Steven Kolawole
PhD Student, Carnegie Mellon University
Language Technologies Institute
School of Computer Science
Advised by Professor Virginia Smith

Email: skolawol[at]andrew[dot]cmu[dot]edu
Steven Kolawole

Hi, welcome to my website! :)

Research

My research explores efficiency problems in machine learning from systems, algorithms, and data perspectives, with a focus of reducing unnecessary computational barriers. I work on fundamental questions about how intelligent algorithmic design can eliminate resource requirements that limit broader participation in AI development and deployment.

Currently, I'm investigating several interconnected approaches: agreement-based methods for efficient model routing, forward-pass techniques for model compression, and semantic approaches to extracting parallelism from natural language queries. My broader research vision centers on demonstrating that accessibility and performance are not opposing forces; that thoughtful design choices can achieve both.

Selected Publications

Agreement-Based Cascading for Efficient Inference
Steven Kolawole*, Don Dennis*, Ameet Talwalkar, Virginia Smith
Under review, 2025
Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes
Steven Kolawole*, Lucio Dery*, Jean-François Kagy, Virginia Smith, Graham Neubig, Ameet Talwalkar
Under Review, 2025
PARALLELPROMPT: Extracting Parallelism from Large Language Model Queries
Steven Kolawole, Keshav Santhanam, Virginia Smith, Pratiksha Thaker
Under review, 2025
Complete Publication List

Recent Highlights

More Updates

Community Impact

My commitment to making AI research more accessible extends beyond algorithmic contributions to building infrastructure for inclusive participation. Every year, I mentor over a score of underrepresented graduate school aspirants at STEM for Development, helping them clarify research goals and optimize their applications for Western graduate programs.

I am also a founding organizer at ML Collective-Nigeria, a grassroots research hub that foster research outside formal academic structures. We run peer-led study groups, host research sprints, and 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 and occasionally contribute to Black in AI's ELAI program. 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 research, I enjoy powerlifting, amateur boxing, watching Liverpool FC matches, and reading diversely. I'm always interested in conversations that bridge technical work with broader social impact.

The Four Six Years of BSc.

Before joining CMU, I completed my BSc in Computer Science at the Federal University of Agriculture Abeokuta, Nigeria, advised by Dr. Adebayo Abayomi-Alli. Academic union strike actions and COVID-19 lockdown extended my bachelor's degree timeline. Fortunately, this gave me more time than a typical undergraduate to refine my interests beyond what my immediate environment offered and also amass an eclectic mix of fulfilling experiences.

I had ample time to participate in several hackathons and internships, and I regularly spoke on tools and topics I liked at tech conferences across different parts of the globe. In early 2021, I started learning to be an independent researcher with ML Collective (and made cameo appearances at Masakhane and Cohere For AI), where I am fortunate to be primarily mentored by Dr. Rosanne Liu and Dr. Jason Yosinski. In the following year, my first completed research project (on sign language understanding) earned me the national AI champion award at the Nigeria Computer Society's AI Summit, held at Lafia's Government House. 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, She Code Africa, 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 making AI more accessible. Having demonstrated that world-class research is possible without elite institutional resources, I'm now focused on systematically eliminating artificial barriers for others.

During the earliest years, I had stints as a choir director at my local churches, majoring on vocals, drums, and piano. My BSc years were roundly punctuated by existential crises [1, 2]. My final year was spent transitioning from award-worthy social awkwardness to an unexpected reputation as a local clown, all while obsessively fine-tuning my Afro-pop dance skills and embracing the reveler lifestyle.