Matti Corren’s research aims to build machine learning-based classifiers based on methylation and cell-free DNA for early-stage detection of precancer lesions in tumor patients. He combines cancer genomics mechanisms with clinical observational phenotypes to define early lesions that prime cells for tumor development.

Dr. Antti Haekkinen has in-depth expertise in computational methods for phenotype profiling and an established track record of analyzing multi-omics data of ovarian cancer. His long term vision is to advance personalized medicine by the rational design of combination therapies that leverage mechanisms in noncoding cancer genomics, DNA repair, and the translational potential of clinical data.

Mirue Kang is an  undergraduate student at Brandeis University. Her research focuses on defining genomic loci that confer risks for cancer phenotypes, identifying differences between normal and epithelial cancer stem cells, and applying genetic risk scores to personalized medicine.

Robert Khashan is a medical student specializing in pediatric tumor biology. He combines genomic, proteomic, and expression data with innovations in data science for an in-depth analysis of oncogenic pathways that drive lymphomas and neuroblastomas. His long-term goal is to advance our biological understanding of pediatric tumors and the efficacy of personalized therapies for children with cancer.

Wanru Liu is a master’s student in the Global Health and Population program of the Harvard School of Public Health. Her primary focus is on biostatistics and cancer epidemiology. By amalgamating data from both fields, she aims to address health disparities in cancer and gain genomic insights that could improve screening methods and population health.

Dr. Platon Lukyanenko intersects cancer biology with machine learning methods to decode multifaceted roles of oncogenic mutations. By training autoencoder models on multiomics data, he aims to illuminate the coordinated activity of mutations in tumorigenesis and pave the way for genome-inspired personalized combination therapies.

Sara Uhan is a PhD student and Fulbright Scholar . She studies SUMOylation as a pivotal post-translational modification in tumor cells and its effect on the localization, stability, and activity of proteins. She pairs experimental with computational strategies to define SUMO-specific networks in cancer cells and translate them into new drug targets for precision medicine.

Eli Wang is a master’s student in the Computational Biology and Quantitative Genetics program of the Harvard School of Public Health. He focuses on single-cell epigenomics and studies regulatory regions in cancer genomes. He has mastered a diverse repertoire of tools and pipelines to process and harmonize these data and combines them with statistical models in cancer genomics.

Yuxiang Zhou has extensive experience in the design of differential expression analyses and their application to diverse diseases. His current work combines these tools with functional experimental data to study the effect of somatic mutations on cancer gene expression. He further aims to characterize complex oncogenic signaling changes that cause malignant transformation.

Aniket Dey studies interactions of tumor cells and the microenvironment. He pairs functional data from cell lines with genomic profiles from cancer patients to dissect mechanisms that allow tumors to spread to distant organs. He also combines tumor biology with aspects of public health for harmonizing the assessment of personalized cancer risk across demographics.

Ian Lo is a master’s student in the Health Data Science program at the Harvard T.H. Chan School of Public Health. Her research interests include the use of machine learning and other computational approaches to studying human genetic diseases and disorders.

Vatsal Parikh’s research focuses on applying machine learning techniques to tumor expression profiles, pairing them with data from scalable experimental assays, and inspiring the design of targeted therapies. He also designs interactive web resources to communicate his science to a broad audience.

Yi-Ting Tsai is a master’s student in the Health Data Science program at the Harvard T.H. Chan School of Public Health. Her research focuses on developing new computational strategies for maximizing the value of sequencing in patient care by pairing clinical with genome data.