Doctor of Philosophy
PhDJoint DegreeOverview
Bioengineering is jointly supported by the School of Engineering and the School of Medicine. The facilities and personnel of the Department of Bioengineering are housed in the Shriram Center, James H. Clark Center, the William F. Durand Building for Space Engineering and Science, the William M. Keck Science Building, the Jerry Yang and Akiko Yamazaki Environment and Energy Building, and the Richard M. Lucas Center for Magnetic Resonance Spectroscopy and Imaging. The departmental headquarters is in the Shriram Center for Bioengineering and Chemical Engineering.
Students are expected to enter with core competencies in mathematics, biology, chemistry, physics, computing, and engineering. Students entering the program are assessed by examining their undergraduate transcripts and research experiences. Specifically, the department requires that students have completed mathematics through multivariable calculus and differential equations, completed a series of undergraduate biology courses, and completed physics, chemistry, and computer sciences courses required of all undergraduate majors in engineering.
Requirements
Program Requirements
Lab Courses (minimum 2 units)
BIOE 232
Advanced Imaging Lab in Biophysics
BIOE 261
3D Bioprinting Laboratory
BIOE 301A
Molecular and Cellular Engineering Lab
BIOE 301B
Clinical Needs and Technology
BIOE 301C
Diagnostic Devices Lab
BIOE 301D
Microfluidic Device Laboratory
BIOE 301E
Computational Protein Modeling Laboratory
BIOE 301P
Research Data, Computation, & Visualization
Quantitative Courses (minimum 6 units)
BIOE 209
Mathematical Modeling of Biological Systems
BIOE 210
Systems Biology
BIOE 230
Measurements, Statistics, and Probability
BIOE 300B
Quantitative Physiology
CME 200
Linear Algebra with Application to Engineering Computations
CME 204
Partial Differential Equations in Engineering
CME 206
Introduction to Numerical Methods for Engineering
CME 263
Matrix Methods: Singular Value Decomposition
CME 302
Numerical Linear Algebra
CME 306
Computational Methods of Applied Mathematics
CME 322
Spectral Methods in Computational Physics
CS 205L
Continuous Mathematical Methods with an Emphasis on Machine Learning
CS 228
Probabilistic Graphical Models: Principles and Techniques
EE 261
The Fourier Transform and Its Applications
EE 278
Probability and Statistical Inference
EE 364A
Convex Optimization I
EE 364B
Convex Optimization II
ME 470
Uncertainty Quantification
Biology or Technical Elective Courses (minimum 12 units)
Select three or more Biology & Technical electives from the pre-approved list of 200+ Technical Elective courses, which can include more of the Lab or Quantitative courses.
Due to the interdisciplinary nature of Bioengineering, some courses are offered directly through the Bioengineering Department, but many are available through other departments. See respective ExploreCourses for course descriptions.
All lecture and lab-based BioE courses at the 200+ level and above are considered Technical Electives.
Directed research units (e.g., BIOE 391, BIOE 392) and BIOE 296 do not count toward the Biology & Technical electives for the PhD requirements.
One of these courses may be an ethics course from among BIOE 131, BIOE 177, CS152, CS182, CS184, CS256, CS278, or CS281.
