[QSB-grads] NSF NRT-IGE- APPLICATION- Interdisciplinary Computational Graduate Education

[David Ardell]

Dear QSB Faculty, Postdocs and Graduate Students,

I wanted to let you know about an exciting new NSF-funded program in Interdisciplinary Computational Graduate Education (ICGE) at UC Merced. Only QSB first-years are invited to apply this year, but as we enter recruitment season, it will be great for all of us to know about this program going forward.

Cheers,
Dave


Dear First Year Doctoral Students,
I am writing to invite you to participate in the NSF funded National Research Training: Innovations in Graduate Education, “Interdisciplinary Computational Graduate Education (ICGE).” Applications will be accepted until 12:00 noon Monday, November 7.  First year doctoral students in all graduate programs with interests in the computational sciences are encouraged to apply.
Please note we recognize that two NRT programs at UC Merced are accepting applications. Acceptance in one program does not preclude acceptance in the other—they are organized such that interested students may easily participate in both.
The program is designed to provide the following benefits to graduate students:
·      Experience working on innovative projects with interdisciplinary teams
·      Networking with and mentorshipfrom partners from the National Laboratories, NASA, and industry and faculty from multiple disciplines.
·      Instruction in project management and teamwork pertaining to managing knowledge-intensive projects
·      Fundamental research skills including writing scientific papers, literature reviews, scientific presentation, and research ethics
·      Computational skills including techniques for managing data, coding, data structures, visualization, and analytic techniques
·      Career preparation to work in and with industry and the National Labs as well as the academy
In addition, the program is designed to contribute to greater diversity within the computational sciences through increased retention and degree completion, especially for members of underrepresented groups and first-generation doctoral students.
Participating students will be expected to attend a program launch Dec. 14 and Dec 16, which will include our partners from industry, the National Labs and NASA.  Throughout the spring semester, participants will meet every Friday afternoon. These sessions will be devoted to hands-on experience working on interdisciplinary project teams and faculty-led instructional modules on interactive programming, the Linux operating system and shell scripting, compiled programming, high performance clusters and remote supercomputers, team science and project management, and practices and habits of successful graduate students in the interdisciplinary computational sciences.
Participants will receive travel funds to visit with the National Labs, NASA and/or industry partners and may receive funding to present their research at professional conferences. Participants may also be eligible for summer internships with partner organizations.
To apply:
1.     Complete the attached short application by 12 noon, Monday, November 7 and email it to graddean at ucmerced.edu<mailto:graddean at ucmerced.edu>.
2.     Ask your advisor/mentor/graduate program chair to send a letter of endorsement, recognizing that if accepted into the program you are committing to regular attendance on Friday afternoons throughout the spring semester.
Feel free to contact any of the participating faculty with any questions you may have —stop by our offices or email us. We really do want to hear from you! Faculty include:

·      Prof. Michael Colvin, Chemistry & Chemical Biology & QSB
·      Prof. Sayantani Ghosh, Physics & BEST
·      Prof. Arnold Kim, Applied Mathematics
·      Prof. Paul Maglio, Cognitive & Info. Sciences & Electrical Engineering &Computer Science
·      Prof. Ashlie Martini, Mechanical Engineering & BEST
·      Prof. Juan C. Meza, Applied Mathematics and Dean, SNS
·      Prof. Suzanne Sindi, Applied Mathematics, Cognitive & Information Sciences, & QSB
·      Prof. Mukesh Singhal, Electical Engineering and Computer Science
·      Prof. Michael Spivey, Cognitive & Information Sciences
·      Prof. Marjorie Zatz, Sociology and Graduate Dean
We look forward to seeing your application!
Yours,
Marjorie S. Zatz, Ph.D.
Vice Provost and Graduate Dean
Professor of Sociology
Graduate Division
University of California, Merced
5200 N. Lake Rd.


