annual report 2018-2019 · bayesian nonparametric framework for high dimensional problems. visiting...
Post on 26-Mar-2020
4 Views
Preview:
TRANSCRIPT
http://stat.utexas.edu
The Department of Statistics and Data Sciences is an academic unit
housed in the College of Natural Sciences that supports the statistical and
data science needs of The University of Texas at Austin campus.
Our Mission
Our mission is to be a world-class center for statistical science that
advances scholarship across The University of Texas at Austin and
prepares graduates to flourish in an information-rich world.
Our Vision The Department of Statistics and Data Sciences will become a vibrant
community of scholars that has a strong intellectual identity and is widely
recognized for excellence in interdisciplinary research and high impact
educational programs.
Our Core Values
• Commitment to education
• Excellence in research
• Broad engagement with science and society
• Collegiality and integrity
Department of Statistics and Data Sciences i
From the Interim Chair It is a bittersweet moment as I pen this letter, for it marks the end
of my term as the interim chair of the department. For as much as
I enjoyed leading the continued growth of the Department of
Statistics and Data Sciences (SDS), I am happy to welcome Dr.
Kate Calder as the new department chair, starting in September
2019. Kate is an outstanding scholar and leader who will be joining
us from The Ohio State University. I expect that our reputation as
a leader in statistics and data sciences will be internationally
recognized under her guidance with her bold vision for growth.
As excited as I am to look forward, the past academic year 2018–
2019 has its share of significant events and noteworthy achievements. First, in addition to
recruiting Kate as department chair, we successfully recruited Dr. Antonio Linero, a stellar
researcher with a research focus in Bayesian nonparametrics for causal inference. We recruited
Tony from Florida State University. He will be joining us as an Assistant Professor in August
2019. The combined recruitment of Kate and Tony has effectively doubled our number of 100%
SDS faculty. We congratulate Dr. James Scott for promotion to Professor, making his formal
rank match his recognition and standing in the scientific community.
Notable achievements for our instructional faculty include Dr. Kristin Harvey being named the
inaugural Director of Undergraduate Studies and the very deserved promotion of three Assistant
Professors of Instruction to Associate Professor of Instruction: Dr. Kristin Harvey, Sally
Ragsdale, and Dr. Lindsey Smith. The instructional team also welcomed three new faculty, Dr.
Choudur Lakshminarayan, Dr. Nathaniel Woodward, and Jason Rarick. Combined, their
contribution resulted in 21 courses having first-rate instruction from committed educators.
As further evidence that SDS faculty are making waves not only at the university but also on the
international platform, four faculty were recognized with prestigious awards. Dr. Kristin Harvey
received the President’s Associates Teaching Excellence Award, Sally Ragsdale received a
CNS Teaching Excellence Award, Dr. Cory Zigler received the 2019 Rothman Epidemiology
Prize, and I was awarded the Zellner Medal.
Achievements were not limited to faculty, many students completed SDS programs: 9 MS in
Statistics graduates, 17 Portfolio in Applied Statistical Modeling graduates, 2 Portfolio in
Scientific Computation graduates, 21 Certificate in Applied Statistical Modeling graduates, and 8
Certificate in Scientific Computation and Data Sciences graduates. Three PhD in Statistics
students were awarded University Fellowships to support their research as they near
graduation: Su Chen, Maurice Diesendruck, and Carlos Pagani Zanini.
Additionally, the consulting team and the annual Summer Statistics Institute continued to
support training in statistics and data science for the university community and beyond.
One of the greatest challenges facing the university this year was the transition of the HR
system to Workday in November 2018. The roll-out has been less than smooth but has
presented the staff the opportunity to meet this challenge head-on and renew efforts to work
across colleges and units to ensure that everyone gets paid. I would like to especially recognize
our new Senior Administrative Associate Victoria Obioma for her continued efforts in mastering
Workday. As the initiator of processes that were once straightforward but are now complicated
and time-intensive, Victoria, and the rest of the administrative staff, has my sincere gratitude.
Department of Statistics and Data Sciences ii
I would like to thank the dedicated faculty and staff of the department for all the hard work
during this critical year of transition. I am also grateful for the support of Deans Paul Goldbart
and David Vanden Bout and their team in the CNS dean’s office, who continue their unwavering
support of our department.
I’m looking forward to the next and upcoming years as we expect incredible growth with
significant impact under the new leadership of Dr. Kate Calder.
Peter Müller
Department of Statistics and Data Sciences iii
Table of Contents
I. SDS Highlights in 2018–2019 ...................................................................................... 1
II. Unit Reports
A. Instruction .............................................................................................................. 5
B. Professional Education ........................................................................................... 8
C. Consulting ............................................................................................................. 9
D. Graduate Degree Programs ................................................................................ 11
E. Portfolio and Certificate Programs ....................................................................... 13
F. SDS Seminar Series ............................................................................................ 17
G. Summer Statistics Institute .................................................................................. 18
H. Corporate Sponsorships ....................................................................................... 19
I. Grants .................................................................................................................... 20
J. Development ......................................................................................................... 23
III. Appendices
A. Organizational Chart ............................................................................................ 24
B. SDS Core Faculty ................................................................................................. 25
C. SDS Seminar Series Speakers ........................................................................... 32
Department of Statistics and Data Sciences 1
I. HIGHLIGHTS IN 2018–2019
New Chair Announcement In January 2019, Dean Paul Goldbart announced that the Department of Statistics and Data
Sciences will welcome Dr. Catherine Calder as Professor and Department Chair for Fall 2019.
Dr. Calder is currently a professor of statistics and co-director of the Mathematical Biosciences
Institute at The Ohio State University.
New Personnel The Department of Statistics and Data Sciences welcomed six new personnel in 2018–2019:
• Dr. Corwin Zigler joined SDS in September 2018 as an Associate Professor. Most
recently from Harvard School of Public Health, Cory received his PhD from the
Department of Biostatistics at the UCLA Fielding School of Public Health. Cory’s
research focuses on statistical methodology to confront the challenges of complex
observational studies. Areas of focus include Bayesian methods, causal inference,
comparative effectiveness research, spatial statistics, and environmental health data
science.
• Dr. Choudur Lakshminarayan joined SDS faculty in Fall 2018 as an Adjunct Assistant
Professor teaching SDS 387 Linear Models. Since 2013, Lakshminarayan has been an
integral part of the annual Summer Statistics Institute roster, teaching big data topics;
most recently offering “Big Data Analytics: Theory and Methods” in May 2019.
• Jason Rarick joined SDS as Lecturer in Fall 2018 teaching SDS 302 Data Analysis for the Health Sciences. Prior to joining SDS, Jason received an M.A. in Educational
Psychology at The University of Texas at Austin before going on to New York University
to pursue a PhD in Applied Psychology.
• Dr. Nathaniel Woodward joined SDS as a Lecturer in Fall 2018 teaching SDS 328M Biostatistics. Prior to joining SDS, Nathaniel earned his PhD in Educational Psychology
in 2018 and M.S. in Statistics in 2016 from The University of Texas at Austin. Nathaniel
taught his first SDS course in 2017 as an Assistant Instructor.
• Dr. Robert Lunde joined SDS in Fall 2018 as a Post-Doctoral Fellow supervised by Dr.
Purnamrita Sarkar. Prior to joining SDS, Robert obtained his PhD in Statistics at
Carnegie Mellon University. Robert’s research interests include time series, networks,
and the bootstrap.
• Victoria Obioma joined SDS as the Senior Administrative Associate in September 2018.
