Doctor of Philosophy
PhDOverview
The Biomedical Data Science Program is interdisciplinary and offers instruction and research opportunities leading to MS and PhD degrees in Biomedical Data Science. The program emphasizes research to develop novel computational methods that can advance biomedicine. Students receive training in investigating new approaches to conceptual modeling and developing new algorithms that address challenging problems in the biological sciences and clinical medicine.
Requirements
Program Requirements
Core Biomedical Data Science Courses (12 units)
Students are expected to complete the core offerings in biomedical data science. These courses should be taken for a grade, when letter grades are offered.
If the student has already completed a course covering this material in BMDS 215, BMDS 218 should be taken to fulfill this requirement.
Computer Science, Statistics, Mathematics & Engineering (21 units)
Required Courses
CS 161
Design and Analysis of Algorithms
STATS 200
Introduction to Theoretical Statistics
STATS 315A
Modern Applied Statistics: Learning
CS 229
Machine Learning
Elective Courses
To achieve in-depth mastery, PhD students are expected to create a program of study with graduate-level courses in computer science, statistics, and other technical informatics-related disciplines. The program of study may focus on aspects of these disciplines, including machine learning, statistical modeling, artificial intelligence, data mining, image analysis, human-computer interaction, and data visualization.
In addition to the 10-13 units students will earn through the Required Courses listed above, students should also choose courses from the electives list below to reach the total of 21 units needed for this requirement.
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 234
Comorbidity and Multimorbidities during Aging
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 100
Vector Calculus for Engineers
CME 102
Ordinary Differential Equations for Engineers
CME 104
Linear Algebra and Partial Differential Equations for Engineers
CME 106
Introduction to Probability and Statistics for Engineers
CME 108
Introduction to Scientific Computing with Machine Learning Applications
CME 200
Linear Algebra with Application to Engineering Computations
CME 204
Partial Differential Equations in Engineering
CME 211
Software Development for Scientists and Engineers
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 323
Distributed Algorithms and Optimization
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 103
Mathematical Foundations of Computing
CS 107
Computer Organization and Systems
CS 108
Object-Oriented Systems Design
CS 109
Introduction to Probability for Computer Scientists
CS 110
Principles of Computer Systems
CS 111
Operating Systems Principles
CS 124
From Languages to Information
CS 129
Applied Machine Learning
CS 131
Computer Vision: Foundations and Applications
CS 140
Operating Systems and Systems Programming
CS 142
Web Applications
CS 143
Compilers
CS 144
Introduction to Computer Networking
CS 145
Intro to Modern Data Systems
CS 147
Introduction to Human-Computer Interaction Design
CS 148
Introduction to Computer Graphics and Imaging
CS 149
Parallel Computing
CS 154
Introduction to the Theory of Computation
CS 155
Computer and Network Security
CS 157
Computational Logic
CS 161
Design and Analysis of Algorithms
CS 166
Advanced Data Structures
CS 193C
Client-Side Internet Technologies
CS 193P
iOS Application Development
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 316
Advanced Multi-Core Systems
CS 319
Topics in Digital Systems
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 331B
Interactive Simulation for Robot Learning
CS 341
Project in Mining Massive Data Sets
CS 342
Building for Digital Health
CS 344
Topics in Computer Networks
CS 345
Building AI-Enabled Robots
CS 348A
Computer Graphics: Geometric Modeling & Processing
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 379C
Computational Models of the Neocortex
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 386
Robust System Design
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 114
Introduction to Scientific Computing
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 221B
Mathematical Methods of Imaging
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 243
Functions of Several Complex Variables
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 257C
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 206
Meta-research: Appraising Research Findings, Bias, and Meta-analysis
MED 252
Outcomes 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 263
Design of Experiments
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 363
Design of Experiments
STATS 370
Bayesian Statistics
STATS 375
Mathematical Problems in Machine Learning
Crosslisted offerings for any of the courses listed above are accepted in place of the offering listed.
No more than nine units in courses numbered 100-199, and the rest should be 200 or above
CS 106A Programming Methodology and CS 106B Programming Abstractions cannot be counted for this requirement
All courses should be formal classroom-based courses, not research units
Up to eight units of this portion of the core curriculum may be taken on a Satisfactory/No Credit basis
Social and Ethical Issues (4 units)
Students should take MED 255. The Responsible Conduct of Research in their first year.
