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Our research involves routine use of ultrafast DNA sequencing, proteomics, high-performance computing, mathematical modeling, and artificial intelligence/machine learning.

Big Data Analytics & Machine Learning

We use advanced machine learning approaches (artificial intelligence techniques) to detect cancer as early as possible and help guide treatment accordingly. 

Cancer Genomics

Using novel computational algorithms, we seek to identify new cancer mutations and understand why and where cancer mutations occur. 

Drug Discovery

Using high-throughput sequencing, we are investigating how the tumor genome (and epigenome) evolves in time and particularly upon drug treatment.


 We use high-throughput experimental approaches and pattern detection techniques to investigate what these genes do and the genomewide epigenomic patterns they mediate.


Additional information coming soon.

Mathematical Modeling

We use ChIP-seq, RNA-seq, computational modeling to investigate how genes are regulated in cancer cells and how gene regulation in cancer cells differs from normal cells.


We have developed of innovative computational approaches for analysis of high-throughput experiments (metabolomics, proteomics, high-throughout sequencing, etc) performed on cancer cells.

Spatial Computing

Additional content coming soon.

Two Graduating Computational Biologists Find Early Success in a Rare Partnership

Two Graduating Computational Biologists Find Early Success in a Rare Partnership | Neel and Katie

Weill Cornell Medicine Elemento Lab 413 E 69th St., Room 1404 New York, NY 10021 Phone: (646) 962-7604