Prior to joining SDS, Victoria worked at the Office of Sponsored Projects and earned a
B.A. in Radio, Television and Film from The University of Texas at Austin. She
participated in multiple internships in Los Angeles and Austin including an internship at
Black Entertainment Television (BET).
New Director of Undergraduate Studies Dr. Kristin Harvey was appointed as the Director of Undergraduate Studies for the department in
the fall of 2018. Dr. Harvey had served the department in many ways over the past six years,
including acting as course coordinator for two of undergraduate foundation statistics courses.
She has been responsible for evaluating course equivalency forms from other departments,
Department of Statistics and Data Sciences 2
coordinating departmental extended time testing, helping with course planning and TA
allotment. Additionally, she serves on the CNS Course and Curriculum committee and on the
Faculty Advisory committees for the Quantitative Reasoning Flag and the Texas Success
Initiative. She has also served on the CNS 21st Century Task force for the Century Curriculum
Planning and Implementation Task force, the CNS Service Course Committee, the STEM
Council under Dr. David Laude, and the CNS Non-Tenure Track Committee. She was recently
elected to be the Vice Chair of Educational Policy Committee of the Faculty Council and is the
co-chair of the Task Force on Student Feedback (revising course instructor surveys)
commissioned by the Faculty Council and the Provost.
In addition to her service, Dr. Harvey has won several teaching awards, including the
President’s Associates Teaching Excellence Award. Dr. Harvey serves as a valuable resource
to the department for innovative teaching approaches as well as serving as a liaison between
SDS and the other departments and colleges we serve through our undergraduate foundation
courses.
Faculty Awards Dr. Kristin Harvey was recognized with the President’s Associates Teaching Excellence Award.
The award recognizes the university's educational innovators who demonstrate exceptional
undergraduate teaching in the core curriculum, including signature courses, and who engage
with curriculum reform and educational innovation.
Dr. Peter Mueller was awarded the Zellner Medal from the International Society for Bayesian
Analysis. The purpose of the Zellner Medal is to recognize ISBA members who have rendered
exceptional and distinguished service to ISBA over an extended period of time, and whose
contributions have had an impact on the society beyond the time of their incumbency.
Sally Ragsdale was honored with a CNS Teaching Excellence Award. This award recognizes
and honors the committed and innovative educators who help students learn a demanding
curriculum and excel in the classroom.
Dr. Cory Zigler received the 2019 Rothman Epidemiology Prize. This prize is given annually for
the best paper published in the Epidemiology Journal in the preceding year. The selection
criteria are importance, originality, clarity of thought, and excellence in writing. Dr. Zigler’s
winning paper, titled “Impact of National Ambient Air Quality Standards Nonattainment
Designations on Particulate Pollution and Health,” appeared in the March 2018 issue.
Faculty Promotions The President announced the promotion of four Department of Statistics and Data Sciences
faculty members in February 2019. The promotions are expected to be approved by the UT
System Board of Regents in August and be effective September 1, 2019.
• Dr. James Scott: promotion to full Professor
• Dr. Kristin Harvey: promotion to Associate Professor of Instruction
• Sally Ragsdale: promotion to Associate Professor of Instruction
• Dr. Lindsey Smith: promotion to Associate Professor of Instruction
Department of Statistics and Data Sciences 3
Faculty Search The Department of Statistics and Data Sciences conducted a year-long search for a new junior
faculty position that sits 100% in SDS. After reviewing 151 applications, six applicants were
invited for on-campus interviews. A faculty position was offered to one candidate who will be
joining SDS in Fall 2019:
• Antonio Linero comes to UT from the Department of Statistics at Florida State University
where he is currently an Assistant Professor. Dr. Linero earned his PhD in Statistics from
the University of Florida. His research is broadly focused on developing flexible
Bayesian methods. His work has focused on developing appropriate Bayesian methods
for complex longitudinal data, as well as developing model selection tools within the
Bayesian nonparametric framework for high dimensional problems.
Visiting Researchers The Department of Statistics and Data Sciences hosted five visiting researchers this year:
• Nicolás Kuschinski comes from Centro de Investigación en Matemáticas, A.C., where he
is pursuing a Doctorate in Probability and Statistics. His research interests are in
Bayesian Statistics. During his residency at UT, he worked with Peter Mueller.
• Jack Jewson comes from the University of Warwick where he is a fourth year student on
the OxWaSP CDT. His research looks at Bayesian methods for making more robust
decisions under misspecified models and is working under the supervision of Jim Smith
(Warwick). and Chris Holmes (Oxford). During his residency at UT, he worked with Dr.
Stephen Walker.
• Cristiano Villa comes from the University of Kent, where he is a Senior Lecturer in
Statistics. His research is Objective Bayesian analysis, Bayesian model selection and
change point analysis, and Bayesian nonparametric modelling for “big data” problems
and cyber-security. During his residency at UT, he worked with Dr. Stephen Walker.
• He Zhou comes from Monash University, where he is concluding his PhD in Faculty
Information Technology. His research is in Statistical machine learning, especially on
Bayesian/deep/neural generative models for discrete data, e.g. texts and relational
networks, with applications in natural language processing, social network analysis, and
collaborative filtering. During his residency at UT, he worked with Dr. Mingyuan Zhou.
• Ying (Rachel) Wang comes from the University of Sydney, Australia, where she is a
Lecturer. She earned her PhD in Statistics from the University of California–Berkeley.
Her research interests are in theoretical and applied statistical problems arising from
genomic studies, in particular, the reconstruction and modeling of gene networks. During
her residency at UT she worked with Dr. Purnamrita Sarkar.
Community Outreach: Thinkery’s “Get Your Game On”
SDS participated in the Thinkery’s May adult-only event, “Thinkery21: Get Your Game On.” The
Thinkery in Austin is a private non-profit organization that offers children hands-on, interactive
STEAM (science, technology, engineering, arts, and math) exhibits and programs. The general
target age of the Thinkery entrant is newborn through eleven years old. However, adults want to
have hands-on fun, too; thus, the monthly “Thinkery21” event that provides adults the chance to
explore and learn.
Department of Statistics and Data Sciences 4
May’s Thinkery21 theme was “Get Your Game On,” which celebrated the love of games. We
decided that the classic Monty Hall problem would be simple to play but challenging enough to
keep people interested. Dr. Nathaniel Woodward developed an interactive computer app that
allowed participants to play the game in real time and make the same decisions participants in
the show would make. The classic setup involves three doors, two choices, one car, and some
goats. The goal of the game is to win the car. Participants make their first door selection using
the app and then also execute their “keep” or “switch” choice after seeing what’s behind the first
door. Dr. Woodward and Steven Hernandez hosted the game and provided explanations about
the two strategies and why one (switch) is statistically better than the other. As an incentive for
playing the game, winning participants were given a baked good provided by Baked from
Scratch ATX. Organizers of the event said the booth was one of the most popular of the night!
SDS Community Drives: The Department of Statistics and Data Sciences is proud to contribute to the UT community in
ways that reach beyond our front door. This year the department held donation drives for
Orange Santa and the Food Pantry and Career Closet.
SDS participated in Orange Santa for the 4th consecutive year in December 2018. The initiative
to participate was originally proposed by Dr. Kristin Harvey in 2014, and has become an end-of-
the year tradition. Orange Santa began in 1994 as a program designed to foster a more caring
university environment by providing toys for UT families in need during the holiday season. The
department gathered toys through the month of November and participated in the Holiday sing-
a-long in December hosted by Orange Santa.
SDS held a food pantry and career closet drive during April 2019 to benefit UT Outpost. UT
Outpost is hosted by UT’s Student Emergency Services in the Office of the Dean of Students
and addresses food insecurity and professional clothing needs of UT students.
