Team

Principal Investigator

Ludmil B. Alexandrov, Ph.D.

I am a Professor in the Department of Cellular and Molecular Medicine and the Department of Bioengineering at the University of California San Diego. I am interested in disentangling the enigmatic secrets hidden in large omics datasets. My research is focused on developing novel machine-learning approaches and in leveraging these approaches to elucidate the basic molecular mechanism underlying cancer development and cancer progression. My hope is that this understanding will allow us to improve cancer treatment and cancer prevention.

Postdoctoral Fellows & Research Associates

Maria Zhivagui, Ph.D.

Specialized in cancer research, I chose this field given its enigmatic aspect inciting the need for discovery. I am a postdoctoral scholar, interested in cancer biology and phenomena. Contributing to cancer genomics, including genetics and epigenetics, is a privilege for me that may allow unraveling mysteries of carcinogenesis. Given that the realm of science is broad and interconnected, one of the aspects that interests me is bioinformatics. Therefore, I would like to expand my knowledge on complex data analysis and programming.

Marcos Diaz-Gay, Ph.D.

I am a postdoctoral scholar, who was originally trained in engineering. I have focused my career in biomedical research, with a strong interest in bioinformatics, particularly genomic data analysis. I have become fascinated by the development of new computational methodologies in cancer research allowing the discovery of the underlying mechanisms of cancer predisposition and progression. Making this knowledge accessible to the research community is one of my main goals at the Alexandrov lab.

Christopher Steele, Ph.D.

I am a research associate specializing in applying signature methodologies to copy number and structural data in cancer genomes. I have an interest in understanding some of the most complex cancers at a structural level, and this approach has proved fruitful in delineating some of the processes leading to structural alterations across cancer. This approach has promise to aid in patient stratification for therapy and to enhance prognostication for patients, and to aid the wider research community in better understanding their cancers of interest.

Shuvro Prakash Nandi, Ph.D.

I am a postdoctoral scholar at UC San Diego. Originally being a molecular biologist, I have acquired skills in measuring genome-wide DNA damage, repair, and mutagenesis during my Ph.D. I am interested in understanding the mutational patterns imprinted by the biological processes operative within somatic cells. Specifically, I am going to look at the early-stage events involving DNA damages and repairs that contribute to endpoint mutational signatures observed within cancer genomes. I hope this will have important implications for assessing individual risk of cancer predisposition to inform on lifestyle choice and stratified treatment for cancer therapy.

Raviteja Vangara, Ph.D.

I am currently a postdoctoral scholar in the Department of Cellular and Molecular Medicine at UCSD under the supervision of Dr. Ludmil B. Alexandrov. I am a versatile computational scientist and engineer, interested in developing novel technologies for various scientific applications using data science and Machine learning. I am specialized in unsupervised ML methods, specifically Non Negative Matrix and Tensor Factorization techniques . My current research activities utilize these machine learning approaches for accurate detection of mutational signatures in human cancer.

Erik Bergstrom, Ph.D.

I am a postdoctoral researcher in the group of Ludmil B. Alexandrov at the University of California San Diego. With a foundation in biomolecular engineering and functional genomics, I have focused my current training in bioinformatics encompassing machine learning and deep learning approaches. Specifically I am interested in developing novel computational methods to translate our understanding of cancer biology towards clinical integration for personalized medicine. My current research focuses on elucidating the mechanisms of clustered mutagenesis and performing AI-driven image analysis to detect genetic biomarkers directly from histopathological slides of cancer patients.

Graduate Students

Phoebe He, B.S.

I am a Ph.D. student in the Biomedical Sciences Program. Prior to starting my graduate studies, I worked for a pharmaceutical company developing biological and chemical treatments for cancer. In the Alexandrov Lab, I study the the genetic alterations in benign and early stage tumors to bring understanding to the causes and the early molecular events of this disease.

Azhar Khandekar, B.S.

I am a Ph.D. student in the UCSD Bioinformatics and Systems Biology program. I am interested in integrating large-scale omics datasets to elucidate the biological mechanisms that drive cancer formation and progression. Previously, I worked at the NIH on integrating methylation and somatic mutation data to study mechanism of mutagenesis, and currently in the Alexandrov Lab I am working on analyzing copy number variation and structural variants in normal and tumor genomes. Coming from a molecular biology background, my goal is to learn how to apply computational techniques to various omics datasets to derive biological insight.

