Richa Batra

Richa Batra, Ph.D., is an Associate Professor at the Centre for Life Sciences, Mahindra University. She is a computational biologist whose work bridges data science, neuroscience, and systems metabolism.
She earned her Ph.D. in Computational Biology from the University of Heidelberg, Germany, where she developed computational methods to analyze time-lapse, high-throughput, image-based siRNA knockdown screens. Her research career has spanned academic and research institutions in Germany, Denmark, and the United States.
Her research at the University of Southern Denmark and Helmholtz Munich focused on computational approaches for network-based multi-omics integration and patient stratification in cancer and psychiatric disorders. At Weill Cornell Medicine, Prof. Batra contributed to large-scale initiatives including the Accelerating Medicines Partnership for Alzheimer’s Disease and the Alzheimer’s Gut Microbiome Project. Her research included large-scale postmortem brain metabolomics, identification of lipid signatures associated with disease heterogeneity, and characterization of systemic metabolic responses to dietary interventions. She also developed computational approaches for hypothesis-free feature selection and subgroup identification. Prof. Batra has authored more than 35 publications and received an independent research grant from the Alzheimer’s Association.
Her current research develops computational frameworks to understand phenotypic heterogeneity in aging, metabolism, neurodegenerative diseases, and pulmonary diseases, to enable more precise prediction and treatment of complex diseases.
She welcomes inquiries from prospective students and researchers interested in computational systems biology of complex diseases.

• 2009 – 2013 Ph.D. in Computational Biology | University of Heidelberg, Germany
  Ph.D. Thesis: Computational methods to analyze image-based siRNA knockdown screens.
• 2006 – 2008 M.Sc. in Bioinformatics | Bioinformatics Centre, University of Pune, India
• 2003 – 2006  B.Sc. in Microbiology | M.E.S. Abasaheb Garware College, Pune, India

• Selected Publications
• † corresponding author; * co-first author
• † [10] M. Dharani, M. Buyukozkan, R. Kaddurah-Daouk, J. Krumsiek†, R. Batra†“AutoSGI: Automated feature selection for clinical subgroup identification” Under Revision at Bioinformatics, 2026, https://doi.org/10.5281/zenodo.19438502
• † [09] C. Orozco, S. Worgall, J. Krumsiek, R.Batra†, P. Permaul†“Obesity reshapes inflammatory fatty acid pathways in pediatric severe asthma”Submitted, 2026, https://doi.org/10.5281/zenodo.18232545
• [08] A. Schweickart*, R. Batra*, et al.“Serum and CSF metabolomics analysis shows Mediterranean Ketogenic Diet mitigates risk factors of Alzheimer’s disease”NPJ Metabolic Health and Disease, 2024, doi: 10.1038/s44324-024-00016-3
• † [07] J. Hagenberg, M. Budde, T. Pandeva, I. Kondofersky, S. K. Schaupp, F. J. Theis, T. G. Schulze, N. S. Mueller, U. Heilbronner†, R. Batra †, J. Knauer-Arloth†“Longmixr: A tool for robust clustering of high-dimensional cross-sectional and longitudinal variables of mixed data types” Bioinformatics (Oxford, England), 2024, doi: 10.1093/bioinformatics/btae137 rpackage
• [06] R. Batra*, R. Uni*, et al.“Urine-based multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS”Molecular Medicine (Cambridge, Mass.), 2023, doi:10.1186/s10020-023-00609-6
• [05] R. Batra*, M. Arnold*, et al.“The landscape of metabolic brain alterations in Alzheimer’s disease”Alzheimer’s & Dementia, 2022, doi: 10.1002/alz.12714.
• [04] N. Houerbi, J. Kim, E. G. Overbey, R. Batra, et al. “Secretome profiling reveals acute changes in oxidative stress, brain homeostasis, and coagulation following short-duration spaceflight”Nature Communications, 2024, doi: 10.1038/s41467-024-48841-w
• † [03] R. Batra†*, N. Garzorz-Stark*, F. Lauffer*, M. Jargosch*, et al.“Integration of phenomics and transcriptomics data to reveal drivers of inflammatory processes in the skin” bioRxiv, 2021, doi:10.1101/2020.07.25.221309
• [02] N. Alcaraz, M. List, R. Batra, F. Vandin, H. J. Ditzel, and J. Baumbach, “De novo pathway-based biomarker identification” Nucleic Acids Research, 2017, doi: 10.1093/nar/gkx642.
• [01] N. Alcaraz*, J. Pauling*, R. Batra*, et al.“KeyPathwayMiner 4.0: Condition-specific pathway analysis by combining multiple omics studies and networks with Cytoscape” BMC Systems Biology, 2014, doi:10.1186/s12918-014-0099-x

• 01/25 – 04/26  Independent Research Scientist, Computational Systems Biology Affiliate – Weill Cornell Medicine, New York, USA
• 01/20 -12/24   Sr. Research Associate (Deputy Lead) | Research Associate | Postdoctoral Associate, Weill Cornell Medicine, New York, USA
• 06/16 – 09/19  Team Lead | Postdoctoral Associate, Technical University of Munich & Helmholtz Center Munich, Munich, Germany
• 08/15 – 05/16  Bioinformatician, Danish Cancer Society, Copenhagen, Denmark
• 08/13 – 07/15  Postdoctoral Associate, University of Southern Denmark, Odense, Denmark

Research focus is on understanding why diseases affect people differently by integrating molecular, clinical, and longitudinal data to identify disease patterns, subgroups, and trajectories.

Multi-Omics Integration: Integrating multiple molecular data types provides a systems-level view of disease biology, enabling the identification of molecular processes and biomarkers that would be missed by studying individual data types. This work includes KeyPathwayMiner (Alcaraz et al., 2014) and AuGER (Batra et al., 2021).

Subgroup Identification: Identifying biologically meaningful disease subgroups helps distinguish different disease mechanisms and trajectories, enabling more precise prediction of disease progression and treatment response. This work includes Pathclass (Alcaraz et al., 2017; List et al., 2020), Longmixr (Haggenberg et al., 2024), and AutoSGI (Dharani et al., 2026).

Disease Biology: Linking molecular signatures to disease pathology helps identify mechanisms, reveal clinically relevant variation, and uncover targets for intervention. Cohort based studies examined metabolic signatures of neuropathology in Alzheimer’s disease (Batra et al., 2022), convergent metabolic signatures across tauopathies (Batra et al., 2024), systemic metabolic response to dietary intervention (Schweickart et al., 2024), molecular signatures of ARDS across COVID-19 and bacterial sepsis (Batra et al., 2022, 2023), and variation in lipid signatures across diverse ethnoracial groups (Schweickart et al., 2026).