PhD Minor
PhD MinorOverview
The Biomedical Data Science Program is a graduate and postdoctoral training program, part of the Department of Biomedical Data Science (DBDS) opens in a new tab. Our mission is to train future research leaders to design and implement novel quantitative and computational methods that solve challenging problems across the entire spectrum of biology and medicine. The flexible program attracts applicants with training in biology, research and clinical medicine, computer science, data science and analytics, statistics, engineering, and related disciplines.
Requirements
How to apply
Stanford PhD students should submit:
1. Application for Ph.D. Minor opens in a new tab form ( Scroll to bottom).
A copy of your unofficial transcript
3. A one page statement of purpose
Submit the form and accompanying materials to the Biomedical Data Science program.
You may submit your application anytime during the academic year to dbds-admissions@stanford.edu.
Advising
A minor program advisor is assigned upon admission to the program.
Program Requirements
Requirements Overview
For a PhD minor in Biomedical Data Science, a PhD candidate must complete a minimum of 20 units of coursework. Please note you cannot use any class numbered below 200 to contribute to the 20 units.
Complete 8 units from any combination of:
any additional DBDS core course (listed below)
BIOMEDIN 201C Biomedical Data Science Student Seminar (1 unit)
any course at the 200-level or above on the DBDS Computer Science/Statistics/Math/Engineering electives (listed below)
A student in the DBDS PhD minor is expected to participate regularly in BMDS 201C Biomedical Data Science Student Seminar. Units from enrollment in BMDS 201C can be used once (maximum of one quarter) toward the DBDS PhD minor.
All courses used for the DBDS PhD minor must be taken for a letter grade, except for BMDS 201C. The student should maintain an overall GPA of 3.0 or better.
Units taken for the minor can be counted as part of the overall requirement for the PhD of 135 units of graduate coursework done at Stanford. Courses used for a minor may not be used to meet the requirements for a master’s degree.
Courses taken before applying to the DBDS PhD minor can be counted toward the minor requirements.
DBDS Core Courses (12 Units)
Complete the list below for at least 12 units.
Biomedical Data Science Student Seminar
Students in the DBDS PhD minor are expected to participate regularly in BMDS 201C Biomedical Data Science Student Seminar. Units from enrollment in BMDS 201C can be used once (maximum of one quarter) toward the DBDS PhD minor.
Students only need to enroll in this course for credit if they want to count the 1 unit toward their total units for the program.
BMDS 201C
Students Seminar in Biomedical Data Science
DBDS Computer Science/Statistics/Math/Engineering Electives
APPPHYS 223
Stochastic and Nonlinear Dynamics
APPPHYS 293
A modern explainable AI approach to Theoretical Neuroscience
BIO 223
Stochastic and Nonlinear Dynamics
BIOE 210
Systems Biology
BIOE 279
Computational Biology: Structure and Organization of Biomolecules and Cells
BIOE 285
Computational Modeling in the Cardiovascular System
BIOE 291
Principles and Practice of Optogenetics for Optical Control of Biological Tissues
BIOE 300B
Quantitative Physiology
BIOE 301E
Computational Protein Modeling Laboratory
BIOE 331
Protein Engineering
BIOS 237
Engineering Wellness: Wearable Devices for Precision Medicine Applications
BIOS 256
Causal Inference in Healthcare: Experimental, Quasi-experimental and Non-experimental Approaches
BMDS 205
Bioinformatics for Stem Cell and Cancer Biology
BMDS 210
Modeling Biomedical Systems
BMDS 217
Translational Bioinformatics
BMDS 218
Data Centric AI for Healthcare
BMDS 219
Mathematical Models and Medical Decisions
BMDS 221
Machine Learning Approaches for Data Fusion in Biomedicine
BMDS 222
Cloud Computing for Biology and Healthcare
BMDS 224
Principles of Pharmacogenomics
BMDS 237
Outcomes Analysis
BMDS 241
Intermediate Biostatistics: Analysis of Discrete Data
BMDS 250
Clinical Trial Design in the Age of Precision Medicine
