Fun fact: I'm a fan of One Piece manga!
I completed my M.S. in Applied Data Science at Howard University, advised by Dr. Amy Yeboah Quarkume in the Core Futures Lab. My work focuses on explainable multimodal AI and AI-driven decision support for business and creative contexts.
I completed my undergraduate degree summa cum laude with a B.S. in Computer Science and a minor in Mathematics at Howard University as a Karsh STEM Scholar.
I've had the opportunity to work with Dr. Daniel Ritchie and Aditya Ganeshan at Brown University; Dr. James Landay and Parker Ruth at Stanford University; and Dr. Paul Liang at the MIT Media Lab Multisensory Intelligence Group.
I have also interned and worked at Google [x2] and The Walt Disney Company. See my resume for more.
Research
I research and develop foundation models (LLMs, VLMs, world models, and multimodal systems), focused on how they perform, fail, and can be made more efficient. I build benchmarks and interpretability methods that characterize how these models behave, working toward explainable AI that strengthens human-AI decision-making.
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A two-channel LLM benchmark spanning 7 models, 5 datasets, and 780K+ generations measuring behavioral shifts under linguistic input and output compression.
Benchmarks 5 text-to-image models on 15K images, showing zero-shot VLMs fail to detect sensory overload without explicit feature grounding.
Trained on 87,547 Vogue runway images (15 houses, 1991–2024), FASH-iCNN identifies houses at 78.2% and decades at 88.6%, with texture revealed as the dominant carrier of editorial identity.
An interactive visualization tool for cross-head mixing in non-standard attention architectures.
A post-hoc interpretability method using information-theoretic criteria to detect unjustified modality suppression in multimodal models.
Retrieval-augmented conversational AI triaging postpartum emergencies for Black mothers using affect-aware NLP and human-in-the-loop clinician escalation.
A multimodal AI framework co-designed with speech-language pathologists that generates personalized therapy scenes from a 2M+ story corpus using Qwen2.5-VL and Google Veo 3.
Simulating how predictive-policing and risk-assessment systems driven by algorithmic bias shape long-term financial and social outcomes for marginalized communities.
Derives closed-form summations for the weighted second moment of elevated Motzkin paths via their relationship to central Delannoy numbers and modular arithmetic.
Quantified a 35% accuracy gap between simulated and real-world tonometer data and improved KNN prediction by 30% via K-means clustering of pulse shapes.
Used LLMs to analyze anxiety-related Reddit posts, extracting inferred features to detect shifts in self-reported mental health statuses.
Audited indoor-scene datasets with CLIP-space Set-Similarity scores, finding up to 76% geographic shift toward Eurocentric features in widely used training corpora.
Experience
I have experience in software engineering, AI strategy, and HCI research across industry teams.
Applying AI-driven decision science to support product and strategy decisions.
Context recommendations for photo experiences.
Built internal tests and RPC validations to improve reliability of data-usage alerts and backend analyses.
- MIT Media Lab (2025)
- Stanford University (2024)
- Brown University (2023)
Leadership
I build and lead initiatives that connect students with industry, secure funding, and grow technology communities through partnerships, sponsorship outreach, and student-led programs.
Leading student research initiatives on Public Interest Technology, guiding interdisciplinary projects on AI safety, algorithmic bias, and governance challenges in emerging AI systems.
Founded Howard University's first coding hackathon and grew it into a nationally recognized event. Led a 30-member team; 250+ students; 32+ corporate partners; $160K+ raised.
Secured record funding and corporate partnerships. Launched Uber-sponsored International Student Pick-Up, $7K ELEVATE Book Scholarship, Apply-a-Thon workshops.
Built a data-driven pipeline for companies to collect student info at scale and for students to mass-apply to corporate opportunities.
Led "Best Practices for Fundraising" for undergraduates nationwide on pitching, sponsorships, and scaling student-led initiatives.
Companies I've partnered with
Interested in partnering? Feel free to email me. I'm always open to collaborations on strategy, research, and student impact initiatives.
- TA, CSCI 470 Fundamentals of Algorithms (Fall 2024)
- TA, CSCI 100 Intro to CS [Google in Residence] (Fall 2024)
- TA and Mentor, Public Interest Tech Summer School (2025) with Dr. Amy Yeboah Quarkume, mentored 6 undergraduates
- Volunteer Teaching, Columbia Heights Educational Campus (Jan to May 2025): College Prep seminar for high school students
- Volunteer SAT Tutor, National Society of Black Engineers (Fall 2022 to Fall 2024)
- Member, CEA Decanal Search Committee (Aug 2024 to Jun 2025)
News
- May 2026Joined Adobe Research as a Research Scientist Intern.
- May 2026Graduated with an M.S. in Applied Data Science and Analytics from Howard University.
- Jan 2026Joined Disney as an AI Decision Scientist Graduate Associate.
- Sep 2025Began research on a postpartum AI support system.
- Aug 2025Joined Google as a Software Engineer Intern (Pixel Photos).
- Aug 2025Started the Applied Data Science Master's program at Howard University.
- Jun 2025Joined MIT Media Lab as a Student Researcher Intern under Dr. Paul Liang in the Multisensory Intelligence Group.