phd statistics princeton

Princeton Statistics Laboratory

About STATLAB

Prof. Jianqing Fan's group is interested in statistical methods in financial econometrics and risk managements, computational biology, biostatistics, high-dimensional statistical learning, data-analytic modeling, longitudinal and functional data analysis, nonlinear time series, wavelets and their applications, among others. Our primary research focuses on developing and justifying statistical methods that are used to solve problems from the frontiers of scientific research. This is expanded into other disciplines where the statistics discipline is useful. In each of the areas mentioned above, our group devotes most of our efforts to the search for intuitively appealing, model-free, robust nonparametric approaches and illustrates the approaches by real data and simulated examples. Modern statistical principles and modeling inevitably involve intensive computation, which is a part of the methodological research development. Our group is also very interested in developing foundational statistical theory and in providing fundamental insights to sophisticated statistical models. These include sampling theory, statisical learning theory, minimax theory, efficient semi-parametric modeling and nonlinear function estimation.

Our group is particularly interested in financial econometrics, risk management, computational biology, biostatistics, high-dimensional data-analytic modeling and inferences, nonlinear time series, analysis of longitudinal and functional data, and other interdisciplinary collaborations.

S. S. Wilks Memorial Seminar in Statistics

• Victor Panaretos, EPFL September 9, 2024 , 12:25 pm – 1:25 pm Location: 101 - Sherrerd Hall
• Yaqi Duan *22, New York University September 16, 2024 , 12:25 pm – 1:25 pm Location: 101 - Sherrerd Hall
• Robert Tibshirani, Stanford University September 30, 2024 , 4:30 pm – 5:30 pm

Statistics and Machine Learning

Fundamentals of machine learning, professor/instructor.

Computers have made it possible to collect vast amounts of data from a wide variety of sources. It is not always clear, however, how to use the data, and how to extract useful information from them. This problem is faced in a tremendous range of social, economic and scientific applications. The focus will be on some of the most useful approaches to the problem of analyzing large complex data sets, exploring both theoretical foundations and practical applications. Students will gain experience analyzing several types of data, including text, images, and biological data. Two 90-minute lectures. Prereq: MAT 202 and COS 126 or equivalent.

Mathematics for Numerical Computing and Machine Learning

This course provides a comprehensive and practical background for students interested in continuous mathematics for computer science. The goal is to prepare students for higher-level subjects in artificial intelligence, machine learning, computer vision, natural language processing, graphics, and other topics that require numerical computation. This course is intended students who wish to pursue these more advanced topics, but who have not taken (or do not feel comfortable) with university-level multivariable calculus (e.g., MAT 201/203) and probability (e.g., ORF 245 or ORF 309).

Foundations of Probabilistic Modeling

A study of the essential tools for analyzing the vast amount of data that have become available in modern scientific research. Mathematical foundations of the field will be studied, along with the methods underlying the current state of the art. Probabilisitc graphical models and a unifying formalism for descrtibing and extending previous methods from statistics and engineering will be considered. Prerequisites COS402 or COS424. Undergraduates by permission only.

Operations Research & Financial Engineering

Graduate Program

The Ph.D. is formulated to prepare students for research and teaching. The aim of the program is to provide a strong disciplinary background in one of the core areas of research in the department. The emphasis is on the theoretical foundations, mathematical models, and computational issues in practical problem solving. Current teaching and research activities include probability and stochastic processes, stochastic analysis, mathematical statistics, linear and nonlinear optimization, stochastic optimization, convex analysis, stochastic networks and queueing theory, mathematical and computational finance, and financial econometrics. Application areas of current interest to faculty include finance, energy, health, bioinformatics, and engineering problems. To learn more about the research interests of individual faculty members, please visit the Research page.

The departmental faculty are affiliated with a number of interdisciplinary programs and centers, including the Program in Applied and Computational Mathematics , the Bendheim Center for Finance , the Andlinger Center for Energy and the Environment , the Princeton Environmental Institute , and the Center for Statistics and Machine Learning . Students may combine their departmental work with courses and research opportunities offered by such programs and centers and also by other departments including Computer Science, Economics, and Mathematics. About half of PhD recipients in Operations Research & Financial Engineering accept positions in academia. See this list of first positions of our graduating PhD students .