BMDS 270
Algorithmic Foundations for Artificial Intelligence in Healthcare
BMDS 271
Foundation Models for Healthcare
BMDS 273
Deep Learning in Genomics and Biomedicine
BMDS 280A
Workshop in Biomedical Data Science
BMDS 280B
Workshop in Biomedical Data Science
BMDS 280C
Workshop in Biomedical Data Science
BMDS 292
Software Engineering For Scientists
CBIO 243
Principles of Cancer Systems Biology
CHEM 263
Machine Learning for Chemical and Dynamical Data
CME 200
Linear Algebra with Application to Engineering Computations
CME 204
Partial Differential Equations in Engineering
CME 213
Introduction to parallel computing using MPI, openMP, and CUDA
CME 250
Introduction to Machine Learning
CME 251
Geometric and Topological Data Analysis
CME 263
Matrix Methods: Singular Value Decomposition
CME 292
Advanced MATLAB for Scientific Computing
CME 302
Numerical Linear Algebra
CME 303
Partial Differential Equations of Applied Mathematics
CME 309
Randomized Algorithms and Probabilistic Analysis
CME 330
Applied Mathematics in the Chemical and Biological Sciences
CME 334
Optimization Algorithms
CME 500
Departmental Seminar
CME 510
Linear Algebra and Optimization Seminar
CS 205L
Continuous Mathematical Methods with an Emphasis on Machine Learning
CS 221
Artificial Intelligence: Principles and Techniques
CS 223A
Introduction to Robotics
CS 224N
Natural Language Processing with Deep Learning
CS 224R
Deep Reinforcement Learning
CS 224S
Spoken Language Processing
CS 224U
Natural Language Understanding
CS 224W
Machine Learning with Graphs
CS 225A
Experimental Robotics
CS 226
The Future of Mechanical Engineering
CS 227B
General Game Playing
CS 228
Probabilistic Graphical Models: Principles and Techniques
CS 229
Machine Learning
CS 230
Deep Learning
CS 231A
Computer Vision: From 3D Perception to 3D Reconstruction and Beyond
CS 231N
Deep Learning for Computer Vision
CS 232
Digital Image Processing
CS 236
Deep Generative Models
CS 236G
Generative Adversarial Networks
CS 238
Decision Making under Uncertainty
CS 240
Advanced Topics in Operating Systems
CS 242
Programming Languages
CS 243
Program Analysis and Optimizations
CS 244
Advanced Topics in Networking
CS 244B
Distributed Systems
CS 245
Principles of Data-Intensive Systems
CS 246
Mining Massive Data Sets
CS 248A
Computer Graphics: Rendering, Geometry, and Image Manipulation
CS 249I
The Modern Internet
CS 254
Computational Complexity
CS 255
Introduction to Cryptography
CS 261
Combinatorial Optimization
CS 263
Counting and Sampling
CS 265
Randomized Algorithms and Probabilistic Analysis
CS 270
Modeling Biomedical Systems
CS 272
Introduction to Biomedical Informatics Research Methodology
CS 273B
Deep Learning in Genomics and Biomedicine
CS 274
Representations and Algorithms for Computational Molecular Biology
CS 275
Translational Bioinformatics
CS 295
Software Engineering
CS 309A
Cloud Computing Seminar
CS 315B
Parallel Computing Research Project
CS 328
Foundations of Causal Machine Learning
CS 329A
Self Improving AI Agents
CS 329H
Machine Learning from Human Preferences
CS 330
Deep Multi-task and Meta Learning
CS 342
Building for Digital Health
CS 348B
Computer Graphics: Image Synthesis Techniques
CS 348I
Computer Graphics in the Era of AI
CS 355
Advanced Topics in Cryptography
CS 356
Topics in Computer and Network Security
CS 362
Research in AI Alignment
CS 375
Large-Scale Neural Network Modeling for Neuroscience
CS 431
High-level Vision: From Neurons to Deep Neural Networks
CS 448B
Data Visualization
CS 468
Topics in Geometric Computing - 3D and 4D Foundation Models
CS 520
Knowledge Graphs
CS 522
Seminar in Artificial Intelligence in Healthcare
CS 524
AI for Science
CS 528
Machine Learning Systems Seminar
CS 547
Human-Computer Interaction Seminar
DATASCI 211
Accelerating Research with Marlowe: Practical GPU Computing for Scientists
DATASCI 294B
Data Science for Computational Molecular Biology
EE 101A
Circuits I
EE 101B
Circuits II
EE 102A
Signals and Systems I
EE 102B
Signals and Systems II
EE 108
Digital System Design
EE 168
Introduction to Digital Image Processing
EE 169
Introduction to Bioimaging
EE 178
Probabilistic Systems Analysis
