Office of the Dean of the Graduate School

Civil and Environmental Engineering

CEE 306 — Hydrology: Water and Climate

Objective/Overview: Analysis of fundamental processes in the hydrologic cycle, including precipitation, evapotranspiration, infiltration, streamflow and groundwater flow. The class focuses on exercises using observational data. There is a modeling and data analysis component using Python and Jupyter Notebooks, readings on flood and drought, and a forecasting competition.

Instructor(s):Reed M. Maxwell

If interested please contact:
Name: Minmin Fan
E-mail: minminf@princeton.edu
Phone: 6092585539

Position 1 Primary Duties:
host weekly office hours; lead a 50-min precept section every week; grade assignments for your own precept section (alternating weekly discussion posts and problem sets).

Number of AI Hours with Position: 3

Background Required:
familiarity in python and basic hydrology; interest in discussing topics related to water, climate, and environmental policy.

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Position 2 Primary Duties:
host weekly office hours; lead a 50-min precept section every week; grade assignments for your own precept section (alternating weekly discussion posts and problem sets).

Number of AI Hours with Position: 3

Background Required:
familiarity in python and basic hydrology; interest in discussing topics related to water, climate, and environmental policy.

East Asian Studies

EAS 262 — How Does It Move?: Action and Moving Image in Modern Japanese Media

The course offers an introduction to moving image cultures in modern Japan with a focus on how technological aesthetic media has transformed the experience and understanding of action. While emphasis is placed on the production and reception of popular action film genres from Japan, the course also explores the relationship of those films to international film cultures in the context of broader historical transformations in media practices and in modes of distribution and reception. We will engage with ethical and political questions concerning issues such as gendered and racialized representations of action and the aesthetics of violence.

Instructor(s):Junko Yamazaki

If interested please contact:
Name: Junko Yamazaki
E-mail: jyamazaki@princeton.edu
Phone: 609-258-3315

Position 1 Primary Duties:
AIs are required to attend two weekly course lectures (Tu/Th 10-10:50 am) and lead a precept for the entire semester. AIs will also grade written student work, hold office hours, and answer students' questions, and meet with head of course as needed.

Number of AI Hours with Position: 3

Background Required:
Background in and enthusiasm for film and media studies; Knowledge of modern Japanese history would be an asset but is not a requirement.

History

HIS 267 — History of Palestine/Israel

An introduction to the history of Palestine, Israel, and the Arab-Israeli conflict from the late-nineteenth century to the present.

Instructor(s):Max D. Weiss

If interested please contact:
Name: Judith Hanson
E-mail: jhanson@princeton.edu
Phone: 6092584151

Position 1 Primary Duties:
Attendance at all lectures, meet with each precept weekly, hold weekly office hours, grading, and attend meetings with head of course as needed.

Number of AI Hours with Position: 2

Background Required:
If interested, please send resume/CV to jhanson@princeton.edu.

Department of Operations Research and Financial Engineering

ORF 401 — Electronic Commerce

Electronic commerce, traditionally the buying and selling of goods using electronic technologies, extends to essentially all facets of human interaction when extended to services, particularly information. The course focuses on both the software and the hardware aspects of traditional aspects as well as the broader aspects of the creation, dissemination and human consumption electronic services. Covered will be the physical, financial and social aspects of these technologies

Instructor(s):Alain L. Kornhauser

If interested please contact:
Name: Kim Lupinacci
E-mail: klupinac@princeton.edu
Phone: 609-258-4018

Position 1 Primary Duties:
Must have knowledge of coding

Number of AI Hours with Position: 6

Background Required:
Forward CV/Resume to klupinac@princeton.edu

Department of Operations Research and Financial Engineering

ORF 335 — Introduction to Financial Mathematics

Financial Mathematics is concerned with designing and analyzing products that improve the efficiency of markets, and create mechanisms for reducing risk. This course develops quantitative methods for these goals: the notions of arbitrage and risk-neutral pricing in discrete time, specific models such as Black-Scholes and Heston in continuous time, and calibration to market data. Credit derivatives, the term structure of interest rates, and robust techniques in the context of volatility options will be discussed, as well as lessons from the financial crisis.

Instructor(s):Mete Soner

If interested please contact:
Name: Kimberly Lupinacci
E-mail: klupinac@princeton.edu
Phone: 609-258-4018

Position 1 Primary Duties:
Grading

Number of AI Hours with Position: 3 or 6

Background Required:

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Position 2 Primary Duties:

Number of AI Hours with Position:

Background Required:

Program in Quantitative and Computational Biology

QCB 408 — Foundations of Statistical Genomics

This course establishes a foundation in the application of statistics to problems in genetics and genomics through lectures, homework sets, and class discussions of publications. Statistical topics may include probabilistic modeling, likelihood based inference, Bayesian inference, bootstrap, EM algorithm, regularization, statistical modeling, principal components analysis, multiple hypothesis testing, and causality. There is an emphasis on applications in population genetics, gene expression, and human genomics. The statistical programming language R is extensively used to explore methods and analyze data.

