IDRE recently awarded seven UCLA early career researchers with the IDRE Postdoctoral Fellowship to support their work. The IDRE Fellows will have direct access to and interact with the Research Technology Groups in the Office of Advanced Research Computing, as well as relevant IDRE affiliated faculty and researchers.
Meet the IDRE 2022-2023 Fellows
(In alphabetical order by last name)
Dr. Nannan Gao is a Postdoctoral associate with Daniel T. Blumstein in the Department of Ecology and Evolutionary Biology at UCLA. Her research mainly focuses on studying the relationship between urban biodiversity and city size by creating global urban biodiversity datasets that include small towns to megacities involving spatiotemporal advanced computing, statistical computing and data science on hoffman2.
About Dr. Gao
Dr. Gao received her PhD in Chinese Academy of Science, also studied human geography and urban planning in Peking University. She seeks to balance humans and animals in urban areas.
Dr. Alp Karakoç’s research interests include experimental and computational materials mechanics, fluid-structure interactions and emerging digitized manufacturing methods, through which different material length scales can be well comprehended and even prototyped. He has been developing micromechanical and multiscale models for hierarchical material systems comprising cellular core, fibrous and composite materials as well as metamaterials which are not readily available in the nature. In addition to the numerical studies, he has gained experience in experimental mechanics, where he carried out strain measurement and domain reconstruction studies with digital image processing and machine learning algorithms. At the moment, he has been focusing on biomedical simulation studies, especially virtualization of transcatheter aortic valve replacement (TAVR) so as to understand both the structural behavior of prosthetic valve and human tissue surrounding it. In recent years, he has also initiated several research cooperations on printed sensors and radiating elements, and their possible applications in biomedicine and healthcare. Owing to UCLA IDRE, he hopes that his investigations will lead to a healthier and happier society with lower risks of morbidity and mortality.
About Dr. Karakoç
Dr. Karakoç is a researcher in the Civil and Environmental Engineering Department, working with Dr. Ertugrul Taciroglu and collaborating with Dr. Olcay Aksoy at School of Medicine, Clinical Research in Interventional Cardiology. Dr. Karakoç received his PhD. degree in the field of Applied Mechanics at the Aalto University Department of Mechanical Engineering. He has been an active academic person serving as editor for the Journal of Research on Engineering Structures and Materials (RESM) and reviewer for several engineering and materials science journals by renowned publishers. For more information about Dr. Karakoç and his recent investigations, please, visit his ResearchGate and GoogleScholar profiles.
Dr. Seyoon Ko’s research applies statistical and computational methods to large-scale biological data. His current project involves developing scalable software for estimating population ancestry at the biobank scale. Recently, the scale of data in the field of genetics has quickly grown, and the dataset people are working on is now as large as 500,000 x 1,000,000 SNPs. In analyzing this scale of data, scalable algorithms and carefully managed utilization of computational resources is essential to perform the analyses in a reasonable time. He aims to select the most informative biomarkers based on a method that simultaneously performs feature ranking and clustering, then using the result to estimate the proportion of ancestry with an improved implementation of admixture estimation in the Julia programming language.
About Dr. Ko
Dr. Ko is a Postdoctoral Scholar working with Dr. Ken Lange and Dr. Hua Zhou in the Department of Computational Medicine. Dr. Ko’s research interests include large-scale computational methods in biostatistics and bioinformatics using parallel and distributed computing. He earned a Ph.D. degree in Statistics from Seoul National University in South Korea, as well as a M.S. degree in Computational Sciences and a B.S. degree in Physics, Mathematical Sciences, and Computational Science.
As an IDRE fellow, Dr. Lee will develop a novel machine learning method —dynamic hypergraph embedding— for science of science applications. The science of science is a rapidly growing field that uses quantitative scientific methods to examine the scientific process itself. With Professor Jacob Foster (UCLA Sociology), she will study how different scientific agents and objects (e.g., scientists, academic institutions, papers, journals, conferences, funding agencies, methods, theories, and topics of study) interact with each other by mapping them from hypergraph space to the Euclidean space in an interpretable, theoretically grounded manner. Her analysis will mathematically answer “when do important hyperedges arise?” which she hopes will in turn answer the question “when does innovative science happen?”
About Dr. Lee
Dr. Harlin Lee is a Hedrick Assistant Adjunct Professor at UCLA Mathematics. She received her PhD in Electrical and Computer Engineering at Carnegie Mellon University in 2021. She also has a MS in Machine Learning from Carnegie Mellon University, and a BS + MEng in Electrical Engineering and Computer Science from MIT. Her research is on learning from high-dimensional data supported on structures such as graphs (networks), low-dimensional subspace, or sparsity, motivated by applications in healthcare and social science. Dr. Lee’s lifelong vision is to use data theory to help everyone live physically, mentally and socially healthier.