Instructional Module and Project Development Meetings
Faculty and students will meet every Friday afternoon throughout the spring semester. The first half-hour will be for social support over a working lunch. Instructional modules will run for 60-90 minutes each week, totaling 15 hours. In-class, faculty-supervised team projects will be conducted for the remaining 2.5 hours each week (37 hours), for a total of 52 hours over the course of the semester. Faculty mentors from among the core researchers will be on-site to advise the teams, and will coordinate as needed with the industry/lab partners. A cornerstone of this pilot project is periodic review and input from our partners/advisory board members, who will join us on a regular basis to provide guidance from the end user perspective. Some modules are best delivered in short segments throughout the semester, while others require longer and more focused delivery time to master key skills. Throughout, students will apply the instructional material to their team projects. See Figure 1 for alignment of instructional modules with Student Learning Objectives. The objectives (including total instructional time and faculty leads) outcome, and assessment of the modules are provided below:

Module 1: Practices and Habits of Successful Graduate Students in the Interdisciplinary Computational Sciences. Lead faculty: Meza and Zatz
Format: In addition to the orientation, four 30-minute instructional sessions plus continuous hands-on experience in project teams and at final group presentation.
Objectives: Provide students expectations and goals for graduate school and beyond. Student Learning Objectives: 1 (Professionalization) and 5 (Career Preparation).
Description: This module will provide students the fundamental, non-technical skills needed in graduate school such as clarifying the nature of the Ph.D. and how it differs from undergraduate studies, ethical practices in research, and preparing publications, presentations, webpages, curriculum vitae, etc. through participation in discussions and activities with an inclusive community of scholars. In addition, students will be introduced to the value of interdisciplinary and transdisciplinary research and to the broad spectrum of career opportunities in the computational and data sciences. To hone their oral communication skills, project participants will participate in GradSlam, a UC system-wide competition in which students must present their research in three minutes. Writing skills will be addressed through Dissertation Boot Camp and through the publishing and grant writing workshops offered by the Graduate Division. Students may also participate in our Preparing Future Faculty and Preparing Future Professionals series, which include forums on choosing between academic and nonacademic careers, the postdoctoral research experience, interviewing for academic positions and negotiating the job offer, surviving and thriving as faculty of color, employment opportunities for Ph.D.s in industry, business, government, and nongovernmental organizations, preparing for jobs in industry, and National Labs Day (see http://graduatedivision.ucmerced.edu/GEARS). In addition, UC Merced has a subscription to the National Center for Faculty Development and Diversity<http://www.facultydiversity.org/> and the Versatile Ph.D.<http://versatilephd.com/> and we will draw on webinars and other resources from these clearinghouses.
Outcomes: Upon completing this module, students will have adjusted to graduate school, and will have developed a curriculum vitae, personal webpage, and Independent Development Plan,
Assessment: Student survey, interview faculty advisor, core faculty review of CV, webpage, and Independent Development Plan.

Module 2: Interactive Programming. Lead faculty: Colvin and Spivey, assisted by Jeffrey Weekley, Director of Cyber Infrastructure and Research Computing
Format: Four 45-minute instructional sessions plus continuous hands-on experience in project teams.
Objectives: Provide experience successfully writing, testing, and validating programs in several interactive and scripting programming environments such as Matlab, R, Python, and CalVR. Student Learning Objectives: 2 (Team Science), 3 (Research Skills) and 4 (Computational Skills).
Description: This module will teach students fundamentals of programming such as data structures, logical operations, loops, and data management and visualization using interactive programming environments. Data visualization will include virtual immersion demonstrations in our Virtual Reality CAVE (Computer Assisted Virtual Environment), an intracampus facility housed in the Digital Humanities Lab.
Outcomes: Upon completing this module, teams of students will have developed, tested and cross-validated codes in Matlab, R, Python, and CalVR to solve problems relevant to their own research and, in so doing, gained team science skills.
Assessment: Assignment designed to develop, test, and validate codes to solve project problems, student survey, presentation and review by other team members, advisors, and industry partners.

Module 3: The Linux Operating System and Shell Scripting. Lead faculty: Ghosh and Sindi
Format: Four 45-minute instructional sessions plus continuous hands-on experience in project teams.
Objectives: Provide experience working with the Linux operating system and developing shell scripts. Student Learning Objectives: 3 (Research Skills) and 4 (Computational Skills).
Description: This module will teach students how to manage files, transfer data, and execute programs in the Linux operating system.
Outcomes: Upon completing this module, the students will be able to use commands in the Linux operating system to organize and parse data files, to transfer data between computer systems, and write scripts to automate program execution and data analysis.
Assessment: Assignment demonstrating ability to work on project with Linux operating system, student survey, presentation and review by other team members, advisors, and industry partners.

