Habib LatifizadehPh.D.

Teaching & mentoring

Teaching quantitative methods from assumptions and derivation to implementation and interpretation.

I ask students to derive a method, implement it carefully, test its behavior, and explain what the resulting evidence does and does not support.

View five detailed course dashboards ↓

Teaching experience

Courses taught, assisted, or supported.

My formal teaching spans undergraduate and graduate mathematics, including algebra, calculus, linear algebra, differential equations, and numerical analysis.

Algebra with ApplicationsFormal course recordFall 2021
Instructor
Multivariable CalculusFormal course recordSpring 2021
Teaching Assistant
Applied Linear AlgebraFormal course recordSpring 2020
Teaching Assistant
Numerical AnalysisFormal course recordSpring 2019
Teaching Assistant
Advanced Linear AlgebraFormal course recordFall 2018
Teaching Assistant
Engineers’ CalculusFormal course recordFall 2018
Recitation Leader
Advanced Engineering MathematicsFormal course recordSpring 2009
Instructor
Differential EquationsFormal course recordFall 2008
Teaching Assistant

Courses prepared to develop

Graduate courses prepared for future development.

These are proposed courses, not courses I have already taught. Each course connects statistical or computational methods with specific health or biomedical applications.

Five-course pathway A connected portfolio spanning introductory graduate training, master’s-level causal methods, and doctoral work in clinical AI, Bayesian networks, and optimal transport.
01Introductory graduate · advanced undergraduate

Statistical Learning for Health Data

Penalized regression, tree ensembles, conditional-independence testing, and survival analysis, with each method paired with a public clinical dataset and a reproducible analysis.

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02Master's

Causal Inference for Observational Health Records

Targeted maximum likelihood, instrumental variables, and sensitivity analysis taught alongside the practical constraints of EHR data, including missingness, irregular sampling, label leakage, and governance.

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03Doctoral · flagship

Uncertainty-Aware Clinical ML for Perioperative & Critical-Care Data

Three units on probabilistic calibration, distribution shift, and subgroup auditing across the data sources that shape clinical AI: EHR, laboratory measurements, medications, high-frequency physiological waveforms, imaging-derived variables, notes, and biomolecular profiles.

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04Doctoral seminar

Bayesian Methods for Biomedical Networks

Ensemble structure learning, MCMC over graph spaces, and bootstrap assessment of stability for gene-regulatory and immunological networks, with edge-level uncertainty reported explicitly.

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05Doctoral seminar

Optimal Transport for Biomedical Data Alignment

Entropic and unbalanced optimal transport, with attention to relaxed-marginal formulations for cross-species and cross-site alignment of biomedical measurements.

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Detailed course dashboards · full syllabus content

Complete outlines for five proposed graduate courses.

Each dashboard specifies the course purpose, intended audience, prerequisites, learning objectives, module sequence, assignments, grading, final project, datasets, tools, readings, and place within the broader teaching program.

