RESEARCH
Before registering, the student must submit an outline of the proposed work for approval by the supervisor and the chair of the Department. Advanced study in a specialized field under the supervision of a member of the department staff. May be repeated for credit.
- Department: Industrial Engineering and Operations Research
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Instructor:
Rachel Cummings
h-index: 16 Info -
Instructor:
Antonius B Dieker
CULPA: 10 h-index: 19 Info -
Instructor:
Adam N Elmachtoub
CULPA: 1 h-index: 13 Info -
Instructor:
Yuri Faenza
h-index: 11 Info -
Instructor:
Vineet Goyal
h-index: 21 Info -
Instructor:
Ali Hirsa
h-index: 8 Info -
Instructor:
Anran Hu
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Instructor:
Garud Iyengar
CULPA: 6
h-index: 30
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Instructor:
Hardeep Johar
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Instructor:
Cedric Josz
CULPA: 2 h-index: 13 Info -
Instructor:
Soulaymane Kachani
CULPA: 12 Info -
Instructor:
Yaren Kaya
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Instructor:
Christian Kroer
h-index: 16 Info -
Instructor:
Daniel Lacker
CULPA: 4
h-index: 13
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Instructor:
Henry Lam
h-index: 17 Info -
Instructor:
Fabrizio Lecci
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Instructor:
Tianyi Lin
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Instructor:
Uday Menon
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Instructor:
Bento Natura
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Instructor:
Jay Sethuraman
CULPA: 10 Info -
Instructor:
Karl Sigman
CULPA: 15
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Instructor:
Clifford Stein
CULPA: 31 Wikipedia h-index: 53 Info -
Instructor:
Wenpin Tang
h-index: 6 Info - 1-6 points
RESEARCH
Before registering, the student must submit an outline of the proposed work for approval by the supervisor and the chair of the Department. Advanced study in a specialized field under the supervision of a member of the department staff. May be repeated for credit.
- Department: Industrial Engineering and Operations Research
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Instructor:
Wenpin Tang
h-index: 6 Info -
Instructor:
Kaizheng Wang
CULPA: 1 h-index: 8 Info -
Instructor:
Lily Xu
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Instructor:
David D Yao
CULPA: 5
Info
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Instructor:
Yi Zhang
CULPA: 2 Info -
Instructor:
Xunyu Zhou
h-index: 54 Info -
Instructor:
Winsor Yang
Info - 1-6 points
RESEARCH IN ARCHAEOLOGY
RESEARCH-PREHISTORIC ARCHAEOL
Prerequisites: the instructors permission. Individual research and tutorial in archaeology for advanced graduate students.
- Department: Anthropology
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Instructor:
Brian Boyd
CULPA: 6
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Instructor:
Hannah Chazin
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Instructor:
Zoe Crossland
CULPA: 9 h-index: 17 Info -
Instructor:
Terence N D'Altroy
CULPA: 37 Info - 3-9 points
RESEARCH IN ARCHAEOLOGY
Prerequisites: the instructors permission. Individual research and tutorial in archaeology for advanced graduate students.
- Department: Anthropology
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Instructor:
Severin Fowles
CULPA: 24
Info
- 3-9 points
Probability for Biostatisticians
Probability: Biostatistic
The biostatistical field is changing with new directions emerging constantly. Doing research in these new directions, which often involve large data and complex designs, requires advanced probability and statistics tools. The purpose of this new course is to collect these important probability methods and present them in a way that is friendly to a biostatistics audience. This course is designed for PhD students in Biostatistics. Its primary objective is to help the students achieve a solid understanding of these probability methods and develop strong analytical skills that are necessary for conducting methodological research in modern biostatistics. At the completion of this course, the students will a) have a working knowledge in Law of Large Numbers, Central Limit Theorems, martingale theory, Brownian motions, weak convergence, empirical process, and Markov chain theory; b) be able to understand the biostatistical literature that involves such methods; c) be able to do proofs that call for such knowledge.
- Department: Biostatistics
- F 1 p.m.-3:50 p.m.