2018–2019 Committee Service Staff Member Committee Position
Kristin Harvey
General Faculty Standing Committee:
Educational Policy Committee Member
Faculty Advisory Committee for Texas Success
Initiative Member
CNS Service Courses Committee Member
Faculty Advisory Committee for Quantitative
Reasoning Flag Member
Course and Curriculum Committee, College of
Natural Sciences Member
Stephanie Tomlinson UT Association of Research Administrators Member
T32 Collaborative Learning Group Leader
Vicki Keller
General Faculty Standing Committee:
Responsibilities, Rights, & Welfare of Graduate
Student Academic Employees Committee
Member
Department of Statistics and Data Sciences 5
II. UNIT REPORTS
A. INSTRUCTION
The demand for statistics and scientific computation courses continues to be strong, particularly
at the foundation course-level. SDS has responded by adding sections of in-demand courses to
meet the needs of the diverse UT student population. SDS now offers more than 100
undergraduate and graduate courses each year, ranging from introductory data analysis and
undergraduate honors statistics, to advanced graduate topics in Bayesian modeling and
computational statistics.
Enrollment Trends
SDS faculty taught 71 undergraduate courses and 41 graduate courses to a total 5719 students
in 2018–2019.
Department of Statistics and Data Sciences 6
Campus Usage SDS courses serve a broad cross-section of students across campus.
Enrollment in undergraduate SDS courses during 2018–2019 is shown by college:
Enrollment in graduate SDS courses during 2018–2019 is shown by college:
Department of Statistics and Data Sciences 7
Statistics Online Support (SOS) Dr. Kristin Harvey and Ms. Sally Ragsdale were awarded a Curriculum Innovation Grant from
the Center for Teaching and Learning in Spring 2015 that funded the creation of the Statistics
Online Support (SOS) resource platform (freely available at https://sites.utexas.edu/sos).
Designed for student researchers who have collected data but need guidance selecting and
performing statistical analyses, SOS provides on-demand content and tutorials that guide users
to independently select the data analysis method appropriate for answering their research
questions. Specifically, Dr. Harvey and Ms. Ragsdale created a website that provides users a
structured decision tree based platform where users follow embedded links that guide them to
the types of statistical methods appropriate for their situation and tutorial videos on how to carry
out those analyses.
There continues to be a high level of usage for the site. In May 2019 alone, there were more
than 4,282 new active users accessing the site and 389 returning users from dozens of
countries spread across six continents. In the past year, there have been 2,921 returning
visitors to the site and 39,342 new visitors. Of those returning, 1,800 people return to the site
three or more times with 800 people returning to the site a week or more after their first visit.
This indicates that SOS is a reliable resource for data analysis that many people return to use
multiple times. The most popular tutorials are on Pearson Correlation and Linear Regression
with 1,497 pageviews in May 2019. The next most popular tutorial was Frequency Tables, Pie
Charts, and Bar Charts with 1,440 pageviews.
MOOC: For the 2018–2019 academic year, Dr. Michael Mahometa continued to offer the department’s
Massively Open Online Course (MOOC), “Foundations of Data Analysis” (FoDA), through the
edX platform. This current version was an asynchronous, archived release, allowing students to
move through the course at their own speed. The course was offered in two parts: one for
Descriptive Statistics and Visualization, and another for Inferential Statistics—each containing
six weeks of instruction. FoDA includes 12 total weeks of instruction, with over 40 instructional
content videos, over 20 R tutorial videos, over 120 direct feedback videos for incorrect answers,
and a scaffolded learning structure that reliably guides students through the analysis process,
while increasing independence. Dr. Mahometa plans on offering a FoDA with Instructor
Feedback in the Spring of 2020.
OnRamps Beginning Fall 2013, Dr. Michael Mahometa, and Ms. Sally Ragsdale worked alongside Dr.
Cathy Stacy, and with the Center for Teaching and Learning, to transform SDS 302 Data
Analysis for the Health Sciences into a course for The University of Texas OnRamps initiative.
This initiative continues to serve a wide variety of Texas High School students, offering dual
enrollment for college credit in SDS 302. In recent years, SDS has seen an explosion of
enrollment in the OnRamps Statistics dual enrollment participation. This past academic year
(2018-2019), under the leadership of Dr. Mahometa and Ms. Rebecca Lyle, OnRamps Statistics
was offered in 59 schools (up from 30 last year), with 1983 total enrollments (up from 792 last
year). Of the students enrolled this year, 48% were eligible for course credit, with over half
(54%) accepting their UT SDS 302 college credit.
Noteworthy:
• OnRamps Statistics will be undergoing a redesign in syllabus and teaching strategies for
a Fall 2020 rollout. Planning includes incorporating a greater emphasis on simulation
through acclaimed R packages (via project MOSAIC), and the inclusion of a final student
project.
Department of Statistics and Data Sciences 8
B. PROFESSIONAL EDUCATION
As the Department of Statistics and Data Sciences grows, so does the need to provide a visible
presence to a larger community. Currently, outreach is through our annual Summer Statistics
Institute.
As the Director of Professional Education and Consulting, Dr. Michael J. Mahometa is tasked
with expanding the department’s professional education offerings. Over the next few years, he
plans to roll out a variety of new educational offerings. These offerings include, but are not
limited, to:
• Statistical Consulting Blog. The Statistical Consulting blog, written by the
department’s Statistical Consultants, will focus on common pain points the Statistical
Consulting Group sees their clients experiencing. This blog will complement the
Statistical Consulting FAQs, and offer a more hands-on asynchronous educational
offering for those both in and out of The University of Texas at Austin community.
• Online Software Short Course offerings. With the continued success of Statistical
Consulting’s Software Short Courses, SDS will create a series of Online Short Courses.
The first series will include Dr. Mahometa’s R and RStudio courses focusing on the
widely used dplyr and ggplot2 packages. These courses are planned to be hosted
through edX, the Department’s partner for the Foundations of Data Analysis MOOC.
• Option III Master’s Program. The Department is also investigating the delivery of a 30-
hour Master’s Program in Data Science and Statistics. This program will serve a broad
population of data consumers, focusing on core concepts in statistics and applications to
a wide variety of fields. We are happy to have the support of the College of Natural
Sciences’ Office of Graduate Education in this exciting endeavor.
In addition to the above listed ventures, Professional Education at the department has been
involved in several new bootcamps, including Biomedical Engineering’s REU (Research
Experiences for Undergraduates) 2019 program and the College of Natural Sciences’ Graduate
Student Data Science training. The department’s role in these bootcamps included:
• Biomedical Engineering’s REU: The creation of 6 hours of material to focus on
Statistical Thinking and the Scientific Method. This included discussions of crafting a
research question, refining in to a hypothesis statement, and applying an appropriate
statistical test. Students examined simulated null distributions to better understand p-
values, and collaboratively worked through a test and graphical summary of a common
engineering failure.
• CNS’ Graduate Student Data Science Training: The application of 12 hours of
material twice this past academic year, covering the common uses of R to import,
transform data, to create new variables from data; graphical investigations of data using
ggplot2 and summary statistics via dplyr; analysis of data including common t-tests,
ANOVA, regression, logistic regression, and multilevel models. This Bootcamp was
offered as part of the CNS Graduate Education Professional Development in both the
Fall of 2018 and Spring of 2019, with continued plans to be offered and expanded upon.
The Department’s Professional Education arm of the Statistical Consulting Group will
continue its involvement with the offering of more advanced courses in future offerings.
