🇻🇮🇩🇴 Jazmia Henry (She/Her)

Iso AI

I build Open-source solutions to improve AI Agent performance and evaluation using simulation environments. 

I also provide consulting services to companies that want to improve their AI strategy, design AI Agent architecture and develop and deploy AI Agents.

Founder/CO

NeurIPS

Paper "Scaling Laws or The Law of Diminishing Returns: How Scaling Low Quality Data Degrades Language Model Performance" accepted for poster presentation with a top 5% rating and short listed for oral presentation consideration at Black in AI workshop. Presentation at NeurIPS 2023.

Accepted Paper (Top 5% Rating) 

Founded in 1987, the Conference on Neural Information Processing Systems (abbreviated as NeurIPS and formerly NIPS) is one of the most prestigious and competitive international conferences in machine learning and computational neuroscience.

Reviewer - Datasets and Benchmarks Track

Keynote Presentation: https://jazmiahenry.com/my-presentations/neurips-keynote-jazmia

Keynote Speaker - Global South in AI Affinity Workshop

McGraw Hill

Writing a chapter in the upcoming textbook, “Mitigating Bias in Machine Learning”, to be released Summer 2023.

Author

EYPHKA

Mentor founders from historically excluded backgrounds to navigate the landscape of startup formation, funding raises, hiring, Data strategy and Machine Learning infrastructure.

Startup Mentor 

www.eyphkaai.com

Thesis: 
What would the future of computing look like for AI? Since Amazon Web Services created Elastic Cloud Compute in 2006 for enterprises to be able to own and build applications without the need of physical servers housed on premises, Machine Learning became more mainstream as companies could now build ML applications with less overhead. Now, all major Tech companies use cloud based servers for application building and advanced companies use EC2s and S3 for Cloud Big Data storage and model compute.

	As IoT devices grow in popularity, edge computing is changing the way we approach compute with edge Machine Learning engineers training smaller models on edge layers and deploying model outputs using dynamic cloud orchestration that separates gateway functionality (normally in some breakout point) from model output consumption (normally on the network’s edge). This adaptability to a smaller device that owns more data and needs quicker response times has evolved what we consider to be optimal compute. 

	Decentralization through Blockchain has the power to take this idea even further and led us into a new realm of what’s possible with Machine Learning.

CEO and Founder

Microsoft

Senior Data and Applied Scientist for ISE, FSI team. Focused on bringing LLM solutions using Azure's many Generative AI offerings including Azure OpenAI, Microsoft Fabric and Azure Machine Learning Studio. 

Formerly a Senior AI Applied Engineer building Autonomous AI Solutions for Industrial Partners using the Reinforcement Learning platform, Microsoft Bonsai, from August 2022 - July 2023.

Senior Data and Applied Scientist, Data and AI 

Microsoft MLADS is the internal Machine Learning, AI and Data Science conference where employees present tutorials, presentations and papers. I serve as a Reviewer.

Machine Learning, Data Science and AI Conference (MLADS) Reviewer 

Architected scalable Machine Learning and Reinforcement Learning models, driving productivity and cost savings for industrial clients.
Product owner for Azure Plato, an open-source toolkit, facilitating strong feedback loops with partners and customers.

Senior Applied AI Engineer, Project Bonsai

Correlation One

Lead Instructor - Data Science and Machine Learning 

Stanford University

Building an open source African American Vernacular English  corpora package to mitigate bias in downstream NLP tasks using Deep Learning Techniques such as few shot and zero shot learning and transfer learning.. 
Participates as a panelist, technical contributor, and speaker at academic and public companies.

Major Accomplishments and Publications:
“Mitigating Bias During the MLOps Lifecycle”. MIT Press. 2022. Pending Publication. 
“Practical Challenges to Achieving Algorithmic Fairness in Industry (And What to Do About Them)”  August 2021. Amstat. Panelists and Other Contributors: Sarah Cox, Coinbase; Samsara Counts, Amazon; Dr. Kalinda Ukanwa, University of Southern California
“AAVE: A Collection of AAVE by Black Speakers for NLP Data Supplementation”. 2022. NeurIPS. Pending Submission.

