I obtained a Ph.D. degree in Bioinformatics in 2010 from the University of Texas at Arlington, where I was trained in bioinformatics and machine learning. In 2011, I joined the Institute for Diabetes, Obesity, and Metabolism at the University of Pennsylvania’s Perelman School of Medicine. Since 2018, I have focused on developing and applying artificial intelligence (AI) to the field of biology and medicine at Albert Einstein College of Medicine.
1. After joining Multi-rate Signal Processing and Computational Genomics Laboratories at UT Arlington, I had an excellent opportunity to research signal processing and bioinformatics. By modeling noise in array comparative genomic hybridization, I proposed a state-of-the-art denoising method to help detect cancer more accurately. I am also the first researcher in biochemistry to explore a new wavelet footprint in order to see essential ions in mass spectrometry. In addition, my collaborators and I developed a machine learning and wavelet-based framework for solenoid and non-solenoid protein recognition.
2. To further explore molecular biological processes, at the Department of Genetics and the Institute for Diabetes, Obesity, and Metabolism at the University of Pennsylvania’s School of Medicine, I combined next sequencing and signal processing to develop the following machine learning and wavelet-based methods.
3. To study the brain's functional organization in Parkinson's disease and dystonia, I worked with collaborators at the Feinstein Center for Neurosciences to develop and implement graph-theory-based frameworks for FDG PET and resting-state functional MRI.
4. Since my time at Albert Einstein College of Medicine in 2018, I have focused on developing and applying artificial intelligence to the fields of biology and medicine, and have also been working with collaborators to study the genomes of longevity and age-related diseases.