Department of Statistics and Data Sciences 9
C. CONSULTING
SDS provides free statistical consulting services to students, faculty, and staff. Clients receive
assistance in planning and interpreting analyses, working with statistical software, developing
research study designs, and learning how to better organize and manipulate their data. Faculty
may also request contract consulting services for more in-depth analyses. In addition, SDS
offers a collection of highly-subscribed instructional short courses in various statistical methods
and software packages for a nominal fee.
Free Consulting Free consulting services continued to be in high demand in 2018-2019. SDS consultants
provided 649.25 hours of free consulting to UT faculty, staff, and students, for an average of
13.53 hours per academic week. Student clients used 81.94% of these hours, while faculty and
staff used the remaining 18.06%. Of clients responding to the post-consultation survey, 99.32%
reported that their most recent consult was either “very good” or “excellent.” Of the responding
clients, 98.58% reported that their research results will be of higher quality, and 99.3% felt more
confident about doing research because of the assistance provided by SDS Consulting
Services.
Contract Consulting SDS provided 83 hours of contract consulting to UT faculty and organizational units and Dell
Medical School clinical faculty in 2018–2019. Contract consulting clients generally have more
complex data analysis needs and pay a fee for these services. SDS consultants typically
provide a detailed reporting of results suitable for publication. Contract consulting clients during
the 2018–2019 academic year included but was not limited to: faculty and staff from the College
of Liberal Arts, Department of Linguistics (40%);, the College of Liberal Arts, Department of
Government (28%); the School of Nursing (12%); and Dell Medical School, Department of
Dermatology (19%).
Department of Statistics and Data Sciences 10
Short Courses In 2018–2019, SDS taught 25 short courses to a total of 522 students, faculty, and staff. Most of
these three-hour courses cover specific software, such as R, Python, SQL, or Stata. In addition,
SDS also continued offering the Topics in Statistics series taught by the statistical consulting
staff.
Overall, SDS’s software and topic short courses saw high enrollment and positive participant
feedback in 2018–2019. Of the participants completing the post-course survey, 94.6% said they
would recommend the course they took to others.
Custom Short Courses In addition to the yearly offerings of Software Short Courses, SDS Consulting Services offered
four Custom Short Courses. Two courses were created as part of CNS’s Data Science training
for graduate students. The third, was an introduction to statistical thinking and the scientific
method for the Biomedical Engineering REU program. This course was offered to 12 REU
undergraduates during their first week of a summer study program. The fourth Custom Short
Course was presented to the Study Abroad International Office staff, covering introductory and
advanced topics in Excel.
Noteworthy:
• SDS Consulting services continued to be in high demand across the University, with
over 99% of all 2017–2018 available appointments filled and an average wait time
exceeding 8 days. �
• SDS short courses continued to have high enrollment and positive participant feedback
in 2018–2019. �
• Evan Stein, doctoral student in Psychology, served as the Graduate Student consultant
during spring 2019. Post-graduation Evan will be hired as a part-time consultant.
Department of Statistics and Data Sciences 11
D. SDS GRADUATE DEGREE PROGRAMS
PhD in Statistics The PhD in Statistics program welcomed its sixth cohort of seven students in August 2018. The
program received 153 completed applications for Fall 2019 admission. Ten offers of admission
were made, resulting in an expected yield of five students.
Admissions Data: PhD Statistics Fall
2013
Fall
2014
Fall
2015
Fall
2016
Fall
2017
Fall
2018
Fall
2019
Applied 79 104 130 138 125 129 153
Admitted 13 13 15 15 20 19 10
Selectivity (%) 16.46 12.5 11.5 10.9 16 14.7 6.5
Enrolled 5 6 8 5 7 7 5*
Yield (%) 38.46 46.15 53.33 33.33 35.00 36.84 50.00
* Expected
PhD Student Progress
Academic Year
Enrollment
Count
Candidacy
Count Graduates
2018–2019* 30 15 -
2017–2018 25 14 2
2016–2017 20 8 2
2015–2016 16 3 -
2014–2015 11 - -
2013–2014 5 - -
TOTAL 4 *Enrollment and graduation numbers current through Spring 2019 Master’s of Science in Statistics The M.S. in Statistics program only accepted applications from current UT graduate students
who are applying to earn the degree concurrently with a doctoral degree in another discipline.
Enrollment Trends Enrollment Count
Academic Year M.S. Only M.S./PhD Graduates
2018–2019* 4 32 9
2017–2018 9 22 9
2016–2017 7 31 13
2015–2016 10 37 19
2014–2015 28 25 18
2013–2014 22 18 12
2012–2013 30 17 19
2011–2012 28 10 11
2010–2011 20 5 7
TOTAL 117 *Enrollment and graduation numbers current through Spring 2019
Department of Statistics and Data Sciences 12
Noteworthy:
• Three PhD in Statistics students entered candidacy during 2018–2019: Ciara Nugent,
Giorgio Paulon, and Spencer Woody.
• Three PhD in Statistics students have been awarded Fellowships from the UT Graduate
School:
o Su Chen, University Graduate Continuing Fellowship for 2019-2020
o Maurice Diesendruck, University of Texas at Austin Graduate School Summer
2019 Fellowship
o Carlos Pagani Zanini, University of Texas at Austin Graduate School Summer
2019 Fellowship
• Seven PhD students have made a total of 12 presentations of their research at national
or international conferences during 2018–2019.
• Seven PhD students have a total of nine publications either accepted or published
during 2018–2019.
Department of Statistics and Data Sciences 13
E. PORTFOLIO AND CERTIFICATE PROGRAMS
SDS provides four unique opportunities for students seeking to develop competencies in
statistical modeling or scientific computation. The Portfolio in Applied Statistical Modeling and
the Portfolio in Scientific Computation are 12-credit programs available to graduate students
interested in strengthening and applying these skills to their research area. The Certificate in
Applied Statistical Modeling and the Certificate in Scientific Computation & Data Sciences are
similar 18-hour programs available to undergraduate students.
Enrollment Trends
Portfolio in Applied Statistical Modeling Since its inception in Fall 2009, 287 students have been admitted into the program and 134
students have completed the portfolio requirements.
Academic Year Enrollment Count Completion Count
2018–2019* 40 17
2017–2018 54 18
2016–2017 40 13
2015–2016 40 17
2014–2015 39 15
2013–2014 47 16
2012–2013 54 15
2011–2012 43 11
2010–2011 24 11
2009–2010 9 1
TOTAL 134 *Enrollment and graduation numbers current through Spring 2019
Participating students come from 11 colleges and schools across campus.
Student Participation Snapshot College/School # students currently enrolled # students completed
Cockrell School of Engineering 13 31
College of Communication — 8
College of Education 8 21
College of Liberal Arts 6 34
College of Natural Sciences 4 15
Jackson School of Geosciences 2 3
LBJ School of Public Affairs 1 16
McCombs School of Business — 2
School of Nursing — 1
School of Pharmacy 2 1
School of Social Work 4 2
TOTAL 40 134
Department of Statistics and Data Sciences 14
Portfolio in Scientific Computation Since its inception in Fall 2010, 38 students have been admitted to the program and 12 students
have completed the portfolio requirements.
Academic Year Enrollment Count Completion Count
2018–2019* 8 2
2017–2018 11 2
2016–2017 10 1
2015–2016 5 1
2014–2015 15 3
2013–2014 12 1
2012–2013 9 2
2011–2012 7 —
2010–2011 7 —
TOTAL 12 *Enrollment and graduation numbers current through Spring 2019
Participating students come from three colleges and schools across campus.
Student Participation Snapshot College/School # students currently enrolled # students completed
Cockrell School of Engineering 4 9
College of Natural Sciences 4 2
School of Pharmacy — 1
TOTAL 8 12
Certificate in Applied Statistical Modeling Since its inception in Fall 2013, 476 students have enrolled and 85 students have completed the
program.