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Instructor:
Bin Cheng
h-index: 32 Info - 3 points
RESEARCH IN SPECIAL FIELDS
Prerequisites: the instructors permission. Individual research in all divisions of anthropology and in allied fields for advanced graduate students
- Department: Anthropology
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Instructor:
Nadia Abu El-Haj
CULPA: 13
Wikipedia
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Instructor:
Lila Abu-Lughod
CULPA: 17 Wikipedia h-index: 48 Info -
Instructor:
Vanessa L Agard-Jones
CULPA: 3 Info -
Instructor:
Naor H Ben-Yehoyada
CULPA: 5 Info -
Instructor:
Brian Boyd
CULPA: 6
Info
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Instructor:
Terence N D'Altroy
CULPA: 37 Info -
Instructor:
Zoe Crossland
CULPA: 9 h-index: 17 Info -
Instructor:
Hannah Chazin
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Instructor:
Maria Jose de Abreu
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Instructor:
Catherine Fennell
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Instructor:
Severin Fowles
CULPA: 24
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Instructor:
Elizabeth M Green
CULPA: 2 Info -
Instructor:
Lashaya Howie
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Instructor:
Marilyn Ivy
CULPA: 11
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Info
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Instructor:
Brian Larkin
CULPA: 16 Info -
Instructor:
Claudio Lomnitz
CULPA: 3 Wikipedia Info -
Instructor:
Mahmood Mamdani
CULPA: 21
Wikipedia
h-index: 57
Info
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Instructor:
Ellen Marakowitz
CULPA: 1 Info -
Instructor:
Rosalind Morris
CULPA: 20 Wikipedia Info -
Instructor:
John Pemberton
CULPA: 15
Wikipedia
Info
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Instructor:
Elizabeth Povinelli
CULPA: 14
Wikipedia
Info
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Instructor:
Lesley Sharp
CULPA: 22 Info -
Instructor:
Audra Simpson
CULPA: 5
Wikipedia
Info
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Instructor:
Paige West
CULPA: 41
h-index: 23
Info
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Instructor:
Kaya N Williams
Info - 3-9 points
Theory of Statistical Inference I
THEORY OF STAT INFERENCE
This course offers a general introduction to essential materials in advanced statistical theory for doctoral students in biostatistics. The course is designed to prepare doctoral students in biostatistics for their written theory qualifying exam. Students in this course will learn theory of estimation, confidence sets and hypothesis testing. Specific topics include a quick review of measure-theoretic probability theory, concepts of sufficiency and completeness, unbiased estimation (UMVUE), least squares principle, likelihood estimation, a variety of estimators and their asymptotic properties, confidence sets, the Neyman-Pearson lemma and uniformly most powerful tests. If time permits, the likelihood ratio test, score test and Wald test, and sequential analysis will be covered.
- Department: Biostatistics
- TR 1 p.m.-2:20 p.m.
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Instructor:
Caleb Miles
h-index: 5 Info - 3 points
Asymptotic Statistics
ASYMPTOTIC STATISTICS
This course will provide a comprehensive introduction to the field of asymptotic statistics. The treatment will be both practical and mathematically rigorous. The course will consist of two parts. The first will be a review of most of the standard topics of limit theory, such as the delta method and central limit theorems, while avoiding many technicalities. The second will present advanced topics such as semiparametric models, counting processes, empirical likelihood, the bootstrap, and empirical processes. These powerful research techniques are becoming increasingly important for the development of biostatistical methods to handle complex data sets. The overall goal of the course is to train students in the use of advanced asymptotic techniques for medical and public health applications. This course is intended for second-year Biostatistics Ph.D. students to provide a review of asymptotic statistics for the Ph.D. qualifying exam, and give them exposure to a variety of advanced topics.
- Department: Biostatistics
- T 1 p.m.-3:50 p.m.
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Instructor:
Ian McKeague
h-index: 43 Info - 3 points
Topics in Statistical Learning and Data
TPCS-STAT LEARN/DATA MINI
The aim of this course is to provide students a systematic training in key topics in modern supervised statistical learning and data mining. For the most part, the focus will remain on a theoretically sound understanding of the methods (learning algorithms) and their applications in complex data analysis, rather than proving technical theorems. Applications of the statistical learning and data mining tools in biomedical and health sciences will be highlighted.
