Biography
Dr. Khullar earned her bachelor’s degrees in Economics (Honors) and Mathematics, with a minor in Business Administration (2015), as well as a Master’s degree in Applied Mathematics/Statistics (2016), from Georgetown University in Washington, D.C. Georgetown’s Jesuit ethos of cura personalis—care for the whole person—sparked her lasting passion for public health and data-driven research. At the University of Wisconsin–Madison, she completed a Master’s degree in Computer Science (2021) and a Ph.D. in Biomedical Data Science (2024). During her doctorate, she built tools to help the community understand how genetic risk variants for brain-related disorders shape biological networks—spanning genes, regulatory elements, and proteins—underlying core disease phenotypes. Following her Ph.D., she contributed to pioneering single-cell tools at Seattle’s Allen Institute for Brain Science. Further, she has contributed to major U.S. consortia, which are shaping the field of neuroscience research today. In 2025, she moved from the U.S. to the U.K. to pursue a Postdoctoral Research Fellowship in Human Genetics with joint appointments at the Wellcome Sanger Institute and University of Cambridge. Over the years, she has enjoyed creating a widely-used educational YouTube channel devoted to advanced topics in areas of dire need for the global community.
Publications
1. Chirag Gupta, Noah Cohen Kalafut, Declan Clarke, Jerome J. Choi, Kalpana Hanthanan Arachchilage, Saniya Khullar, Yan Xia, Xiao Zhou, Mark Gerstein*, Daifeng Wang*, Network-based drug repurposing for psychiatric disorders using single-cell genomics, Cell Genomics, 2025.
2. John F. Fullard, Prashant NM, Donghoon Lee, Deepika Mathur, Karen Therrien, Aram Hong, Clara Casey, Zhiping Shao, Marcela Alvia, Stathis Argyriou, Tereza Clarence, David Burstein, Sanan Venkatesh, Pavan K. Auluck, Lisa L. Barnes, David A. Bennett, Stefano Marenco, PsychAD Consortium § (including Saniya Khullar), Kiran Girdhar, Vahram Haroutunian, Gabriel E. Hoffman, Georgios Voloudakis, Jaroslav Bendl*, Panos Roussos*, Population-scale cross-disorder atlas of the human prefrontal cortex at single-cell resolution, Scientific Data, 2025.
3. Saniya Khullar, Xiang Huang, Raghu Ramesh, John Svaren, Daifeng Wang*, NetREm: Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation, Bioinformatics Advances, 2024.
4. Pramod Bharadwaj Chandrashekar, Sayali Alatkar, Jiebiao Wang, Gabriel E. Hoffman, Chenfeng He, Ting Jin, Saniya Khullar, Jaroslav Bendl, John F. Fullard, Panagiotis Roussos, Daifeng Wang*, DeepGAMI: Deep biologically guided auxiliary learning for multimodal integration and imputation to improve phenotype prediction, Genome Medicine, 2023.
5. Minjie Shen, Carissa L Sirois, Meng Li, Yu Guo, Qiping Dong, Natasha Mendez-Albelo, Yu Gao, Saniya Khullar, Lee Kissel, Jonathan Bryan#, Soraya O Sandoval, Natalie Wolkoff, Amaya Contractor, Sabrina Huang, Tomer Korabelnikov, Birth Defects Research Laboratory BDRL, Jon Levine, Andre M.M. Sousa, Qiang Chang, Anita Bhattacharyya, Daifeng Wang, Donna Werling, Xinyu Zhao*, Species-specific FMRP regulation of RACK1 is critical for prenatal cortical development, Neuron, 2023.
6. Chenfeng He, Noah Cohen Kalafut, Soraya O. Sandoval, Ryan Risgaard, Carissa L. Sirois, Chen Yang, Saniya Khullar, Marin Suzuki, Xiang Huang, Qiang Chang, Xinyu Zhao, Andre M.M. Sousa, Daifeng Wang*, Brain and Organoid Manifold Alignment (BOMA), a machine learning framework for comparative gene expression analysis across brains and organoids, Cell Reports Methods, 2023.
7. Saniya Khullar, Daifeng Wang*, Predicting brain-regional gene regulatory networks from multi-omics for Alzheimer’s disease phenotypes and Covid-19 severity, Human Molecular Genetics, 2023.
8. Shuang Liu, Hyejung Won, Declan Clarke, Nana Matoba, Saniya Khullar, Yudi Mu, Daifeng Wang*, Mark Gerstein*, Illuminating links between cis-regulators and trans-acting variants in the human prefrontal cortex, Genome Medicine, 2022.
9. Chirag Gupta, Jielin Xu, Ting Jin, Saniya Khullar, Xiaoyu Liu, Sayali Alatkar, Feixiong Cheng, Daifeng Wang*, Single-cell network biology characterizes cell-type gene regulation for drug repurposing and phenotype prediction in Alzheimer’s disease, PLOS Computational Biology, (Cover image of July 2022 issue), 2022.
10. Chirag Gupta, Pramod Chandrashekar, Chenfeng He, Ting Jin, Saniya Khullar, Qiang Chang, Daifeng Wang*, Bringing artificial intelligence to research on intellectual and developmental disabilities: taking inspiration from neurological diseases, IDDRC 2022 special issue: Computational Neuroscience, Journal of Neurodevelopmental Disorders, 2022.
11. Anna S. Heffron, Sean J. McIlwain, Maya F. Amjadi, David A. Baker, Saniya Khullar, Tammy Armbrust, Peter J. Halfmann, Yoshihiro Kawaoka, Ajay K. Sethi, Ann C. Palmenberg, Miriam A. Shelef, David H. O’Connor, Irene M. Ong*, The landscape of antibody binding in SARS-CoV-2 infection, PLOS Biology, 2021.
12. Michala Skovlund Sørensen, Elizabeth C. Silvius, Saniya Khullar, Klaus Hindsø, Jonathan A. Forsberg, Michael Mørk Petersen*, Biochemical Variables are Predictive for Patient Survival after Surgery for Skeletal Metastasis. A Prediction Model Development and External Validation Study, The Open Orthopaedics Journal, 2018.
Teaching and Supervisions
Dr. Khullar is really interested in how data science and empathy can help transform the field of public health and uncover findings to help human lives. To this end, she is enjoying creating a potpourri of hopefully helpful educational content on bioinformatics/biostatistics, programming, AI/Machine Learning, neuroscience, STEM concepts, research/career guidance, mental health, and beyond (along with cat videos to boost mental health!). During my career, she has been fortunate to teach students from all ages (middle/high school, undergraduate, graduate) as volunteer teacher (even in villages), teaching assistant, lead instructor. She has created over 160 educational videos for her YouTube channel.
