We are welcoming 7 faculty members to the Computational Medicine Department!

Our department is committed to transforming patient care by leveraging recent advances in artificial intelligence and genomics. A unique strength of UCLA is that on a single campus lies a prestigious medical school, a world-class research hospital, and nationally-ranked departments within the engineering school with significant strength in data sciences. The recent addition of seven faculty from multiple disciplines into the department continues the collaboration across the UCLA campus to enable scientific discovery in biomedical data sciences and then work hand in hand with the UCLA Health System to apply these discoveries to improve patient care. 

Valerie Arboleda, M.D., Ph.D. 
Assistant Professor
Dr. Arboleda is an Assistant Professor of Pathology & Lab Medicine, Human Genetics, and Computational Medicine at the David Geffen School of Medicine at UCLA. Dr. Arboleda graduated from the UCLA Medical Student Training Program (MSTP) with a Ph.D in Human Genetics and an MD in 2014. She went on to complete residency training in clinical pathology with an emphasis in molecular genetic pathology in the Department of Pathology and Laboratory Medicine at UCLA. She joined the faculty of the David Geffen School of Medicine at UCLA in 2018.  Dr. Arboleda was awarded the NIH Director’s Early Independence Award (DP5), which provides independent research funding to young scientists to start independent research careers without formal post-doctoral training. She was the recipient of the Daljit S. and Elaine Sarkaria Fellowship, and the Charles J. Epstein Pre-doctoral Award for Excellence in Human Genetics Research from ASHG, and was selected for the John H. Walsh Young Investigator Award in 2021.

Dr. Arboleda’s research program studies the causal relationships between genetic variation and human disease from the clinical perspective. A major challenge in genomics is how to interpret DNA variation in the context of disease diagnosis, therapeutics, and prognosis. Interpreting how the DNA code influences human disease is an important next step in Genomic Medicine. The lab leverages a bidirectional approach, starting from genes to phenotypes and then exploring clinical phenotypes within the electronic health record and identifying novel genetic associations. Arboleda's projects use patient derived samples to develop rare-disease model systems and functional genomic approaches (RNA-seq, ATAC-seq ChIP-seq, methylation-seq) to understand molecular dysregulation cause by these mutations and identify drug targets.  Her lab is also part of larger scale collaborations to explore the role of genetic ancestry with disease risk and looking at the shared genetic basis of monogenic and complex diseases.

Joshua Bloom, Ph.D. 
Asst Adjunct Professor
Dr. Bloom is a geneticist and computational biologist. He is interested in understanding the genetic basis of complex traits. He develops technology and computational methods to better understand the relationship between genetic variation and trait variation in populations with a primary focus on yeast genetics. His recent work involves high-throughput variant engineering with CRISPR/Cas9 to identify causal genetic variants as well as the development of new experimental and computational methods to comprehensively identify the heritable genetic factors underlying gene expression differences and other complex traits in very large populations. After studying Neuroscience at UCLA as an undergraduate, Josh earned his PhD in Molecular Biology from Princeton.

Jeffrey Chiang, Ph.D. 
Asst Adjunct Professor 
Dr. Jeff Chiang is interested in translating recent advances in big data and artificial intelligence to active clinical research, and his work addresses the computational challenges which arise when doing so. He works closely with clinical departments to identify risk factors and develop predictive models for negative outcomes such as age-related macular degeneration. On the way, Chiang and his team develop techniques which overcome limited data availability, combine and leverage health information from disparate sources, and are as free from bias as possible.

Dr. Chiang also leads the Computational Medicine Technology Core, which is involved in building the technical infrastructure for these computational models to be deployed in the clinic. Chiang obtained his B.S., M.A., and PhD in psychology (cognitive science) at UCLA. He then held research positions in industry and the Department of Computational Medicine prior to joining its faculty.

Loes Olde Loohuis, PhD. 
Assistant Professor

Loes Olde Loohuis is Assistant Professor-in-Residence in Psychiatry and Biobehavioral Sciences. Her research focuses on elucidating the underlying molecular mechanisms of severe mental illness, by utilizing and developing computational approaches to leverage multi-level data. Prior to joining UCLA as faculty she was a postdoctoral fellow at UCLA’s Center for Neurobehavioral Genetics, during which time she was awarded a K99/R00 award to identify electronic health record and genetic signatures that predict which patients with depression will develop bipolar disorder. 

