The Biomedical Data Driven Discovery (BD3) Training Program at Northwestern University (NU) is a
collaborative proposal that brings together Big Data scientists and educators from the Feinberg School of
Medicine (FSM), the McCormick School of Engineering and Applied Science (MEAS), the Weinberg College
of Arts and Sciences (WCAS) and the School of Communication. The proposal leverages existing dataintensive doctoral programs: the well-established and nationally recognized program in Data Analytics in
MEAS, led by Diego Klabjan, and the innovative and growing programs in Health and Biomedical Informatics
(HBMI), led by Justin Starren. Since long before this RFA was proposed, Drs. Klabjan and Starren have been
working to more closely align the two training programs and increase the opportunities for Big Data training
in the biomedical domain. This proposal represents the culmination of those extensive discussions. It brings
together the biomedical Big Data domain expertise with methodological expertise in computation,
informatics, statistics, and mathematics, from schools and departments across NU. Before long, we believe
that nearly every biomedical researcher will need to utilize Big Data tools. The BD3 is not targeted at these
“tool users. Rather, the goal of BD3 is to train the next generation biomedical Big Data tool builders.
Coursework
The goal of BD3 is to create a truly multi-disciplinary data science training environment. In doing so, BD3 will
encompass multiple departments and degree-programs, designed for students coming from a wide variety
of backgrounds and undergraduate degrees. Thus, the role of the BD3 curriculum is four fold: 1) it provides
guidance for students who are considering data science, whether they plan on applying to BD3 or not; 2) it
provides guidance for the executive committee on evaluating the preparation of applicants; 3) it provides
guidance to trainees and mentors on the design of individual training plans, in particular which courses
count toward different requirements; and, 4) it provides guidance on areas where new courses are needed.
Success in data science requires mastery of three distinct skill sets: 1) an understanding of the target
domain, 2) an understanding of the nature and structure of the data within that domain, and 3) a master of
the computational and statistical techniques for manipulating and analyzing the data. This translates into a
number of more specific competencies. Those are divided into three broad categories: Core Requirements,
those courses that every BD3 student is expected to complete; Selectives, competencies that must be
addressed, but can be fulfilled by any of a selection of courses; and, Electives, courses that may strengthen a
students preparation for a particular project but are not tied to a specific competency requirement. Unlike
many training programs, many of the selectives for BD3 are in the first year and the requirements are
predominately in the second year. This structure facilitates the integration of students from both HBMI and
DA by allowing them to focus on degree requirements during year one that also satisfy BD3 selective
requirements in preparation for Year 2 requirements.
Core Requirements
MSIA 420 --Predictive Analytics. This is one of the three core courses that all BD3
students are expect to take. It includes classifiers, non-parametric regression, time series, neural
networks, and Bayesian networks. MSIA 421 --Data Mining. This is the second core course. This includes clustering,
association rules, factor analysis, scale development, principle component analysis and dimension
reduction.
MSIA 431 --Analytics for Big Data. This is the third core course. It focuses on Hadoop,
MapReduce and other methods specifically optimized for Big Data. Responsible Conduct of Research. Biomedical Big Data research often involves sensitive
data and real- world clinical systems.
