Welcome! I'm Ilyes Baali
at Weill Cornell Medicine / Memorial Sloan Kettering Cancer Center
I completed my PhD at the Tri-I Computational Biology and Medicine program at Weill Cornell Medicine and Memorial Sloan Kettering Cancer Center, supervised by Quaid Morris, and I continue to work in the Morris Lab.
I am interested in the application of machine learning methods to functional genomics, particularly in understanding post-transcriptional regulation. My work improves the prediction of cellular binding sites of RNA-binding proteins by integrating both in vivo and in vitro data using a recalibration framework. I am also interested in developing genomic foundation models. Other than that, I collaborate with Michael Kharas' lab to understand the role of MSI2-RBP in AML.
Previously, I completed my BSc and MSc at Antalya Bilim University in Electrical & Electronics Engineering and Computer Engineering, under the supervision of Hilal Kazan.
Research Areas
RNA-Binding Proteins
Post-transcriptional regulation and how RNA-binding proteins choose their targets in cells.
Machine Learning for Genomics
Protein language models and genomic sequence-to-function models applied to functional genomics.
Translational Oncology
Translational control of leukemic stem-cell programs and driver-module discovery in cancer.
AI Agents for Science
Agentic pipelines that plan, call scientific tools, and synthesize evidence for biological questions.
Recent Publications
ZFP36L2 orchestrates stress-adaptive plasticity in regeneration and cancer
Nature • 2026
A dynamic RNA-binding protein module coordinates translation of leukemic stemness programs
In review • 2026
mRNABench: a curated benchmark for mature mRNA property and function prediction
Gen² Workshop on Generative AI in Genomics, ICLR • 2026
Uncovering the HOXA9 translational regulatory complex that promotes AML leukemic stem cells
Blood (ASH Annual Meeting abstract) • 2024
DriveWays: a method for identifying possibly overlapping driver pathways in cancer
Scientific Reports • 2020
MEXCOwalk: mutual exclusion and coverage based random walk to identify cancer modules
Bioinformatics • 2020
Machine learning for wearable IoT-based applications: a survey
Transactions on Emerging Telecommunications Technologies • 2019
Predicting clinical outcomes in neuroblastoma with genomic data integration
Biology Direct • 2018