EE 179
Analog and Digital Communication Systems
EE 256
Board Level Design
EE 261
The Fourier Transform and Its Applications
EE 263
Matrix Methods: Singular Value Decomposition
EE 264
Digital Signal Processing
EE 276
Information Theory
EE 279
Introduction to Digital Communication
EE 282
Computer Systems Architecture
EE 284
Introduction to Computer Networks
EE 364A
Convex Optimization I
EE 364B
Convex Optimization II
EE 368
Digital Image Processing
EE 369A
Medical Imaging Systems I
EE 369B
Medical Imaging Systems II
EE 369C
Medical Image Reconstruction
EE 376C
Universal Information Processing
EE 378A
Statistical Signal Processing
EE 379
Digital Communication
EE 382A
Parallel Processors Beyond Multicore Processing
EE 382C
Interconnection Networks
EE 384A
Internet Switching and Routing Protocols
EE 384S
Performance Engineering of Computer Systems & Networks
EE 387
Algebraic Error Correcting Codes
ENGR 108
Introduction to Matrix Methods
ENGR 154
Vector Calculus for Engineers
ENGR 155A
Ordinary Differential Equations for Engineers
ENGR 155B
Linear Algebra and Partial Differential Equations for Engineers
ENGR 155C
Introduction to Probability and Statistics for Engineers
ENGR 205
Introduction to Control Design Techniques
ENGR 209A
Analysis and Control of Nonlinear Systems
EPI 206
Meta-research: Appraising Research Findings, Bias, and Meta-analysis
EPI 224
Genetic Epidemiology
EPI 225
Introduction to Epidemiologic and Clinical Research Methods
EPI 226
Intermediate Epidemiologic and Clinical Research Methods
EPI 251
Design and Conduct of Clinical Trials
EPI 258
Introduction to Probability and Statistics for Clinical Research
EPI 259
Introduction to Probability and Statistics for Epidemiology
EPI 262
Intermediate Biostatistics: Regression, Prediction, Survival Analysis
EPI 263
Social Epidemiology
HRP 252
Outcomes Analysis
HRP 255
Decoding Academia: Power, Hierarchies, and Transforming Institutions
HRP 263
Advanced Decision Science Methods and Modeling in Health
IMMUNOL 207
Essential Methods in Computational and Systems Immunology
MATH 104
Applied Matrix Theory
MATH 106
Functions of a Complex Variable
MATH 107
Graph Theory
MATH 108
Introduction to Combinatorics and Its Applications
MATH 109
Groups and Symmetry
MATH 110
Number Theory for Cryptography
MATH 113
Linear Algebra and Matrix Theory
MATH 115
Functions of a Real Variable
MATH 116
Complex Analysis
MATH 118
Mathematics of Computation
MATH 120
Groups and Rings
MATH 121
Galois Theory
MATH 122
Modules and Group Representations
MATH 131P
Partial Differential Equations
MATH 136
Stochastic Processes
MATH 143
Differential Geometry
MATH 144
Introduction to Topology and Geometry
MATH 145
Algebraic Geometry
MATH 147
Differential Topology
MATH 148
Algebraic Topology
MATH 151
Introduction to Probability Theory
MATH 152
Elementary Theory of Numbers
MATH 154
Algebraic Number Theory
MATH 155
Analytic Number Theory
MATH 159
Discrete Probabilistic Methods
MATH 161
Set Theory
MATH 171
Fundamental Concepts of Analysis
MATH 172
Fourier Analysis
MATH 173
Theory of Partial Differential Equations
MATH 175
Elementary Functional Analysis
MATH 193
Polya Problem Solving Seminar
MATH 205A
Real Analysis
MATH 205B
Real Analysis
MATH 210A
Modern Algebra I
MATH 210B
Modern Algebra II
MATH 210C
Lie Theory
MATH 215A
Algebraic Topology
MATH 215B
Differential Topology
MATH 215C
Differential Geometry
MATH 216A
Introduction to Algebraic Geometry
MATH 216B
Introduction to Algebraic Geometry
MATH 216C
Introduction to Algebraic Geometry
MATH 220A
Partial Differential Equations of Applied Mathematics
MATH 220B
Computational Methods of Applied Mathematics
MATH 228
Stochastic Methods in Engineering
MATH 230A
Theory of Probability I
MATH 230B
Theory of Probability II
MATH 230C
Theory of Probability III
MATH 232
Topics in Probability
MATH 236
Introduction to Stochastic Differential Equations
MATH 244
Riemann Surfaces
MATH 245A
Topics in Algebraic Geometry
MATH 245B
Topics in Algebraic Geometry
MATH 245C
Topics in Algebraic Geometry
MATH 249A
Topics in number theory
MATH 249B
Topics in Number Theory
MATH 249C
Topics in Number Theory
MATH 256A
