CB&B 523a / ENAS 541a / MB&B 523a / PHYS 523a, Biological Physics  Yimin Luo

This course has three aims: (1) to introduce students to the physics of biological systems, (2) to introduce students to the basics of scientific computing, and (3) to familiarize students with characterization methods and analysis tools. We focus on studies of a broad range of biophysical phenomena including diffusion, polymer statistics, entropic forces, membranes, and cell motion using computational tools and methods. We provide intensive tutorials for Matlab including basic syntax, arrays, functions, plotting, and importing and exporting data.
TTh 4pm-5:15pm

CB&B 562b / AMTH 765b / ENAS 561b / INP 562b / MB&B 562b / MCDB 562b / PHYS 562b, Modeling Biological Systems II  Thierry Emonet

This course covers advanced topics in computational biology. How do cells compute, how do they count and tell time, how do they oscillate and generate spatial patterns? Topics include time-dependent dynamics in regulatory, signal-transduction, and neuronal networks; fluctuations, growth, and form; mechanics of cell shape and motion; spatially heterogeneous processes; diffusion. This year, the course spends roughly half its time on mechanical systems at the cellular and tissue level, and half on models of neurons and neural systems in computational neuroscience. Prerequisite: a 200-level biology course or permission of the instructor.
TTh 2:30pm-3:45pm

CB&B 568b, Applied Artificial Intelligence in Healthcare  Andrew Taylor and Wade Schulz

Recent advances in machine learning (ML) offer tremendous promise to improve the care of patients. However, few ML applications are currently deployed within healthcare institutions and even fewer provide real value. This course is designed to empower students to overcome common pitfalls in bringing ML to the bedside and aims to provide a holistic approach to the complexities and nuances of ML in the healthcare space. The class focuses on key steps of model development and implementation centered on real-world applications. Students apply what they learn from the lectures, assignments, and readings to identify salient healthcare problems and tackle their solutions through end-to-end data engineering pipelines.
HTBA

CB&B 570b, Privacy-Enhancing Technologies in Biomedical Data Science  Hoon Cho

Biomedical data science increasingly depends upon access to large and diverse collections of sensitive human subject data. Conventional data sharing frameworks offer limited privacy protection, often resulting in isolated data silos that hinder scientific collaboration.  This course explores Privacy-Enhancing Technologies (PETs) as a solution to these challenges. Specific technologies covered include secure multiparty computation, homomorphic encryption, differential privacy, federated learning, and trusted execution environments. We examine the landscape of privacy risks in biomedicine and study the conceptual and mathematical foundations of PETs as well as their applications in a range of biomedical domains, including genomics and health informatics. Additional special topics delve into the latest developments in this field, concerning both technical and social aspects of PETs. Students engage in hands-on experiences throughout the course, including privacy attack demonstration, literature survey, and the implementation of PET algorithms for various biomedical tasks. Prerequisites: We expect students to have some level of mathematical maturity, including an understanding of probability/statistics and experience writing proofs. We also expect students to be comfortable with Python programming; homeworks include hands-on programming components in Python. A basic understanding of biology and genetics is helpful but not required. Feel free to contact us if you have any questions regarding the requirements.
M 3:30pm-5:20pm

CB&B 571a, Data Science Grant-Writing Practicum  Lucila Ohno-Machado

This is a hands-on course where students review funded and non-funded grant proposals for different types of NIH awards, as well as the critiques provided by the reviewers. Proposals in informatics and data science are different than traditional basic sciences proposals and clinical research proposals, so this course is specific for those proposing data science and informatics innovation that can be applied in biology and/or medicine. Although there is an emphasis on K (mentored) and F awards, we also cover the basics of R (non-mentored) research awards. Instructors and classmates review proposals that students prepare as part of the course.
T 12pm-12:50pm

CB&B 574a or b, Biomedical Natural Language Processing: Methods and Applications  Staff

This course examines current natural language processing (NLP) methods and their applications in the biomedical domain. It provides a systematic introduction to basic knowledge on NLP and AI (e.g., linguistics, machine learning, and deep learning algorithms), advanced NLP tasks (e.g., information extraction, information retrieval, question answering), and corresponding approaches including the recent large language models (LLMs) and hands-on experience in developing biomedical NLP systems for different applications, ranging from biomedical literature mining to clinical decision support. Assessment in this course consists of technical exercises, exams, and projects, to demonstrate the applicability of skills learned during the course.
W 2pm-3:50pm

CB&B 575a, Bioinformatics Applications in Biomedicine  Jihoon Kim

This course covers the latest advances in bioinformatics in the context of human diseases. Students learn background knowledge and practical skills to analyze omics data for human disease research. By the end of this course, students should be able to: (1) process bioinformatics data with linux-based pipelines and data tools, (2) apply exploratory data analysis techniques in Python and R, (3) perform analysis of DNA, RNA, and protein data, and (4) conduct a biobank-scale analysis using the platform such as the All of Us Research Workbench.
M 12:30pm-2:20pm

CB&B 576b, Foundations of Real World Data Science: Electronic Health Records  Daniella Meeker

The course covers scientific principles, best practices, and limitations of using observational data from administrative records, including hypothesis generation, feasibility assessments, and causal inference. Students learn pragmatic skills required to prepare analytic data from large, complex transactional databases. We cover methods for data quality characterization and profiling for study planning. Coursework includes application of methods for creation and validation of computable phenotypes, electronic clinical quality measures, and derived analytic variables. Skills include preparation of real-world data for visualization and reporting in business intelligence tools commonly used in population health and health administration. Students reproduce results from published literature using existing databases for predictive modeling, public health, and outcomes research. Completion of this course positions students for externships in healthcare analytics and health data science. Prerequisites: BIS 638, Clinical Database Management Systems and Ontologies (or equivalent); proficiency in SQL and Python, or R; HIPAA and HSR training; YNHHS Research Basic Access; COS550 or equivalent; execution of DUAs for public and proprietary databases; demonstrated use of YU-YNHHS data science platform.
M 1:30pm-2:20pm

