Bioengineering electives

Bioengineering electives are listed below; they are grouped into categories that can also serve as minor areas for the PhD program.

Information Systems & Networking

Course content in this area draws on advanced topics in database design and implementation from computer science; and the distributed nature of information systems given the ubiquity of Internet-based models for storage and processing. Classes in this area go beyond the conventional relational databases to explore is-sues related to spatiotemporal and multimedia representations of information and new paradigms for indexing/retrieval of such data across expansive, heterogeneous datasets.

CS 240A: Databases & Knowledgebases

Theoretical and technological foundation of intelligent database systems that merge database technology, knowledge-based systems, and advanced programming environments. Rule-based knowledge representation, spatiotemporal reasoning, and logic-based declarative querying/programming are salient features of this technology. Other topics include object-relational systems and data mining techniques

CS 240B: Advanced Data & Knowledge Bases

Logical models for data and knowledge representations. Rule-based languages and non-monotonic reasoning. Temporal queries, spatial queries, and uncertainty in deductive databases and object relational databases. Abstract data types and user-defined column functions in object-relational systems. Data mining algorithms. Semi-structured information.

CS 244A: Distributed Database Systems

File allocation, intelligent directory design, transaction management, deadlock, strong and weak concurrency control, commit protocols, semantic query answering, multi-database systems, fault recovery techniques, network partitioning. Examples, trade-offs, and design experiences.

CS 245A: Intelligent Information Systems

Knowledge discovery in database, knowledge-base maintenance, knowledge-base and database integration architectures, and scale-up issues and applications to cooperative database systems, intelligent decision support systems, and intelligent planning and scheduling systems; computer architecture for processing large-scale knowledge-base/database systems.

CS 246: Web Information Management

Study of Web characteristics and new management techniques needed to build computer systems suitable for Web environment. Topics include Web measuring techniques, large-scale data mining algorithms, efficient page refresh techniques, Web-search ranking algorithms, and query processing techniques on independent data sources.

Biomedical Text Understanding & Information Retrieval

Classes in this topic are on core theories and approaches for natural language processing (NLP) and for text-based information retrieval. For NLP, topics are drawn from machine learning, and statistics to establish an understanding of past and current methods in the area of (automated) text structuring and extraction. Information retrieval classes emphasize the role of information-seeking behaviors and evaluation of systems supporting querying and retrieval over large datasets.

CS 263A: Language & Thought

Introduction to natural language processing (NLP), with emphasis on semantics. Presentation of process models for variety of tasks, including question answering, paraphrasing, machine translation, word-sense disambiguation, narrative and editorial comprehension. Examination of both symbolic and statistical approaches to language processing and acquisition.

CS 263B: Connectionist NLP

Connectionist/ANN architectures designed for natural language processing. Issues include localist versus distributed representations, variable binding, instantiation and inference via spreading activation, acquisition of language and world knowledge (e.g., back propagation in PDP networks and competitive learning in self-organizing feature maps).

Statistics M231: Pattern Recognition & Machine Learning

Fundamental concepts, theories, and algorithms for pattern recognition and machine used in computer vision, image processing, speech recognition, data mining, statistics, and computational biology. Topics include Bayesian decision theory, parametric and nonparametric learning, clustering, complexity (VC-dimension, MDL, AIC), PCA/ICA/TCA, MDS, SVM.

IS 228: Measurement & Evaluation of Information Systems

Information systems and services from points of view of their cost and effectiveness in meeting desired objectives. Study of literature in which measures have been developed to evaluate effectiveness of document collections, reference and IR services, document delivery systems, networking, and technical services.

IS 245: Information Access

Provides fundamental knowledge and skills enabling information professionals to link users with information. Overview of structure of literature in different fields; information-seeking behavior of user groups; communication with users; development of search strategies using print and electronic sources.

IS 246: Information-seeking Behavior

Study of factors and influences, both individual and social, associated with human beings needing, using, and acting on information. Topics include information theory, human information processing, information flow among social and occupational groups, and research on information needs and uses.

IS 260: Information Structures

Introduction to various systems and tools used to organize materials and provide access to them, with emphasis on generic concepts of organization, classification, hierarchy, arrangement, and display of records. Provides background for further studies in cataloging, reference, information retrieval, and database management.

