Team Members

Jiangshan Bai, or Shawn, is a research associate in the Xia laboratory. He received his Bachelor’s degree from Cuiying Honors College, Lanzhou University in 2020. Jiangshan has a broad interest in genomics, epigenomics, evolution, and bioinformatics. Previously, Jiangshan worked on the evolutionary history of the plant PINOID gene family at Lanzhou University, as well as G-quadruplexes (G4) distribution across the human genome at the Institute of Biophysics, Chinese Academy of Sciences. In the Xia lab, Jiangshan will mainly work on 3D genome organization by combining methods of experiments and bioinformatics. Outside the lab, Jiangshan enjoys music and Chinese calligraphy.

Twitter: @baijiangshan1

Favorite bio-molecule: G-quadruplex

Aidan Cordero is an undergraduate at MIT studying computer science and molecular biology. He is joining the Xia lab in January 2023.  

Favorite bio-molecule: NADH

Parmida is a Ph.D. student at MIT EECS. Broadly she is interested in developing/deploying machine learning and statistical methods to solve problems in (single-cell) biology. In her master's thesis conducted under Caroline Uhler's advice, she sought to analyze and create an embedding for the 3D genome organization in mouse embryos, using in situ genomic sequencing data. She obtained her bachelor's degree in Mathematics at the University of Michigan (class of 2021).

Favorite biomolecule: mRNA

Jimin Tan is a third-year graduate student studying computational biomedicine at NYU Grossman School of Medicine, co-mentored by Dr. David Fenyo and Dr. Aristotelis Tsirigos. He graduated from Grinnell College in 2018 with a double major in mathematics and computer science. He then studied at NYU Center for Data Science from 2018 to 2020 and received an M.S. degree in data science. He is broadly interested in using machine learning to understand biological systems. In the past, he worked on projects related to computer vision, natural language processing, and genomics. His recent projects include chromatin organization prediction and in silico genetic screening using machine learning models (C.Origami), tissue microenvironment study on high-plex fluorescence imaging using self-supervised learning, and machine learning-aided prognosis using X-ray/MRI imaging with semi-supervised learning. He looks to continue his exploration in genomics in the Xia Lab.

Twitter: @tan_jimin

Favorite bio-molecule: CCCTC-binding factor (CTCF)

Bailey is currently an undergraduate student at MIT pursuing a degree in bioengineering and chemistry. She is dedicated to researching microbiology, genetics, and immunology. She has previously conducted work in biological research studies at Arizona State University (2020-2022) emphasizing cell permeability and computational simulations. During that time she was awarded as an ISEF finalist (2021) and was designated “Future Innovator of the Year” by the Arizona Governor’s Celebration of Innovation forum (2021). 

Favorite bio-molecule: Epinephrine/Adrenaline 

Bo Xia is Gene Regulation Observatory Fellow and a Principal Investigator at the Broad Institute of MIT and Harvard. He is also a Junior Fellow of the Harvard University Society of Fellows. Xia’s career started as an independent research associate in Dr. Chengqi Yi’s lab at Peking University, leading the development of chemical technologies for analyzing epigenomes. Xia received his Ph.D. degree from NYU School of Medicine in 2022, co-mentored by Drs. Itai Yanai and Jef D. Boeke. During Ph.D., Xia pioneered a few innovative studies on genome regulation and its impact on human development and evolution, including revealing the genetic basis of tail-loss evolution in humans and apes. He is a recipient of multiple prestigious awards, including the 2020 Regeneron Prize for Creative Innovation, the Harold M. Weintraub Graduate Student Award (2022), and the NIH Director's Early Independence Award (2022). 

Twitter: @BoXia7

Favorite bio-molecule: 5-formylcytosine

Bingham Zheng recently received her Bachelor of Science in computational biology from the University of Pittsburgh. Bingham is primarily interested in using applied machine learning and data science to explore regulatory systems and mechanisms in genomics. She is very excited to be working on what she loves and is eager to keep learning.

Favorite bio-molecule: Pseudouridine