Dr. Victoria Rolandi’s research at UCLA focuses on developing and implementing algorithms helpful in characterizing, modeling and controlling turbulent flows, which are of interest to a wide range of engineering applications. Controlling turbulent flows supports reducing human impacts of environmental and noise pollution and is a necessary step for optimization and progress in the design of vehicles. However, turbulent flows are characterized by complex nonlinear dynamics that span a wide range of spatial and temporal scales. This translates into substantial degrees of freedom, making certain analyses unaffordable regarding computational cost and memory allocation. For this reason, the applications of techniques such as resolvent analysis have mostly been restricted to laminar or moderately turbulent flows. In this context, her research aims to combine current concepts in developing computationally tractable techniques and reformulate algorithms for computational speedup and reduction in memory requirement, to extend the applicability of resolvent analysis to highly complex turbulent flows.
About Dr. Rolandi
Dr. Rolandi obtained a BSc in Mathematics for engineering from Politecnico di Torino and an MSc in Computational science and engineering from Politecnico di Milano. She later completed a Ph.D in Fluid Dynamics at the Institute Supérieure de l’Aéronautique et de l’Espace (ISAE-Supaero) before joining the Mechanical and Aerospace department at UCLA as a postdoctoral researcher at Professor Taira’s Lab. During her MSc, she studied advanced mathematical and numerical methods and specialized in computational fluid dynamics. During her Ph.D, she developed a global stability solver for compressible flows and investigated how compressibility affects the aerodynamic performance and the emergence of instabilities in the airfoil wake for applications in stratospheric flight and Martian exploration by drones. At Professor Taira’s Computational and Data-Driven Fluid Dynamics Group, she works on algorithms for reducing computational costs and memory allocations for analyzing complex high-dimensional fluid flows.
Dr. Olivia Sanderfoot studies the impact of wildfire smoke on birds and other wildlife. Her research is currently focused on modeling how wildfire smoke influences the behavior of birds in California, ultimately shaping species distributions. Dr. Sanderfoot’s work is highly interdisciplinary, drawing on knowledge, approaches, and tools from applied and quantitative ecology, atmospheric science, and statistical modeling. Many of her projects incorporate novel integrations of bird observations from long-term monitoring and community science programs with data on air pollution from ground-based monitors and air quality models. Her long-term goal is to assess the potential for wildfire smoke to act as an ecological disturbance and identify which birds may be most vulnerable to smoke in a rapidly warming world.
About Dr. Sanderfoot
Dr. Sanderfoot is a postdoctoral scholar in Dr. Morgan Tingley’s lab in the UCLA Department of Ecology & Evolutionary Biology. Before moving to Los Angeles, Dr. Sanderfoot conducted her doctoral research in the School of Environmental and Forest Sciences at the University of Washington in Seattle. Her dissertation explored how wildfire smoke and urban air pollution impacted detection of birds in Washington state. Born and raised in Wisconsin, Dr. Sanderfoot is driven by her passion for environmental policy and conservation, her love for birds, and her strong belief in the Wisconsin Idea, the philosophy that a university’s research should be applied to solve problems and improve the health, well-being, and environment of the community it serves.
Dr. Fangming Xie’s research involves developing high-throughput imaging technologies to investigate brain architecture. The mouse brain, with a size about that of a pea, contains more than 100 million cells that are diverse in molecular signature and spatial organization. Despite rapid progress in throughput and resolution, existing methods will take years to measure all the cells in the mouse brain, before any computational analyses to reveal cell types and their spatial organizations. Rather than performing experiments first and analyzing data second, Dr. Xie, together with his mentor Dr. Roy Wollman and labmates, is developing an integrated approach where machine learning is used to design smarter experiments that maximize the information content of spatial transcriptomics experiments.
Dr. Xie is also a Collaboratory Fellow of the UCLA Institute for Quantitative & Computational Biosciences, where he works in part with neurobiologists including Dr. Larry Zipursky, HHMI to help them analyze and interpret data from the mouse visual cortex.
About Dr. Xie
Dr. Xie is a postdoc in the Department of Chemistry and Biochemistry at UCLA. Prior to joining UCLA, Fangming received a BS degree in Physics at the University of Science and Technology of China. He visited UCLA as an undergraduate student for a summer, working with Dr. Robijn Bruinsma and Dr. William Klug to model the self-assembly of viral capsids. He then pursued a PhD at UCSD where his research focused on integrative analyses of single-cell transcriptomes and epigenomes of brain cells. Fangming loves neuroscience, genomics, and physics. He believes many parts of these disciplines can be brought together to sharpen our tools and advance our understanding of the brain.