For example, here is a hypothetical course plan for BD3 as well as a comparative list of coursework for
trainees from the three target degree programs:
BD3 Competency
HISP Informatics Student
DGP Informatics Student
IEMS Analytics Students
Predictive Analytics
Data Mining
Big Data Methods
Responsible Conduct of Research
Domain Selective
Domain Selective
Statistics
Programming – Java
Programming – Python
Ontologies
Databases
Text Analytics
MSIA 420 Predictive Analytics
MSIA 421 Data Mining
MSIA 431 Analytics for Big Data
Responsible Conduct of Research
IGP 410 Molecular Biology &Genetics
IBIS 407 Genome Scale Science
EPI_BIO 301 Intro. TO Biostatistics
MSIA 490-25 Programming Java
MSIA 490-20 Analytics with Python
HSIP 441 HBMI Methods 1
HSIP 442 HBMI Method 2
[covered in HSIP 442 above]
MSIA 420 Predictive Analytics
MSIA 421 Data Mining
MSIA 431 Analytics for Big Data
Integrity in Biomedical Research
IGP 410 Molecular Biology & Genetics
IBIS 407 Genome Scale Science
EPI_BIO 301 Intro. TO Biostatistics
MSIA 490-25 Programming Java
MSIA 490-20 Analytics with Python
HSIP 441 HBMI Methods 1
HSIP 442 HBMI Method 2
[covered in HSIP 442 above]
MSIA 420 Predictive Analytics
MSIA 421 Data Mining
MSIA 431 Analytics for Big Data
Responsible Conduct of Research
IGP 410 Molecular Biology & Genetics
IBIS 407 Genome Scale Science
IEMA 401 Intermediate Statistics
MSIA 490-25 Programming Java
MSIA 490-20 Analytics with Python
HSIP 441 HBMI Methods 1
MSIA 490 Introduction to Databases
MSIA 490 Text Analytics
Other Degree Requirements
HSIP 440 Into to Medical Informatics
PH 445 Writing for Publication
HSIP 400 HSIP Colloquium
HSIP 401 Intro to Measurement Science
HSIP 440 Into to Medical Informatics
IGP 405 Cell Biology
IGP 425 Topics in Drug Discovery
IEMS 450-1 Mathematical Programming I
IEMS 460-1 Stochastic Models
IEMS 480-1 Production/Logistics I
IEMS 480-2 Production/Logistics II
IEMS 435-1 Simulation
IEMS 450-1 Mathematical Programming II
IEMS 460.2 Stochastic Models II
Program Faculty
The vision of BD3 is to combine deep domain knowledge with methodological expertise. This involved recruiting a
multidisciplinary group of Primary Mentors as well as secondary mentors. Rather than include all Big Data researchers at
NU as potential mentors, we have focused on those who have expressed a high degree of interest in the program and who
have particular expertise that will be important for a wide variety of trainees. From among the many eligible faculty, we
have carefully selected mentors with expertise across a wide range of methodological skills as well as across a wide range
of biomedical Big Data types, selecting only those with both the interest and time to actively participate in
BD3.
Primary Mentors
Luis Amaral
Sangeeta Bhorade
Chemical and Biomedical Engineering, and Medicine
Medicine-Pulmonary and Critical Care
Larry Birnbaum
Rosemary Braun
Electrical Engineering and Computer Science
Preventive Medicine
David Cella
Rex L. Chisholm
Nosh Contractor
Medical Social Sciences and HBMI
Cell and Molecular Biology and Surgery
Behavioral Sciences, IEMS, and School of Communications
Ramana Davuluri
Philip Greenland
Preventive Medicine-HBMI
Preventive Medicine and Medicine-Cardiology
M. Geoffrey Hayes
Jane Holl
Hongmei Jiang
Medicine-Endocrinology, Metabolism and Molecular Medicine, and Anthropology
Pediatrics and Preventive Medicine
Statistics
Neil Jordan
Aggelos K. Katsaggelos
Preventive Medicine, Psychiatry and Behavioral Sciences
Electrical Engineering and Mechanical Engineering
Neil Kelleher
Abel Kho
Chemistry, Molecular Biosciences, and Medicine
Medicine and Preventive Medicine-HBMI
Diego Klabjan
Konrad Kording
David Liebovitz
IEMS, co-director of BD3
Physical Medicine and Rehabilitation, and Physiology
Medicine and Preventive Medicine-HBMI
Lei Liu
Sanjay Mehrotra
Preventive Medicine-Biostatistics
IEMS and Medical Social Sciences
Lee Miller
Eric Perreault
Mark Seagraves
Physiology
Biomedical Engineering and Physical Medicine and Rehabilitation
Neurobiology
Justin Starren
D. James Surmeier
Preventive Medicine-HBMI, co-director of BD3
Physiology
Krisitn Swanson
Neurological Surgery
Secondary Mentors
Kristi Holmes
Lifang Hou
Siddhartha Jonnalagadda
Elizabeth McNally
Richard Neapolitan
Nicholas Soulakis
Preventive Medicine-HBMI
Preventive Medicine
Preventive Medicine-HBMI
Genomic Medicine
Preventive Medicine-HBMI
Preventive Medicine-HBMI