Course Schedule

Fall 2014 Courses

 

Core Courses

ENG BF 751: Molecular Biology & Biochemistry

Description: This course consists of two modules: (a) “Molecules” – an introduction to the molecular make-up of living organisms, including the structures and mechanisms of action of key players in metabolism and other dynamic behaviors, and (b) “Processes” – a survey of selected biochemical and cellular functions viewed from a systems-biology perspective.  During classes we will present and discuss fundamental information about the make-up and properties of relevant biological components at the molecular (and often supra-molecular) level.  Classes will also involve presentations by training faculty or guest lecturers about exemplary systems related to the material presented earlier and will be followed by extensive discussion. Whenever possible, the presentations will come from the presenter’s own research. In most cases these they will start at the level of physiological function and “drill down” to the molecular details (to the extent that those are known). The “Processes” module will include introductions to metabolic and signaling networks, sub-networks and control processes.

Instructor: Mohr; 2 credits; Tues, 11:30 am – 1:30 pm

CAS BI 552: Molecular Biology I

Description: Synthesis, structure, and function of biologically important macromolecules (DNA, RNA, and proteins). Regulation and control of the synthesis of RNA and proteins. Introduction to molecular biology of eukaryotes. Discussion of molecular biological techniques, including genetics and recombinant DNA techniques. Three hours lecture, one hour discussion.  Required for Bioinformatics M.S. students.

Prereq: BI 203 or BI 213; BI 206 or BI 216.

Instructor: Loechler; Credits: 4; Tues/Thurs 3:30 pm – 5:30 pm 

ENG BE 562: Computational Biology: Genomes, Networks, Evolution

Description: The algorithmic and machine learning foundations of computational biology, combining theory with practice are covered. Principles of algorithm design and core methods in computational biology, and an introduction of important problems in computational biology. Hands on experience analyzing large-scale biological data sets.

Prereq: EK 127 or equivalent, BE 209 or equivalent, BE 200 or equivalent).

Instructor: Galagan; Credits: 4; Tues/Thurs, 2 pm – 3:30 pm and Fri, 10 am – 11:30 am

GRS MA 681: Accelerated Introduction to Statistical Methods for Quantitative Research

Description: Introduction to statistical methods relevant to research in the computational sciences. Core topics include probability theory, estimation theory, hypothesis testing, linear models, GLMs, and experimental design. Emphasis on developing a firm conceptual understanding of the statistical paradigm through data analyses.  Required for entering Bioinformatics Ph.D. students.

Prereq: MA 225 & MA 242 or their equivalents.

Instructor: Eden; Credits: 4; Tues/Thurs, 9:30 am – 11 am

ENG BF 820: Bioinformatics Research Opportunities

Required for entering Bioinformatics Ph.D. students. The course will consist of a series of presentations by Bioinformatics faculty that focus on research projects being investigated in their laboratories. Emphasis is placed on the description of collaborative projects involving experimental and computational approaches to Bioinformatics research problems.

Instructor: TBA; Credits: 1; Mon, 2 pm – 4 pm 

ENG BF 690: Bioinformatics Challenge Project

Project course for first year Bioinformatics Ph.D. students. Open-ended problems will involve bioinformatics as a key element, typically requiring the use of large data sets and computational analysis to make predictions about molecular function, molecular interactions, regulation, etc. Projects will be proposed by the Bioinformatics program faculty and selected by student in teams of three. The end result will be a set of predictions, some of which can be validated retrospectively using data available through online sources and some of which will require experimental validation. During the last 2 months of the academic year, teams will design feasible validation experiments in consultation with the experimental faculty.

Instructor: Benson; Credits: 2; Wed, 12:00 pm – 1:30 pm

ENG BF 810: Bioinformatics PhD Laboratory Rotation

This course is for PhD students to take part in a laboratory rotation system. Students will become familiar with research activity in Bioinformatics labs. These rotations will help students identify the laboratory in which they will perform their dissertation research. PhD students must complete one 9-week rotation in their first semester of matriculation and two in their second semester.

Credit: 1

ENG BF 821: Bioinformatics Graduate Seminar

In this course, the students present advanced papers in Computational Biology and Bioinformatics. The papers are chosen to cover recent breakthroughs in genomics, computational biology, high-throughput biology, analysis methods, computational modeling, databases, theory and bioinformatics.