Her UCLA lab aims to characterize and predict psychiatric disease trajectories using genetic and high-dimensional phenotypic data resources, especially electronic health records. Her lab has a focus on studying Latin American populations and the identification of genetic and environmental risk factors that contribute to the cause and course of illness in these admixed populations. To this end, she is co-leading the development of an NIMH funded biobank for Severe Mental Illness, Misión Origen, consisting of 100,000 participants from the Paisa region of Colombia. Dr. Olde Loohuis is also part of Populations Underrepresented in Mental illness Association Studies (PUMAS), a recently formed international collaboration of investigators from the US, South America and Africa which, through whole genome sequencing, aims to elucidate the genetics of severe mental illness across diverse ancestries and environments.

Dr. Olde Loohuis earned a PhD in Computer Science from CUNY Graduate Center under the supervision of Dr. Bud Mishra (from New York University).

Veena Ranganath, MD, MS, RhMSUS 
Associate Clinical Professor
Dr. Veena K Ranganath is a practicing adult rheumatologist and a clinical researcher. She is an Associate Clinical Professor in the UCLA Department of Medicine, Division of Rheumatology and is Co-Director of the Training Program in Translational Science (TPTS) track 3 program, UCLA Masters of Science in Clinical Research Program (MSCR). She is also Director of Rheumatology Fellowship Musculoskeletal Ultrasound Training. Her research focus is in rheumatoid arthritis (RA) outcome measures and more broadly in musculoskeletal rheumatology imaging (ie MSK Ultrasound, MRI etc).

Noah Zaitlen Ph.D. 
Associate Professor 
Dr. Noah Zaitlen is the principal investigator of a UCLA lab focused on computational and medical genomics that aims to improve health by identifying and characterizing the processes that are disrupted in human disease and mitigated through clinical treatments. His lab collaborates closely with both clinical neurologists and molecular biologists, generating primary functional genomic data tied to individual patients’ medical records from national and international medical institutions. Recent efforts have focused on identifying disease subtypes that have distinct biological mechanisms, prognoses, and treatment responses. These efforts rely on the development of novel statistical methods applied to high-dimensional genomic data and precisely collected phenotypes in large numbers of patients, with the ultimate goal of translating the findings to more effective medical care. The lab’s expertise draws from diverse fields including applied math, computer science, biostatistics, bioinformatics, evolutionary biology, medicine, and functional genomics. Dr. Zaitlen earned a PhD in bioinformatics and systems biology from the University of California, San Francisco, and he completed postdoctoral training at the Harvard School of Public Health. His work is supported by the ALS Association and the National Institutes of Health. Prof. Zaitlen recently received a five-year grant by NIH’s National Human Genome Research Institute as part of its newly established Impact of Genomic Variation on Function (IGVF) Consortium.

Hua Zhou, Ph.D. 
Dr. Hua Zhou, Professor of Public Health, has long term interests in numerical optimization problems, particularly those arising from statistical analysis of high-dimensional data. He developed highly scalable optimization algorithms for maximum likelihood estimation of some multivariate discrete distributions, calculation of importance sampling weights for large data sets, geometric and signomial programming, and a model-based movie rating method. He also proposed a new deterministic annealing method for global optimization, a quasi-Newton scheme for accelerating high-dimensional optimization algorithms, and a strategy for massive parallel computing using graphical processing units (GPUs). He studied new path following algorithms for regularization problems in statistics and machine learning, and successfully generalized them to least angle regression and convex programming. His recent development also includes scalable estimation algorithm for multivariate response generalized linear models and variance components models, fast matrix computation tools, and distance majorization for convex programming.
One of Dr. Zhou's research interests is to develop statistical and computational tools for analysis of large-scale genomic data. He developed penalization methods for association screening of genome-wide association (GWAS) and next generation sequencing (NGS) data, and a nonlinear dimension reduction approach for genotype aggregation and association mapping. Currently he is working on genome-wide QTL association mapping based on family designs, genotype imputation, transcriptomics data analysis based on RNA-seq technology, and statistical methods for analyzing microbiome data. Dr. Zhou is a developer of the comprehensive genetics analysis software Mendel, which is freely available at the UCLA Human Genetics Software Page. His current work includes implementing fast likelihood ratio test of variance components for genome wide QTL mapping and fast genotype imputation for pedigrees.

Media Contact: 

Leticia Ortiz | Marketing & Communications | Building a community around data science in biomedicine