Program of Study
As an interdisciplinary program, CBM has implemented a focused, but flexible curriculum and requires students to obtain knowledge of fundamental principles in biology, computational science, and mathematics to ensure competency within these disciplines.
COURSEWORK
Students must complete at least 4 courses in year 1. If you are interested in taking a course that is not listed below, please contact CBM Associate Director, Dr. Ekta Khurana for approval.
CBM Required Coursework
| Course Code | Credits | Course Name | Campus |
| TPCM 5001.01 | 0.5 credits | CBM Seminar and Journal Club I | Weill Cornell |
| TPCM 5004.01 | 0.5 credits | CBM Seminar and Journal Club II | Weill Cornell |
| PBSB 5006.04 | 1 credit | Critical Dissection of Scientific Data (spring) | Weill Cornell |
| RCRP 9010.01 | 1 credit | Tri-Institutional Responsible Conduct of Research (RCR) | Weill Cornell |
Additional Requirements
| Dates | Research Seminar Requirements |
| Years 2-5 | CBM Annual RCR Case-Study Refresher (2-hour workshop) |
| All Years | CBM Student Research in Progress Seminar Series |
| All Years | CBM Annual Symposium |
Approved Core-Level Coursework
| Course Code | Credits | Course Name | Campus |
| CMPB 5001 01 | 4 credits | Dynamic Models in Biology | Weill Cornell |
| CMPB 5002 01 | 4 credits | Data Structures & Algorithms for Computational Biology | Weill Cornell |
| CMPB 5005 03 | 4 credits | Functional Interpretation of High-Throughput Data | Weill Cornell |
| BTRY 4830/6830 | 3 credits | Quantitative Genomics and Genetics (crosslisted at WCM as PBSB 5021) |
Cornell-Ithaca |
| ECE 5412 | 3 credits | Bayesian Estimation and Stochastic Optimization (crosslisted at WCM as TPCB 9001 01) |
Cornell-Ithaca |
| CS 5785 | 3 credits | Applied Machine Learning | Cornell Tech |
Approved Electives
Weill Cornell Medicine
| Course Code | Credits | Course Name | Institution |
| CMPB 5004 03 | 4 credits | Analysis of Next Generation Sequencing Data | Weill Cornell |
| NUER 5013 03 | 1.5 credits | Mathematical Structures in Neuroscience | Weill Cornell |
| CMPB 5003 01 | 4 credits | Cellular and Molecular Biology | Weill Cornell |
| PBSB 5019 04 | 0.5 credits | Clinical and Research Genomics | Weill Cornell |
| NEUR 5003.01 NEUR 5003.03 |
1.5 credits 1.5 credits |
From Neuron to the Brain I From Neuron to the Brain II |
Weill Cornell |
| IAMP 5001 IAMP 5002 |
5.5 credits 4.5 credits |
Fundamental Immunology I Fundamental Immunology II |
Weill Cornell |
| TPCM 5003.01 | 3 credits | Genomic Innovation (via the New York Genome Center) |
Weill Cornell |
| BCMB 5009 01 | 1.5 credits | Stem Cell Biology | Weill Cornell |
Rockefeller University
For more information about RU classes, please download the course catalog (pdf file).
| Course Code | Credits | Course Name | Campus |
| RU | 1 credit | Mathematical Modeling | Rockefeller |
| RU | 5.5 credits | Cell Biology | Rockefeller |
| RU | 1.5 credits | Development of CNS Circuits | Rockefeller |
| RU | 2 credits | Microbial Pathogenesis | Rockefeller |
| RU | 2.5 credits | Molecular Basis of Cancer | Rockefeller |
| RU | 2.5 credits | Stem Cells in Tissue Morphogenesis and Cancer | Rockefeller |
Cornell-Ithaca
| Course Code | Credits | Course Name | Host |
| ORIE 6500 | 4 credits | Applied Stochastic Processes | Cornell-Ithaca |
| ORIE 6741 | 3 credits | Bayesian Machine Learning | Cornell-Ithaca |
| BTRY 6381 | 3 credits | Biomedical Data Mining and Modeling (crosslisted at WCM as PBSB 9202 01) |
Cornell-Ithaca |
| BTRY 6840 | 4 credits | Computational Genetics and Genomics (crosslisted at WCM as PBSB 9203 01) |
Cornell-Ithaca |
| BTRY 6520 | 4 credits | Computational Intensive Statistical Methods | Cornell-Ithaca |
| STSCI 4740 | 4 credits | Data Mining and Machine Learning | Cornell-Ithaca |
| CS 4320 | 3 credits | Introduction to Database Systems | Cornell-Ithaca |
| CS 6785 | 3 credits | Deep Probabilistic and Generative Model (crosslisted at WCM as PBSB 9211 03) |
Cornell-Ithaca |
| BTRY 4030 | 3 credits | Linear Models with Matrices | Cornell-Ithaca |
| CS 5780 | 3 credits | Introduction to Machine Learning | Cornell-Ithaca |
| CS 5786 | 3 credits | Machine Learning for Data Science | Cornell-Ithaca |
| CS 6210 | 3 credits | Matrix Computation | Cornell-Ithaca |
| CS 4210 | 3 credits | Numerical Analysis and Differential Equations | Cornell-Ithaca |
| BTRY 6820 | 3 credits | Statistical Genomics: Coalescent Theory and Human Population Genomics | Cornell-Ithaca |
| BTRY 6010 BTRY 6020 |
4 credits 4 credits |
Statistical Methods I Statistical Methods II |
Cornell-Ithaca |
| ORIE/STSCI 7170 | 3 credits | Theory of Linear Models | Cornell-Ithaca |
| BIOMG 6330 | 2 credits | Biosynthesis of Macromolecules | Cornell-Ithaca |
| BIOMG 4880 | 3 credits | Cancer Genetics (crosslisted at WCM as PBSB 9210 03) |