Biomedical Image Understanding

This elective area is geared towards students interested specifically in imaging informatics and includes classes on medical image acquisition; lower-level image processing and feature extraction; and more advanced image modeling and analysis. Coursework draws upon expertise from biomedical physics, and techniques for computer vision/image processing from applied math and statistics.

PBM 210: Computer Vision in Medical Imaging

Study of image segmentation, feature extraction, object recognition, classification, and visualization with biomedical applications. Topics include region-growing, edge detection, mathematical morphology, clustering, neural networks, and volume rendering.

PBM 214: Medical Image Processing Systems

Advanced image processing and image analysis techniques applied to medical images. Discussion of approaches to computer-aided diagnosis and image quantitation, as well as application of pattern classification techniques (neural networks and discriminant analysis). Examination of problems from several imaging modalities (CT, MR, CR, and mammography).

PBM M219: Principles & Applications of MRI

Basic principles of MR physics, and image formation. Emphasis on hardware, Bloch equations, analytic expressions, image contrast mechanisms, spin and gradient echoes, Fourier transform methods, structure of pulse sequences, and scanning parameters. Introduction to advanced techniques in rapid imaging, quantitative imaging, and spectroscopy.

PBM 230: Computed Tomography Theory & Applications

Computed tomography is three-dimensional imaging technique being widely used in radiology and is becoming active research area in biomedicine. Basic principles of computed tomography (CT), various reconstruction algorithms, special characteristics of CT, physics in CT, and various biomedical applications.

PBM M266: Advances in MRI

Starting with basic principles, presentation of physical basis of magnetic resonance imaging (MRI), with emphasis on developing advanced applications in biomedical imaging, including structural and functional studies.

EE 211A: Digital Image Processing

Fundamentals of digital image processing theory and techniques. Topics include two-dimensional linear system theory, image transforms, and enhancement. Concepts covered in lecture applied in computer laboratory assignments.

CS M266A: Statistical Modeling & Learning in Vision and Science

Computer vision and pattern recognition. Study of four types of statistical models for modeling visual patterns: descriptive, causal Markov, generative (hidden Markov), and discriminative. Comparison of principles and algorithms for these models; presentation of unifying picture. Introduction of minimax entropy and EM-type and stochastic algorithms for learning.

CS M266B: Statistical Computing & Inference in Vision and Image Science

Introduction to broad range of algorithms for statistical inference and learning that could be used in vision, pattern recognition, speech, bioinformatics, data mining. Topics include Markov chain Monte Carlo computing, sequential Monte Carlo methods, belief propagation, partial differential equations.

CS 276B: Structured Computer Vision

Methods for computer processing of image data. Systems, concepts, and algorithms for image analysis, radiologic and robotic applications.

Biomedical Modeling & Visualization

This area addresses two intertwined topics: how to represent different types of biomedical information and the ensuing knowledge so that it can be used (e.g., to support medical decision making, comparative effectiveness evaluations, etc.); and how to support users accessing this information through novel interfaces. Courses in modeling consider conventional methods for representing information; symbolic methods; and statistical and probabilistic frameworks. Issues related to data imputation are also considered. Classes from information studies cover user-centric design methods, including human-computer interaction (HCI).

CS 205: Health Analytics

Applied data analytics course, with focus on healthcare applications. Exploration of various machine learning and data analytic tools to learn underlying structure of datasets to solve healthcare problems. Different machine learning concepts and algorithms, statistical models, and building of data-driven models. Big data analytics and tools for handling structured, unstructured, and semi-structured datasets.

CS 241B: Pictorial & Multimedia Database Management

Multimedia information systems requirements. Data models. Searching and accessing databases and across Internet by alphanumeric, image, video, and audio content. Querying, visual languages, and communication. Database design and organization, logical and physical. Indexing methods. Internet multimedia streaming.

CS 262A: Reasoning with Partial Beliefs

Review of several formalisms for representing and managing uncertainty in reasoning systems; presentation of comprehensive description of Bayesian inference using belief networks representation.

