Agriculture × AI
Combining AI and robotics for more efficient farming: more food, less waste.
San Francisco PhD, Howard University
Marcus Edwards
I see myself as a tinkerer and a builder: someone who does serious research and has fun doing it. The path has been unorthodox, from Army Research Lab and Howard to residencies at Meta, Apple, Google X, and Netflix, but the through-line is the same. I want to change the world with AI, and help inspire the next generation of research scientists to do the same.
Dissertation · Howard University · 2024
Adversarial Machine Learning Defenses for Multidomain Applications
Focus
Four things I’m excited about
Intersections of AI I care about most: generative models and LLMs applied where they can actually move the world, from farms and genomes to space data and entertainment.
Biology × AI
AI for genomic science, resilient plants, and breakthrough cures to disease.
Space × AI
Models that sift the flood of space and Earth-facing imagery: optical, radar, SAR, and the archives we keep collecting.
Entertainment × AI
Generative AI for radically new entertainment experiences: not just better recommenders, but new forms of story and play.
Experience
Selected appointments
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Frontier LLMs
San Francisco
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ML Research Engineer
Google X · San Francisco
Kinetics-to-motion transformers for robotics; multimodal preference fine-tuning with human feedback.
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Research Scientist (Residency)
Facebook / Meta · New York
Reels recommendation with large multitask models and natural-language content-based filtering.
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Data Scientist (Residency)
Netflix · Los Angeles
Knowledge graph work.
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ML Engineer (Residency)
Apple · Cupertino
3D kinematic modeling and GAN synthetic video for few-shot learning.
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Data Scientist (Residency)
Netflix · Los Angeles
Entity resolution for the billion+ entity knowledge graph.
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Research Scientist
Army Research Laboratory · Fort Belvoir
GANs for infrared imagery, spectral image recognition, and IR inpainting, plus generative methods for constrained sensing.
Publications
Selected publications
Peer-reviewed papers and doctoral dissertation. Google Scholar →
Dissertation
Adversarial Machine Learning Defenses for Multidomain Applications
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2025
Adversarial Attack Resilient Computational Modeling for Person Re-Identification in Visual IoT Applications
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2024
SleepWalker: constrastive fine-tuning technique for text to kinematics models for human computer interaction
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2024
Agent Deprogramming Finetuning Away Backdoor Triggers for Secure Machine Learning Models/LLMs
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2022
Adversarial promotion for video based recommender systems
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2022
Study of Adversarial Machine Learning for Enhancing Human Action Classification
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2020
Quantum adversarial machine learning: Status, challenges and perspectives
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2020
Study of adversarial machine learning with infrared examples for surveillance applications
Recognition
Awards & fellowships
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2024
AI Advisor to the Pentagon
Advisory role
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2021
DOD Center of AI/ML Excellence Fellowship
Howard University
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2020
Apple TMCF Fellowship
Howard University
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2018
Security Engineering Assistantship, Resilient Mobile Physical Systems
Howard University
Press & talks
Public appearances
Selected talks and stories. More soon.
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Podcast
Netflix podcast appearance
Link pending. Guest appearance from the Netflix residency era. Drop the URL when you find it and I’ll wire it up.
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Talk
Speaking at the University of Michigan
On tinkering, unorthodox paths, residencies, and building with AI. YouTube.
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2025
How a Million Dollar AI Company Grew from a Howard Student’s Drive and Mentor’s Vision
The Dig at Howard University · profile on mentorship, research, and DARE Labs.