AA 203
Optimal and Learning-based Control
AA 212
Advanced Feedback Control Design
AA 222
Engineering Design Optimization
AA 273
State Estimation and Filtering for Robotic Perception
APPPHYS 229
Statistical Mechanics of Learning and Computation
APPPHYS 237
Quantitative Evolutionary Dynamics and Genomics
APPPHYS 294
Cellular Biophysics
BIO 204
Neuroplasticity: From Synapses to Behavior
BIO 214
Advanced Cell Biology
BIO 220
Integrative and Experimental Microbiology
BIO 230
Cellular and Molecular Immunology: An Introductory Course
BIO 239
The Hidden Kingdom - Evolution, Ecology and Diversity of Fungi
BIO 255
Cell and Developmental Biology of Plants
BIO 265
Quantitative Approaches in Modern Biology
BIO 273A
Environmental Microbiology I
BMDS 205
Bioinformatics for Stem Cell and Cancer Biology
BMDS 274
Machine Learning for Neuroimaging
BIOE 131
Ethics in Bioengineering
BIOE 206
Mixed-Reality in Medicine
BIOE 209
Mathematical Modeling of Biological Systems
BIOE 210
Systems Biology
BIOE 212
Introduction to Biomedical Informatics Research Methodology
BIOE 213
Stochastic and Nonlinear Dynamics
BIOE 214
Representations and Algorithms for Computational Molecular Biology
BIOE 217
Translational Bioinformatics
BIOE 219
Fundamentals of Regeneration Biology
BIOE 220
Introduction to Imaging and Image-based Human Anatomy
BIOE 221
Physics and Engineering of Radionuclide-based Medical Imaging
BIOE 221G
Gut Microbiota in Health and Disease
BIOE 222
Physics and Engineering Principles of Multi-modality Molecular Imaging of Living Subjects
BIOE 224
Probes and Applications for Multi-modality Molecular Imaging of Living Subjects
BIOE 225
Transcranial Ultrasound Neuromodulation: Physics, Neurophysiology, and Applications
BIOE 226
MRI Spin Physics, Relaxation Theory, and Contrast Mechanisms
BIOE 227
Functional MRI Methods
BIOE 230
Measurements, Statistics, and Probability
BIOE 232
Advanced Imaging Lab in Biophysics
BIOE 235
Biotransport Phenomena
BIOE 236
Bioengineering Innate Immunity
BIOE 240
Principles of Synthetic Biology
BIOE 241
Biological Macromolecules
BIOE 250
Biochemical Engineering
BIOE 256
Technology Assessment and Regulation of Medical Devices
BIOE 260
Tissue Engineering
BIOE 261
3D Bioprinting Laboratory
BIOE 266
Synthetic proteins and genetic circuits
BIOE 271
Frugal Science
BIOE 279
Computational Biology: Structure and Organization of Biomolecules and Cells
BIOE 281
Biomechanics of Movement
BIOE 282
Introduction to Biomechanics and Mechanobiology
BIOE 283
Mechanotransduction in Cells and Tissues
BIOE 285
Computational Modeling in the Cardiovascular System
BIOE 291
Principles and Practice of Optogenetics for Optical Control of Biological Tissues
BIOE 300A
Molecular and Cellular Bioengineering
BIOE 300B
Quantitative Physiology
BIOE 301A
Molecular and Cellular Engineering Lab
BIOE 301B
Clinical Needs and Technology
BIOE 301C
Diagnostic Devices Lab
BIOE 301D
Microfluidic Device Laboratory
BIOE 301E
Computational Protein Modeling Laboratory
BIOE 301P
Research Data, Computation, & Visualization
BIOE 305
Dynamics and Feedback Control of Living Systems
BIOE 313
Neuromorphics: Brains in Silicon
BIOE 320
Cryogenic electron microscopy and tomography
BIOE 331
Protein Engineering
BIOE 335
Molecular Motors I
BIOE 337
The Art of Observation: Table Top Explorations In Natural Philosophy
BIOE 355
Advanced Biochemical Engineering
BIOE 361
Biomaterials in Regenerative Medicine
BIOE 374A
Biodesign Innovation: Needs Finding and Concept Creation
BIOE 374B
Biodesign Innovation: Concept Development and Implementation
BIOE 375
Prevention Medicine and Entrepreneurship for Societal Health
BIOE 376
Startup Garage: Design
BIOE 377
Startup Garage: Testing and Launch
BIOE 381
Orthopaedic Bioengineering
BIOE 385