Instructor(s):John D. Storey

If interested please contact:
Name: Jennifer Giraldi
E-mail: jgiraldi@princeton.edu
Phone: 609-258-9407

Position 1 Primary Duties:

Number of AI Hours with Position: 2

Background Required:
AI must have a solid background in statistics, genetics/genomics, and R programming.

Center for Statistics and Machine Learning

SML 505 — Modern Statistics

The course provides an introduction to modern statistics and data analysis. It addresses the question, "What should I do if these are my data and this is what I want to know"? The course adopts a model based, largely Bayesian, approach. It introduces the computational means and software packages to explore data and infer underlying parameters from them. An emphasis will be put on streamlining model specification and evaluation by leveraging probabilistic programming frameworks. The topics are exemplified by real-world applications drawn from across the sciences.

Instructor(s):Peter M. Melchior

If interested please contact:
Name: Peter Melchior
E-mail: peter.melchior@princeton.edu

Position 1 Primary Duties:
The Center for Statistics and Machine Learning at Princeton University has openings for AIs to teach precepts for SML/AST 505-Modern Statistics

Number of AI Hours with Position:

Background Required:
Interested candidates should contact Peter Melchior with a resume/CV and their undergraduate and graduate transcripts as soon as possible. Please copy Susan Johansen on the email. Applications are accepted until all positions are filled.

Center for Statistics and Machine Learning

SML 201 — Introduction to Data Science

Introduction to Data Science provides a practical introduction to the burgeoning field of data science. The course introduces students to the essential tools for conducting data-driven research, including the fundamentals of programming techniques and the essentials of statistics. Students will work with real-world datasets from various domains; write computer code to manipulate, explore, and analyze data; use basic techniques from statistics and machine learning to analyze data; learn to draw conclusions using sound statistical reasoning; and produce scientific reports. No prior knowledge of programming or statistics is required.

Instructor(s):Day Yi

If interested please contact:
Name: Daisy Huang
E-mail: daisyhuang@princeton.edu

Position 1 Primary Duties:
AIs are required to lead an 80-minute precept for the entire semester and to attend two weekly course lectures (either Tu/Th 11a.m.-12:20p.m. or 3-4:20p.m.) for part of the semester. AIs will also grade written student work, hold office hours, and answer students' questions online. Interested candidates should contact Daisy Huang with a resume/CV and their undergraduate and graduate transcripts as soon as possible. Please copy Susan Johansen (susan_j@princeton.edu) on the email. Applications are accepted until all positions are filled. Appointment per precept: 3 hours Number of AI Hours with Position: 3 hours

Number of AI Hours with Position: 3

Background Required:
Requirements: Background in statistics and programming. Enthusiasm for teaching beginners. Proficiency in R is an asset. We welcome applications from students in both technical disciplines and the candidates working with data in the social sciences.

Center for Statistics and Machine Learning

SML 310 — Research Projects in Data Science (A)

A project-based seminar course in which students work individually or in small teams to tackle data science and machine learning problems, working with real-world datasets. The course emphasizes critical thinking about experiments and large dataset analysis and the ability to clearly communicate one's research. This course is intended to support students in developing the analytical skills necessary for quantitative independent work; students should consult with their home department about how this course could appropriately complement, but not replace, their independent work requirements.

Instructor(s):N/A

If interested please contact:
Name: Daisy Huang
E-mail: daisyhuang@princeton.edu

Position 1 Primary Duties:
The AI will work with students who are working on projects that fall within the AI's specialization, hold office hours, assist with grading, hold Python workshops for students who are beginners in Python, and lead precept sessions.

Number of AI Hours with Position: 6

Background Required:
Experience with and enthusiasm for machine learning and data science; expertise in data analysis using Python and/or R. Experience with TensorFlow, PyTorch and/or Stan would be an asset but is not a requirement. We encourage applications from candidates in core data science disciplines as well as any other candidates whose work involves advanced quantitative analysis.

Center for Statistics and Machine Learning

SML 312 — Research Projects in Data Science (B)

Project-based course in which students work individually/small teams to tackle DS and ML problems, working with real-world datasets.The course emphasizes critical thinking about experiments and dataset analysis and the ability to clearly communicate one's research. Programming components are taught in Python. Experience in only one of the two programming languages (R and Python) is required.This course is intended to support students in developing the analytical skills for quantitative independent work; students should consult with their home department about how this course could complement, not replace, their independent work requirements.