Instructor: Segrè; Credits: 2; Wed, 2:00 pm – 4:00 pm

ENG BF 831: Translational Bioinformatics Seminar

This course enrolls students who intend to pursue careers in medicine, dental medicine and/or medical research (either academic or industrial) – in particular, students in the BF for Translational Medicine track of the Bioinformatics MS program. After commencing briefly with general introductory material (published reviews and other relevant back-ground information), students will proceed to examine, discuss and evaluate recent papers that directly illustrate the use of bioinformatics either in pre-clinical or clinical research settings. Papers will be drawn from high-impact journals such as Nature, Science, PNAS, Cell, and Science Translational Medicine. Students will take turns presenting the papers to the class and provide a critical review of each, both orally and in writing. They will also complete a term paper in the form of a research proposal directed to the goal of using bioinformatics to advance a medical procedure – either diagnostic or therapeutic. Brief guest presentations by researchers in BUSM laboratories will be arranged as appropriate.

Prereq: consent of the instructor and completion of advising form. Email Scott Mohr (mohr@bu.edu) or Katie Steiling (steiling@bu.edu) to enroll.

Instructor: Steiling; Credits: 2; Thurs, 4:30 pm – 6:30 pm

Electives

ENG BF 527: Applications in Bioinformatics

The field of bioinformatics is concerned with the management and analysis of large biological datasets (such as the human genome) for the purpose of improving our understanding of complex living systems. This course introduces graduate students and upper-level undergraduate students to the core problems in bioinformatics, along with the databases and tools that have been developed to study them. Computer labs emphasize the acquisition of practical bioinformatics skills for use in students research. Familiarity with basic molecular biology is a prerequisite; no prior programming knowledge is assumed. Specific topics will include the analysis of biological sequences, structures, and networks.

Instructor: TBA; Credits: 4; Mon/Wed 12:00 pm – 2:00 pm

ENG BE 569: Next Generation Sequencing

The advent of high throughput sequencing is virtually changing biology and medicine. The technology enables us to catalog the entire functional parts list of living organisms from bacteria to human, develop and validate regulatory networks for controlling gene expression in systems biology models and develop novel biomarkers for personalized medicine that guide pharmacological treatments. In this course we will review the foundations of the field, starting from the biophysical foundations of current or emerging single molecule DNA sequencing techniques, through an introduction to the analytical tools to model and analyze NGS Data, and finally discussing clinical applications such as predicting drug response focusing on cancer. The course will involve bi-weekly homework assignments that include theoretical analysis and modeling, working with multiple analysis tools for NGS data including assembly, re-sequencing, alignments, RNA-seq, ChIP-seq, DNA methylation, mutation analysis and detection, copy number variation detection, and their applications to cancer.

Prereq: ENG BE 200, ENG BE 401 or permission of instructor.

Instructor: Kasif; Credits: 4; Tues/Thurs 4:00 pm – 6:00 pm

ENG BE 700/PY 895:  Methods and Logic in Quantitative Biology

Biology is in the midst of a transformation into a fully quantitative, theory-rich science. For example, the advent of genomic techniques has presented the opportunity to study genetic processes on a genomic scale and to achieve quantitative understanding, not just of individual molecular mechanisms but also of their interactions and regulation at the systems level. This graduate course in quantitative biology is based on original path-breaking papers in diverse areas of biology. Each of these papers depends on quantitative reasoning and theory as well as experiment. Through close reading and discussion of these papers, students of diverse backgrounds (biology, engineering, physics, computational sciences) will learn essential ideas and how to communicate in a common language of modern/quantitative biology. This course should be considered essential education for students interested in pursuing research in bioinformatics, biophysics, systems biology, and synthetic biology. Although it is a graduate course, we encourage advanced undergraduates to enroll, too.

Prereq: Graduate standing or consent of instructor.

Instructors: Mehta & Khalil; Credits: 4; Mon/Weds 10:00 am – 12:00 pm

 

CAS BI 527: Biochemistry Laboratory I

Emphasizes the purification and characterization of proteins and DNA. Development and use of modern instrumentation and techniques. Same as CH527 and laboratory portion of CAS BI/CH421. Four hours lab, one hour discussion.