Biomaterials for Drug Delivery
BIOE 450
Advances in Biotechnology
BIOE 485
Modeling and Simulation of Human Movement
BIOMEDIN 202
An overview of Biomedical Data Science
BMDS 210
Modeling Biomedical Systems
BMDS 221
Machine Learning Approaches for Data Fusion in Biomedicine
BMDS 222
Cloud Computing for Biology and Healthcare
BMDS 224
Principles of Pharmacogenomics
BIOS 202
Understanding Kinetics for Biologists and Biology
BIOS 249
Single-cell Spatial-omics: with Applications to Stem Cell Engineering and Cancer Immunotherapy
BIOS 253
Primary concepts in Bioinformatics- To Make Sense Of large scale Biological and Biomedical Data
BIOS 270
Bioinformatics Engineering
CBIO 243
Principles of Cancer Systems Biology
CBIO 275
Tumor Immunology
CEE 177E
Water & the Environment: Current Challenges and Solutions
CEE 271B
Environmental Biotechnology
CEE 274B
Microbial Bioenergy Systems
CHEM 225
Advanced Organic Chemistry III
CHEM 261
Computational Electronic Structure of Molecular Systems
CHEM 263
Machine Learning for Chemical and Dynamical Data
CHEM 271
Advanced Physical Chemistry
CHEM 277
Manipulating Spins: NMR Theory and Applications
CHEM 281
Synthesis and Analysis at the Chemistry-Biology Interface
CHEM 283
Therapeutic Science at the Chemistry - Biology Interface
CHEMENG 281
Biochemistry I
CHEMENG 340
Molecular Thermodynamics
CHEMENG 420
Growth and Form
CHEMENG 466
Polymer Physics
CHEMENG 470
Mechanics of Soft Matter: Rheology
CME 200
Linear Algebra with Application to Engineering Computations
CME 204
Partial Differential Equations in Engineering
CME 206
Introduction to Numerical Methods for Engineering
CME 298
Probability and Stochastic Differential Equations for Applications
CME 302
Numerical Linear Algebra
CME 303
Partial Differential Equations of Applied Mathematics
CME 306
Computational Methods of Applied Mathematics
CS 106A
Programming Methodology
CS 106AX
Programming Methodologies in JavaScript and Python (Accelerated)
CS 106B
Programming Abstractions
CS 109
Introduction to Probability for Computer Scientists
CS 161
Design and Analysis of Algorithms
CS 173A
Foundations of Computational Human Genomics
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 224W
Machine Learning with Graphs
CS 227A
Robot Perception
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 235
Computational Methods for Biomedical Image Analysis and Interpretation
CS 237A
Principles of Robot Autonomy I
CS 238
Decision Making under Uncertainty
CS 246
Mining Massive Data Sets
CS 248A
Computer Graphics: Rendering, Geometry, and Image Manipulation
CS 265
Randomized Algorithms and Probabilistic Analysis
CS 271
Algorithmic Foundations for Artificial Intelligence in Healthcare
CS 273B
Deep Learning in Genomics and Biomedicine
CS 324
Advances in Foundation Models
CS 336
Language Modeling from Scratch
CS 348C
Computer Graphics: Animation and Simulation
CS 372
Artificial General Intelligence for Reasoning, Planning, and Decision Making
CS 375
Large-Scale Neural Network Modeling for Neuroscience
CS 468
Topics in Geometric Computing - 3D and 4D Foundation Models
CSB 240A
A Practical Approach to Drug Discovery and Development
CSB 240B
A Practical Approach to Drug Discover and Development
DATASCI 294B
Data Science for Computational Molecular Biology
DBIO 210
Developmental Biology
EARTHSYS 213
Innovating for Climate and Sustainability
EARTHSYS 217
Biology and Global Change
EARTHSYS 223
Biosphere-Atmosphere Interactions
EARTHSYS 233
Mitigating Climate Change through Soil Management
EARTHSYS 256
Soil and Water Chemistry
EARTHSYS 258
Geomicrobiology
EBS 332
Climate Tech for Rapid Decarbonization
EE 205
Product Management for Engineers in the AI Age
EE 217
Understanding the Sensors in your Smartphone
EE 220
Bioelectronics
