Dr. Knowles is a viral ecologist whose research focuses on whether viruses choose to kill or parasitize their hosts, what drives this decision, and what its outcomes are from molecular to global scales.
Dr. Danielle L. Schmitt (she/her) earned her BS in Chemistry-Biochemistry from Ball State University in 2012, where she performed undergraduate research supported by the Lewis Stokes Alliances for Minority Participation program. She earned her doctorate in Chemistry-Biochemistry at University of Maryland Baltimore County in 2017 with Dr. Songon An, where she focused on understanding the signaling pathways involved in the organization of multienzyme complexes for metabolism. Her graduate work was supported by the NIH T32 Chemistry-Biology Interface program. In 2018, Dr. Schmitt undertook her postdoctoral training with Dr. Jin Zhang in the Pharmacology Department at University of California San Diego, where she used genetically encoded biosensors to uncover mechanisms for compartmentalized AMP activated protein kinase (AMPK) activity. Her postdoctoral work was funded through the NIH/NCI-supported Biochemistry of Growth Regulation and Oncogenesis Cancer Training Grant, University of California President’s Postdoctoral Fellowship Program, and San Diego Institutional Research and Academic Career Development Award (IRACDA).
Dr. Mehdi Bouhaddou performed his postdoctoral training with Dr. Nevan J. Krogan at UC San Francisco (UCSF) in virology, mass spectrometry proteomics, bioinformatics, and network modeling as a member of the Quantitative Biosciences Institute (QBI) Coronavirus Research Group (QCRG). During his postdoc, Dr. Bouhaddou received F32 (NCI) and K99 (NIAID) awards to study phosphorylation signaling and protein-protein interactions in the context of infectious disease and cancer, co-mentored by Danielle L. Swaney. He developed virus-host interaction networks for SARS-CoV-2 and other coronaviruses, and systematically compared the molecular response to emerging SARS-CoV-2 variants to pinpoint variant-specific mechanisms of pathogenesis. Prior to his postdoc, Dr Bouhaddou worked at Roche with Drs. Li Yu and Antje-Christine Walz to develop pharmacokinetics and pharmacodynamics (PK/PD) mathematical models of epigenetic modifier drugs in cancer. He received his PhD in Biomedical Sciences advised by Dr. Marc Birtwistle at the Icahn School of Medicine at Mount Sinai in New York City, where he developed ordinary differential equation (ODE) models of cancer signaling to predict personalized therapeutic strategies tailored to specific cancer mutational contexts. Lastly, Dr. Bouhaddou received his Bachelor’s degree from UC Berkeley in Cognitive Neuroscience.
The Bouhaddou lab will officially open in the QCBio space in February 2023!
Dr. Balliu obtained a BSc. in Statistics from the Athens University of Economics and Business in Greece and a Ph.D. in Statistical Genetics from the Leiden University Medical Center in the Netherlands. She joined UCLA in 2018 as an Independent Fellow in the Department of Computational Medicine and later as a faculty in the Departments of Pathology and Computational Medicine. Dr. Balliu was the recipient of the Charles J. Epstein Postdoctoral Award for Excellence in Human Genetics Research from ASHG and
Dr. Balliu’s research interests focus on the development of novel statistical methodologies and computational tools for analyzing sparsely and irregularly sampled high-dimensional functional data such as those arising from high-throughput genomic assays, mobile phone sensors, and electronic health records. She applies these methods to understand the genetic, molecular, cellular, and environmental mechanisms underlying complex human traits and diseases. Dr. Balliu is especially interested in understanding how context-specific genetic regulation relates to metabolic and psychiatric phenotypes.
Dr. Mario Dipoppa seeks to understand the neural mechanisms underlying cortical brain functions. He obtained his Ph.D. at Pierre and Marie Curie University where he developed neural circuit models underlying working memory, under the guidance of Boris Gutkin. He then joined as a postdoc in the laboratory of Kenneth Harris and Matteo Carandini at University College London and was the recipient of the Marie Curie Fellowship. As a postdoc, Dr. Dipoppa combined large-scale neural recordings and computational models to study the mouse visual system. He then served as an Associate Research Scientist at the Center for Theoretical Neuroscience of Columbia University advised by Ken Miller. There, he combined deep learning with dynamical systems methods to study fundamental properties of visual computations. Dr. Dipoppa’s computational neuroscience laboratory continues to investigate how neural networks and dynamics in the cerebral cortex give rise to neural computation. Despite the complexity of their operations, cortical circuits are stereotypical which may underlie common computations. To discover the governing principles of these canonical circuits, Dr. Dipoppa’s laboratory combines state-of-the-art approaches, including biologically realistic neural networks, artificial (deep and recurrent) neural networks, and encoding and decoding models.
Follow us on Twitter for weekly updates on quantitative and computational biosciences research, training, and education!
The Institute for Quantitative and Computational Biosciences is a partnership between the UCLA College, the Health Sciences, and Engineering. Its associated faculty span more than twelve departments, and a broad range of biological and biomedical research areas – yet, the hallmark of QCBio faculty and their laboratories is the commitment to quantitative reasoning and the development of algorithmic and computational methods.
QCBio’s mission is to support quantitative and computational biosciences research, training, and education. As new measurement capabilities and public data bases are rendering the biosciences – whether basic, translational or clinical – increasingly data-rich, the challenges and opportunities for data analysis and interpretation are a hallmark of all aspects of biosciences research. Further, vast quantities of knowledge – the result of prior research investments – should be harnessed for computer-aided data interpretation and prospective prediction. Thus QCBio addresses the opportunities and challenges of data-driven and knowledge-based computational modeling in the biosciences.
QCBio fosters research into the development of algorithms, software, statistical, mechanistic, and dynamical models, as well as intra-institutional and international collaborations. QCBio provides research training and expert collaborative support via the Collaboratory. QCBio functions as the academic home and sponsor of the inter-departmental programs in Bioinformatics, Biomedical Informatics, and Computational and Systems Biology, at the graduate and undergraduate level. QCBio organizes a major summer undergraduate research program, Bruins in Genomics, that provides substantive graduate school preparation.
Some of the video recordings are available on the QCBio YouTube Channel.
Institute for Quantitative and Computational Biosciences