Prereq: CASCH204 & CASCH212 & CASCH214 or CASCH282

Instructor: Tolan; Credits: 4; Multiple meetings.  See University class schedule for complete list.

CAS BI 560: Systems Biology

Examines critical components of systems biology, including design principles of biological systems (e.g., feedback, synergy, cooperativity), and the generation and analysis of large-scale datasets (e.g., protein- protein interaction, mRNA expression).

Prereq: CAS BI 552 or consent of the instructor.

Instructor: Siggers; Credits: 4; Mon/Wed/Fri 11:00 am – 12:00 pm & Wed 12:00 pm – 1:00 pm

CAS BI 572: Advanced Genetics

An in-depth study of eukaryotic genetics, ranging from the history and basic principles to current topics and modern experimental approaches. Genetics of Drosophila, C. elegans, mice, and humans are explored in detail, including readings from primary literature. Three hours lecture, one hour discussion.

Prereq: CAS BI 206 & CAS BI 203; CAS BI 552 is recommended.

Instructor: McCall; Credits: 4; Tues/Thurs 9:30 am – 11:00 am & Wed 10:00 am – 11:00 am

GRS BI 753: Advanced Molecular Biology

In-depth analysis of current topics in molecular biology regarding the flow of information in the nucleus of eukaryotic cells. Focus on primary literature. Includes genomic flexibility, signal transduction to the nucleus, chromatin structure, gene expression, cell cycle checkpoints, health-related topics.

Prereq: CAS BI 552 or consent of instructor.

Instructor: Hansen; Credits: 4; Mon/Fri 2:30 pm – 4:00 pm & Wed 11:00 am – 12:00 pm

GRS BI 755: Cellular and Systems Neuroscience

Survey course in neurobiology. Topics covered include cell biology of the neuron, development of the nervous system, synaptic plasticity, learning and behavior, and network modeling. Three hours lecture, one hour discussion.

Instructor: Eldred; Credits: 4; Tues/Thurs 3:30 pm – 6:00 pm

CAS CS 565: Data Mining

Introduction to data mining concepts and techniques. Topics include association and correlation discovery, classification and clustering of large datasets, outlier detection. Emphasis on the algorithmic aspects as well as the application of mining in real-world problems.

Prereq: (CASCS112) or equivalent programming experience, and familiarity with linear algebra, probability, and statistics.

Instructor: Terzi; Credits: 4; Mon/Wed 1:00 pm – 2:30 pm & Friday lab either 10:00 – 11:00, 12:00 – 1:00 or 2:00 – 3:00

SPH BS 855: Bayesian Modeling for Biomedical Research & Public Health

The purpose of this course is to present Bayesian modeling techniques in a variety of data analysis applications, including both hypothesis and data driven modeling. The course will start with an overview of Bayesian principles through simple statistical models that will be used to introduce the concept of marginal and conditional independence, graphical modeling and stochastic computations. The course will proceed with the description of advanced Bayesian methods for estimation of odds and risk in observational studies, multiple regression modeling, loglinear and logistic regression, latent class modeling including hidden Markov models and application to model-based clustering, graphical models and Bayesian networks. Applications from genetics, genomics, and observational studies will be included. These topics will be taught using real examples, class discussion and critical reading. Students will be asked to analyze real data sets in their homework and final project.

Grad Prereq: BS805 or MA684 and MA581/MA582 or equivalent or consent.

Instructor: Sebastiani; Credits: 4; Fri 2:00 pm – 4:45 pm

SPH BS 858: Statistical Genetics I

This course covers a variety of statistical applications to human genetic data, including collection and data management of genetic and family history information, and statistical techniques used to identify genes contributing to disease and quantitative traits in humans. Specific topics include basic population genetics, linkage analysis and genetic association analyses with related and unrelated individuals.

Grad Prereq: SPH BS723 or equivalent as determined by instructor.

Instructor: Lunetta; Credits: 4; Thurs 2:00 pm – 4:45 pm

CAS MA 565: Mathematical Models in the Life Sciences

An introduction to mathematical modeling, using applications in the biological sciences. Mathematics includes linear difference and differential equations, and an introduction to nonlinear phenomena and qualitative methods. An elementary knowledge of differential equations and linear algebra is assumed.