Course overview ↑
01 Introductory graduate · advanced undergraduate Statistical Learning for Health Data Penalized regression, tree ensembles, conditional-independence testing, and survival analysis, with each method paired with a public clinical dataset and a reproducible analysis.
Catalog description
A hands-on introduction to statistical learning for students entering data science from outside mathematics, taught through health data. Each method is approached through derivation, working code, and a complete analysis of a public clinical dataset.
Why it matters
Health data science requires practitioners who can fit a model and defend the choices that produced it. From the first week, calibration, subgroup performance, and label noise are treated as parts of the modeling problem rather than as afterthoughts.
Target audience
First-year graduate students and advanced undergraduates in data science, biomedical informatics, statistics, or clinical fields entering quantitative work.
Prerequisites
One course each in probability/statistics and programming (Python); linear algebra recommended.
Learning objectives
  • Fit and evaluate supervised models on clinical data and reason about bias–variance and regularization.
  • Test conditional independence and perform principled feature selection.
  • Analyze censored outcomes with survival models.
  • Produce a reproducible analysis another student can rerun.
Module schedule
  1. Reproducible workflows: notebooks, environments, data manifests.
  2. Regression & loss functions.
  3. Penalized regression: ridge, lasso, elastic net.
  4. Classification, logistic regression & calibration.
  5. Resampling, cross-validation & leakage.
  6. Tree ensembles: random forests, gradient boosting.
  7. Conditional-independence testing & feature selection.
  8. Survival analysis: Kaplan–Meier, Cox models.
  9. Evaluation for health: AUC pitfalls, subgroup metrics.
  10. Missing data & interpretability (permutation importance, PDP).
Assignments
Weekly labs, each on a public clinical dataset; a mid-term "why is this AUC misleading?" critique.
Grading
45% weekly labs · 15% critique · 30% final project · 10% participation.
Final project
Choose a public clinical dataset, define a prediction task, fit at least two model families, evaluate with calibration and subgroup analysis, and submit a reproducible notebook with a short written report.
Datasets
MIMIC-IV derived tables, a public sepsis cohort, UCI clinical datasets, SEER (for survival).
Tools
Python, scikit-learn, lifelines, pandas, Jupyter, Git.
Readings
James et al., ISLR; Hastie, Tibshirani & Friedman, ESL (selected); selected clinical-ML method papers.
Program fit
This course provides the statistical and computational foundation for the causal-inference and uncertainty-aware courses that follow.
02 Master's Causal Inference for Observational Health Records Targeted maximum likelihood, instrumental variables, and sensitivity analysis taught alongside the practical constraints of EHR data, including missingness, irregular sampling, label leakage, and governance.
Catalog description
Methods for estimating causal effects from observational health records, taught alongside the data-generating processes that can invalidate naive estimates.
Why it matters
Clinical decisions often require causal questions, including for which patients does this intervention help? EHR data, however, are confounded, irregular, and incomplete. The course pairs principled estimation with explicit sensitivity analysis.
Target audience
Master's and doctoral students in data science, biostatistics, and biomedical informatics; clinical fellows with quantitative training.
Prerequisites
Regression and probability; a prior ML or statistics course; Python.
Learning objectives
  • Formalize causal questions with potential outcomes and DAGs.
  • Estimate effects with IPW, doubly-robust methods, TMLE, and instrumental variables.
  • Conduct sensitivity analysis for unmeasured confounding.
  • Diagnose EHR-specific biases and interpret treatment-effect heterogeneity.
Module schedule
  1. Causal questions & potential outcomes.
  2. DAGs & identification.
  3. Confounding, adjustment & propensity scores.
  4. IPW & doubly-robust estimation.
  5. Targeted maximum likelihood (TMLE).
  6. Instrumental variables.
  7. Time-varying treatments & g-methods.
  8. Heterogeneous treatment effects.
  9. EHR realities: missingness, irregular sampling, label leakage.
  10. Sensitivity analysis (E-values); governance, fairness & transportability.
Assignments
Problem sets on identification and estimation; an EHR "bias audit" of a published observational claim.
Grading
40% problem sets · 20% bias audit · 30% final project · 10% participation.
Final project
Pose a causal question on a public EHR cohort, justify identification, estimate with a doubly-robust method, run a sensitivity analysis, and report with clinical interpretation.
Datasets
MIMIC-IV, eICU, public claims subsets.
Tools
Python, DoWhy, EconML, scikit-learn; R optional.
Readings
Hernán & Robins, Causal Inference: What If; Pearl, Causality (selected); Peters, Janzing & Schölkopf (selected); TMLE method papers.
Program fit
The methodological core of the causal-phenotyping and treatment-effect direction in my teaching program.
03 Doctoral · flagship Uncertainty-Aware Clinical ML for Perioperative & Critical-Care Data Three units on probabilistic calibration, distribution shift, and subgroup auditing across the data sources that shape clinical AI: EHR, laboratory measurements, medications, high-frequency physiological waveforms, imaging-derived variables, notes, and biomolecular profiles.
Catalog description
A doctoral course on building and auditing clinical machine-learning systems that report uncertainty explicitly, organized around calibration, distribution shift, and subgroup performance.
Why it matters
Models that perform well on average may still fail during induction, decompensation, weaning, or recovery. Evaluation must therefore examine uncertainty and failure patterns, not only average accuracy.
Target audience
Doctoral students in data science, statistics, biomedical informatics, and computer science; postdocs; clinical fellows in research electives.
Prerequisites
A machine-learning course; probability/statistics; Python. Causal inference helpful.
Learning objectives
  • Calibrate and evaluate probabilistic predictions with proper scoring rules.
  • Apply conformal prediction and quantify uncertainty.
  • Detect and quantify distribution shift across time and sites.
  • Audit subgroup performance, fairness, and instance-level harm.
