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Mario Juric distinguished Croatian astrophysicist & the leader of the Data Management team for LSST
Professor of Astronomy at the University of Washington, studied Physics at the University of Zagreb
Mario Jurić (to be pronounced as Yurich), distinguished Croatian astrophysicist,
earned his PhD from Princeton University
Large Synoptic Survey Telescope: Entering the Era of Petascale Optical Astronomy
I've decided to accept the admission offer of Princeton University Graduate School to enter the Department of Astrophysical Sciences's graduate program this fall !!!
Special thanks to everyone who made this possible, especially to Korado Korlevic of Visnjan Observatory where I've learned what science is and how to do it in creative ways and to Zeljko Ivezic, who gave me a chance to show what I know and can do in the "big league".
I'€™m interested in astronomical "Big Data"€™: developing and applying methods and algorithms that let us use large data sets to answer research questions. Major astronomical surveys of today are routinely collecting hundreds of terabytes of images, creating databases with billions of objects and several billion measurements. Large surveys astronomers are becoming part data scientists. In my research, I go where the data takes me - I'™ve worked on topics ranging from asteroids in the Solar System, Galactic structure, to the scale structure of the universe. My current focus is using survey data to understand the structure and evolution of the Milky Way. I also lead the Data Management team for the Large Synoptic Survey Telescope, a project to build the largest sky survey ever undertaken.
Mario Jurić Professor of Astronomy and Sr. Data Science Fellow at the eScience Instutite University of Washington
I am a professor of astronomy at the Department of Astronomy of the University of Washington, and a Senior Data Science Fellow of the University of Washington eScience Institute. I presently hold the Washington Research Foundation Data Science Term Chair.
This website keeps summaries and pointers to various papers, results, code, and talks related to my group's research. Use the top level menus to navigate the site. If you'€™re one of my collaborators looking for data, use the direct link provided (and username/password) to reach your destination.
I am the Project Scientist responsible for the Data Management System for the Large Synoptic Survey Telecope. The LSST Project Science group's task is to ensure LSST'€™s DM system delivers excellent science quality data products and services (we're the Product Owners of the LSST's Data Management system).
LSST'€™s team of 80+ researchers and IT experts, distributed among LSST in Tucson, Princeton University, University of Washington, SLAC, NCSA and IPAC, is developing the data processing system for LSST. The work includes the science pipelines (the image processing software to detect and characterize sources on images, detect moving objects, build co-added images, difference images, etc.), the middleware layer that manages the robust execution on large computing clusters, the distributed database (call qserv) to store and serve the resulting catalogs, as well as the user interfaces enabling the access to and analyses of data.
Given the size and expected quality of the dataset, we're breaking new grounds in virtually all these areas:
Image processing: We'€™re developing a new (general purpose) O/IR image processing system and researching algorithms capable of controlling measurement systematics to the level required for characterization of Dark Energy. We're pushing the state-of-the art in identification and linking of Solar System objects, to make LSST the most efficient asteroid-finding mission in history.
Middleware: Our science pipelines must scale from operating on a single laptop with a hard drive, to a 100,000+ core cluster with a mass store system (tape library), and live through 8+ years of construction and 10+ years of operations. The transient alert system must reliably difference images and detect transients in 60 seconds.
Infrastructure: 6.4 GB images will be transmitted within seconds over 10+ gigabit networks from Chile to NCSA in Illinois. LSST is one of the drivers of multi-gigabit long-haul network infrastructure build-up between Chile and North America.
Database: Our database must store 10+ PB of LSST catalogs, and enable complex analyses and data mining. We are developing a fault-tolerant distributed database product, based on existing well-developed open-source tools, to meet this need.
User interfaces: We are developing UIs designed to enable querying and visualization of the LSST data set, as well as enable machine-driven access to it.
More information can be found at the LSST Project web page, and the LSST Data Management pages.
Group Members
Mario Juric Melissa Graham Colin Slater Eric Bellm Jim Bosch (Princeton) David Ciardi (CalTech/IPAC)
Mario Juric, a self-described "data driven astrophysicist with interest in extracting knowledge from large survey datasets" has joined LSST as Project Scientist for Data Management. Growing up, Mario wanted to explore space and "boldly go where no one has gone before." However, he soon realized that none of the world's space agencies were interested in a 6'8" astronaut and decided to become an astrophysicist. "Alas, somewhere along the way I got my first computer and caught the love-to-think-about-code-and-algorithms bug," Mario explained. "€śI've spent the rest of my career trying to reconcile my interests in computing and astronomy, until I got to my current position contributing to the astronomical data generating machine of the next decade - LSST"€ť.
As Data Management Project Scientist, Mario's primary responsibility is to ensure that LSST software, infrastructure, and data products are capable of delivering the science envisioned for LSST. One of the biggest challenges in his new role will be to design a data analysis system that can robustly and automatically operate on orders of magnitude more data than experienced by current surveys.
"€śI always try to take up projects that I'€™ll be excited about every single day, and that push the boundaries of what is currently possible. To become a part of this extremely talented team that is hard at work creating a modern, efficient, data processing stack for LSST was hard to say no to - so much so that I've cut short my stay as a Hubble Fellow at Harvard University to join LSST this January."
A native of Croatia, Mario earned a B.Sc. in Physics at the University of Zagreb, followed by a Ph.D. in astrophysical sciences from Princeton University. Prior to his two years at Harvard, Mario spent three years as a postdoctoral fellow at the Institute for Advanced Study at Princeton. Most recently, he has worked on understanding Milky Way structure using SDSS and PanSTARRS PS1 surveys. The LSST, he says, will enlarge by two orders of magnitude the volume of the Galaxy able to be explored. For example, "by enabling researchers to trace the structure of the Galactic stellar halo out to the virial radius, including stellar streams and dwarf galaxies, the LSST promises to reveal much about how the Galaxy formed and evolved to its present state."
Mario'€™s research lives at the intersection of computationally intensive and observational astrophysics. "I'€™m especially intrigued by the problem (and promise!) of the extraction of knowledge, of learning, from large datasets. In my career, I've worked on asteroid surveys, with the Sloan Digital Sky Survey, and most recently with PanSTARRS. LSST has the potential to leave an even greater mark on astronomy than these surveys had. This brings with it a number of extremely exciting challenges, the most basic being: how do we convert all this wonderful data to useful information? Also, how do we create a code-base on which both LSST and future surveys will be able to stand? And how do we transform the community from the usual theorist/observer mental frame of reference, to one of computer-assisted knowledge discoverers?"
To facilitate that transformation, Mario advises the current and upcoming generation of students and future scientists to "be broad in your interests. Learn information theory. Use Python. Know C++, because - €śwhile LSST will be a giant leap forward in all the key science areas used to design it, it is the unexpected discoveries that will prove most fascinating. Only time will tell what those will be."
Many thanks to Dr. Zeljko Ivezic for suggesting this theme for the readers of the CROWN.
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