CB&B 601b, Fundamentals of Research: Responsible Conduct of Research  Staff

A weekly seminar presented by faculty trainers on topics relating to proper conduct of research. Required of first-year CB&B students, first-year Immunobiology students, and training grant-funded postdocs. Pass/Fail.
HTBA

CB&B 634a, Computational Methods for Informatics  Robert McDougal

This course introduces the key computational methods and concepts necessary for taking an informatics project from start to finish: using APIs to query online resources, reading and writing common biomedical data formats, choosing appropriate data structures for storing and manipulating data, implementing computationally efficient and parallelizable algorithms for analyzing data, and developing appropriate visualizations for communicating health information. The FAIR data-sharing guidelines are discussed. Current issues in big health data are discussed, including successful applications as well as privacy and bias concerns. This course has a significant programming component, and familiarity with programming is assumed. Prerequisite: CPSC 223 or equivalent, or permission of the instructor.
TTh 3pm-4:20pm, W 11pm-11:45pm

CB&B 638a, Clinical Database Management Systems and Ontologies  Kei-Hoi Cheung and George Hauser

This course introduces database and ontology in the clinical/public health domain. It reviews how data and information are generated in clinical/public health settings. It introduces different approaches to representing, modeling, managing, querying, and integrating clinical/public health data. In terms of database technologies, the course describes two main approaches—SQL database and non-SQL (NoSQL) database—and shows how these technologies can be used to build electronic health records (EHR), data repositories, and data warehouses. In terms of ontologies, it discusses how ontologies are used in connecting and integrating data with machine-readable knowledge. The course reviews the major theories, methods, and tools for the design and development of databases and ontologies. It also includes clinical/public health use cases demonstrating how databases and ontologies are used to support clinical/public health research.
Th 1pm-2:50pm

CB&B 647a / GENE 645a, Statistical Methods in Human Genetics  Hongyu Zhao

Probability modeling and statistical methodology for the analysis of human genetics data are presented. Topics include population genetics, single locus and polygenic inheritance, linkage analysis, quantitative trait analysis, association analysis, haplotype analysis, population structure, whole genome genotyping platforms, copy number variation, pathway analysis, and genetic risk prediction models. Offered every other year. Prerequisites: genetics; BIS 505; S&DS 541 or equivalent; or permission of the instructor.
Th 10am-11:50am

CB&B 663b / AMTH 552b / CPSC 552b / GENE 663b, Deep Learning Theory and Applications  Smita Krishnaswamy

Deep neural networks have gained immense popularity within the past decade due to their success in many important machine-learning tasks such as image recognition, speech recognition, and natural language processing. This course provides a principled and hands-on approach to deep learning with neural networks. Students master the principles and practices underlying neural networks, including modern methods of deep learning, and apply deep learning methods to real-world problems including image recognition, natural language processing, and biomedical applications. Course work includes homework, a final exam, and a final project—either group or individual, depending on enrollment—with both a written and oral (i.e., presentation) component. The course assumes basic prior knowledge in linear algebra and probability. Prerequisites: CPSC 202 and knowledge of Python programming.
HTBA

CB&B 711a and CB&B 712b and CB&B 713b, Lab Rotations  Steven Kleinstein

Three 2.5–3-month research rotations in faculty laboratories are required during the first year of graduate study. These rotations are arranged by each student with individual faculty members.
HTBA

CB&B 740a, Introduction to Health Informatics  Andrew Taylor

The course provides an introduction to clinical and translational informatics. Topics include (1) overview of biomedical informatics, (2) design, function, and evaluation of clinical information systems, (3) clinical decision-making and practice guidelines, (4) clinical decision support systems, (5) informatics support of clinical research, (6) privacy and confidentiality of clinical data, (7) standards, and (8) topics in translational bioinformatics. Permission of the instructor required.
TTh 10:30am-11:45am

CB&B 750b, Core Topics in Biomedical Informatics  Samah Jarad

The course focuses on providing an introduction to common unifying themes that serve as the foundation for different areas of biomedical informatics. It is designed for students with programming experience who plan to build databases and computational tools for use in biomedical research. Emphasis is on understanding basic principles underlying informatics approaches to interoperation among biomedical databases and software tools, standardized biomedical vocabularies and ontologies, biomedical natural language processing, predictive analytics, information extraction, deep learning, and other related topics.
TTh 10:30am-11:45am

CB&B 752b / CPSC 752b / MB&B 752b and MB&B 753b and MB&B 754b / MB&B 753b and MB&B 754b / MB&B 754b / MCDB 752b, Biomedical Data Science: Mining and Modeling  Mark Gerstein and Matthew Simon

Biomedical data science encompasses the analysis of gene sequences, macromolecular structures, and functional genomics data on a large scale. It represents a major practical application for modern techniques in data mining and simulation. Specific topics to be covered include sequence alignment, large-scale processing, next-generation sequencing data, comparative genomics, phylogenetics, biological database design, geometric analysis of protein structure, molecular-dynamics simulation, biological networks, normalization of microarray data, mining of functional genomics data sets, and machine-learning approaches to data integration. Prerequisites: biochemistry and calculus, or permission of the instructor.
MWF 1pm-2:15pm