jennifer was also a software developer in Microsoft’s Natural Interactive Services division, where she earned two patents for her work in search and data mining algorithms. Jennifer has also held positions with Ford Motor Company, National Instruments, and Soar Technology. Jennifer holds a Bachelor’s Degree in Computer Engineering and Master’s Degree in Computer Science and Engineering from the University of Michigan in Ann Arbor. Her graduate work specialized in artificial intelligence and computational theory. http://twitter.com/jennifermarsman
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Women In Technology Panel -- Gender Equality: How far have we come?
Women remain a minority in the tech industry at all levels. A 2020 https://AnitaB.org Institute study found that women are 28.8% of the tech workforce, a steady increase from 25.9% in 2018. However, women continue to leave the industry at rates far higher than cis men and the gains are not equal across race, ethnicity, age, or level. Per the Kapor Institute the American tech industry loses $16 billion every year replacing people who report leaving for unfairness. In this panel discussion we look at how far we’ve come and what we still need to do to reach fair pay, hiring, and retention in the tech workforce. WE aren’t the problem, WE are the solution. When we see, understand, and rebuild the systems around us in alignment with community values, everyone can thrive.
Using Neural Networks to Rewrite the "Game of Thrones" Ending
Machine learning enables us to make future predictions based on patterns learned from historical data. If we apply these principles to a large body of text, we can predict what the next word or character in a document will be and generate writing in a similar style. This will help us build our own George R.R. Martin to generate GoT scripts (yes, I know, he didn't write the TV scripts). In this session, I will teach the basics of neural networks, the different types, and where each excels. Then I will demonstrate how to generate text using machine learning by training a model on the scripts of the "Game of Thrones" television series, and having it output new text in the same style of writing. You will walk away with an enhanced understanding of machine learning and neural networks, and maybe even a new and preferred ending!
Using GPT Visual Capabilities to Solve a Wordle Puzzle
The visual capabilities of GPT-4 open up new scenarios of possibility with a multimodal model. In this session, we will explore what this model can do, and rather than just showing a perfect polished final demo, I will walk you through my entire journey of trying to use the model to solve Wordle puzzles, starting with "Hello World". Along the way, you will gain a good understanding of the model's capabilities, along with learning some prompt engineering techniques that drove progress in this journey (along with what didn't work!). We'll close with a live demo to attempt to solve today's Wordle! This session will tackle a fun problem, but the underlying prompt engineering techniques for image understanding that you will learn are applicable to a wide variety of business problems.
Secrets of Success in the Style of GLEE
Occasionally, I am asked for career advice. Now, I *definitely* don’t have it all figured out (I work from home, and there are days when I’m still wearing whatever I slept in until about 5 minutes before the school bus brings my kiddos home from school), but I have collected a few nuggets of wisdom over the years. To make it fun, I started mapping advice to popular songs. Each tip is backed by personal stories. So, with the help of Taylor Swift, Frank Sinatra, and more, I will share some secrets to success, in the style of Glee. Singing and dancing optional.
Generative Artificial Intelligence (Overflow)-
Generative artificial intelligence has incredible disruptive potential. Based on hands-on work with these machine learning models, discover insights about prompt engineering, hallucinations, which models are best for what purposes, what skills you will need to develop in the near future, and other learnings.
Generative Artificial Intelligence
Generative artificial intelligence has incredible disruptive potential. Based on hands-on work with these machine learning models, discover insights about prompt engineering, hallucinations, which models are best for what purposes, what skills you will need to develop in the near future, and other learnings.
AI for Earth: Using machine learning to monitor, model, and manage natural resources
The AI for Earth program applies machine learning and data science to hard challenges in agriculture, water, climate, and biodiversity. In this talk, we will discuss how the AI for Earth team, Microsoft Research, and AI for Earth grant recipients are using machine learning to enable precision agriculture, to predict outbreaks of disease, to detect poachers in real time, and to classify animals for conservation. Finally, we will briefly provide details on the AI for Earth grant program to obtain resources for everyone to work on these challenges.
AI for Earth: Innovations in Data Collection for Machine Learning
The AI for Earth program applies machine learning and data science to hard challenges in agriculture, water, climate, and biodiversity. The lifeblood that powers machine learning is data. Many AI for Earth grant recipients have developed innovative ways to collect data. I'll showcase the usage of UAV/drone imagery, camera trap data, simulation, crowdsourcing, and social media to gather data for some interesting machine learning problems in the conservation and sustainability space. We will cover the innovations in how data is collected, as well as the machine learning algorithms used to solve these challenges.
NDC London 2025 Sessionize Event
CodeMash 2025 Sessionize Event
Jennifer Marsman
Microsoft, Principal Engineer for the CTO
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