Ambuj Singh - Abstract and Bio
Abstract
Managing uncertainty in bioimage databases
Professor
Department of Computer Science
Department of Biomolecular Science and Engineering
University of California at Santa Barbara
Rapid advances in imaging technologies have opened the door for a new era of biological research, leading to quantitative understanding of complex systems. The realization of this potential, however, critically depends on the availability of tools and technologies to extract information from imaging data so as to achieve new biological insights and understanding. The inherent nature of bioimages leads to uncertainty in data, e.g., to which retinal image layer does a given pixel belong, what is the thickness of a neurite, how many photoreceptors are there, or what is the length of a microtubule. Probability distributions are the natural way to model such phenomena. The aggregation of data (such as the thickness of a layer measured at different spatial locations or the length of all microtubules in an image) again leads to probability distributions. I will discuss new probabilistic image analysis algorithms for measuring biological processes. These include image segmentation, computing sizes of neurons and their spatial distributions. Finally, I will discuss new techniques for querying probabilistic data through index structures that can store distributions at multiple resolutions and scale to large datasets.
Bio: Ambuj Singh
Ambuj Singh is a Professor of Computer Science at the University of California at Santa Barbara. He received his B.Tech. from the Indian Institute of Technology and PhD degree from the University of Texas at Austin. His current research interests are in graph querying and mining and bioimage databases. He has written over 130 technical papers in the areas of distributed computing, databases, and bioinformatics. He has graduated over 25 graduate students. He currently leads database development on a large NSF sponsored bioimage ITR project. He is also the founder of a startup.