Prereq: (CASMA226 OR CASMA231), Grad Prereq: (CASMA226 OR CASMA231).

Instructor: TBA; Credits: 4; Tues/Thurs 11:00 am – 12:30 pm

CAS MA 575: Linear Models

Post-introductory course in linear models, with focus on both principles and practice. Simple and multiple linear regression, weighted and generalized least squares, polynomials and factors, transformations, regression diagnostics, variable selection, and a selection from topics on extensions of linear models.

Prereq: (CASMA214 & CASMA242 & CASMA581) or consent of instructor.

Instructor: Kolaczyk; Credits: 4; Tues/Thurs 9:30 am – 11:00 am & discussion either Mon 1-2:00, Mon 2-3:00 or Mon 3-4:00

CAS MA 581: Probability

Basic probability, conditional probability, independence. Discrete and continuous random variables, mean and variance, functions of random variables, moment generating function. Jointly distributed random variables, conditional distributions, independent random variables. Methods of transformations, law of large numbers, central limit theorem.

Prereq: (CAS MA 225 OR CAS MA 230) or consent of instructor

Instructor: Taqqu; Credits: 4; Mon/Wed/Fri 11:00 am – 12:00 pm & discussion either Mon 1-2:00, Mon 2-3:00, Tues 9:30-10:30 or Tues 4:30-5:30

CAS MA 614: Statistical Methods II

Second course in statistics, embodying basic statistical methods used in educational and social science research. Reviews all basic concepts covered in a first statistics course (e.g., CAS MA 613) and presents, in detail, more advanced topics such as analysis of variance, covariance, experimental design, correlation, regression, and selected nonparametric techniques. A problem-solving course; students carry out analysis of data taken from educational and other social science sources.

Prereq: (CASMA613) For graduate students in education and the social sciences. Not open to CAS students. CAS MA 613 or equivalent or good background in high school algebra.

Grad Prereq: (CASMA613) or equivalent, or good background in high school algebra.

Instructor: Heeren; Credits: 4; Mon 3:00 pm – 6:00 pm

GRS MA 681: Statistics Seminar I

Real problems in experimental design and data analysis presented by clients from various other departments. The art of statistical consulting in a variety of applied areas.

Prereq: GRS MA 782

Instructor: Zhang; Credits: 4; Tues/Thurs 12:30 pm – 2:00 pm

GRS MB 721: Graduate Level Biochemistry

Course description is not currently available.

Instructor: Kornberg; Credits: 4; Tues/Thurs 12:30 pm – 2:00 pm & Wed 4:00 pm – 6:00 pm

ENG EC 533: Advanced Discrete Mathematics

Selected topics in discrete mathematics. Formal systems. Mathematical deduction. Logical concepts. Theorem proving. Sets, relations on sets, operations on sets. Functions, graphs, mathematical structures, morphisms, algebraic structures, semigroups, quotient groups, finite-state machines, their homomorphism, and simulation. Machines as recognizers, regular sets. Kleene theorem.

Prereq: (CASMA124) or equivalent

Instructor: Levitin; Credits: 4; Mon/Wed 2:00 pm – 4:00 pm

 

 

 

 

Spring 2014

 

Core Courses

ENG BF 751: Molecular Biology and Biochemistry: Molecules and Processes

Description: This course consists of two modules:

(a) “Molecules” – an introduction to the molecular make-up of living organisms, inel dynamic behaviors, and

(b) “Processes” – a survey of selected biochemical and cellular functions viewed from a systems-biology perspective.

Instructor: Mohr; Credits: 2 per semester (4 total); Tues 12:00 pm – 2:00 pm

ENG BF 690: Bioinformatics Challenge Project

Description: Project course for first year Bioinformatics graduate students. Open-ended problems will involve bioinformatics as a key element, typically requiring the use of large data sets and computational analysis to make predictions about molecular function, molecular interactions, regulation, etc. Projects will be proposed by the Bioinformatics program faculty and selected by student in teams of three. The end result will be a set of predictions, some of which can be validated retrospectively using data available through online sources and some of which will require experimental validation. During the last 2 months of the academic year, teams will design feasible validation experiments in consultation with the experimental faculty.  1st year Bioinformatics PhD students only.

