Habib LatifizadehPh.D.

Teaching & mentoring

Teaching quantitative methods from assumptions to implementation.

I ask students to derive the method, code it carefully, test it, and explain what the result does and does not support.

View five detailed course dashboards ↓Teaching statement ↗

Teaching experience

Courses taught and supported.

My formal teaching spans undergraduate and graduate mathematics, from algebra and calculus to linear algebra and numerical analysis.

  • Algebra with ApplicationsInstructor · Fall 2021
  • Multivariable CalculusTeaching Assistant · Spring 2021
  • Applied Linear AlgebraTeaching Assistant · Spring 2020
  • Numerical AnalysisTeaching Assistant · Spring 2019
  • Advanced Linear AlgebraTeaching Assistant · Fall 2018
  • Engineers’ CalculusRecitation Leader · Fall 2018
  • Advanced Engineering MathematicsInstructor · Spring 2009
  • Differential EquationsTeaching Assistant · Fall 2008

Courses prepared to develop

Graduate courses I am prepared to develop.

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

01 Introductory graduate · advanced undergraduate

Statistical Learning for Health Data

Penalized regression, tree ensembles, conditional-independence testing, and survival analysis — each method paired with a public clinical dataset, so students leave with a portfolio of reproducible analyses.

View full course dashboard 02 Master's

Causal Inference for Observational Health Records

Targeted maximum likelihood, instrumental variables, and sensitivity analysis alongside the practical realities of EHR data — missingness mechanisms, irregular sampling, label leakage, and governance.

View full course dashboard 03 Doctoral · flagship

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

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

View full course dashboard 04 Doctoral seminar

Bayesian Methods for Biomedical Networks

Ensemble structure learning, MCMC over graph spaces, and bootstrap-stability analysis for gene-regulatory and immunological networks — with calibrated edge-level uncertainty.

View full course dashboard 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.

View full course dashboard

Detailed course dashboards · full syllabus content

Full graduate course outlines.

Each dashboard includes the course purpose, audience, prerequisites, learning objectives, module sequence, assignments, grading, final project, datasets, tools, readings, and program fit.

Course overview ↑
01 Introductory graduate · advanced undergraduate Statistical Learning for Health Data Penalized regression, tree ensembles, conditional-independence testing, and survival analysis — each method paired with a public clinical dataset, so students leave with a portfolio of reproducible analyses.
Catalog description
A hands-on introduction to statistical learning for students entering data science from outside mathematics, taught entirely through health data. Every method is met three ways — a derivation, working code, and a worked analysis on a public clinical dataset.
Why it matters
Health data science needs practitioners who can not only fit a model but defend it. The course builds that judgment from the first week, treating calibration, subgroup performance, and label noise as part of the modeling task rather than caveats.
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
The entry point to my teaching sequence; it feeds the causal-inference and uncertainty-aware courses below.
02 Master's Causal Inference for Observational Health Records Targeted maximum likelihood, instrumental variables, and sensitivity analysis alongside the practical realities of EHR data — missingness mechanisms, irregular sampling, label leakage, and governance.
Catalog description
Methods for estimating causal effects from observational health records, taught alongside the data-generating realities that make naive estimates fail.
Why it matters
Clinical decisions need causal answers — for which patients does this intervention help? — but EHR data are confounded and messy. The course builds principled estimation paired with honest 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 my D2 direction — causal phenotyping and treatment-effect estimation for perioperative recovery.
03 Doctoral · flagship Uncertainty-Aware Clinical ML for Perioperative & Critical-Care Data Three units — probabilistic calibration, distribution shift, and subgroup auditing — across the data sources that actually shape clinical AI: EHR, labs, medications, high-frequency physiologic waveforms, imaging-derived variables, notes, and biomolecular profiles.
Catalog description
A doctoral course on building and auditing clinical machine learning that reports what it does not know, organized around calibration, distribution shift, and subgroup performance.
Why it matters
Models that are accurate on average fail at the edges that matter most — induction, decompensation, weaning, recovery. Trustworthy deployment requires uncertainty-aware evaluation, not just higher 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
The flagship of my teaching program — the course expression of D1 and D2, directly aligned 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-stability analysis for gene-regulatory and immunological networks — with calibrated edge-level uncertainty.
Catalog description
A research seminar on Bayesian network inference for biomedical systems, drawing on ensemble structure learning and stability analysis.
Why it matters
Biomedical discovery needs reproducible causal structure with honest uncertainty — not brittle graphs from a single algorithm run once.
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 form of my causal-discovery methodology (BaMANI); feeds the D2 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
Models fail across sites and platforms. Principled distribution alignment — with honest reporting of unmatched mass — is a precondition for transferable biomedical AI.
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 form of my cross-dataset harmonization work (OT-RMC); supports multi-site perioperative phenotyping.

Mentoring

Mentoring through research questions, code, 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 founder and president of the WVU SIAM Student Chapter.