Specific Required Courses
MED 255
The Responsible Conduct of Research
Elective Courses
BIOE 122
BioSecurity and Pandemic Resilience
BIOE 131
Ethics in Bioengineering
BIOE 450
Advances in Biotechnology
BMDS 223
Deploying and Evaluating Fair AI in Healthcare
BMDS 235
Economics of Health and Medical Care
BMDS 236
Introduction to Cost-Effectiveness Analysis: Evaluating Benefits and Costs of Health Interventions
BMDS 244
Methods for Reproducible Population Health and Clinical Research
CEE 252
Silicon Valley and the U.S. Government
CS 182
Ethics, Public Policy, and Technological Change
CS 182W
Ethics, Public Policy, and Technological Change (WIM)
CS 256
Algorithmic Fairness
CS 281
Ethics of Artificial Intelligence
CS 283
Governing Artificial Intelligence: Law, Policy, and Institutions
CS 521
Seminar on AI Safety
ENGR 208
Patent Law and Strategy for Innovators and Entrepreneurs
EPI 203
Methods for Reproducible Population Health and Clinical Research
EPI 237
Practical Approaches to Global Health Research
EPI 238
Genes and Environment in Disease Causation: Implications for Medicine and Public Health
GENE 104Q
Law and the Biosciences
GENE 220
Introduction to Genetics, Ethics, and Society
HRP 203
Methods for Reproducible Population Health and Clinical Research
HRP 211
Law and Biosciences: Neuroscience
HRP 221
Law and the Biosciences: Genetics
HRP 256
Economics of Health and Medical Care
HRP 263
Advanced Decision Science Methods and Modeling in Health
HRP 285
Global Leaders and Innovators in Human and Planetary Health: Sustainable Societies Lab
HRP 392
Introduction to Cost-Effectiveness Analysis: Evaluating Benefits and Costs of Health Interventions
HUMBIO 174
Foundations of Bioethics
INDE 212
Medical Humanities and the Arts
MED 228
Physicians and Social Responsibility
MED 242
Human Rights and Health
MED 285
Global Leaders and Innovators in Human and Planetary Health: Sustainable Societies Lab
MS&E 256
Technology Assessment and Regulation of Medical Devices
MS&E 278
Patent Law and Strategy for Innovators and Entrepreneurs
NBIO 101
Social and Ethical Issues in the Neurosciences
PEDS 251A
Medical Ethics I
PUBLPOL 122
BioSecurity and Pandemic Resilience
PUBLPOL 222
BioSecurity and Pandemic Resilience
SOMGEN 207
Theories of Change in Global Health
TRAM 283
Biotechnology Law
Students are expected to be familiar with issues regarding responsible conduct of research, reproducibility of research, and ethical, legal, social, organizational, and behavioral aspects of the impact of biomedical informatics technologies on society. Courses that fulfill this requirement can be found by entering “dbds::ethics” in the Explore Courses search box. These courses may be taken on a Satisfactory/No credit basis.
Domain Biology or Medicine (6 units)
Students should take classes relevant to their application area interests. The core curriculum and domain biology/medicine requirements should be completed by the end of the second year of graduate study. Classes on the following list are pre-approved. Other classes can be used to fulfill this requirement with department approval.
Domain Biology courses can be taken as Credit/No Credit (CR/NC).
BIO 230
Cellular and Molecular Immunology: An Introductory Course
BIO 246
Synthetic proteins and genetic circuits
BIO 247
Genomic approaches to the study of human disease
BIO 253
Molecular Epigenetics in Health and Disease
BIO 287A
Advanced Topics in Mathematical Evolutionary Biology
BIOE 210
Systems Biology
BIOE 279
Computational Biology: Structure and Organization of Biomolecules and Cells
BIOE 281
Biomechanics of Movement
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 210
Cell, Gene, and Immune Therapies
BIOS 237
Engineering Wellness: Wearable Devices for Precision Medicine Applications
BIOS 243
An Excuse to Sleep: The Science of Sleep and Health
BMDS 210
Modeling Biomedical Systems
BMDS 217
Translational Bioinformatics
BMDS 218
Data Centric AI for Healthcare
BMDS 224
Principles of Pharmacogenomics
BMDS 235
Economics of Health and Medical Care
BMDS 304
Clinical Experience Seminar for Students in Biomedical Data Science
CBIO 243
Principles of Cancer Systems Biology
CHEM 281
Synthesis and Analysis at the Chemistry-Biology Interface
CHEM 283
Therapeutic Science at the Chemistry - Biology Interface
EMED 201A
Re-Certification for Basic Cardiac Life Support for Healthcare Professionals
EMED 301A
Emergency Medicine Core Clerkship
EPI 263
Social Epidemiology
FEMGEN 206
Global Medical Issues Affecting Women
GENE 202
Molecular, Cellular, and Genetic Medicine
GENE 220
Introduction to Genetics, Ethics, and Society
GENE 230
Genetic Epidemiology
GENE 242
Genetics of Viral Emergence and Emerging Viruses
GENE 247
Genomic approaches to the study of human disease
