Busayo Awobade
ML Scientist, Intron Inc.

Email: busayo[at]mlcollective[dot]org
Busayo Awobade

Welcome! 😎

Research

I work to make speech and language technology accessible to everyone, with a special focus on resource-constrained settings. My research combines representation learning, data-centric AI, and the development of robust, efficient models. I am broadly interested in building systems that are fundamentally more adaptable to the diverse and imperfect data found in the real world.

Currently, I am improving multilingual speech and language models to deliver strong performance without increasing computational costs, using curriculum learning and data pruning strategies. I also explore trade-offs between model compression, efficiency, and robustness. My ultimate goal is to ensure these powerful models are scalable, accessible, and broaden participation in global AI research.

Selected Publications

AfriVox: Probing Multilingual and Accent Robustness of Speech LLMs
Busayo Awobade, Mardhiyah Sanni, Tassallah Abdullahi, Chibuzor Okocha, Kelechi Ezema, Devendra Kayande, Lukman Enegi Ismaila, Gloria Ashiya Katuka, Tobi Olatunji
EACL 2026
AfriSpeech-MultiBench: A Verticalized Multidomain Multicountry Benchmark Suite for African Accented English ASR
Gabrial Zencha Ashungafac, Mardhiyah Sanni, Busayo Awobade, Alex Gichamba, Tobi Olatunji
IJCNLP-AACL 2025
The NaijaVoices Dataset: Cultivating Large-Scale, High-Quality, Culturally-Rich Speech Data for African Languages
Chris Emezue, NaijaVoices Community, Busayo Awobade, Abraham Owodunni, Handel Emezue, Gloria Emezue, Nefertiti Emezue, Sewade Ogun, Bunmi Akinremi, David Adelani, Chris Pal
Interspeech 2025
What Happens When Small Is Made Smaller? Exploring the Impact of Compression on Small Data Pretrained Language Models
Busayo Awobade*, Mardiyyah Oduwole*, Steven Kolawole*
AfricaNLP 2024
Complete Publication List

Recent Highlights

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Community Impact

I am all about making AI research more accessible, not just with algorithms, but also by building infrastructure that helps everyone get involved. Every year, I mentor college students at the Federal University of Agriculture Abeokuta, helping them figure out their research intrests.

I am also a founding organizer at ML Collective-Nigeria, a grassroots research hub that fosters research outside formal academic structures. We run peer-led study groups, host research sprints, and also leverage MLC's resources to support members––who now publish at top venues––through mentorship, collaborative projects, and idea exchange.

I organize annual fundraisers enabling African student researchers to attend Deep Learning Indaba. During my undergraduate years, I helped organize dozens of technical training programs impacting over 3,000 students and personally taught several hundred students in machine learning, data science, and technical skills.

Outside of research, I enjoy studying and reflecting on the doctrines of the Bible (2 Tim 2:15). I also enjoy playing chess and football, and watching Arsenal matches. I value conversations that connect technical work to wider social impact.

The Scenic Route to a BSc.

I completed my BSc in Computer Science at the Federal University of Agriculture Abeokuta, Nigeria, advised by Prof. Olusegun Folorunso. Academic union strike actions and COVID-19 lockdown extended my bachelor's degree timeline beyond the expected 4 years to 6 years. Fortunately, this gave me more time than a typical undergraduate to explore my many interests beyond what my immediate environment offered and also amass a diverse mix of interesting experiences.

I participated in several hackathons and projects, and I occasionally spoke on tools and topics I liked at tech conferences across different parts of the globe. In 2023, I started learning to be an independent researcher with ML Collective. Toward the end, I (along with my friends) designed a real-time opinion mining system for digital assets and secured a 115k USD grant to bring it to life. This enabled me to focus on exploring independent research without worrying much about my living expenses.

I am a "community-taught" ML practitioner; hence, much of those years were dedicated to giving back to our tech communities, including Data Science Nigeria, Google Developer Student Club, the National Association of Computing Students, and ML Collective. This unconventional path—learning and building research capabilities outside formal structures—directly informs my current work on eliminating barriers to AI accessibility.