The Community Research Education and Engagement for Data Science (CREEDS) will advance research education by providing (a) an intensive, practical summer school experience in computational genomics for graduate students (b) DREAM Challenges and problem solving to tackle real-life biomedical questions for graduate students, (c) an online collaboration environment, and (d) active recruitment and retention of underrepresented groups. This program is funded through the NIH Big Data to Knowledge (BD2K), award #1R25EB020393.
Community Research Education and Engagement for Data Science
- To provide biomedical researchers with the practical skills and insight needed to harness the power and advance the promise of big data science to accelerate scientific discovery
- To develop an online social environment to facilitate the exchange of big data ideas, approaches and techniques between novices and experts
- To enhance the diversity of the biomedical big data workforce through targeted recruitment and retention of disadvantaged and underrepresented student populations
The intended participants are first- and second-year graduate students in computer science or biology. The summer school will provide training on basic computational skills and genomic information. This program strives to enhance the diversity of the biomedical big data workforce through recruitment of individuals from diverse racial, ethnic, cultural, and socioeconomic backgrounds. We encourage applications from economically underrepresented groups.
There is a competitive selection process for applicants; and for those selected there is no fee to attend the course.In addition, housing will be provided at no cost for accepted applicants from outside the NY metro area, and travel stipends are also available.
Applying to Summer School for Computational Genomics
For the June 18th - June 29th, 2018 session, enrollment applications are due by February 9th, 2018.Applicants for all activities must be U.S. citizens or permanent residents and will need to submit the following materials:
- A Letter of Recommendation. Postdoctoral Applicants: The Recommender must be your advisor. Other Applicants (non-Postdoctoral): The Recommender should be a faculty member who knows you well enough to determine if you would benefit from the training
- A 300-word description of what you hope to learn and what you plan to do with the knowledge gained from the summer school program
- A PDF copy of your transcript. Icahn School of Medicine requires international applicants to have their foreign transcripts translated into English by a certified foreign credential translation service (ex. WES, ECE, FIS). Applicants should request that these translated documents be sent directly to the Icahn School of Medicine at Mount Sinai along with official original transcripts from their institution.
Applications are due April 30, 2017.
The Academy will begin early June 2017.
The speed of advancement in data-rich biomedical sciences is limited in part by the lack of awareness and expertise in big data sets and tools, the absence of approaches that productively intersect disparate domains, and the underrepresentation of diverse groups in the workforce. The mission of the DREAM Challenges Academy is to provide early-career biomedical researchers with the practical skills and insight needed to harness the power and advance the promise of big data science to accelerate scientific discovery.
While methodologies for the analysis of cancer genomes and transcriptomes have undergone rapid benchmarking and standardization, our understanding of how best to analyze the cancer proteome remains less developed. The NCI-CPTAC DREAM Challenge is a community-based collaborative competition of researchers from across the world working together to answer key questions in cancer research, initially focused around the integration of genomics, transcriptomics and proteomics data. Algorithms developed in this effort will be applied to a broad range of tumor types and clinical questions, providing together the most comprehensive unified view of the cancer data to date.
Team problem-solving; Big data techniques to solve the DREAM Challenge: machine learning, data exploration/statistics (PCA, clustering), network inference, modeling the biology of the problem; Programming in High-Performance Computing environments.
You will work with peers as well as gain expertise through lectures (two 2-hour lectures per week for a month).
DREAM Academy Mentors
Pablo Meyer, PhD, Research Staff Member, IBM Research
Gaurav Pandey, PhD, Assistant Professor, Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai
Gustavo Stolovitzky, PhD, Adjunct Professor, Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai; Program Director, Translational Systems Biology and Nanobiotechnology, IBM Research
DREAM Challenges Academy Curriculum
Cost: There is no fee to participate in the DREAM Challenge Academy.
Eligibility: Intended participants for the DREAM Challenges Academy are senior undergraduate and graduate students, and post-doctoral fellows. Some knowledge of biology and/or programming is preferred. Applicants must be U.S. citizens or permanent residents, and based in or around New York City.
For more information on the DREAM Challenge community, please visit dreamchallenges.org.
This program strives to enhance the diversity of the biomedical big data workforce through recruitment of individuals from diverse racial, ethnic, cultural, and socioeconomic backgrounds.
- Patricia Kovatch, Associate Dean for Scientific Computing
- Andrew Sharp, PhD, Associate Professor, Genetics and Genomic Sciences
- Luz Claudio, PhD, Professor, Preventive Medicine
- C. Titus Brown, PhD (UC-Davis)
- Ilkay Altintas, PhD (UCSD)
- Terry Krulwich, PhD (ISMMS)