CS 262B: Knowledge-based Systems

Machine representation of judgmental knowledge and uncertain relationships. Inference on inexact knowledge bases. Rule-based systems – principles, advantages, and limitations. Signal understanding. Automated planning systems. Knowledge acquisition and explanation producing techniques.

CS M262C: Causal Inference

Techniques of using computers to interpret, summarize, and form theories of empirical observations. Mathematical analysis of trade-offs between computational complexity, storage requirements, and precision of computerized models.

IS 272: Human/Computer Interaction

Survey of social, behavioral, design, and evaluation issues in human/computer interaction, with readings from several disciplines. Extensive use of technology demonstrations and class discussions. Recommended for students in any discipline involved in design or implementation of information technologies.

IS 277: Information Retrieval Systems — User-centered Designs

Design implications of interaction between users and features of automated information systems and interfaces that are specific to information-seeking process. Emphasis on search strategy and subject access through use of thesauri and other vocabularies.

Biostatistics M209: Statistical Modeling in Epidemiology

Principles of modeling, including meanings of models, a priori model specification, translation of models into explicit population assumptions, model selection, model diagnostics, hierarchical (multilevel) modeling. S/U or letter grading.

Biostatistics M232: Statistical Analysis of Incomplete Data

Discussion of statistical analysis of incomplete data sets, with material from sample survey, econometric, biometric, psychometric, and general statistical literature. Topics include treatment of missing data in statistical packages, missing data in ANOVA and regression imputation, weighting, likelihood-based methods, and nonrandom nonresponse models.

Biostatistics M234: Applied Bayesian Inference

Bayesian approach to statistical inference, emphasizing biomedical applications and concepts. Topics include large sample Bayes inference from likelihoods, non-informative and conjugate priors, empirical Bayes, Bayesian approaches to (non)linear regression, model selection, Bayesian hypothesis testing, and numerical methods.

Biostatistics M235: Causal Inference

Selection bias, confounding, ecological paradox, contributions of Fisher and Neyman. Rubin model for causal inference, propensity scores. Analysis of clinical trials with noncompliance. Addressing confounding in longitudinal studies. Path analysis, structural equation, and graphical models. Decision making when causality is disputed.

Biostatistics M236: Longitudinal Data

Longitudinal data analysis, graphing longitudinal data, specifying predictors, modeling variances and covariance, inference, computing, hierarchical models, and random effects.


This topical area leverages coursework from Bioinformatics and Human Genetics. As efforts continue to-wards translational bioinformatics, many areas of medicine are considering how to correlate current observations with -omic data. For instance, within imaging, the area of imaging genomics attempts to link imaging-based phenotype with genetic biomarkers and pathways. Courses in this minor provide a foundation upon which biomedical informatics students can gain some understanding of current genomic and proteomic approaches from a computational perspective.

HG 236A: Advanced Human Genetics

Topics in human genetics related to molecular genetics and relevant technologies. Topics include genomic technologies, human genome, mapping and identification of disease-causing mutations, transcriptomics, proteomics, functional genomics, epigenetics, and stem cells.

Bioinformatics M260A: Introduction to Bioinformatics & Genomics

Genomics and bioinformatics results and methodologies, with emphasis on concepts behind rapid development of these fields. Focus on how to think genomically via case studies showing how genomics questions map to computational problems and their solutions.

Bioinformatics M260B: Algorithms in Bioinformatics & Systems

Development and application of computational approaches to biological questions. Understanding of mechanisms for determining statistical significance of computationally derived results. Students will develop a foundation for innovative work in bioinformatics and systems biology.

Bioinformatics M271: Statistical Methods in Computational Biology

Examination of computational methodology in bioinformatics and computational biology through presentation of current research literature. How to select and apply methods from computational and mathematical disciplines to problems in bioinformatics and computational biology; development of novel methodologies.

Statistics M254: Methods in Computational Biology

Introduction to statistical and computational methods in computational biology and bioinformatics. Emphasize on the understanding of basic statistical concepts and the ability to use statistical inference to solve biological problems. The course covers gene expression data, regulatory sequence analysis, ChIP-chip/seq data, RNA-seq data, and their applications in gene regulation analysis. Statistical methods include multivariate methods, statistical sequence analysis, machine learning, Markov chain Monte Carlo, etc.