Members

Adina Matache is a part-time Senior Research Associate in the Division of Mathematical Oncology and Computational Systems Biology at City of Hope. She received her Ph.D. in Electrical Engineering from University of California, Los Angeles (UCLA) and her B.S. and M.S. degrees, both in Electrical Engineering from University of Washington. 

Adina has extensive research experience with theoretical analysis and modeling and simulation of digital communication systems, including terrestrial and satellite communication systems. She gained her expertise in digital communications and information theory while working for commercial companies and Federally Funded R&D Centers. At City of Hope, she applies her knowledge in communication and information theory to research immune cell signaling in breast cancer. She hopes this research can lead to new insights into how immune cells are signaling and processing molecular information in healthy donors and how the intracellular signaling pathways may be impaired in breast cancer patients.

Martina Conte is a GSP-Visiting Scholar in the Division of Mathematical Oncology and Computational Systems Biology, working with Dr. Rockne on data-driven and data-integrated models for studying Car T-cell based therapy within brain tumors. She has a position as a Postdoctoral Fellow at the Politecnico of Torino (Italy), while she carried on her doctoral research projects at the Basque Center for Applied Mathematics (BCAM - Bilbao) receiving her PhD at the University of Basque Country (Spain). Her Ph.D. research projects collect the development and analysis of several multiscale mathematical models analyzing various aspects of brain tumor growth and migration into the brain. 
 
Currently, her main research lines focus on (i) describing the process of cell migration in the ECM, with a special attention to the case of tumor invasion, (ii) studying tumor-endothelial cells interactions and specific aspects related to the tumor angiogenesis process (e.g. hypoxia, treatment resistance), and (iii) analyzing signal propagation in the brain (through network analysis) and its relationship with physiological and pathological conditions. In the different project, great attention is paid to the integration of biological data into the mathematical models, that is one of the main goals of her Visiting Programme at City of Hope.
 

Yu-Hsuan is currently a graduate student in the Ph.D. in biological sciences program at Irell & Manella Graduate School of Biological Sciences in City of Hope. Yu-Hsuan received his bachelor’s and master’s degrees in clinical laboratory sciences and medical biotechnology from National Taiwan University in Taiwan, where he started his research focused on targeted therapy resistance and drug repositioning in leukemia.

With his enthusiasm for combining his previous training in hematology and cancer research with bioinformatics and mathematical modeling, he is currently working with Dr. Ya-Huei Kuo (Department of Hematologic Malignancies Translational Science, Beckman Research Institute) and Dr. Russell Rockne (Department of Computational and Quantitative Medicine, Beckman Research Institute) to uncover the molecular basis of leukemia stem cells and to develop novel therapeutic strategies.

Maxim Kuznetsov, Ph.D., is a Postdoctoral Fellow in the Division of Mathematical Oncology and Computational Systems Biology within Beckman Research Institute of City of Hope. He focuses on mathematical modeling of various types of antitumor treatment with the key goal of suggesting the ways to improve their efficacy in clinical setting.

Dr. Kuznetsov received his doctorate in Physics and Mathematics with dissertation on the topic “Mathematical modeling of tumor growth under the influence of antiangiogenic therapy and radiotherapy”. In the last years he was working in P.N. Lebedev Physical Institute, where his main research interest was spatiotemporal optimization of external beam radiotherapy. Another direction of his research was the investigation of formation and evolution of spatiotemporal structures in general nonlinear systems. In City of Hope he applies his expertise in particular to the task of optimization of targeted radionuclide therapy and immunotherapy.

Alexandra Levy is a graduate student intern, pursuing her M.S. in Human Genetics and Genomic Data Analytics from Keck Graduate Institute in Claremont, California. Her interests center around glioblastoma research, specifically the immune response to angiogenesis in glioblastoma models, and polyclonal tumor evolution. 

She is currently working on her capstone project alongside Dr. Russell Rockne (Mathematical Oncology, Beckman Research Institute), Dr. Sergio Branciamore (Computational and Quantitative Medicine, Beckman Research Institute), and Dr. Floris Barthel (TGen, Computational and Quantitative Medicine, Beckman Research Institute).
 
 

Babgen Manookian, Ph.D. is a postdoctoral fellow in the Department of Computational and Quantitative Medicine in the Division of Mathematical Oncology and Computational Systems Biology at City of Hope. He works on developing and applying dynamic Bayesian probabilistic algorithms to investigate allosteric mechanisms and causal relationships in transmembrane proteins.

Prior to his fellowship at City of Hope, Babgen completed his doctorate studies at the University of Massachusetts Amherst where he received a Ph.D. in computational chemistry. His thesis focused on investigating the kinetics and thermodynamics of biofuel chemistry within the confined pores of zeolite acid catalysts. Specifically, he tackled the question of how confinement plays a role in key reactions related to the production of biofuels and other energy products. In his research projects he worked with advanced chemistry software in both the static (quantum chemistry) and dynamic (molecular dynamics) realms of modeling. In the latter years of his doctoral work, he developed a microkinetic model of biofuel reactions in zeolites and investigated the concurrence of kinetic and thermodynamic control of product formation. He is currently shifting his focus to a data science approach to study biochemical systems relevant to the field of drug discovery. Babgen also works as a professor of chemistry and has experience teaching at local community colleges in Massachusetts as well as in Los Angeles.

Lisa Uechi received her Ph.D. and M.Sc. in Informatics (Computer Science) from the Department of Intelligence Science and Technology, Kyoto University, Japan. The research subjects of her graduate program included computational biology and applied mathematics for the prediction of economic activities. In her research projects, she developed mathematical models for analyzing the dynamical pattern changes of population density and evaluating the damage expansion rate via a metabolic networks. She also proposed a novel method that could predict significant financial sectors along with the changes in economic trends by collaborating economists at Boston University.
 
After her graduate programs, she focused on information theory-based methods for data mining and applications for genetic data.  She proposed mathematical models and algorithms based on information theory to analyze the dependencies among the observed phenotypes and genetic variants. She created a model for evaluating the reliability of multi-variable interaction analysis based on the concept of communication channels for data with noise and missing values. She is currently working on the development of mathematical models based on thermodynamics and information theory for Acute Myeloid Leukemia.
 

Konstancja Urbaniak is a postdoctoral fellow in Dr. Sergio Branciamore’s group. She embarked on her PhD journey through an A*STAR program, a joint venture between the University of Manchester in the UK and the A*STAR Bioinformatics Institute in Singapore. Her research focused on combining two fields biochemistry and bioinformatics to decipher the code for SUMO reader: substrate interactions. As a postdoctoral fellow, she has been working on employing causal probabilistic models - Bayesian networks to variety omics datasets, including scRNAseq, spatial RNAseq, and FACS to reveal complex and non-linear relationships in biological systems.