Instructor(s):Jonathan Hanke

If interested please contact:
Name: Daisy Huang
E-mail: daisyhuang@princeton.edu

Position 1 Primary Duties:
The AI will work with students who are working on projects that fall within the AI's specialization, hold office hours, assist with grading, hold Python workshops for students who are beginners in Python, and lead precept sessions.

Number of AI Hours with Position: 6

Background Required:
Experience with and enthusiasm for machine learning and data science; expertise in data analysis using Python and/or R. Experience with TensorFlow, PyTorch and/or Stan would be an asset but is not a requirement. We encourage applications from candidates in core data science disciplines as well as any other candidates whose work involves advanced quantitative analysis.

Center for Statistics and Machine Learning

SML 301 — Data Intelligence: Modern Data Science Methods

This course provides the training for students to be independent in modern data analysis. The course emphasizes the rigorous treatment of data and the programming skills and conceptual understanding required for dealing with modern datasets. The course examines data analysis through the lens of statistics and machine learning methods. Students verify their understanding by working with real datasets. The course also covers supporting topics such as experiment design, ethical data use, best practices for statistical and machine learning methods, reproducible research, writing a quantitative research paper, and presenting research results.

Instructor(s):Daisy Yan Huang

If interested please contact:
Name: Daisy Huang
E-mail: daisyhuang@princeton.edu

Position 1 Primary Duties:
The AI will assist with designing assignments, hold office hours, assist with grading, hold Python workshops for students who are beginners in Python, and lead precept sessions.

Number of AI Hours with Position: 3

Background Required:
Experience with and enthusiasm for machine learning and data science; expertise in data analysis using Python and/or R. Experience with TensorFlow, PyTorch and/or Stan would be an asset but is not a requirement. We encourage applications from candidates in core data science disciplines as well as any other candidates whose work involves advanced quantitative analysis.

Center for Statistics and Machine Learning

SML 320 — Bayesian Analysis

This course provides an introduction to Bayesian analysis---a powerful statistical framework for making inferences and modeling uncertainty in a wide range of applications. Students will explore the fundamental principles of Bayesian statistics, probability theory, Bayesian inference, and practical applications of Bayesian modeling. The course will cover both the theory and hands-on implementation using data science software and the R programming language.

Instructor(s):N/A

If interested please contact:
Name: Daisy Huang
E-mail: daisyhuang@princeton.edu

Position 1 Primary Duties:
The AI will assist with designing assignments, hold office hours, assist with grading, hold R workshops for students who are beginners in R, and lead precept sessions.

Number of AI Hours with Position: 6

Background Required:
Experience with and enthusiasm for machine learning and data science; expertise in data analysis using Python and/or R. We encourage applications from candidates in core data science disciplines as well as any other candidates whose work involves advanced quantitative analysis.

Princeton School of Public and International Affairs

SPI 200 — Statistics for Social Science

The course is an introduction to probability and statistical methods for social science research focused on public policy. Topics include data analysis, descriptive statistics, distributions, random variables, sampling, estimation, hypothesis testing, bivariate regression, and multiple regression. Examples will be discussed from tax, health, environment, energy, education, security, law, and other policy areas. Students will be introduced to the statistical software R. (No prior experience with statistics, programing or calculus is required)

Instructor(s):Saad A. Gulzar

If interested please contact:
Name: Elizabeth Choe
E-mail: liz.choe@princeton.edu
Phone: 609-258-4818

Position 1 Primary Duties:
We are seeking a graduate student to teach three precept sections.

Number of AI Hours with Position: 3

Background Required:
Interested candidates should contact Saad Gulzar with a resume/CV. Please copy Liz Choe on the email.

Princeton School of Public and International Affairs

SPI 304 — Microeconomics for Public Policy

Microeconomics is the study of how the economy works as a result of myriad decisions by individual agents (households, firms) in interaction among themselves and with public authorities. This course, taught at the intermediate level, starts from understanding how markets work. It then moves to considering the appropriateness of government interventions in functioning markets. The second half of the course is devoted to understanding market failures, government interventions to rectify those failures and the evaluation of those interventions.

Instructor(s):Sofia Moroni

If interested please contact:
Name: Elizabeth Choe
E-mail: liz.choe@princeton.edu
Phone: 609-258-4818

Position 1 Primary Duties:
We are seeking a graduate student to teach two precept sections.

Number of AI Hours with Position: 2

Background Required:
Interested candidates should contact Sofia Moroni with a resume/CV. Please copy Liz Choe on the email.