- Department: Biostatistics
- R 1 p.m.-3:50 p.m.
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Instructor:
Min Qian
h-index: 19 Info - 3 points
SOCIAL NETWORKS
- Department: Sociology
- T 4:10 p.m.-6 p.m.
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Instructor:
Peter Bearman
CULPA: 19
Wikipedia
h-index: 54
Info
- 3 points
Advanced Biostatistical Method I
Adv. Biostatistical Metho
This is an advanced course for first-year Ph.D. students in Biostatistics. The aim is to provide a solid foundation of the theory behind linear models and generalized linear models. More emphasis will be placed on concepts and theory with mathematical rigor. Topics covered including linear regression models, logistic regression models, generalized linear regression models and methods for the analysis contingency tables.
- Department: Biostatistics
- M 8:30 a.m.-11:20 a.m.
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Instructor:
Zhezhen Jin
h-index: 51 Info - 3 points
Master's Essay in Biostatistics: Patient
MASTER'S ESSAY-PATIENT OR
This seminar-style course will lead students through the process of writing a Master's Essay in the form of an NIH-style grant application (required for the MS/POR degree track). The essay is undertaken during the fall semester of the second year of study. At the end of the fall term, each student submits a written research proposal following NIH guidelines for either an R01 or K (career development) award. The emphasis in this course is on the quality of the proposed research. The following February, students make an oral presentation to the POR Advisory Board, summarizing the research proposal. Final grades are awarded after the presentations in February.
- Department: Biostatistics
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Instructor:
Qixuan Chen
h-index: 27 Info - 0 points
TOPICS IN MORAL PHILOSOPHY
Prerequisites: the instructor's permission. At least one foundational course in moral philosophy is recommended as background for this course. In this seminar we will take up several questions about moral understanding and insight. Questions we will consider include: Can trusting moral testimony ever be rational or right? Are the reasons to be cautions about relying on moral testimony moral reasons or epistemic reasons (or both)? What assumptions about moral knowledge do critics and defenders of moral testimony make? How does moral knowledge differ, if it does, from moral understanding? Is there such a thing as moral expertise? Is there any reason to think that moral expertise is more problematic than other kinds of expertise? Can emotions inform us about value? Under what conditions, if any, can emotions contribute to our understanding of value? Under what conditions are emotions impediments to moral knowledge or understanding? Can fictions help us gain moral insight? Can pictures ever be legitimate tools of moral persuasion?
- Department: Philosophy
- W 4:10 p.m.-6 p.m.
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Instructor:
Axel Honneth
CULPA: 2 Wikipedia Info - 3 points
Statistical Practices and Research for I
Stat Prac & Res for Int S
In this course, students will apply the concepts and methods introduced in Statistical Practices and Research for Interdisciplinary Science (SPRIS) I to a real research setting. Each student will be paired with a Biostatistics faculty member. The student will participate in one of the mentor’s collaborative projects to learn how to be an effective member of an interdisciplinary team. The relationship will mimic that between a medical resident and an attending physician.
The SPRIS II experience will vary depending on the assigned faculty member, but all students will gain exposure to preparing collaborative grant applications, designing research studies, analyzing real data, interpreting and presenting results, and writing manuscripts. Mentors will help to develop the student’s data intuition skills, ability to ask good research questions, and leadership qualities. Where necessary, students may replicate projects already completed by the faculty mentor to gain experience.
- Department: Biostatistics
- M 2 p.m.-3:30 p.m.
- 1.5 points
Tutorial in Biostatistics
TUTORIALS IN BIOSTATISTIC
For appropriately qualified students wishing to enrich their programs by undertaking literature reviews, special studies, or small group instruction in topics not covered in formal courses.
PRERESEARCH SEMINARS I
You may be asked to serve as research subjects in studies under direction of the faculty while enrolled in this course. Participation in voluntary.