EE 225
Biochips and Medical Imaging
EE 236A
Modern Optics
EE 261
The Fourier Transform and Its Applications
EE 268
The Engineering Economics of Electricity Markets
EE 269
Quantization and Signal Processing for AI
EE 276
Information Theory
EE 355
Imaging Radar and Applications
EE 364A
Convex Optimization I
EE 364B
Convex Optimization II
EE 367
Computational Imaging
EE 368
Digital Image Processing
EE 369A
Medical Imaging Systems I
EE 369B
Medical Imaging Systems II
EE 369C
Medical Image Reconstruction
EE 469B
RF Pulse Design for Magnetic Resonance Imaging
ENERGY 263
Sustainable Energy Decisions
EPI 231
Epidemiology of Infectious Diseases
GENE 211
Genomics
GENE 231
AI and Vibecoding for healthcare
GENE 242
Genetics of Viral Emergence and Emerging Viruses
GEP 349
The Financial Economics of Climate and Sustainability
IMMUNOL 201
Advanced Immunology I
IMMUNOL 205
Immunology in Health and Disease
IMMUNOL 206
Introduction to Applied Computational Tools in Immunology
IMMUNOL 207
Essential Methods in Computational and Systems Immunology
MATSCI 210
Organic and Biological Materials
MATSCI 303
Principles, Materials and Devices of Batteries
MATSCI 380
Nano-Biotechnology
MATSCI 384
Materials Advances in Neurotechnology
MCP 222
Imaging: Biological Light Microscopy
MCP 256
How Cells Work: Energetics, Compartments, and Coupling in Cell Biology
ME 203
Design and Manufacturing
ME 206A
Design for Extreme Affordability
ME 218A
Smart Product Design Fundamentals
ME 218B
Smart Product Design Applications
ME 218C
Smart Product Design Practice
ME 220
Introduction to Sensors
ME 223
Applied Robot Design for Non-Robot-Designers: How to Fix, Modify, Design, and Build Robots
ME 234
Introduction to Neuromechanics
ME 280
Deliverables: A Mechanical Engineering Design Practicum
ME 287
Mechanics of Biological Tissues
ME 303
Soft Composites and Soft Robotics
ME 310A
Global Engineering Design Thinking, Innovation, and Entrepreneurship
ME 318
Computer-Aided Product Creation
ME 331A
Advanced Dynamics & Computation
ME 331B
Advanced Dynamics, Simulation & Control
ME 335A
Finite Element Analysis
ME 351A
Fluid Mechanics
ME 354
Experimental Methods in Fluid Mechanics
ME 451D
Microhydrodynamics
ME 470
Uncertainty Quantification
MS&E 328
Foundations of Causal Machine Learning
NBIO 206
The Nervous System
NBIO 227
Understanding Techniques in Neuroscience
OPHT 207
Introduction to Electro-neural Interfaces
PHYSICS 212
Statistical Mechanics
PHYSICS 230
Graduate Quantum Mechanics I
PHYSICS 231
Graduate Quantum Mechanics II
PSYCH 204A
Human Neuroimaging Methods
PSYCH 242
A modern explainable AI approach to Theoretical Neuroscience
PSYCH 254A
Advanced Statistical Modeling for Behavioral and Neural Sciences
RAD 229
MRI Sequences and Signals
STATS 200
Introduction to Theoretical Statistics
STATS 202
Statistical Learning and Data Science
STATS 203
Regression Models and Analysis of Variance
STATS 207
Time Series Analysis
STATS 215
Statistical Models in Biology
STATS 217
Introduction to Stochastic Processes I
STATS 220
Machine Learning Methods for Neural Data Analysis
STATS 262
Intermediate Biostatistics: Regression, Prediction, Survival Analysis
STATS 305A
Applied Statistics I
STATS 305B
Applied Statistics II
STATS 305C
Applied Statistics III
STATS 326
Topics in Generative Modeling Methods in Protein Modeling and Design
STEMREM 201A
Stem Cell Biology & Regenerative Medicine
Degree Milestones
Degree milestones indicate progress toward a degree. They are listed on unofficial transcripts and document satisfactory and timely completion of various events, such as securing research advisors, candidacy examinations, submission of completed degree progress forms, dates of reading committee meetings, and assisting with teaching BIOE courses.