Instructor: Benson; Credits: 2; Thurs 11:00 am – 12:30 pm

ENG BF 571: Dynamics and Evolution of Biological Networks

Description: The course focuses on mathematical models for exploring the organization, dynamics, and evolution of biochemical and genetic networks. Topics include: introductions to metabolic and genetic networks, deterministic and stochastic kinetics of biochemical pathways; genome-scale models of metabolic reaction fluxes; models of regulatory networks; modular architecture of biological networks. Prereq: CAS MA 226 & CAS MA 242. ENG EK 102 can be used in lieu of the CAS MA 242 pre-req. Familiarity with differential equations and linear algebra at equivalent levels and the consent of instructor can be used in lieu of both pre-reqs.

Instructors: Segre & Korolev; Credits: 4; Mon/Wed 10:00 am – 12:00 pm

Description: This course will address the ethical, legal and scientific aspects of the new genetics. Students in bioinformatics will discuss the questions raised from another view that they normally would not see. As part of the new technologies, individuals, families and society as a whole will have to make decisions that will affect everyone.  We will analyze cases, question the legal system’s role in regulating this field, discuss options for now and analyze cases. Gene therapy, DNA forensics, new reproductive techniques and cloning are only a few of the topics that will be addressed.

Instructor: Yashon; Credits: 4; Tue 9:30 am – 11:30 am

ENG BF 768: Biological Database Systems

Description: Describes relational data models and database management systems. Teaches the theories and techniques of constructing relational databases with emphasis on those aspects needed for various biological data, including sequences, structures, genetic linkages and maps, and signal pathways. Introduces relational database query language SQL. Summarizes currently existing biological databases and the Web-based programming tools for their access. Object-oriented modeling is introduced primarily as a design aid for dealing with the particular complexities of biological information in standard RDB design. Emphasis will be on those problems associated with dealing with data whose nomenclature and interrelationships are undergoing rapid change.

Instructor: Benson; Credits 4; Tues/Thurs 4:00 pm – 7:00 pm

ENG BF 810: Laboratory Rotation System

Description: Three laboratory rotations are required during a Bioinformatics Ph.D. student’s first year. Rotations typically last for a minimum of nine weeks. It is expected that the student will participate in the lab full time except for time spent on courses. One rotation must be experimental, one computational, and the third can be either. Stduents who participate in the Summer Wet-Lab Experience prior entering the program receive credit toward one of the required rotations.

Instructor: Varied; Credits: 1 per rotation (3 total); Day: To be arranged

ENG BF 821: Bioinformatics Graduate Seminar

Description: BF821 is a graduate seminar covering current topics in bioinformatics. This is achieved through the critical reading, presentation, and discussion of recent literature. Additionally, the course is intended to give students the opportunity to practice and improve their scientific presentation abilities. As such, peer feedback on presentations is an integral aspect of the course. Students will present twice during the semester so that they may improve upon their presentation skills based on peer comments.

Instructor: Korolev; Credits: 2; Wed 1:00 pm – 3:00 pm


Electives


ENG BE 500: Systems Biology of Disease

Description: This course will train students to apply or develop new computational network and machine learning concepts to probe into the systems biology of disease.

The course will cover computational frameworks such as biological networks (including metabolic, regulatory and signal transduction networks), microarray analysis, proteomic analysis, next. generation sequencing, imaging, machine learning, genetics, pathway analysis and other technologies to medical diseases initially focusing on clinical problems such as cancer, diabetes, inflammation and aging.

The course is aimed at seniors and graduate students in biomedical engineering or bioinformatics. However, students from other disciplines ranging from medicine and biology to physics or computer science can attend the class with some prerequisites. .

Instructor: Kasif; Credits: 4; Mon/Weds 6:00 pm – 8:00 pm

ENG BE 560: Biomolecular Architecture

Description: Provides an introduction to the molecular building blocks and the structure of three major components of the living cells: the nucleic acids, the phospho-lipids membrane, and the proteins. The nucleic acids, DNA and RNA, linear information storing structure as well as their three-dimensional structure are covered in relationship to their function. This includes an introduction to information and coding theory. The analysis tools used in pattern identification representation and functional association are introduced and used to discuss the patterns characteristic of DNA and protein structure and biochemical function. The problems and current approaches to predicting protein structure including those using homology, energy minimization, and modeling are introduced. The future implications of our expanding biomolecular knowledge and of rational drug design are also discussed.