Academic Year Enrollment Count Completion Count
2018–2019* 255 21
2017–2018 139 29
2016–2017 97 25
2015–2016 56 8
2014–2015 27 1
2013–2014 9 1
TOTAL 85 *Enrollment and graduation numbers current through Spring 2019
Department of Statistics and Data Sciences 15
Participating students come from six colleges and schools across campus.
Student Participation Snapshot College/School # students currently enrolled # students completed
Cockrell School of Engineering 15 2
College of Communication 1 —
College of Education 2 —
College of Liberal Arts 68 31
College of Natural Sciences 94 38
McCombs School of Business 74 14
Undeclared 1 —
TOTAL 255 85
Certificate in Scientific Computation & Data Sciences Since its inception in Fall 2009, 404 students have been admitted into the program, and 58
students have completed the program.
Academic Year Enrollment Count Completion Count
2018–2019* 120 8
2017–2018 99 9
2016–2017 84 8
2015–2016 69 7
2014–2015 69 4
2013–2014 79 5
2012–2013 71 6
2011–2012 51 6
2010–2011 27 4
2009–2010 19 1
TOTAL 58 *Enrollment and graduation numbers current through Spring 2019
Participating students come from seven colleges and schools across campus.
Student Participation Snapshot College/School # students currently enrolled # students completed
Cockrell School of Engineering 16 11
College of Communication 1 --
College of Fine Arts 1 --
College of Liberal Arts 17 7
College of Natural Sciences 71 36
Jackson School of Geosciences 2 1
McCombs School of Business 11 3
Undeclared 1 --
TOTAL 120 58
Department of Statistics and Data Sciences 16
Noteworthy: • To meet the increasing demand of both certificate programs, automatic enrollment
began in Spring 2019. With this change, both programs have increased student
participation since AY 2017–2018.
• Current enrollment in the Certificate in Applied Statistical Modeling is 83% higher and
current enrollment in the Certificate in Scientific Computation & Data Sciences is 21%
higher, compared to last year.
Department of Statistics and Data Sciences 17
F. SDS SEMINAR SERIES
SDS Seminar Series Since Fall 2011, the SDS Seminar Series has made a vital contribution to the intellectual,
cultural, and scholarly environment at The University of Texas at Austin for students, faculty,
and the wider community. The lecture series provides participants with the opportunity to hear
from leading scholars and experts who work in different applied areas, including business,
biology, medicine, computer modeling, and economics. Each talk is free of charge and open to
the public.
Dr. Abhra Sarkar chaired the SDS Seminar Series committee that made the final selection of
the invited speakers. PhD student Ciara Nugent acted as the student host and liaison for the
invited speakers. The Fall 2018 SDS Seminar Series featured fourteen speakers with an
average attendance of 25 people. The Spring 2019 SDS Seminar Series featured 10 speakers
with an average attendance of 20. (Please see Appendix C for program details.)
Noteworthy: • PhD in Statistics students in candidacy present a seminar on their research during their
fourth year in the program. Six students presented during the 2018–2019 season: o Su Chen o Evan Ott o Carlos Pagani Zanini o Mengie Wang
o Matteo Vestrucci
o Mingzhang Yin
Department of Statistics and Data Sciences 18
G. SUMMER STATISTICS INSTITUTE
The 12th annual UT Summer Statistics Institute (SSI) was held May 28–31, 2019, in
collaboration with Academic Technology Support in the Patton Hall (RLP) building. SSI provides
a unique hands-on opportunity for participants to acquire valuable skills directly from experts in
the field. Participants joined the Institute from across the country, coming from as far away as
Hawaii and New York, illustrating the continued popularity SSI nationwide. SSI offered 24
twelve-hour courses designed to appeal to a broad range of students, faculty, staff, and the
public. New courses this year included “Intermediate Excel for Personal and Professional Use”
with Maliki Ghossainy, “Scalable Machine Learning: Methods and Tools” with Weijia Xu, and
“Statistical Methods for Categorical Data—Logistical Regression and Beyond” with Dr. Abhra
Sarkar.
The 2019 SSI had enrollment of 394 participants filling 513 seats with 24 participating
instructors from 11 departments. This year’s attendance breakdown was: 25% UT students,
15% UT faculty and staff, 12% non-UT students, and 48% non-UT other (e.g., private industry,
state departments, and non-profits).
The 2019 SSI brought in a gross income of $194,430 with a projected total net profit of $86,369,
a 17% increase over 2018.
Noteworthy: • SDS implemented Cvent as the new SSI registration system, bringing multiple
innovations and ease of use for SSI participants and SDS staff. • 60% of SSI enrollees came from outside The University of Texas at Austin. A first in
program history. • 100% of SSI survey respondents said that the course will benefit their professional
opportunities, while 96% said that they would recommend SSI to others.
Department of Statistics and Data Sciences 19
H. CORPORATE PARTNERSHIPS Dr. Bindu Viswanathan met with representatives from the following companies to discuss
potential collaborative efforts:
• Social Solutions
• Tyson Foods
• USAA
• Merck & Co.
• Tata Consultancy
During spring 2019, students in the consulting seminar class worked on projects from Tyson
Foods, from Dr. Valerie Danesh from the School of Nursing, and Dr. Farya Phillips from the
School of Social Work.
.
Department of Statistics and Data Sciences 20
I. GRANTS
During 2018–2019, SDS assisted faculty with the submission of more than five individual
faculty-driven research proposals and three continuations to the following organizations:
• National Institutes of Health (NIH)
• National Science Foundation (NSF)
and in collaboration with:
• NorthShore University Health System
• Harvard University
SDS had 5 active grants and contracts this fiscal year:
Faculty Active Grants & Contracts
Lauren Meyers,
Professor
• Program Director: Predoctoral Training in Biomedical Big Data Science, NIH, ($1,018,560)
Peter Müller,
Professor
• Sub-award PI: Bayesian Inference for Tumor Heterogeneity with Next-Generation Sequencing Data, NIH R01 ($45,433)
Purnamrita Sarkar,
Assistant Professor
• PI: Collaborative Research: Inference for Network Models with Covariates: Leveraging Local Information for Statistically and Computationally Efficient Estimation of Global Parameters,
NSF, ($160,000)
Stephen Walker,
Professor
• PI: Bayesian Nonparametric Time Dependent Models, ConTex,
($33,500)
Corwin Zigler,
Associate Professor
• PI: ACE Center: Regional Air Pollution Mixtures: The Past and Future Impacts of Emission Controls and Climate Change on Air Quality and Health, EPA, ($54,208)
BIG DATA TO KNOWLEDGE (BD2K): T32 Predoctoral Training in Biomedical Big Data 2018–2019 was the 3
rd year of the five-year T32 (NIH) predoctoral training grant in biomedical
big data for a total project amount of $1,018,560 with $197,756 total costs awarded in this 3rd
year. The BD2K project was developed to deepen predoctoral students’ knowledge in three key
areas: biology, statistics, and computer science in order to provide the holistic training
necessary to meaningfully mine biomedical big data.
Department of Statistics and Data Sciences 21
Starting in Fall 2018 the Executive Committee selected three new trainees to join the two
continuing trainees from the previous year:
• Mackenzie Johnson (Ecology, Evolution, and Behavior)
• Karl Muller (Neuroscience)
• Jennifer Starling (Statistics and Data Sciences)
Continuing trainees:
• Anthony Dutcher (Neuroscience)
• Ciara Nugent (Statistics and Data Sciences)
New trainees began their training by taking the three core courses and the seminar/workshop
course. Core courses included “CSE 380: Tools and Techniques for Computer Science,” “BIO
382K: Introduction to Biology for Data Science,” and “SDS 385: Statistical Models for Big Data.”