GENE 272
Introduction to Medical Genetics
GENE 294
Patient-Centered Care and Genetic Therapies for Rare Diseases
HUMBIO 125
Current Topics and Controversies in Women's Health
HUMBIO 172
Reproductive Health: History, Medicine, Policy, and Politics
IMMUNOL 200
Cellular and Molecular Immunology: An Introductory Course
IMMUNOL 209
Translational Immunology
MED 213
The Digital Future of Health Care
MED 242
Human Rights and Health
MED 245
Of Decisions and Dilemmas: The Art of Leadership
MED 248
Student Rounds
MED 253
Building for Digital Health
MED 254
IM Bedside Clinical Reasoning and Physical Diagnosis Rounds
MED 282
Early Clinical Experience at the Cardinal Free Clinic Program
MED 292
Designing Human-AI Interaction in Healthcare
MED 295
Advanced Cardiac Life Support
MED 300A
Internal Medicine Core Clerkship
MED 303B
Cardiology Clerkship
MED 313A
Ambulatory Medicine Core Clerkship
MED 314A
Advanced Medicine Clerkship
MED 323A
Trans-Disciplinary Breast Oncology Clerkship
MED 344A
Elective in Quality Improvement, Patient Safety, and Organizational Change
MED 397A
MD Capstone Experience: Preparation for Residency
Thesis Pre-Proposal Seminar
During the third year of training, each doctoral student must give a thesis pre-proposal seminar that describes evolving research plans.
Teaching Requirement
Teaching Assistantship
Each student must be a teaching assistant for two courses as assigned by the Biomedical Data Science Executive Committee; one should be completed in the first two years of study. Students may register for up to three BMDS 295 Biomedical Informatics Teaching Methods units to obtain credit for each teaching assistantship.
Advancement to Candidacy
Advancement to Candidacy
After applying for Terminal Graduate Registration (TGR) status and completing 135 units, the PhD candidate should register each quarter for BIOMEDIN 802 TGR PhD Dissertation so that their research effort may be counted toward the degree.
BMDS 802
TGR PhD Dissertation
Oral Exam
Oral Exam
Each student must present and defend their dissertation work before a University Oral Examination Committee that includes at least one member of the DBDS Faculty. The committee determines whether the student’s general knowledge of the field and the details of their completed research are sufficient to be granted the doctoral degree. Further information about the Oral Examination and the submission of the written dissertation is in the DBDS Student Handbook opens in a new taband university policies cited therein.
The student is expected to demonstrate an ability to present scholarly material and research in a lecture at a formal seminar.
Dissertation
Dissertation Overview
The most important requirement for the PhD degree is the dissertation. Each student must secure the agreement of a member of the DBDS faculty to act as the doctoral dissertation advisor or obtain departmental approval for a dissertation advisor to be appointed from outside the major department. This approval will depend on research rationale, potential advisor’s commitment to program’s rules, and identification of a DBDS faculty to participate in the thesis committee. See DBDS Student Handbook opens in a new tab for details.
After applying for Terminal Graduate Registration (TGR) status and completing 135 units, the PhD candidate should register each quarter for BMDS 802 TGR PhD Dissertation so that their research effort may be counted toward the degree.
Each student must present and defend their dissertation work before a University Oral Examination Committee that includes at least one member of the DBDS faculty. The committee determines whether the student’s general knowledge of the field and the details of their completed research are sufficient to be granted the doctoral degree. Further information about the Oral Examination and the submission of the written dissertation is in the DBDS Student Handbook opens in a new taband university policies cited therein.
The student is expected to demonstrate an ability to present scholarly material and research in a lecture at a formal seminar.
The student is expected to demonstrate an ability to present scholarly material in concise written form. Each student must write a paper suitable for publication, usually discussing their doctoral research project. The student’s advisor must approve this paper as suitable for submission to a refereed journal before the department confers the doctoral degree.
The dissertation must be accepted by a doctoral dissertation reading committee composed of the principal dissertation advisor and two other readers. A fourth reader may be added at the discretion of the student and the principal advisor.
BMDS 802
TGR PhD Dissertation

:format(webp))