Cornell-Ithaca |
| VETMM 6100 | 2 credits | Cellular and Molecular Pharmacology | Cornell-Ithaca |
| PSYCH 6140 | 4 credits | Computational Psychology | Cornell-Ithaca |
| BIOMG 4870 | 3 credits | Human Genomics (crosslisted at WCM as PBSB 9204 01 |
Cornell-Ithaca |
| BIOMG 4390 | 3 credits | Molecular Basis of Disease (crosslisted at WCM as PBSB 9209 03) |
Cornell-Ithaca |
| BIOMG 6390 | 2 credits | The Nucleus | Cornell-Ithaca |
| BIOMG 4810 | 4 credits | Population Genetics (crosslisted at WCM as PBSB 9201 11 |
Cornell-Ithaca |
| BME 6120 | 3 credits | Precision and Genomic Medicine | Cornell-Ithaca |
| BIOMG 6310 | 3 credits | Protein Structure and Function | Cornell-Ithaca |
| BIOMG 4380 | 3 credits | RNA in Biology and Medicine | Cornell-Ithaca |
Cornell Tech
| Course Code | Credits | Course Name | Institution |
| CS 5112 | 3 credits | Algorithms and Data Structures for Applications | Cornell Tech |
| CS 5304 | 3 credits | Data Science in the Wild | Cornell Tech |
| CS 5787 | 3 credits | Deep Learning | Cornell Tech |
| ECE 5242 | 3 credits | Intelligent Autonomous Systems | Cornell Tech |
| CS 5670 | 4 credits | Introduction to Computer Vision | Cornell Tech |
| CS 5740 | 3 credits | Natural Language Processing | Cornell Tech |
| CS 5854 | 3 credits | Networks and Markets | Cornell Tech |
| ECE 5411 | 3 credits | Statistical Signal Processing and Reinforcement Learning | Cornell Tech |
| CS 5435 | 3 credits | Security and Privacy Concepts in the Wild | Cornell Tech |
| CS 6741 | 3 credits | Topics in Natural Language Processing and Machine Learning | Cornell Tech |
THESIS YEARS
If a student selects a thesis mentor at Weill Cornell or Sloan Kettering, they join the Weill Cornell Graduate School, a partnership with Sloan Kettering Institute; if thesis research is to be conducted at The Rockefeller University, student joins the David Rockefeller Graduate Program in Bioscience. When a student selects a thesis mentor in Cornell Ithaca, they are affiliated with the Computational Biology Field. Regardless of where the student is based, all are considered trainees in the CBM program.
Admissions to Candidacy Exam (NYC and Ithaca)
Deadline: June 30 of 2nd year
NYC-based students will write and defend a thesis proposal for their Admission to Candidacy Examination (ACE); Ithaca-based students will complete their A Exam. These exams must be successfully completed and registered according to the guidelines of the graduate school in which the student is matriculated. Originals of all required forms must be filed with the appropriate graduate school, and duplicates must be filed with the CBM office.
Thesis Committee Meetings
Thesis Committee meetings are required at least every 9 months in Years 3 and 4, then at least every 6 months in Year 5 and beyond; thesis committee reports must be filed with CBM and the graduate school in which the student is enrolled.
LAB ROTATIONS
Prior to choosing a thesis laboratory, students complete research rotations with faculty of their choosing. These rotations provide the student the opportunity to experience a range of thesis research options and thereby enable an informed thesis laboratory decision.
Students must complete at least 3 rotations (in Ithaca and/or NYC) in their first 12 months in the program (beginning in July). Dates may be modified after consultation with CBM Co-Directors.
Find out more in the Timeline.
PROGRAM SYMPOSIUM
A student-organized retreat is held off-site every summer to bring together students from the three institutions. The 2-day event features a keynote speaker (selected and invited by students), alumni panel, oral presentations and a poster session. Student and faculty sessions are scheduled to discuss any changes to program structure and other suggestions to improve the overall training experience. In addition, the event provides students with time for recreational activities and other social events.
Previous Symposiums
2020
Location: Virtual Symposium
Keynote Speaker: Dr. Gad A. Getz, Director, Cancer Genome Computational Analysis and Institute Member, Broad Institute
2019
Location: Crystal Springs Resort, Hamburg, NJ
Keynote Speaker: Dr. Tal Nawy, Senior Program Manager, Dana Pe’er Lab, MSK
CBM Alumni Speakers: Dr. Julie Yang and Dr. Lauren Fairchild
2018
Location: Mohonk Mountain House, New Paltz, NY
Keynote Speaker: Dr. Tuuli Lappalainen, Columbia University, New York Genome Center
CBM Alumni Speakers: Dr. Kaitlyn Gayvert and Dr. Kelson Zawack
2017
Location: Mohonk Mountain House, New Paltz, NY
Keynote Speaker: Dr. Sam Globus, Director of Scientific Operations, Celmatix
CBM Alumni Speakers: Dr. Nyasha Chambwe and Dr. Byron Roberts
Job Search Panel (CBM recent graduates): Dr. Neel Madhukar and Dr. Priyanka Vijay