Christopher M. Stewart, Ph.D.
AI / ML Research & Development
Researcher | Data Scientist | ML Engineer
Accomplished research engineer with 15+ years of experience bridging academic research and large-scale AI/ML. Most recently, 8.5 years in Google working on crowdsourcing, then building, evaluating, and deploying production AI/ML systems. Deep expertise in applied AI/ML, speech tech, and statistical modeling. Collaborative research with projects spanning AI safety, medical NLP, and probabilistic methods for generative AI. Collaborators include major research hospitals, American and European universities, startups, and Google.
Competitive Edge: Rare combination of hands-on production ML engineering (300K+ lines of production code at Google), original research published in top venues, and deep theoretical grounding in computational linguistics and statistical modeling with experience translating cutting-edge research into production-ready implementations at scale.
Core Competencies:
Highlights
Production ML at Google scale: Developed and deployed end-to-end content moderation AI/ML systems for multimodal classification (text, image, video data) across 10+ high-impact production launches, preventing >$100M of policy-violating monetization.
Content safety & alignment: Innovated fraud and abuse detection methods presented to Google’s CEO. Co-leading research projects via affiliations at the University of Memphis (Institute for Intelligent Systems) and Carnegie Mellon University (Machine Learning Department).
Technical depth & impact: Strong technical contributor with a proven track record of leading projects to completion; committed to ongoing professional development; won multiple hackathon awards at Google.
Experience
Leading and collaborating on multiple original research projects at the intersection of AI safety, alignment, and response deployment with researchers at University of Memphis, Carnegie Mellon University (CMU), Google, Aarhus University, and the University of Zürich.
- AI safety research: Direct research on AI deployment in clinical settings, agentic approaches to misinformation detection and privacy protection for minors.
- AI application research: Collaborating on other AI-related projects: automatic prompt optimization for financial QA, Bayesian language model-based RAG, language patterns in AI-generated vs. human-written text.
- Publications: Submitted manuscript (Building Language Technologies – MIT Press); 7 manuscripts forthcoming.
Senior Computational Linguist / Research Scientist (L4 → L5), 03/2022–08/2025
Hybrid Researcher / Data Scientist / ML Engineer. Collaborated to build and deploy production AI pipelines like LLM-based classification using Python, SQL, TFX, and fine-tuned Gemini models.
- Production ML: Implemented cutting-edge ML/AI for semantic and visual classification methodologies for content moderation, leading to a defensive patent for highly ambiguous classification.
- Safety systems: Designed novel detection methodologies for spam, fraud, and abuse in app advertising.
- LLM research: Conducted original research on multimodal many-shot prompting and graph-based LLM inference during a Research Scientist rotation.
Analytical Linguist (L3 → L4), 03/2017–03/2022
Proposed, designed, and built automated statistical analysis tooling for teams collecting large-scale crowdsourced data in Google Ads, used by 100+ global teams.
- Data infrastructure: Automated statistical analysis for thousands of new crowdsourcing tasks; set requirements and evaluated workflows for a global rater workforce.
- ML evaluation: Evaluated rater performance at scale using ML and psychometric methods.
- Mentorship: Mentored 2 Nooglers, hosted 1 intern, and represented Google at external research conferences.
Senior Data Scientist
Built analytics pipelines, forecasting models, and supervised learning systems for both an Industrial IoT startup and Walmart.
- ML systems: Developed supervised learning package in R and statistical software for business analytics; built forecasting models for high-frequency sensor data using Python and R.
- Data mining: Mined large-scale socio-demographic data for the world’s largest retailer.
Voice Engineer
Engineered synthetic voices for Apple’s Siri using large-scale multimodal speech data. Collaborated with distributed, multinational team across R&D pipeline and took on major customer-facing role.
Assistant Professor
Initiated multidisciplinary research resulting in 5 publications and 5 conference presentations. Co-PI on neuroscience funding application. Supervised team of 10 instructors; raised enrollment by 86%.
Leadership & Mentoring
- Co-organized Linguistics Career Launch, mentoring graduate students at American and European universities transitioning into industry careers in NLP and computational linguistics.
- Invited talks at 13+ institutions including Google Faculty in Residence Program, Georgetown University, University of Wisconsin-Madison, LSA, and University College London.
Selected Publications (full bibliography on Google Scholar)
Forthcoming
Book
Stewart, C.M., Tyler, J. & Thyme-Gobbel, A. (2026). Building Language Technologies. MIT Press.
Peer-reviewed journal articles
Windsor, A., Stewart, C., Casal, J.E. (2026). “AI, Teddy Bears, and Long Tails: Investigating AI Texts through Constructional Diversity.”
Published
Casal, J.E., Stewart, C.M., & Windsor, A.J. (2025). “‘It Is Important to Consult’ a Linguist: Verb-Argument Constructions in ChatGPT and Human Experts’ Medical and Financial Advice.” PLoS One 20(5): e0324611.
Sim, J.A., Huang, X., Horan, M.R., Stewart, C.M., et al. (2023). “Natural Language Processing with ML Methods to Analyze Unstructured Patient-Reported Outcomes from EHRs: A Systematic Review.” Artificial Intelligence in Medicine 146: 102701.
Conference Presentations
“Social cognition and sociolinguistic processing: Automatic Vigilance and Ingroup Identification” (co-author Jared Kenworthy). Paper presented at New Ways of Analyzing Variation 41 (Bloomington, IN, October 2012).
“The Effects of Language Attitudes on Semantic Processing: An Implicit Approach” (co-author Jared Kenworthy). Paper presented at the International Conference of the Association for Language and Social Psychology (Friesland, Netherlands, June 2012).
Education and Professional Development
University of Illinois at Urbana-Champaign
- Awarded 8 fellowships; published 3 articles and gave 5 conference presentations.
Furman University
- 2026Stanford School of Engineering Artificial Intelligence Professional Program
- 2016Coursera Machine Learning Specialization
- 2015Coursera Data Science Specialization
Technical Skills / Natural Languages
- Artificial Languages
- Python, R, SQL, Unix, HTML/CSS/XML
- AI/ML
- TensorFlow/TFX, LLM development via API (Gemini, GPT-4/5, Claude), supervised fine-tuning, nonneural retrieval-augmented generation (RAG), multimodal data integration, reinforcement learning
- Infrastructure
- Distributed computing, Hive, Spark, Git/GitHub, Jupyter, RStudio; Google-internal: Critique, Subversion
- Statistical Methods
- Mixed-effects models, causal inference, predictive modeling, Bayesian methods, feature engineering for high-dimensional data
- Natural Languages
- English (native), French (near-native), Spanish (highly proficient), plus 5 additional languages
Recommendations
Recommendations from managers and co-workers can be found on my LinkedIn profile.
Other references available upon request.