Doctoral candidates should submit forms as appropriate to departmental student services. Students approaching a milestone should be aware of intradepartmental communications and support, and students with questions or concerns should discuss them with student services staff. Additional information regarding Degree Milestones can be found in the graduate handbook.
Thesis Lab Rotation Selection
Based on the research interests expressed in their application, students are assigned an initial faculty advisor who assists them in choosing courses and identifying research opportunities.
One of the most important goals of the first year is to identify a primary research advisor. Students are strongly encouraged to explore research activities in three or a maximum of four labs during their first academic year.
Students who do not confirm a research advisor, and thus a thesis lab, by the end of the 4th quarter of study at Stanford are subject to dismissal from the program.
Students in the PhD program are required to complete 135 units of unduplicated coursework. In addition to the requirements listed above, this total may be satisfied by:
research units (e.g., BIOE 391)
seminar units (e.g., BIOE 296)
additional lecture/lab courses taken CR/NC or for letter grades
non-departmental units in nontechnical areas
Completing an MS degree is not a prerequisite for beginning, pursuing, or completing doctoral work.
Teaching Requirement
Teaching Assistant Requirement
The Bioengineering Department believes that teaching is integral to graduate-level education in Bioengineering. Consequently, serving as a teaching assistant for two courses is a requirement for the PhD in Bioengineering. Students serve as teaching assistants for one course during the second and third years of graduate study. The TA requirement may only be satisfied by TA-ing Bioengineering courses assigned by the Bioengineering department. Students on full fellowships, such as the NSF, may hold a concurrent teaching assistantship up to a maximum of 25% with no additional hourly employment.
In preparation for the TA requirement, students are encouraged to enroll in BIOE 296 (Promoting Effective and Equitable Teaching in Bioengineering) during their first or second year.
BioE PhD students TA'ing to fulfill their academic requirement will be appointed to a 25% TA during the assigned TA quarter. If the PhD student is fully funded by fellowship support they will not be appointed to a 25% TA and instead will enroll in BioE teaching practicum units during the assigned TA quarter.
Advancement to Candidacy
Qualifying Exam and Advancement to Candidacy
Before being formally admitted to candidacy for the PhD degree, the student must demonstrate knowledge of Bioengineering fundamentals and potential for research by passing a qualifying oral examination before the end of the second year. Potential for success in research is demonstrated by completing an outstanding bioengineering research proposal, the testimony of the student’s research advisor of their research potential, and a detailed evaluation.
Students must be admitted to candidacy by the end of the second year of the student’s PhD Program. Being admitted to candidacy signifies that the department considers the student capable of completing the requirements for earning a PhD degree. Candidacy is valid for five calendar years (through the end of the quarter in which candidacy expires) unless terminated by the department for unsatisfactory progress.
For information regarding candidacy extension request, please see Stanford GAP 4.6.1. All extension requests must include the BioE Path to Graduate Form and are reviewed by the Director of Graduate Studies.
Oral Exam
University Oral Exam
The PhD candidate is required to take the university oral examination after the dissertation is substantially completed (with the dissertation draft in writing) but before final approval. The examination consists of a public presentation of dissertation research, followed by substantive private questioning on the dissertation and related fields by the university oral committee (four selected faculty members, plus a chair from another department). Once the oral has been passed, the student finalizes the dissertation for reading committee review and final approval.
Dissertation
Dissertation Overview
Dissertation Reading Committee
Once the oral has been passed, the student finalizes the dissertation for reading committee review and final approval.
Each PhD candidate is required to establish a reading committee for a doctoral dissertation within six months after passing the department’s PhD Qualifying exams. After that, the student should at least annually convene with all committee members about the direction and progress of the dissertation research.
Students must have at least three faculty members who read and certify their dissertations. At least two members must be on the Stanford Academic Council (see GAP 4.8.1 for membership requirements). Two members of the reading committee are also required to be Bioengineering faculty.

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