Prereq: CAS PY 212 & CAS CH 131 or CAS CH 102.

Instructor: Vajda; Credits: 4; Tues/Thurs 2:00 pm – 4:00 pm

ENG BE 566: DNA Structure and Function

Description: Physical structure and properties of DNA. The physical principles of the major experimental methods to study DNA are explained, among them: X-ray analysis, NMR, optical methods (absorption, circular dichroism, fluorescence), centrifugation, gel electrophoresis, chemical and enzymatic probing. Different theoretical models of DNA are presented, among them the melting (helix-coil) model, the polyelectrolyte model, the elastic-rod model, and the topological model. Theoretical approaches to treat the models, (e.g., the Monte Carlo method) are covered. Special emphasis is placed on DNA topology and DNA unusual structures and their biological significance. Major structural features of RNA are considered in parallel with DNA. The main principles of DNA-protein interaction are presented. the role of DNA and RNA structure in most fundamental biological processes, replication, transcription, recombination, reparation, and translation is considered.

Prereq: CAS CH 102 & CAS PY 212, Coreq: ENG EK 424

ENG BF 527: Applications in Bioinformatics

Description: The field of bioinformatics is concerned with the management and analysis of large biological datasets (such as the human genome) for the purpose of improving our understanding of complex living systems. This course introduces graduate students and upper-level undergraduate students to the core problems in bioinformatics, along with the databases and tools that have been developed to study them. Computer labs emphasize the acquisition of practical bioinformatics skills for use in student research. Familiarity with basic molecular biology is a prerequisite; no prior programming knowledge is assumed. Specific topics will include the analysis of biological sequences, structures, and networks.

Prereq: Familiarity with basic molecular biology.

Instructor: TBA; Credits: 4 Mon/Wed 12:00 pm – 2:00 pm

CAS BI/CH 528: Biochemistry Laboratory 2

Description: Emphasizes protein, carbohydrate, nucleic acid, and lipid chemistry. Development and use of modern instrumentation and techniques. Four hours lab, one hour discussion. Same as CAS CH528 and laboratory portion of CAS BI/CH422. Required for BMB students enrolled concurrently in GMS BI 556. Four hours lab, one hour discussion.

Prereq: CASCH204 & CASCH212 & CASCH214 or CASCH282.

Instructor: Kornberg; Credits: 2; Lecture: Fri 8:00 am – 9:00 am, Lab: Mon 10 am – 2 pm, Mon 3 pm – 7: pm, Tue 6 pm – 10 pm, Wed 1 pm – 5 pm, Wed 5:30 pm – 9:30 pm

CAS BI 553: Molecular Biology II

Description: Continuation of CAS BI 552 with emphasis on eukaryotes. General areas of focus include genome organization, mechanisms of gene regulation, and cell signaling. Topics including genomics, mouse transgenics systems, signal transduction, chromatin structure, and cell cycle.

Prereq: (CASBI552) (CAS BI/CH 421/422 recommended.)

Instructor: Naya, Credits: 4.0; Lecture: Tue & Thurs 9:30 to 11:00

GRS BI735: Advanced Cell Biology

Description: Current understanding of essential topics and important problems in modern cell biology, with emphasis on recent experimental findings, research strategies and approaches, and new techniques for investigating how cells work.Three hours lecture, one hour discussion.

Instructor: Bradham, Credits: 4.0; Lecture: Tue & Thurs 2:00 to 3:30

CAS BB 522: Molecular Biology Laboratory

Description: Introduction to techniques of molecular biology research, including analysis of DNA, RNA, and protein molecules by techniques such as restriction enzyme digestions, PCR, subcloning, DNA sequencing and analysis, reporter gene assays, protein-protein interactions, and culturing and yeast molecular biology.