- Department: Chemistry
- F 9:30 a.m.-11 a.m.
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Instructor:
Tomislav Rovis
Info - 1 points
DIRECTING THESIS ADVISEMENT
This course will serve to provide an opportunity for Students who are Directing Concentrates to develop their thesis projects within a structured environment. The course may be taught in every week or alternating week formats. Students will be encouraged to submit ideas, treatments, scripts, rough cuts and fine cuts of their thesis films. The class is collaborative and serves as a base from which Directors can try out concepts and ideas, and receive input from fellow students as well as their thesis advisor.
- Department: Film
- R 10 a.m.-1 p.m.
- W 2 p.m.-5 p.m.
- T 2 p.m.-5 p.m.
- W 2 p.m.-5 p.m.
- W 6 p.m.-9 p.m.
- W 2 p.m.-5 p.m.
- R 10 a.m.-1 p.m.
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Instructor:
Ramin Bahrani
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Instructor:
Hilary Brougher
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Instructor:
Bette Gordon
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Instructor:
Tom Kalin
Wikipedia Info -
Instructor:
Eric Mendelsohn
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Instructor:
Bogdan G Apetri
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Instructor:
Anocha Suwichakornpong
Wikipedia Info - 0 points
THESIS PLANNING AND RESEARCH METHODS
Clinical and laboratory projects or field investigation related to nutrition, particularly in the area of growth and development.
- Department: Human Nutrition, Institute of
- R 10:30 a.m.-noon
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Instructor:
Moneek Madra
Info - 2 points
DESCRYING THE WORLD IN PHYSICS
- Department: Philosophy
- W 6:10 p.m.-8 p.m.
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Instructor:
Justin Clarke-Doane
h-index: 10 Info - 3 points
EARTH & ENVIRONMENTAL COLLOQ
All graduate students are required to attend the departmental colloquium as long as they are in residence. Advanced doctoral students may be excused after three years of residence. No degree credit is granted.
- Department: Earth and Environmental Engineering
- R 10:10 a.m.-11:25 a.m.
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Instructor:
Oscar Nordness
Info - 0 points
PRODUCING THESIS ADVISEMENT
Overview:
The class will meet once monthly and will focus on the following:
1) Students’ thesis work - class will analyze, advise, give notes on, support, and discuss each person’s work over the year during the development, prep, production, post-production, and marketing periods of work for each thesis project.
2) Exploration of skills necessary to transition to working in the film industry after graduation. Topics include resume workshops, web site creation, film festival strategy, financing strategies, rights clearance, and press kit creation.
3) CU alums and other guest speakers will discuss their transitions from film school to working in the film industry, and will discuss their areas of expertise: TV producing, feature film producing, development, representation, networks and studios, teaching as a career, etc.
- Department: Film
- T 6 p.m.-9 p.m.
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Instructor:
Jack Lechner
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Instructor:
Maureen A Ryan
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Instructor:
Mynette Louie
Wikipedia Info - 0 points
DOCTORAL RESEARCH
Required of doctoral candidates.
- Department: Applied Physics and Applied Mathematics
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Instructor:
Daniel Bienstock
h-index: 38 Info -
Instructor:
Liliana Borcea
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Instructor:
Aravind Devarakonda
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Instructor:
Alexander Gaeta
Wikipedia h-index: 89 Info -
Instructor:
Michal Lipson
CULPA: 1 Wikipedia h-index: 114 Info -
Instructor:
Michael E Mauel
CULPA: 9
Info
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Instructor:
Gerald A Navratil
CULPA: 2 h-index: 46 Info -
Instructor:
Elizabeth Paul
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Instructor:
Carlos Paz Soldan
h-index: 28 Info -
Instructor:
Lorenzo M Polvani
CULPA: 6
h-index: 69
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Instructor:
Kui Ren
CULPA: 2 Info -
Instructor:
Steven A Sabbagh
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Instructor:
Michele Simoncelli
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Instructor:
Adam H Sobel
CULPA: 6
Wikipedia
Info
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Instructor:
Marc W Spiegelman
CULPA: 15
Info
- 0-15 points