I am a Ph.D. student at MIT Statistics and Data Science Center, advised by Caroline Uhler. My research focuses on developing and applying machine learning methods such as manifold learning, deep learning, graphical models, and network analysis to high-throughput genomics data sets, with a special interest in understanding gene regulation. I often collaborate with biologists to design experiments.
- Y. Wang, C. Squires, A. Belyaeva and C. Uhler. Direct Estimation of Differences in Causal Graphs. (2018). Neural Information Processing Systems (NIPS), accepted. [PDF]
- A. Belyaeva, S. Venkatachalapathy, M. Nagarajan, G.V. Shivashankar and C. Uhler. Network analysis identifies chromosome intermingling regions as regulatory hotspots for transcription. (2017). Proceedings of the National Academy of Sciences (PNAS), 114(52), p.13714. [HTML]
Manifold Alignment for Single-Cell Genomic Datasets
Predictive Models for DNA Organization
Causal Inference in Biological Networks
Integrative Network Analysis of Functional Genomics Data