  • Combine supervised prediction, phenotyping, representation learning, and causal inference on multimodal clinical data.
Module schedule
Unit I: Calibration.
  1. Probabilistic prediction & proper scoring.
  2. Calibration methods & reliability diagrams.
  3. Conformal prediction.
  4. Uncertainty quantification.
Unit II: Distribution shift.
  1. Covariate & label shift.
  2. Shift detection & monitoring.
  3. Domain adaptation & optimal transport.
  4. Temporal & multi-site shift.
Unit III: Subgroup auditing.
  1. Subgroup metrics & fairness.
  2. Instance-level error & harm scoring.
  3. Governance & reproducibility.
Assignments
Labs on calibration, conformal sets, shift detection, and subgroup audits; a paper-replication assignment (reproduce one figure from a clinical AI paper).
Grading
40% labs · 30% final project · 20% paper replication & critique · 10% participation.
Final project
Define a cohort (justify inclusion/exclusion), construct features, train and evaluate a model, and write a report combining quantitative results with clinical interpretation. Submit reproducible code and a data manifest another student can rerun end-to-end on a SLURM cluster or containerized workstation.
Datasets
MIMIC-IV and MIMIC-CXR (PhysioNet-credentialed), eICU; physiologic-waveform subsets.
Tools
Python, PyTorch, scikit-learn, MAPIE (conformal), Docker, SLURM/HPC.
Readings
Guo et al. (calibration); Angelopoulos & Bates (conformal prediction); clinical distribution-shift literature; subgroup/fairness auditing papers.
Program fit
This course integrates the principal themes of my teaching program and aligns directly with my MIT reliability work and Duke postdoctoral research.
04 Doctoral seminar Bayesian Methods for Biomedical Networks Ensemble structure learning, MCMC over graph spaces, and bootstrap assessment of stability for gene-regulatory and immunological networks, with edge-level uncertainty reported explicitly.
Catalog description
A research seminar on Bayesian network inference for biomedical systems, drawing on ensemble structure learning and stability analysis.
Why it matters
Biomedical network analysis requires reproducible structure estimates with explicit uncertainty, rather than a single graph produced by one algorithm and one data sample.
Target audience
Doctoral students in data science, statistics, and computational biology; methodologically oriented postdocs.
Prerequisites
Probability and a graphical-models or ML course; Python; some Bayesian statistics.
Learning objectives
  • Represent and score Bayesian networks.
  • Run constraint- and score-based structure learning.
  • Explore graph posteriors with MCMC.
  • Assess edge stability via bootstrapping and report calibrated uncertainty.
Module schedule
  1. Graphical models & d-separation.
  2. Conditional-independence testing with multiple-hypothesis correction.
  3. Score-based search.
  4. Constraint-based algorithms.
  5. Ensemble / hybrid structure learning.
  6. MCMC over graph spaces.
  7. Bootstrap stability & edge uncertainty.
  8. Deconvolution & network-immunology case study.
  9. Evaluation & benchmarks.
  10. Project presentations.
Assignments
Implement and apply a structure-learning pipeline; lead a seminar critique of a published network-inference paper.
Grading
35% implementation · 25% critiques & seminar leadership · 30% project · 10% participation.
Final project
Infer a network from a real biomedical dataset with calibrated edge stability, and report the structure together with its uncertainty.
Datasets
Public gene-expression (TCGA subsets); immunology / cytometry references.
Tools
Python, bnlearn, PyMC, causalnex, TETRAD, NetworkX.
Readings
Koller & Friedman (selected); Latifizadeh et al., BaMANI (arXiv, 2025); Klinke et al., Nature Communications (2022).
Program fit
The seminar develops the causal-structure methodology used in BaMANI and supports the broader causal-phenotyping direction.
05 Doctoral seminar Optimal Transport for Biomedical Data Alignment Entropic and unbalanced optimal transport, with attention to relaxed-marginal formulations for cross-species and cross-site alignment of biomedical measurements.
Catalog description
A seminar on optimal transport for aligning biomedical distributions across instruments, sites, and species, emphasizing unbalanced and relaxed-marginal methods.
Why it matters
Measurements and models can shift across sites, instruments, and species. Distribution alignment should therefore report both transferred structure and unmatched mass.
Target audience
Doctoral students in data science, statistics, computational biology, and computer science.
Prerequisites
Linear algebra, probability, and optimization basics; Python.
Learning objectives
  • Formulate optimal transport (Monge and Kantorovich).
  • Compute entropic OT with the Sinkhorn algorithm.
  • Apply unbalanced and relaxed-marginal OT.
  • Use Wasserstein distances in machine-learning pipelines for alignment.
Module schedule
  1. OT foundations.
  2. Kantorovich duality.
  3. Entropic regularization & Sinkhorn.
  4. Wasserstein distances & barycenters.
  5. Unbalanced OT.
  6. Relaxed-marginal formulations & unmatched mass.
  7. OT for domain adaptation.
  8. Gaussian-mixture embeddings & graph clustering.
  9. Cross-species / cross-site case study.
  10. Project presentations.
Assignments
Implement Sinkhorn; apply unbalanced OT to an alignment task; lead a critique of an OT-in-biology paper.
Grading
35% implementation · 25% critiques & seminar leadership · 30% project · 10% participation.
Final project
Align two real biomedical datasets with (unbalanced) optimal transport, reporting transfer performance and unmatched mass.
Datasets
Flow-cytometry references; multi-site expression subsets.
Tools
Python, POT (Python Optimal Transport), scikit-learn, scanpy, Leiden/igraph.
Readings
Peyré & Cuturi, Computational Optimal Transport; Cuturi (Sinkhorn); Chizat et al. (unbalanced OT); Latifizadeh et al., OT-RMC (in preparation).
Program fit
The seminar develops the optimal-transport methods used in my cross-dataset harmonization work and supports multi-site phenotyping applications.

Mentoring

Mentoring through research questions, code review, and interpretation.

As a Trainee Mentor and Hackathon Co-Organizer for the MISM Center of Excellence Summer Trainee Program at Duke in 2026, I advised trainees on simulation design, parameter identifiability, and code review.

I have also served as an External Graduate Student Panelist for an NSF REU, an Invited Graduate Judge at WVU research symposia, a mentor to three M.Sc. students, and the founder and president of the WVU SIAM Student Chapter.