Continuing trainees were on rotation, mentored by participating faculty members in areas
outside the trainees’ graduate programs.
Two new trainees have been selected to start the program in Fall 2019.
Dr. Lauren Meyers acted as Program Director for 2018–2019.
Noteworthy:
• SDS graduate students were encouraged to apply for the NIH Ruth L. Kirschstein
National Research Service Award Individual Predoctoral Fellowship (F31). Students also
attended the F31 application workshop provided by the university. One predoctoral
student applied for external funding from this opportunity, results pending.
• Two trainees presented papers and posters in the past year:
o Ciara Nugent (2nd
year trainee)
§ “Inferring treatment effects for unplanned subgroups using multiple
studies,” Bayesian Causal Inference Workshop, June 2019
o Jennifer E. Starling (1st year trainee)
§ “Bayesian Causal Forests with Targeted Smoothing for Identification of
Heterogeneous Treatment Effects: An Application to Family Planning,”
2019 Atlantic Causal Inference Conference, May 2019
§ “BART with Targeted Smoothing: An Application to Patient-Specific Still
Birth Risk,” 2019 International Chinese Statistical Association
Symposium, June 2019
§ “Targeted Smooth Bayesian Causal Forests for Time-Varying
Heterogeneous Treatment Effects,” 2019 International Chinese Statistical
Association Symposium, June 2019
• Two trainees published papers in this past year:
o Karl S. Muller (1st year trainee)
§ Zhang, R., Liu, Z., Zhang, L., Whritner, J. A., Muller, K. S., Hayhoe, M.
M., & Ballard, D. H. (2018). Agile: Learning attention from human for
visuomotor tasks. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 663-679).
o Jennifer Starling (1st year trainee)
§ Starling, J. E., Murray, J. S., Lohr, P. A., Aiken, A. R., Carvalho, C. M., &
Scott, J. G. (2019). Targeted Smooth Bayesian Causal Forests: An
analysis of heterogeneous treatment effects for simultaneous versus
Department of Statistics and Data Sciences 22
interval medical abortion regimens over gestation. arXiv preprint arXiv:1905.09405.
§ Starling, J. E., Murray, J. S., Carvalho, C. M., Bukowski, R., & Scott, J. G.
(2018). Functional response regression with funBART: an analysis of
patient-specific stillbirth risk. arXiv preprint arXiv:1805.07656.
§ Lohr, P. A., Starling, J. E., Scott, J. G., & Aiken, A. R. (2018).
Simultaneous compared with interval medical abortion regimens where
home use is restricted. Obstetrics & Gynecology, 131(4), 635-641.
Department of Statistics and Data Sciences 23
J. DEVELOPMENT
SDS has partnered with the College of Natural Sciences (CNS) External Relations team to
create both short- and long-term plans for building philanthropic and community partnerships
with alumni, friends, corporations, and foundations. SDS is continuing to actively pursue
opportunities for support of programs and conferences through NIH, NSF, and the Institute of
Mathematical Statistics Meeting sponsorship.
Department of Statistics and Data Sciences 24
Appendix A: SDS Organizational Chart
DeanCollege of Natural Sciences
Paul Goldbart, PhD
Chair, interimPeter Müller, PhD
SDS Core Faculty (50% Time)
Peter Müller, PhD Mathematics
Stephen Walker, PhD Mathematics
Carlos Carvalho, PhD IROM
James Scott, PhD IROM
Sinead Williamson, PhD
IROM
Cory Zigler, PhD DMS
SDS Core Faculty(0% Courtesy Appt)
Paul Damien, PhDIROM
Lauren Meyers, PhDIntegrative Biology
Jared Murray, PhDIROM
Tom Sager, PhDIROM
Tom Shively, PhDIROM
Mingyuan Zhou, PhDIROM
SDS Full Faculty
(100% Time)
Abhra Sarkar, PhD
Purnamitra Sarkar, PhD
Assistant Director for AdminstrationVicki L. Keller, MA
Coordinates daily SDS operations & student programsSupervise departmental staffGraduate Coordinator Budgets & EOMCommunicationsReportingFaculty Affairs
Adminstrative ManagerSasha Schellenberg, B.A.
Supervise classified staffCourse scheduling & catalog managementCIS, ALEKS, FASET, Final Exam scheduling, HB 2504 adminCoordinates SDS Seminar Series & Summer Statistics InstituteEvent planning
Grants & Contracts SpecialistStephanie Tomlinson
Grants: pre- and post-award processingT-32 Grant Management
Senior Administrative AssociateVictoria Obioma
HR processingPurchasingPayroll AccountingInventoryRecords MaintenanceBillingDEFINE Vouchers
Student AssistantJuan Pablo Avila
Laura Garcia (ULN)
Director of Professional Education & Consulting
Michael J. Mahometa, PhD Manages all consulting operationsOversees FAC 101B & Stat App ServerProfessional education program developmentOn-ramps & MOOC development
Staff ConsultantVACANT
Manages short coursesOn-ramps development
Staff Consultant (p/t)Erika Hale, M.S.Evan Stein, PhD
Director of Undergraduate Studies
Kristin Harvey, PhDCoordinates SDS 302Teaches undergraduate coursesCourse & Curriculum representativeManage undergraduate courses and course development
Assistant Professor of Instruction
Bindu Viswanathan, PhD
Manages Corporate Partnership programNTT course assignments & staffingTeaches graduate & undergraduate courses
Instructional Faculty
Lauren Blondeau, PhDSarah Collins, PhDSteven Hernandez, MSMatt Hersh, PhDKyongJoo Hong, PhDVera Ioudina, PhDMaggie Myers, PhDMary Parker, PhDSally Ragsdale, MSJason Rarick, MALindsey Smith, PhDNathaniel Woodward, PhD
Department of Statistics and Data Sciences 25
Appendix B: SDS Core Faculty Carlos Carvalho, Professor, IROM
Dr. Carvalho’s research focuses on Bayesian statistics in high-dimensional problems with applications ranging from finance to genetics. Some of his current projects include work on causal inference, machine learning, policy evaluation and empirical asset pricing. He is also the Executive Director of the Center for Enterprise and Policy Analytics , a unit dedicate to support research, education, and dialogue around the impact of economic policies on markets and the free enterprise system. Dr. Carvalho did his undergraduate studies in Brazil. He received his
PhD in Statistics from Duke University in 2006. Before moving to The University of Texas, he was an Assistant Professor at The University of Chicago Booth School of Business. Honors:
• CBA Foundation Advisory Council Centennial Fellow—The University of Texas, Austin, 2012–
• Donald D. Harrington Faculty Fellow—The University of Texas, Austin, 2009–10 • IBM Corporation Scholar—The University of Chicago, 2008–09 • Dennis V. Lindley Prize for innovative research in Bayesian Statistics. Honorable
Mention for “Dynamic Matrix-Variate Graphical Models,” 2007 • Leonard J. Savage Award for outstanding doctoral dissertation in Bayesian
econometrics and statistics—Honorable Mention, 2006 Paul Damien, Professor, IROM
Dr. Damien’s research interests lie at the intersection of mathematics, Bayesian statistics, and applications in business, engineering, and stochastic optimization. He is a Fellow of the Royal Statistical Society of England. He is twice recipient of the United Kingdom Engineering and Physical Sciences Research Council Research Award, and was awarded the United Kingdom Overseas Visiting Fellowship by the Royal Statistical Society of England. Damien has been a Visiting Scholar at the Department of Economics in the University of Groningen, Netherlands. Dr. Damien consults for several companies in a variety of industries world-wide.
