ImageXD 2019

Images Across Domains

XD

September 11, 2019 to September 13, 2019
9:00am to 5:30pm
Berkeley, CA

Register

ImageXD 2019 showcased recent advances in image processing algorithms and tools, which, together with an increased accessibility to modern imaging equipment, have made image data ubiquitous across many fields, with applications ranging from microscopy to radio astronomy.

SCHEDULE / AGENDA / PROCEEDINGS

The ImageXD annual conference aims to:

  1. Foster a cross-disciplinary community of image processing experts from academia, research, and industry;
  2. Develop a shared understanding of image processing data, algorithms, and software; and
  3. Learn about new tools and methods, and the applicability of these to various discipline-specific problems, through tutorials and collaborative work sessions.

CALL FOR ABSTRACTS  APPLY NOW, to attend ImageXD 2019. Applicants will be selected based on their submitted abstracts, and the top-submissions will be invited to give a 20-minute presentation. Top science picture submissions will also be featured on the event website. Application deadline: June 28 extended to July 18, 2019.

Organizing Committee
Daniela Ushizima, LBNL / UC Berkeley
Maryana Alegro, UCSF / UC Berkeley
Kevin Keys, UCSF / UC Berkeley
Henry Pinkard, UC Berkeley
Stéfan van der Walt, UC Berkeley

Speaker(s)

Stéfan van der Walt

Senior Research Data Scientist

Stéfan van der Walt is a researcher at BIDS, where he leads the Software Working Group. He is the founder of scikit-image and co-author of Elegant SciPy.  Stéfan has been developing scientific open source software for more than a decade, focusing mainly on Python packages such as NumPy & SciPy. Outside work, he enjoys traveling, running, and the great outdoors.

Daniela Ushizima

Consulting Data Scientist

Dani Ushizima is a data scientist at BIDS, where she leads the Center for Recognition and Inspection of Cells (CRIC), where her research focuses on imaging cancer cells for early-stage disease diagnosis; and she is also a staff scientist at Berkeley Lab, where she leads the U.S. Department of Energy Early Career Research Project on Image across Domains, Algorithms and Learning (IDEAL). With 20 years of research and development experience in Computer Vision, Dani has focused primarily on quantitative microscopy and microstructure classification, from materials science to biomedical imaging.

 

Maryana Alegro

BIDS Alumni - BIDS-BCHIS Data Science Fellow

Computer Scientist Maryana Alegro is a former BIDS-BCHIS Data Science Fellow, now an Associate Professional Researcher at UCSF.  At UC Berkeley, she was a post-doc at the UCSF Grinberg Lab, where she investigated and created computational tools to assist researchers in studying human brain and dementia, especially Alzheimer’s disease (AD). Such tools incorporate a combination of machine learning techniques with visualization and computer vision. She was also responsible for the design/construction of prototype imaging equipment at the lab.  She received her MS and PhD in electrical engineering from the University of São Paulo Polytechnic School. Her major experience is in medical imaging, especially in MRI and histological image analysis.

Henry Pinkard

Alumni - BIDS-BCHIS Data Science Fellow

Henry Pinkard is a PhD student in Computational Biology and member of the Computational Imaging Lab advised by Laura Waller. His work focuses on combining machine learning and optical microscopy to create computational imaging systems that can diagnose disease and understand biology in new ways. Before coming to Berkeley, he worked in the Vale Lab and the Biological Imaging Development Center at UCSF, where he created open-source tools for controlling microscopes, performing new types of 3D imaging experiments, and handling the large data streams these experiments produce.

Kevin Keys

Postdoctoral Scholar, Department of Medicine, UCSF

Kevin L. Keys is a postdoctoral scholar in the Burchard Lab at the UCSF School of Medicine. His biological research interests span computational genomics, bioinformatics, and statistical genetics, and his mathematical interests span scientific computing, high-dimensional statistical inference, and mathematical optimization.

At UCSF Kevin studies the genetic basis of pediatric asthma in admixed populations using multilayered data, including genomic, transcriptomic, methylomic, sociodemographic, environmental, and clinical measures.

Kevin completed his M.S and Ph.D in Biomathematics from the UCLA School of Medicine, where he developed open-source penalized regression methods for genetic association studies. He holds a B.S in Mathematics and a B.A. in Linguistics from The University of Arizona, where he did human evolution research with Michael F. Hammer and Joseph Watkins. He was a Fulbright visiting scholar in Jaume Bertranpetit’s lab at the Universitat Pompeu Fabra in Barcelona, Spain, where he studied the molecular evolution of metabolic networks in primates.