COMBINE fellows are generally involved in program activities throughout the duration of their doctoral studies. They will typically engage in introductory disciple-bridging course work in their first year and undertake the most intensive program components in their second year. Fellows will continue involvement throughout the remainder of their graduate careers, with fellows in their later years of study serving as mentors to those in their early years.
COMBINE students are drawn from three different disciplines:
- Life Sciences: from UMD graduate programs in: Behavior, Ecology, Evolution, and Systematics; Molecular and Cellular Biology; Computational Biology, Bioinformatics and Genomics; and Neuroscience and Cognitive Sciences
- Physical and Mathematical Sciences: from graduate programs in Physics, Biophysics, and Applied Mathematics and Scientific Computing
- Computational Sciences: from graduate programs in Computer Science as well as Electrical and Computer Engineering
Required COMBINE components:
The following represent the program components required for COMBINE fellows. They will also constitute the required components of the graduate certificate program we are developing, which we expect to be in place for Fall 2017 (credit toward the certificate will be granted for components fulfilled before Fall 2017).
- Discipline-bridging coursework (4 credits): In addition to completely their PhD degree requirements in one of three disciplinary areas, COMBINE fellows will complete: (A) one regular (3 or 4 credit) course at the graduate or advanced undergraduate level from one of the other two disciplines (chosen from a list of appropriate courses) and (B) one out-of-field graduate seminar course (1 credit or more) from the third discipline. This introductory coursework is designed to help bridge the physical/ mathematical, computational, and life sciences. For example, physics/math and computer science graduate students will become familiar with core concepts in the life sciences (via courses such as BIOL708: Cell Biology from a Biophysical Perspective or BSCI453: Cellular Neurophysiology), life science and computer science graduate students will study the techniques of physical and mathematical modeling (via courses such as PHYS615: Nonlinear Dynamics of Extended Systems or MATH420: Mathematical Modeling), and life and physical science graduate students will explore computational techniques for processing and analyzing large datasets (via courses such CBMG688: Programming for Biologists or AMSC660: Scientific Computing). For students in programs outside the computational sciences who have no prior computational training, we require that the 3-4 credit course be a computation-based course. Note that some courses fulfilling these “out of discipline” requirements may actually be taught from within the student’s primary discipline. For example, the course BSCI474: Mathematical Biology might be used by a life science graduate student to fulfill the NRT course requirement for physical/mathematical modeling.
- Advanced Interdisciplinary coursework: Advanced Topics in Information Processing; Computational and Mathematical Analysis of Biological Networks across Scales (CMSC828O) (3 credits): Typically in the first semester of their second year of graduate study, COMBINE students undertake advanced interdisciplinary coursework in the form of a team-based module course. This innovative course is led by Prof. Hector Corrada Bravo, team-taught by multiple COMBINE faculty and co-listed in participating departments. The central idea of this course is that students, working in small interdisciplinary teams, learn how to apply network science methodologies to large, complex datasets drawn from life science applications. Modules are organized around the four major methodological themes of our NRT program: network measures, mechanistic network models, network statistics and machine learning, and network visualization. This new course will be taught for the first time in Fall 2017, when it will meet TR 9:30am to 10:45am
- Advanced Interdisciplinary coursework: Data practicum at the intersection of the physical, computer, and life sciences (PHYS798N) (3 credits): NRT students participate in a semester-long intensive data project, to be guided by one or more of our NRT faculty and/or partners. The motivating idea behind this course is to fill a major gap in graduate science education by helping students develop and hone the skills necessary for conducting ground-breaking research, instead of focusing solely on building a knowledge base. In addition, the course has a significant focus on developing skills for communication to diverse audiences. Students will learn to write for individuals in the same field, for individuals in another specified field to which their research is applicable, and for a general science audience. In Spring 2017, this was taught as PHYS798N, a special topics course entitled, “Data practicum at the intersection of the physical, computer, and life sciences .” Starting in Spring 2018, it will have a regular course number and be cross listed in different departments.
- COMBINE seminars (2 semesters required, 1 credit each): Each semester, we will have a weekly COMBINE seminar series. In the Fall, the seminar will take on a reading group format and trainees will discuss seminal papers in network biology. In the Spring, the seminar’s focus will be on research-in-progress. In both semesters, students will have opportunities to develop their oral communication skills. This seminar series will begin in Fall 2017.
- Peer-to-peer (P2P) tutorials (seminar during the winter term): To complement primarily research-focused activities like COMBINE seminars, our program includes the skill-focused P2P Tutorial Week. This event is held every January between the fall and spring semesters. Students work in small groups of 3-4 preparing tutorials for their peers. Groups and topics are formed by the trainees themselves, with each group advised by one or more COMBINE faculty members of its choosing. Example topics might include: understanding high throughput DNA sequencing, analyzing phase transitions in biochemical systems, and writing computer scripts to collect online data. This activity will be offered during the winter term potentially for optional course credits (1 credit), starting in January 2018.
Additional Broadening Activities:
- Internship opportunities with NRT partners: COMBINE leverages strong existing partnerships between UMD and four nearby centers for research excellence: the Smithsonian Institution, where students can explore evolutionary, ecological, and conservation problems; the National Institutes of Health, where students can study the roles of biomolecular and neuronal networks in disease; the National Institute of Standards and Technology, where students can use network science to develop new approaches for bioscience measurement and interpretation; and the University of Maryland School of Medicine, where students can apply network analysis techniques in the context of health informatics and bioimaging. In addition to providing internship opportunities with these local research partners, our program offers students the opportunity to explore alternate career paths via internships with industry and government partners who are invested in the translation of scientific advancements to a range of applications.
- Outreach and mentoring: COMBINE students actively participate in outreach and mentoring activities to promote cross-disciplinary STEM education to undergraduate and middle/high school groups. A principal goal is to promote participation from groups traditionally under-represented in STEM fields. Outreach and mentoring activities include: outreach to middle/high school students and the general public via UMD channels like CompSci Connect and GEMS; undergraduate research mentoring; and peer-mentoring in which Each trainee beyond his or her second year is matched with one or more incoming trainees to serve as a peer mentor.
- Annual Career Develop Workshop: Each year, we hold a Career Development Workshop for our trainees that brings in a diverse set of speakers to discuss how network-focused data science methods can be applied in a variety of contexts. The workshop is largely student-organized with NRT faculty members providing overall guidance. The workshop’s theme changes from year to year, depending on student interests. Speakers are drawn from our established corporate and government partners, as well from other organizations who might wish to become involved in our program.
- Annual COMBINE Symposium: Every year before the start of the fall semester, we host a one-day Annual COMBINE Symposium to showcase our research activities. The symposium format includes two keynote presentations from leading researchers outside of our NRT program, as well as short talks from some of our faculty.