Dr. Damien received his PhD from Imperial College, UK. Honors:
• Fellow—Royal Statistical Society of England • McCombs’s School of Business Dean’s Research Fellow
Department of Statistics and Data Sciences 26
Lauren Ancel Meyers, Professor, Integrative Biology
Dr. Meyers received her B.A. degree in Mathematics and Philosophy from Harvard University in 1996 and her PhD from the department of Biological Sciences at Stanford University in 2000. She joined the faculty at the University of Texas at Austin in 2003 where she was recently promoted to Full Professor and awarded a Donald D. Harrington Faculty Fellowship. She has also been an active member of the external faculty of the Santa Fe Institute since 2003 and now serves on its Scientific Advisory Board. Lauren has developed new mathematical methods for forecasting and optimizing the control of infectious diseases including meningitis, HIV, influenza, walking
pneumonia, and SARS. Her research has been published in over 45 peer-reviewed publications and funded by research grants from National Institutes of Health, the National Science Foundation, and the James S. McDonnell Foundation. The Wall Street Journal, Newsweek, the BBC, and other news sources have highlighted Lauren's work; and a number of government agencies have sought her expertise, including the Centers for Disease Control and Prevention (CDC), the Biomedical Advanced Research and Development Authority (BARDA), and the US National Intelligence Council. In 2004, the MIT Technology Review named Lauren as one of the top 100 global innovators under age 35. Honors:
• William H. and Gladys G. Reeder Faculty Fellow—The University of Texas, Austin, 2011-2013
• Donald D. Harrington Faculty Fellowship—The University of Texas, Austin, 2010-2011
• Fellow, University of Texas Institute for Molecular and Cellular Biology, 2006-2010
• College of Natural Sciences Teaching Excellence Award—The University of Texas, 2005
• MIT Technology Review TR100: One of 100 Top Global Innovators Under 35, 2004
Jared Murray, Assistant Professor, IROM
Dr. Murray’s current research interests are in developing flexible Bayesian multivariate models for heterogenous and structured data, with applications to multiple imputation for missing data, latent variable modeling and causal inference. He was recently awarded an NSF grant “Improving Probabilistic Record Linkage and Subsequent Inference” to develop new methods for matching records across files in the absence of unique identifiers, and for making inference using
the combined files.
Department of Statistics and Data Sciences 27
Peter Müller, Professor, Mathematics
Dr. Müller’s interest is in methods and applications of Bayesian inference. More specifically, he is working on nonparametric Bayesian inference, decision problems, and applications to biomedical research problems. Nonparametric Bayesian inference refers to prior models for infinite dimensional random quantities, typically random probability measures. Decision problems include particular clinical trial design and multiple comparison procedures. Other applications that interest Dr. Müller include inference related to dependence structure, specifically graphical models to formalize inference about dependence for high throughput genomic data.
Another large area of application is population pharmacokinetic and pharmacodynamic models, which give rise to many good applications that exploit many of his methodological interests. Dr. Müller’s undergraduate education is from Universität Wien and Technische Universität Wien, Austria. He received his PhD from Purdue University where he worked under Jim Berger on MCMC for constrained parameter problems. He spent several years at the Institute of Statistics and Decision Sciences (ISDS), Duke University, and at M.D. Anderson Biostatistics. Honors:
• Zellner Medal, ISBA, 2019 • Fellow of the American Statistical Association • President of the International Society for Bayesian Analysis, 2010 • Robert R. Herring Distinguished Professorship in Clinical Research, 2007–2011
Tom Sager, Professor, IROM
The major part of Dr. Sager’s research focuses on the development and application of econometric models to study the empirical behavior of insurance companies. The models involve simultaneously interacting manifest and latent variables interacting in autocorrelated nonlinear panel data structures. He has applied these to the prediction of insurer insolvency, the risks of mortgage-backed securities, the effects of the Affordable Care Act, the assessment of global systemic risk, consequences of regulation, and other issues. Dr. Sager seeks to illuminate how agency theory, transactions cost economics, theories of limited and unlimited risk, and other ideas
play out to explain insurers’ management of their enterprise risks. Honors:
• Associate Editor, Journal of Risk and Insurance, 2001–2018
• Joe D. Beasley Award for teaching excellence
Department of Statistics & Data Sciences 28
Abhra Sarkar, Assistant Professor
Dr. Sarkar’s interests center around the development of novel statistical approaches that improve results and practice in an initial motivating application area while also having much broader general utility. In particular, he enjoys developing Bayesian non- and semi-parametric methods that accommodate a wide range of data generating processes, adapting to different level of data complexity and potentially automating various aspects of the analysis, including feature extraction, selection of variables, quantification of model
uncertainty and test hypothesis of interest. Dr. Sarkar earned his PhD in Statistics from Texas A&M University, and was a postdoctoral associate at Duke University. Honors:
• International Indian Statistical Association (IISA) Student Paper Award (2014)
Purnamrita Sarkar, Assistant Professor
Dr. Sarkar works on large-scale statistical machine learning problems with a focus on statistical models, asymptotic theory and scalable inference algorithms for large networks. Dr. Sarkar graduated from the School of Computer Science at Carnegie Mellon University in 2010. After earning her doctorate she was a postdoctoral scholar at U. C. Berkeley jointly in the Department of Electrical Engineering and Computer Sciences and the Department of Statistics. She received her Bachelor's degree in
Computer Science from the Indian Institute of Technology, Kharagpur in 2004. Honors:
• Best paper award, 29th International Conference on Data Engineering (ICDE), 2013
• Best paper award, International Conference on Social Networks Analysis and Mining (ASONAM), 2009
Department of Statistics and Data Sciences 29
James G. Scott, Associate Professor, IROM
Dr. Scott's research focuses on statistical methodology for high-dimensional data sets, with applications in a diverse set of areas spanning the social, physical, and biomedical sciences. Three areas of methodological focus include (1) large-scale multiple testing, anomaly-detection and screening problems, where the rate of false discoveries must be controlled in order to yield viable inferences; (2) inference in sparse models; and (3) the application of data-augmentation theory and algorithms to improve the efficiency of Bayesian inference in large-scale models for discrete data sets. His recent applied work has included collaborations in health care,
demography, linguistics, biology, and neuroscience. Dr. Scott received his PhD is from Duke University, where he studied Bayesian model selection under Jim Berger. Before that he studied at Trinity College, Cambridge for two years. He was an undergraduate from 2000 to 2004 at UT-Austin in the Dean's Scholars and Plan II honors programs. Honors:
• Regents’ Outstanding Teaching Award, 2014 • NSF CAREER Grant, 2013 • Savage Award, 2010 (awarded by the International Society of Bayesian Statistics
for best thesis in Bayesian statistical theory) • National Science Foundation Graduate Research Fellowship, 2006–2009 • Marshall Scholarship for study in Great Britain, 2004–2006
Tom Shively, Professor, IROM
Dr. Shively’s research focuses on nonparametric function estimation and its application in energy economics, finance, marketing and transportation science. Current problems of interest include estimating univariate functions nonparametrically subject to shape constraints such as monotonicity and convexity, developing tests for monotonicity, and extending these results to multivariate functions using Bayesian additive regression trees. He is also interested in MCMC-based computational methods that allow for nonparametric function estimation in a wide range of non-Gaussian models including generalized mixed models, hazard function models and
models for extreme values. His recent applied research focuses on modeling electricity prices in the deregulated Australian market using multivariate nonparametric methods.
