Omics, Images and data integration

Oana Zeleznik.
Instructor in Medicine, Harvard Medical School
Associate Epidemiologist, Channing Division of Network Medicine Brigham and Women’s Hospital

https://connects.catalyst.harvard.edu/Profiles/display/Person/150534

Title: A prospective analysis of circulating plasma metabolomics and ovarian cancer risk

Bio: My current research focuses on the understanding of cancer etiology through the application of cutting edge bioinformatics and machine learning methods to the integration of multiple omics, particularly metabolomics and genomics, with epidemiological data such as diet and lifestyle information. I received my PhD in Bioinformatics from the Graz University of Technology (Austria) where I focused on the integrative analysis of multiple omics data types. I am an Austrian Marshall Plan Foundation scholar and have a BSc in Computer Engineering and an MSc in Machine Learning, and did my postdoctoral training in cancer epidemiology at the Channing Division of Network Medicine (Brigham and Women’s Hospital and Harvard Medical School, Boston, USA) where I specialized in metabolomics. During my postdoctoral training, I received the Scholar in Training Award from the American Association for Cancer Research in 2018 and the The Early-Career Members Network of the Metabolomics Society Travel Grant 2019.

Roman Zeleznik
Department of Radiation Oncology
Dana-Farber Cancer Institute, Harvard Medical School
http://www.cibl-harvard.org/romanzeleznik

Title: Deep Learning in chest CT scans (to predict cardiac-related events)

Roman's talk will feature Python, GPUs using Tensorflow for deep learning of medical images. He work was recently featured in local and national press https://www.auntminnie.com/index.aspx?sec=rca&sub=rsna_2019&pag=dis&ItemID=127536

Bio
I received my Master of Science in Computer Vision and Graphics and Machine Learning from the Graz University of Technology in Austria after I earned a Bachelor of Science degree in Information and Computer Engineering from the same university. I am working in Hugo Aerts Lab at DFCI and am a PhD candidate in Computational Imaging and Bioinformatics at the Maastricht University. My current research focuses on deep learning as well as manual and automatic tumor segmentation in CT and MR images.