Module 4: Compiled Programming. Lead faculty: Colvin and Kim
Format: Four 45-minute instructional sessions plus continuous hands-on experience in project teams.
Objectives: Provide experience successfully writing, testing, and validating programs in several compiled programming languages such as C and C++. Student Learning Objectives: 3 (Research Skills) and 4 (Computational Skills).
Description: This module will teach students the translation of prototype codes written in interactive programming languages to compiled programming languages more suitable for large-scale computations.
Outcomes: Upon completing this module, teams of students will have developed, tested and validated codes to solve problems relevant to their own research.
Assessment: Assignment related to development, testing, and validating codes to solve problems, student survey, presentation and review by other team members, advisors, and industry partners.

Module 5: High Performance Clusters and Remote Supercomputers. Lead faculty: Martini and Singhal, assisted by Jeffrey Weekley (Director of Cyber Infrastructure and Research Computing)
Format: Two 60-minute instructional sessions plus continuous hands-on experience in project teams.
Objectives:  Provide experience accessing and using high performance clusters and remote supercomputer systems, such as those on the NSF-funded XSEDE network. Student Learning Objective: 4 (Computational Skills).
Description: Each student will be provided an account on an XSEDE supercomputer as part of an educational allocation, learn about accessing and using this computer, write scripts to run programs in the supercomputer’s queuing system, and run a series of benchmark simulations.
Outcomes Achieved:  Upon completing this module, students will be able to effectively use local and remote supercomputing resources for their graduate research.
Assessment: Successful completion of module activity on either local cluster or remote supercomputer.

Module 6. Team Science and Project Management. Lead faculty: Meza and Rolland
Format: In addition to the orientation, four 30-minute instructional sessions plus continuous hands-on experience in project teams and at final group presentation.
Objectives:  Provide experience with managing projects using a team science approach, and provide deep understanding of the technological and social challenges and opportunities associated with team science. Student Learning Objectives: 1 (Professionalization) 2 (Team Science), 3 (Research Skills), and 5 (Career Preparation).
Description:  In this module, students will learn project management and teamwork skills. The weekly project team activities will teach participants about the technology-based tools that enable collaboration and project management and how to identify and structure a project, organize the team, break the work into subprojects, and assess team performance. They will learn to identify and address different types of problems and the scientific skills necessary to complete those projects. We will also familiarize students with basic team science tools, skills and principles as well as different frameworks for developing solutions to real-world problems, thus enabling our students to transition from incremental research to transformational breakthroughs.  Students will utilize various cloud-based tools and platforms for cooperation and coordination.
Outcomes Achieved:  Upon completing this module, students will be able to efficiently and effectively participate in team science projects, and be able to assess and diagnose issues related to team effectiveness and efficiency.
Assessment: Pre-module self-assessment will borrow from online learning tools in TeamScience.net<http://teamscience.net/>. This will be followed by post-module self-assessment and interviews with team members and lab and industry partners.

Students who have made strong connections with industry or laboratories will be encouraged to participate in optional internships with those organizations during the summer or during subsequent years, providing students with an additional window into that professional opportunity. Internships will also provide students opportunities to gain experience in writing and presenting their work, teamwork, and building professional networks. Selected IGE student participants will also travel to and participate in conferences and workshops related to their project activities, including dissemination of findings.

Students who successfully complete Year 1 will be invited to return in Year 2 as peer mentors and to assist in the project management training sessions, thus further honing their skills. We anticipate that students who are seriously considering careers in industry/labs will continue for this second year. Peer mentoring also will be provided by an existing self-organized group of graduate students interested in interdisciplinary collaboration and who have experience in optimizing code, developing models, and conducting data analysis. These advanced graduate students, who call themselves QACC (pronounced “quack”) for Quantitative, Analytic, and Computational Consulting, http://gsa.ucmerced.edu/resources/research-journal-clubs/quantitative-analytic-and-computational-consulting), have agreed to assist in the Linux lab.



--
David H. Ardell
Associate Professor, Molecular and Cell Biology
Chair, Quantitative and Systems Biology Program
University of California, Merced, CA 95343
office:SE1 228 ; office phone: 2092282953 ; web: http://davidardell.org
Please contact me by email at dardell at ucmerced.edu<mailto:dardell at ucmerced.edu>



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