Dr. Shively received a B.A. degree in Mathematics from Middlebury College in 1981, an M.B.A. from the University of Chicago in 1984, and a PhD in Statistics from Chicago in 1986. He has been at The University of Texas at Austin since 1986.
Honors: • Outstanding Professor in the Executive MBA Program Award, 2007, 2003
• Joe D. Beasley Award for Teaching Excellence, 2001, 1993
Department of Statistics and Data Sciences 30
Stephen G. Walker, Professor, Mathematics Dr. Walker’s main research focus is on Bayesian parametric and nonparametric methods. He has worked on applications, methodology, theory, implementation via MCMC, and foundational issues. Dr. Walker’s main areas of applications include medical statistics and financial data. Recent work on Bayesian nonparametrics includes constructing time series and regression models. Recent work also includes working with Bayesian models under misspecification and using loss
functions as an alternative to probability models within a learning process akin to Bayesian updating. Dr. Walker received his BA (Hons.) in Mathematics at the Oriel College of Oxford University, being awarded Open Exhibition on entry to the college. He received his PhD in Statistics from the Imperial College of London in 1995, supervised by Jon Wakefield. Dr. Walker has taught at various institutions: Imperial College at London, the University of Bath, and most recently at the University of Kent.
Honors: • Editor of Journal of Statistical Planning and Inference, 2017– • Chair of Bayesian Nonparametric Section of ISBA, 2010–2012 • EPSRC Advances Research Fellow, 2001–2006
Sinead Williamson, Assistant Professor, IROM
Dr. Williamson’s main research focus is the development of nonparametric Bayesian methods for machine learning applications. In particular, she is interested in constructing distributions over correlated measures and structures, in order to model correlated data sets or data with spatio-temporal dependence. Examples include models for documents whose topical composition varies through time, and models for temporally evolving social networks. A key research goal is the development of efficient inference algorithms for such models, and she is currently investigating methods that allow us to apply Bayesian nonparametric techniques to large datasets.
Dr. Williamson received her MEng from the University of Oxford, MSc from University College London, and PhD from the University of Cambridge. Before joining the faculty at UT Austin, she was a postdoctoral scholar at Carnegie Mellon University.
Department of Statistics & Data Sciences 31
Mingyuan Zhou, Assistant Professor, IROM Dr. Zhou’s research lies at the intersection of Bayesian statistics and machine learning. He is interested in developing statistical theory and methods, hierarchical models, and efficient Bayesian inference for big data. He is currently focused on the development of nonparametric Bayesian priors for both count and mixture modeling and dictionary learning. Dr. Zhou is building the negative binomial process family to introduce new exchangeable random partitions and novel clustering algorithms. Dr. Zhou received his PhD in Electrical and Computer Engineering from
Duke University in 2013, Master's in Signal and Information Processing from the Chinese Academy of Sciences in 2008, and B.Sc. in Acoustics from Nanjing University in 2005. Honors
• McCombs Research Excellence Grant, November 2018 • CBA Foundations Research Excellence Award for Assistant Professors, April 2018 • NSF@ISBA junior travel support, ISBA World Meeting 2016 • Best Student Poster Award in NIPS 2015 Workshop: Networks
Corwin Zigler, Associate Professor, DMS
Dr. Zigler’s main research focuses on statistical methodology to confront the challenges of complex observational studies. Areas of focus include Bayesian methods, causal inference, comparative effectiveness research, spatial statistics, and environmental health data science. He has established a national and international reputation in the fields of statistics and biostatistics, and has impacted other areas of public health research including air pollution regulatory policy, environmental engineering, cancer health services research, interventional cardiology, and health care policy.
Dr. Zigler received his PhD from the Department of Biostatistics at the UCLA Fielding School of Public Health. Before joining the faculty at UT Austin, he was at the Harvard School of Public Health.
Honors • Rothman Epidemiology Prize, 2019 • American Statistical Association Statistics in Epidemiology Section Young Investigator
Award, 2012
Department of Statistics & Data Sciences 32
Appendix C: SDS Seminar Series Speakers Date Speaker Name Institution Title of Talk
8/31/18 Katherine Heller Duke University “Machine Learning for Health Care”
9/21/18 Su Chen University of Texas at Austin
“Fast Bayesian Variable Selection: Solo Spike and Slab”
9/28/18 Lucas Janson Harvard University “Using Knockoffs to find important variables with statistical guarantees”
10/5/18 Carlos Pagani Zanini
University of Texas at Austin
“A Bayesian Random Partition Model for Sequential Refinement and Coagulation”
10/12/18 Mingzhang Yin University of Texas at Austin
“ARM: Augment-REINFORCE-merge gradient for discrete latent variables”
10/19/18 Debdeep Pati Texas A&M University
“Constrained Gaussian processes and the proton puzzle problem”
10/26/18 Yuguo Chen University of Illinois at Urbana- Champaign
“Statistical Inference on Dynamic Networks”
11/2/18 Evan Ott University of Texas at Austin
“Bayesian Deep Learning: Extending Probabilistic Backpropagation and Transfer Learning”
11/9/18 Mengie Wang University of Texas at Austin
“A Data Dependent Posterior Density Generative Model”
11/16/18 David Yeager University of Texas at Austin
“Heterogeneous Effects of a Scalable Growth-Mindset Intervention on Adolescents’ Educational Trajectories”
11/29/18 Sonia Petrone Universitá Bocconi “Quasi-Bayes properties of a sequential procedures for mixtures.”
11/30/18 Choudur Lakshminarayan
University of Texas at Austin
“Data Compression, and Statistical Pattern Recognition in Healthcare Applications”
12/7/18 Cory Zigler University of Texas at Austin
“Bipartite Causal Inference with Interference: Estimating Health Impacts of Power Plant Regulations”
1/23/19 Andee Kaplan Duke University “Life After Record Linkage: Tackling the Downstream Task with Error Propagation”
1/25/19 Eric Jones University of California – Berkeley
“Exploiting computational scale for richer model-based inference”
1/28/19 Maricela Cruz University of California – Irvine
“Interrupted Time Series Models for Analyzing Complex Healthcare Interventions”
1/30/19 Prasad Patil Harvard University “Replicability of genomic signatures and scientific results”
2/1/19 Rachel Nethery Harvard University “Bayesian and Machine Learning Approaches to Estimate Causal Effects in Environmental Health Applications”
2/4/19 Antonio Linero Florida State University
“Theory and Practice for Bayesian Regression Tree Ensembles”
Department of Statistics & Data Sciences 33
2/22/19 Lorenzo Trippa Harvard University “Bayesian Designs for Glioblastoma Clinical Trials”
3/1/19 Raquel Prado University of California - Santa Cruz)
“Bayesian models for complex-valued fMRI”
3/8/19 Veera Baladandayuthapani
University of Michigan
“Bayesian Models for Richly Structured Data in Biomedicine”
3/11/19 Roger Peng Johns Hopkins Bloomberg School of Public Health
“Estimating the health impacts of modifying air pollution mixtures”
3/29/19 Matteo Vestrucci University of Texas at Austin
“Cognitive disease progression models for clinical trials in autosomal-dominant Alzheimer's disease”
4/5/19 Paul Rathouz University of Texas at Austin
“Semiparametric Generalized Linear Models: Small, Large, and Biased Samples”
4/26/19 Junming Yin University of Arizona
“Towards Better Learning from Crowd Labeling”
5/3/19 Surya Tokday Duke University “Quantile Regression for Correlated Response”
5/7/19 Subhajit Dutta IIT Kanpur “On Perfect Classification and Clustering for Gaussian Processes”
5/10/19 Georgia Papadogeorgou
Duke University “Unmeasured spatial confounding in air pollution studies”
top related