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Due to the COVID-19 situation, Stanford University will not be able to offer on-campus programs this summer. Sadly, we are forced to cancel SBCC Workshop 2020.
We will issue full refunds in the very short future. This page will be updated within the next several days with more specific information.
The Genetics Department at Stanford University is offering an intensive three week workshop in Bioinformatics and Cloud Computing. This course is open to students in the S.F Bay area, Out of State and International students.
Bioinformatics is an interdisciplinary field which combines Biology, Mathematics, Statistics, Computer Science and Information engineering to interpret biological data. It is an useful technology that studies living beings at a molecular level. A field that uses computers to collect, organize and analyze biological information to answer questions on genetic makeup of the population, disease prevention and evolutionary biology.
Big Data is radically transforming healthcare. To provide real-time personalized healthcare, we need hardware and software solutions that can efficiently store and process large-scale biomedical datasets. In this class, students will learn the concepts of cloud computing and parallel systems' architecture. This class prepares students to understand how to design parallel programs for computationally intensive medical applications and how to run these applications on computing frameworks such as Cloud Computing and High-Performance Computing (HPC) systems. Prerequisites: familiarity with programming in Python.
Course Goals and Learning Outcomes
Through active engagement with and successful completion of this course, students will be able to:
Bioinformatics: Learn the fundamentals of Bioinformatics, connection between genomics, transcriptomics, proteomics and metabolomics
Computationally Intensive Medical Applications: Learn how to run large-scale applications of genomics transcriptomics, proteomics, metabolomics, and macrobiotics and how to run these applications in the cloud; process longitudinal data from wearable biosensors and run deep learning pipelines for biology and medicine.
Parallel programming: Explain the popular parallel programming paradigms; design and implement parallel programs; compile parallel programs and submit parallel jobs using popular parallel execution frameworks; and perform basic performance benchmarking and analysis.
Students must be enrolled in High School or Undergraduate Program.
Students in Computer Science and engineering majors can apply.
Students must have prior knowledge of programming languages such as Python/R.
Knowledge of Biology especially Genetics.
Program Dates, Cost and Time
Workshop dates: June 22nd 2020 - July 10th 2020.
Instruction: Mon - Fri. 9am to 12pm. Students will be meeting afternoons 2p to 5p for project work.
The course will include guest lectures from prominent Scientists.
Admissions will be offered on a rolling basis until the workshop is filled.
Cost: Program: $5000. Stanford accomodation can be provided upon request for Out of State and International students. The approximate cost of room+board is $3000 for three weeks.
School of Medicine, Genetics
Director, Stanford Center for Genomics & Personalized Medicine. Chair and Stanford W. Ascherman Professor, Chair of Genetics and Director of Genomics and Personalized Medicine at Stanford University School of Medicine. Michael Snyder is an American genomicist, systems biologist, and entrepreneur.
Director, Genetics, Bioinformatics Service Center.Director of the GBSC, responsible for the user experience on our genetics cluster, where he designs and implements technical solutions related to data privacy, data commons, and the support of various tools and technologies.
Program Manager. Manages GRIPS, SBCCW, and other research programs for Stanford Center for Genomics and Personalized Medicine (SCGPM).
Lead Bioinformatician. Working on structural variant calling with VA's Million Veterans Project. Software developer embedded within Stanford Health Care, worked on the Stanford Clinical Genomics Service, and was responsible for implementing computational pipelines on the cloud and conducting benchmarking for clinical data.