Partial Differential Equations
MATH 256B
Partial Differential Equations
MATH 257A
Symplectic Geometry and Topology
MATH 257B
Symplectic Geometry and Topology
MATH 258
Topics in Geometric Analysis
MATH 263A
Topics in Representation Theory
MATH 269
Topics in symplectic geometry
MATH 270
Geometry and Topology of Complex Manifolds
MATH 271
The H-Principle
MATH 272
Topics in Analysis and Partial Differential Equations
MATH 282A
Low Dimensional Topology
MATH 282B
Homotopy Theory
MATH 282C
Fiber Bundles and Cobordism
MATH 283A
Topics in Topology
MATH 286
Topics in Differential Geometry
ME 161
Dynamic Systems, Vibrations and Control
ME 300B
Partial Differential Equations in Engineering
MED 252
Outcomes Analysis
MED 206
Meta-research: Appraising Research Findings, Bias, and Meta-analysis
MED 263
Advanced Decision Science Methods and Modeling in Health
MGTECON 634
Machine Learning and Causal Inference
MS&E 120
Introduction to Probability
MS&E 211
Introduction to Optimization
MS&E 220
Probabilistic Analysis
MS&E 223
Stochastic Simulation and Monte Carlo Methods
MS&E 226
Fundamentals of Data Science: Prediction, Inference, Causality
MS&E 228
Applied Causal Inference with Machine Learning and AI
MS&E 229
Bayesian Linear Regression
MS&E 252
Foundations of Decision Analysis
MS&E 263
Healthcare Operations Management
MS&E 310
Linear Programming
MS&E 328
Foundations of Causal Machine Learning
MS&E 334
Topics in Social Data
MS&E 335
Queueing and Scheduling in Processing Networks
MS&E 355
Influence Diagrams and Probabilistics Networks
MS&E 454
Decision Analysis Seminar
MS&E 463
Healthcare Systems Design
NBIO 228
Mathematical Tools for Neuroscience
NENS 230
Analysis Techniques for the Biosciences Using MATLAB
PSYCH 253
Multivariate Analysis, Measurement, and Study of Change in Social Science Research
STATS 110
Introduction to Statistics for Engineering and the Sciences
STATS 191
Introduction to Applied Statistics
STATS 200
Introduction to Theoretical Statistics
STATS 202
Statistical Learning and Data Science
STATS 203
Regression Models and Analysis of Variance
STATS 205
Introduction to Nonparametric Statistics
STATS 206
Applied Multivariate Analysis
STATS 207
Time Series Analysis
STATS 208
Resampling Methods: Bootstrap, Cross Validation and Beyond
STATS 209
Introduction to Causal Inference
STATS 211
Meta-research: Appraising Research Findings, Bias, and Meta-analysis
STATS 214
Machine Learning Theory
STATS 215
Statistical Models in Biology
STATS 217
Introduction to Stochastic Processes I
STATS 218
Introduction to Stochastic Processes II
STATS 219
Stochastic Processes
STATS 260A
Workshop in Biomedical Data Science
STATS 260B
Workshop in Biomedical Data Science
STATS 260C
Workshop in Biomedical Data Science
STATS 261
Intermediate Biostatistics: Analysis of Discrete Data
STATS 262
Intermediate Biostatistics: Regression, Prediction, Survival Analysis
STATS 270
Bayesian Statistics
STATS 285
Massive Computational Experiments, Painlessly
STATS 290
Computing for Data Science
STATS 300A
Theory of Statistics I
STATS 300B
Theory of Statistics II
STATS 300C
Theory of Statistics III
STATS 305A
Applied Statistics I
STATS 305B
Applied Statistics II
STATS 305C
Applied Statistics III
STATS 310A
Theory of Probability I
STATS 310B
Theory of Probability II
STATS 310C
Theory of Probability III
STATS 314A
Advanced Statistical Theory
STATS 315A
Modern Applied Statistics: Learning
STATS 315B
Modern Applied Statistics: Learning II
STATS 317
Stochastic Processes
STATS 318
Modern Markov Chains
STATS 319
Literature of Statistics
STATS 320
Machine Learning Methods for Neural Data Analysis
STATS 322
Function Estimation in White Noise
STATS 324
Stein's Method
STATS 325
Multivariate Analysis and Random Matrices in Statistics
STATS 352
Topics in Computing for Data Science
STATS 361
Causal Inference
STATS 362
Topic: Monte Carlo
STATS 370
Bayesian Statistics
STATS 375
Mathematical Problems in Machine Learning
Crosslisted offerings (as long as they are numbered 200 or above) for any of the courses listed above are accepted in place of the offering listed.

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