Andriy Mulyar Research by SuperAGI

Executive Summary

Andriy Mulyar is the Co-Founder and Chief Technology Officer (CTO) of Nomic AI, a New York-based, venture-backed technology company focused on democratizing and explaining artificial intelligence. Mulyar is recognized as a leader in AI transparency and large-scale natural language processing (NLP) infrastructure, with notable features including being named to Forbes' 30 Under 30 in enterprise technology (2024). He possesses strong academic and industry credentials, blending deep technical specialization in machine learning with significant experience in productizing open-source AI models.

Professional Background

Current Role

Co-Founder & CTO, Nomic AI

Mission: To make AI systems more accessible and explainable; focus on open-source, transparent large language models (LLMs) and AI infrastructure.

Key Product: GPT4All – an open-source LLM platform enabling custom training and deployment of models by individuals and enterprises.

Notable Project: nomic-embed-text-v1 – the first open-data, open-weights, open-training code text embedding model that reportedly outperforms OpenAI Ada-002 for text embedding tasks. Emphasized as fully auditable and production-ready for RAG and semantic search applications.

Previous Experience

Rad AI – Early engineer, responsible for training multi-billion-parameter language models specifically to assist radiologists, handling large-scale, sensitive clinical NLP tasks.

NYU Courant Institute for Mathematical Sciences – Former Ph.D. student, specialized in interpretable machine learning and deep neural networks. Left Ph.D. to co-found Nomic AI.

Academic Projects: Led or contributed to multiple seminal projects in clinical NLP, such as MT-Clinical BERT, Clinical Concept Linking, and medaCy—all focusing on scalable clinical information extraction, contextualized neural representations, and automated data handling in medical informatics.

Achievements and Recognition

Forbes 30 Under 30 (Enterprise Technology, 2024): Recognition for driving innovation in democratizing AI infrastructure and data transparency.

Fundraising: Secured $17 million in venture funding (April 2023) to scale Nomic AI's mission.

Conference Speaker: Delivered technical sessions at industry-leading conferences (e.g., Open Data Science Conference 2024), including deep dives on training auditable, high-performing open-source NLP models.

Academic Distinctions: Product of the Wright Engineering Access Scholarship at VCU, enabling early, hands-on work in machine learning and NLP research.

Technical and Product Expertise

Domain Expertise: Deep learning, interpretability of neural networks, natural language processing, open-source AI, data transparency.

Product and Project Leadership:

Vision and delivery on open-source tooling for LLMs (GPT4All, nomic-embed).

Pioneering efforts in text embeddings, with focus on fully-auditable pipelines and reproducibility.

Extensive experience in designing infrastructure for scaling ML models, engaging both academic and enterprise audiences.

Notable Published Projects:

Nomic Embed: Long context text embedding with open-source frameworks.

MT-Clinical BERT: Multitask clinical NLP, published in the Journal of the American Medical Informatics Association.

Clinical Concept Linking and Document Classification: Contributions presented at NeurIPS, ACL, and relevant health informatics conferences.

Education and Origins

Education:

VCU College of Engineering: Dual degree in Mathematics and Computer Science.

NYU Courant Institute: Ph.D. coursework in machine learning (did not complete, left to co-found Nomic).

Background: Ukrainian immigrant with lifelong interest in AI, ignited during high school internships at VCU. Emphasizes rigorous mathematical and engineering training as foundational to success in fast-evolving AI domains.

Public Persona and Thought Leadership

LinkedIn Network: 500+ connections; professional presence in both academic and industry circles with cross-discipline collaborations.

Technical Interests: Advocates for data transparency in AI, accessibility of machine learning infrastructure, and ethical governance of LLMs. Public commentary indicates passionate involvement in topics like self-supervised learning, data cleaning, interpretability, and democratic control of AI outputs.

Advisory Team Building: Recently expanded Nomic AI's advisory team with industry leaders in AI, including experts in data visualization and embeddings.

Key Insights for Engagement

Primary Decision-Maker: As both Co-Founder and CTO, Mulyar is a strategic and technical stakeholder with direct influence over product direction, partnership decisions, and technology adoption at Nomic AI.

Openness to Collaboration: Strong orientation toward open-source and community engagement, demonstrating willingness to partner with entities advancing AI transparency and democratization.

Focus on Infrastructure and Scale: Solutions or propositions aligning with open data, scalable ML infrastructure, and enterprise-grade model management are directly relevant to Mulyar's vision and operational responsibilities.

Emphasis on Long-Term AI Governance: Interested in systems that foster transparency, auditability, and control over AI outputs, indicating potential receptiveness to tools or services supporting these objectives.

Selected Linked Resources

LinkedIn Profile: [Andriy Mulyar](http://www.linkedin.com/in/andriymulyar)

Personal Website: [andriymulyar.com](https://andriymulyar.com/)

Nomic AI: [Nomic AI official](https://nomic.ai/)

Conference Presence: [ODSC Speaker Page](https://odsc.com/blog/speaker/andriy-mulyar/)

Notable Publications (Sample)

MT-Clinical BERT: Scaling Clinical Information Extraction with Multitask Learning, J. American Medical Informatics Association

Clinical Concept Linking with Contextualized Neural Representations, ACL 2020

Phenotyping of Clinical Notes with Improved Document Classification Models, NeurIPS 2019 ML4H Workshop

In sum, Andriy Mulyar is a principal architect of industry-leading open-source AI infrastructure, recognized for his technical prowess, leadership in data transparency, and vision for accessible AI. His background suggests strong alignment with initiatives or solutions in advanced NLP, ethical AI, and transparent ML frameworks.