Mostly LLM inference efficiency: the model, its memory, the serving policy, and the workload. Some resource-constrained and applied ML, too. (bold = me; * = equal contribution)
2026
Steven Kolawole, Virginia Smith
preprint, 2026
Pearse Jim*, Steven Kolawole*, Opegbemi M. Busoye, Glory Bagai, Virginia Smith
preprint, 2026
Mardiyyah Oduwole, Oluwatosin Olajide, Jamiu Suleiman, Faith Hunja, Busayo Awobade, Fatimo Adebanjo, Comfort Akanni, Chinonyelum Igwe, Peace Ododo, Promise Omoigui, Abraham Owodunni, Steven Kolawole
LM4UC @ AAAI 2026 · Springer CCIS
2025
Steven Kolawole, Keshav Santhanam, Virginia Smith, Pratiksha Thaker
NeurIPS 2025, Datasets & Benchmarks Track
Duncan Soiffer, Steven Kolawole, Virginia Smith
EMNLP 2025, Industry Track
Steven Kolawole*, Don Dennis*, Ameet Talwalkar, Virginia Smith
TMLR, 2025
Nnaemeka Obiefuna, Samuel Oyeneye*, Similoluwa Odunaiya*, Iremide Oyelaja*, Steven Kolawole
ES-FoMo @ ICML 2025
2024
Steven Kolawole*, Lucio Dery*, Jean-François Kagy, Virginia Smith, Graham Neubig, Ameet Talwalkar
arXiv, 2024
Busayo Awobade*, Mardiyyah Oduwole*, Steven Kolawole
AfricaNLP @ ICLR 2024
2023
Colin Leong, Herumb Shandilya, Bonaventure Dossou, Atnafu Tonja, Joel Mathew, Abdul-Hakeem Omotayo, Oreen Yousuf, Zainab Akinjobi, Chris Emezue, Shamsudeen Muhammad, Steven Kolawole, Younwoo Choi, Tosin Adewumi
AfricaNLP @ ICLR 2023
Nahid Alam*, Steven Kolawole*, Simardeep Sethi*, Nishant Bansali, Karina Nguyen
arXiv, 2023
2022
Steven Kolawole, Opeyemi Osakuade, Nayan Saxena, Babatunde Kazeem Olorisade
IJCAI 2022, AI for Social Good Track
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