Steven Kolawole
PhD Student, Carnegie Mellon University
Language Technologies Institute & Machine Learning,
School of Computer Science
Advised by Professor Virginia Smith
Summer 2026: Applied Scientist Intern, AWS Bedrock — Inference Optimization Science

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

Hi, welcome to my website! :)

Research

My research explores efficiency problems in machine learning systems, focusing on how intelligent algorithmic design can eliminate resource requirements that limit broader participation in AI development and deployment.

I'm currently investigating several interconnected approaches: agreement-based cascading methods for efficient model routing, semantic approaches to extracting parallelism from natural language queries, forward-pass techniques for model compression, memory-efficient KV-cache management for long-context reasoning, and compression for on-device (edge) deployment. My broader research vision centers on demonstrating that accessibility and performance are not opposing forces; that thoughtful design choices can achieve both.

Selected Publications

Epiphany-Aware KV Cache Eviction Without the Attention Matrix
Steven Kolawole, Virginia Smith
under review (EMNLP 2026)
What Survives When You Compress a Recursive Reasoner for the Edge?
Pearse Jim*, Steven Kolawole*, Opegbemi M. Busoye, Glory Bagai, Virginia Smith
under review (EMNLP 2026)
Agreement-Based Cascading for Efficient Inference
Steven Kolawole*, Don Dennis*, Ameet Talwalkar, Virginia Smith
TMLR 2025
Semantic Agreement Enables Efficient Open-Ended LLM Cascades
Duncan Soiffer, Steven Kolawole, Virginia Smith
EMNLP 2025 Industry Track
PARALLELPROMPT: Extracting Parallelism from Large Language Model Queries
Steven Kolawole, Keshav Santhanam, Virginia Smith, Pratiksha Thaker
NeurIPS 2025 D&B Track
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
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 20+ underrepresented graduate school aspirants at STEM for Development, helping them clarify research goals and optimize their applications for Western graduate programs.

I lead ML Collective Africa, a pan-African sub-community (262 members) of ML Collective, fostering research outside formal academic structures. We support under-served, early-career researchers through research focus groups, mentorship programs, research training, and resources---enabling members to publish at top venues despite limited institutional resources.

I have organized annual fundraisers since 2022 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 LFC 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 across different parts of the globe, totaling around 27 tech conferences. 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 eliminating barriers to AI accessibility.

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.