Technology Fellow: NLP

The Motley Fool

Founding member of the Machine Learning Team at the Fool. Lead team hiring, Company-wide AI strategy, and Machine Learning Model Building. Lead Machine Learning Modeler that built and deployed large scale Recommendation Engines, Deep Learning Models, Regression Models, and Quantitative Models.
Assisted in Cloud Migration from On-Prem servers and integrated Streaming servers into a Data Lakehouse structure for enhanced ML performance, Data democratization, and Data Enablement.
Directly managed a team of ML Engineers, Data Scientists, and Project Managers. Collaborated with Business Intelligence and Data Engineering Department Heads to form a collective Data team of over 30 Data Engineers, Business Intelligence Analysts, and Machine Learning Practitioners to build, deploy, and manage robust ML models for millions of Motley Fool users.

Major Accomplishments:
Built and Deployed a Hybrid Recommendation System using the Alternating Least Squares Matrix Factorization technique using Spark.
Worked with leadership to establish AI Ethical Standards and brought on consultants to create Ethical Product Review Standards for AI models and with business leaders to create company wide AI and Data literacy programs.

Head of Machine Learning

SIID Technologies

Co-Founder and Advisor 

Morgan Stanley

• Relationship Manager that communicated model performance, success metrics, predicted ROI, and model scope to senior executives, junior developers, and non-technical management located domestically and abroad.
• Perfected ETL process for large and distributed data sets through Hadoop and Spark using SQL and
Pyspark. Built queries, reports, and models using R and Python. Used Tableau to visualize findings through dashboards to make business recommendations to senior shareholders.

Data Strategist

Hey there, I'm Jazmia Henry, your friendly neighborhood dreadhead living at the intersection of AI, Reinforcement Learning, Machine Learning and Analytics. I have a passion for helping people unlock the full potential of AI.

I founded Iso AI, where we're reshaping how enterprises test, train, and deploy AI agents at scale. My mission? To drive the future of AI-powered systems with seamless integration, modular design, and robust performance across industries. Aka make AI Agents suck less. 

Why I'm awesome?

I am the founder & CEO of Iso AI,  a platform that enables companies to create cutting-edge simulation environments for training AI agents, testing robust models, and driving innovation in AI behavior.

I have a long background in  in Machine Learning, Data Science, and AI. I worked Microsoft, Morgan Stanley, and Motley Fool as well as a couple others before graduate school. Plus I went to like everyone's dream schools: Columbia, Oxford and had a short stint at University of Pennsylvania, a Fellowship at Stanford and a business certificate from Harvard. 

I have a proven track record of building AI agents for major financial institutions, leading teams, and developing systems that adapt to complex real-world challenges. I’m driven by a belief that AI is only as powerful as the environments it’s tested in, which is why I’ve dedicated my career to building the tools that ensure AI systems are not only smart but also reliable and scalable.

But I'm not just about the tech- I'm about the people. 

I’m deeply invested in understanding how AI intersects with human behavior, and I enjoy pushing the boundaries of human-computer interaction. 

Follow me to learn more.

Senior Applied AI Engineer, Autonomous Systems

I made it through loops with almost every big AI lab this year and most of the BigTech companies.

I won’t get into specifics BUT just a pro tip for folks that want to understand what they would need to know if approached by these juggernauts.

Things we did not go over:
1. prompt engineering
2. Agent orchestration
3. Which model is the best at passing some benchmark

Things we did talk about:
1. Reinforcement pre-training to improve agentic decision making
2. Improving the performance of multi-agent interaction 
3. Reinforcement learning for evaluation of agents
4. Quantization, distillation and teacher-student models

So, my advice, be a good person. It’s seemed to be popular to be annoying online but in real life, there’s still a premium on being a good person. One of y’all’s favorite AI labs called me because a technical sourcer I saw passingly at conferences over the years spoke highly of me as a person before digging into my background and realizing my research made me a good fit. The HM said this was rare so reached out to me over other highly qualified AI folks. 

Second piece of advice, build and learn. You should at least know something about what’s happening in the field over the basics. I was cold called by most of these companies because of my research and open source project I released last year in RL and AI. Because of this + major demand for RL skills in these spaces, I’m working with LinkedIn on an RL course so that I can help democratize this knowledge. 

Dig deeper, yall. Push further. Advancements in AI aren’t just about how you can stack your resume- we can actually change the world with this thing. And if you want that change to be for the better, gotta push the limits of what’s possible.

Jazmia Henry

Execution and Engineering Excellence- less noise | I really have math tattoos

taplio

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