Prereq: (CASBI552)

Instructor: Gilmore, Credits: 4.0; Independent: Tues & Thurs 1:00 to 5:00 (Web Restricted Permission Required)

CAS CH 525: Physical Biochemistry

Description: Cannot be taken as advanced course for chemistry majors or in addition to CAS CH351/352. Introduction to physical chemical principles with topics in biochemistry, solution and solid phase chemistry of biomolecules as studied by equilibrium, hydrodynamics, and spectroscopic/quantum mechanical methods.

Prereq:(CASCH421 OR CASBI421); (CASMA121 OR CASMA123); (CASPY106 OR CASPY212).

Instructor: Allen, Credits: 4.0; Independent: Mon, Wed & Fri 10:00 to 11:00

GRS CH 752: Advanced Topics and Chemical Physics

Description: Current topics of research in chemical physics. Content varies with the instructor but may include material from such areas as advanced methods in molecular spectroscopy and magnetic resonance, non-linear laser-induced phenomena, and photoionization and electron-molecule scattering.

Grad Prereq:(GRSCH652)

Instructor: Chen, Credits: 4.0; Independent: Tue & Thurs 11:00 to 12:30

CAS CS 542: Machine Learning

Description: Introduction to modern machine learning concepts, techniques, and algorithms. Topics include regression, kernels, support vector machines, feature selection, boosting, clustering, hidden Markov models, and Bayesian networks. Programming assignments emphasize taking theory into practice, through applications on real-world data sets.

Prereq: (CASCS112) or equivalent programming experience, and familiarity with linear algebra, probability, and statistics.

Instructor: Chin, Credits: 4.0; Lecture: Tue & Thurs 3:30 to 5:00

SPH BS 704: Introduction to Biostatistics

Description: This course meets the biostatistics core course requirement for all degrees and concentrations at SPH. The course replaces BS701 and BS703. Topics include the collection, classification, and presentation of descriptive data; the rationale of estimation and hypothesis testing; analysis of variance; analysis of contingency tables; correlation and regression analaysis; multiple regression, logistic regression, and the statistical control of confounding; sample size and power considerations; survival analysis. Special attention is directed to the ability to recognize and interpret statistical procedures in articles from the current literature. This course gives students the skills to perform, present, and interpret basic statistical analyses using the R statistical package.

Instructor: Heeren or Milton , Credits: 3.0; Independent: (Heeren ) Tue Jan 14 – Apr 29 6:00 pm to 8:00 pm [ BS704 replaces BS701 and BS 703 for all degree programs MED Campus] and (Milton) Fri Jan 17 – May 2 10:00 am to 12:00pm [ BS704 replaces BS 701 and BS703 for all degree programs MED Campus]

SPH BS 860: Statistical Genetics II

Description: This course covers current topics in statistical genetics, with emphasis on how statistical techniques can be used with various types of genetics data for mapping genes responsible/contributing to complex human diseases. Topics such as genetics map functions, gene mapping in experimental organisms, advanced linkage analysis methods, statistical approaches for the analysis of genome-wide high density SNP scans in unrelated and family samples will be discussed.

Grad Prereq: SPH BS858 or consent of instructor (dupuis@bu.edu).

Instructor: Dupuis, Credits: 4.0; Independent: Tue Jan 14 – Apr 29 6:00pm -8:45 pm

CAS MA 555: Numerical Analysis I

Description: Numerical solutions of equations, iterative methods, analysis of sequences. Theory of interpolation and functional approximation, divided differences. Numerical differentiation and integration. Polynomial theory. Ordinary differential equations.

Prereq: (CASMA225 OR CASMA230) Grad Prereq:(CASMA225 OR CASMA230)

Instructor: Fried, Credits: 4.0; Independent: Mon, Wed and Fri 3:00 to 4:00

CAS MA 576: Generalized Linear Models

Description: Covers topics in linear models beyond MA 575: generalized linear models, analysis of binary and polytomous data, log-linear models, multivariate response models, non-linear models, graphical models, and relevant model selection techniques. Additional topics in modern regression as time allows.

Prereq: (CASMA575) or consent of instructor.