Yuhe Cheng, B.S.

I am a Ph.D. student in the Bioengineering department. I am interested in combining experimental and computation methods to explore the phenotypic profiling of mutational signatures in tumors and normal tissues. My goal is to map the multi-omic landscape of mutations that provides biological insights into the onset of cancer.

Ammal Abbasi, B.S.

I am a Ph.D. student in the Bioinformatics and Systems Biology program. I am primarily interested in cancer genomics and the use of computational tools to uncover genetic driver events responsible for cancer onset and metastasis. More specifically, I am interested in the clinical applicability of mutational signature driven biomarkers for better personalized use of cancer therapies.

Mariya Kazachkova, B.S.

I am a Ph.D. student in the Biomedical Sciences program. I am interested in using omics datasets and computational approaches to better understand cancer and its development. Prior to starting graduate school, I worked with genome-scale CRISPR screening data at the Broad Institute. I aim to continue integrating computational tools and biological knowledge to gain insight into cancer development and progression.

Shams Al-Azzam, M.S.

I am a Ph.D. student in the Biomedical Sciences program (2021 cohort). I am a biologist by training. I obtained both my Bachelor's and Master's degrees in biology from UCSD, class of 2018. Before starting the Ph.D. program, I worked at various companies in the San Diego Biotech hub where I had research positions in drug discovery, diagnostics, and product development. My interest lies in the field of cancer genomics and in utilizing computational methods to analyze large data sets. If I am not in the lab, I usually golf at Torrey Pines, play tennis at UCSD, or attend a yoga class in Pacific Beach.

Ting Yang, B.S.

I am a Ph.D. student in the Bioengineering department. Before starting my graduate journey at UCSD, my undergrad research mainly focused on developing paper-based point-of-care diagnostic tools. In the Alexandrov Lab, I am interested in applying computational and biological tools to study the underlying mechanism that drives tumorigensis and rapid cancer progression. In my spare time, I enjoy reading, surfing, and swimming.

Jessica Au, B.S.

I am a Ph.D. student in the Bioinformatics and Systems Biology program. I graduated from UC Davis with a degree in computational mathematics. Prior to UCSD, I conducted research in a variety of fields like theoretical cardiology & developmental biology, as well as helped manage the ENCODE database as part of the Data Coordination Center at Stanford University. My research interests involve cancer genomics and the underlying mechanisms that drive tumor evolution and development. More recently, I've become interested in investigating how specific genomic alterations can be used as biomarkers in a clinical setting, while utilizing computational tools to make the study of personalized medicine all possible. When I'm not busy being a workaholic, I enjoy staying active and being a plant mom.

Douglas Meyer, B.S.

I am a Ph.D. student in the Bioinformatics and Systems Biology program (2022 cohort). Prior to starting graduate school I worked as an ORISE fellow at the FDA where my research focused on understanding the role of synonymous variants in human disease. My current research aims to develop new methods to detect germline-somatic interactions affecting oncogenic pathways. I am co-advised with the Hannah Carter Lab.

Rongjie Wu, B.S.

I am a Ph.D. student in the Bioengineering department. Prior to my graduate study, I obtained my Bachelor’s degree from UCSD in Bioengineering: Bioinformatics. My academic interest is to develop engineering approaches and computational tools on cancer omics dataset. In the Alexandrov Lab, I am mainly interested in discovering the somatic mutation landscape for non-alcoholic steatohepatitis through molecular-level sequencing techniques. Besides research, I also enjoy listening to classical music, practicing the piano and the violin, and learning new languages.

Staff

Mark Barnes, B.S.

I have a degree in Mathematics-Computer Science from UC San Diego and I am interested in the intersection between computer science, mathematics, and biology. I will be working as a bioinformatics programmer and will be developing software to help study mutational signatures and to understand the activity of mutational signatures in different cancer types.

Mousumy Kundu, B.S.

I have completed my graduation in Computer Science and Engineering. I am interested in applying Machine Learning algorithms to biological data for prediction of diseases towards personalized treatment. Within Alexandrov lab, I will be working on benchmarking of SigProfilerHotSpots tool as well as implementing, running, and optimizing the existing computational pipelines for analysis of cancer genomics data.