Instructor: Carvalho, Credits: 4.0; Independent: Mon, Wed and Fri 1:00 to 2:00

CAS MA 582: Mathematical Statistics

Description: Point estimation including unbiasedness, efficiency, consistency, sufficiency, minimum variance unbiased estimator, Rao-Blackwell theorem, and Rao-Cramer inequality. Maximum likelihood and method of moment estimations; interval estimation; tests of hypothesis, uniformly most powerful tests, uniformly most powerful unbiased tests, likelihood ratio test, and chi-square test.

Prereq: (CASMA581 OR CASMA381)

Grad Prereq:(CASMA581)

Instructor: Weiner, Credits: 4.0; Lecture: Mon, Wed and Fri 11:00 am to 12:00 pm

CAS MA 583: Introduction to Stochastic Processes

Description: Basic concepts and techniques of stochastic process as they are most often used to construct models for a variety of problems of practical interest. Topics include Markov chains, Poisson process, birth and death processes, queuing theory, renewal processes, and reliability.

Prereq: (CASMA581 OR CASMA381) or consent of instructor.

Grad Prereq:(CASMA581) or consent of instructor.

Instructor: Eden, Credits: 4.0; Lecture Mon, Wed and Fri 10:00 am to 11:00 am

CAS MA 684: Applied Multiple Regression and Multivariable Methods

Description: Application of multivariate data analytic techniques. Multiple regression and correlation, confounding and interaction, variable selection, categorical predictors and outcomes, logistic regression, factor analysis, MANOVA, discriminant analysis, regression with longitudinal data, repeated measures, ANOVA.

Prereq: one year of statistics.

Grad Prereq: one year of statistics.

Instructor: Heeren , Credits: 4.0; Independent: Mon 4:00 to 7:00

GRS MA 884: Topics in Multiscale Analysis: Theory Computation and Applications

Description: Data obtained from a physical system sometimes possess many characteristic length and time scales. In such cases, it is desirable to construct models that are effective for large-scale structures, while capturing small scales at the same time. Modeling this type of data and physical phenomena via mulitple scale diffusion processes and PDE’s with multiple scales may be well-suited in many cases. Thus, such models have been used to describe the behavior of phenomena in scientific areas such as chemistry and biology, ocean-atmosphere sciences, finance and econometrics.

In this course, we will study concepts, analytic and probabilistic tools that are used in various scientific disciplines. Emphasis will be placed on

  • Review of probability theory, introduction to stochastic calculus (Brownian motion, stochastic differential equations, It\^{o} formula, Fokker-Planck eqs, Feynman-Kac formula, relation to PDE’s)
  • Multiscale analysis (averaging and homogenization) of stochastic processes and differential equations using various deterministic and probabilistic tools.
  • Backward SDE’s and their application to homogenization of related PDE’s.
  • Numerical methods and Monte Carlo methods for multiscale processes.
  • Applications to various disciplines such that mathematical finance, physics, chemistry and engineering will be discussed.

The course material will be based on theory, methods and examples from various scientific disciplines.

Prereq: The course will be largely self-contained, accessible to a broad audience and a revision to the basic tools from probability, stochastic processes and differential equations that are needed, will be given. However, students are expected to have the knowlesge equivalent to undergrdauate level probability, stochastic processes and differential equations. PDE’s and graduate level probability will be helpful but NOT necessary.

Instructor: Spiliopoulos , Credits: 4.0; Independent: Tues and Thurs 9:30 to 11:00

GRS MB 722: Advanced Biochemistry

Description: Course description is not currently available. Please contact the School or College offering the course.

Instructor: Tolan, Credits: 4.0;Disscussion Wed 4:30 to 6:30 and Lecture Tue & Thurs 12:30 pm to 2:00 pm (the lecture will meet in LSE 804 and Discussion mts in LSE 704)

ENG EC 730: Information –Theoretical Design of Algorithms

Description: Recently developed information-theoretical approach to the analysis and design of computer algorithms. Previous knowledge of information theory or the theory of algorithms is not required, though desirable. Main topics include the complexity of algorithms; P, E, NP, and NP?hard problems; basic concepts of information theorfy, optimal coding; information-theoretical approach to sorting, order statistics, binary search, decision trees, hashing, minimization of Boolean functions, test, and similar problems; and design of efficient computer algorithms.

Instructor: Levitin, Credits: 4.0; Independent: Tue and Thurs 12:00pm to 2:00 pm