In the first year of the Ph.D. program, students enroll in the 6 departmental core courses in probability, statistics, and optimization in consultation with the Department's Director of Graduate Studies, to be followed by a qualifying exam at the end of the summer. In addition, at least two advanced courses and two semesters of directed research are completed under the direction of a faculty adviser in the student's area of interest by the end of the second year in preparation for the general examination. The general examination is normally taken in the Spring of the second year of study. Usually, beyond the general examination, two to three years are needed for the completion of a suitable dissertation. Upon completion of theses studies and acceptance of the dissertation by the department, the candidate is admitted to the final public oral examination.

A comprehensive guide to the ORFE Ph.D. program can be found in the Graduate Student Handbook and in the Graduate School Catalog .

Research Seminars

The weekly departmental colloquium series brings distinguished researchers to the department to present their latest work. In addition, informal research seminars are organized in order to exchange information and to discuss ideas arising from the research work in progress. Students, research staff, visiting scholars, and faculty members participate in these seminars.

Application

A bachelor's degree in engineering, sciences, or mathematics is normally required for admission to the graduate program. A strong mathematical background is expected for admission. The Graduate Record Examination (GRE) results will be optional (not required) for Fall 2023. Applicants whose primary language is not English or who have not earned their undergraduate degree in an institution where the language of instruction is English should also submit the results of the TOEFL, IELTS, or Duolingo. Applicants whose graduate study was on a full-time basis for at least one year where instruction is entirely in English as certified by the institution is accepted.

Further details on applying to the graduate program are available via the Graduate School.

You may request an application fee waiver within the application.

Financial Support

The department aims to support all doctoral students requesting aid through a combination of fellowships and assistantships. All first-year Ph.D. candidates are supported by full-time fellowships, allowing students to focus on courses and providing flexibility in the choice of a research adviser. From the second year onward, students are supported by a combination of teaching assistantships, research assistantships, and fellowships. Continuation of support is recommended on the basis of satisfactory progress.

Further details on financial support are provided by the Graduate School.

Research Groups

The Department features several research groups that facilitate collaborations between students and faculty and provide a variety of resources for research and teaching. These include the following:

Financial Engineering. This group is home to computers, software, and financial data feeds needed for teaching and research in financial engineering. It is a focal point for graduate students in the Ph.D. program in financial engineering and M.Fin. It also serves as a gateway to collaborative research projects with financial institutions.

The  Statistics  and  Financial Econometrics  labs emphasize statistical methods in financial econometrics and risk management, computational biology, biostatistics, high-dimensional statistical learning, data-analytic modeling, longitudinal and functional data analysis, nonlinear time series, wavelets and their applications, among others. The group's primary research focuses on developing and justifying statistical methods that are used to solve problems from the frontiers of scientific research.

Transportation Center . The center conducts research on information and decision engineering technologies and how these technologies can be used to improve transportation-related decision making.

Master in Finance

The department is a major participant in the Master in Finance (M.Fin) degree program offered through the Bendheim Center for Finance. Further information is available via the  Bendheim Center for Finance .

Statistics Laboratory Research Group

Oxford-Princeton Workshop

Wesley L. Harris Scientific Society

Professor Ahmadi and Graduate Students in Bordeaux

Graduate Students Enjoying Dinner Together

Professor Shkolnikov at Oxford-Princeton Conference with Colleague

Center for Statistics and Machine Learning

Graduate Certificate Requirements

An example of research performed by doctoral student Anat Fuchs, who is part of the research lab headed by Mona Singh, professor of computer science and the Lewis Sigler Institute for Integrative Genomics. Fuchs is enrolled in the graduate certificate program at the Center for Statistics and Machine Learning. 

Current Princeton students already enrolled in a Ph.D.  or Masters program are eligible to enroll for this certificate.

The graduate certificate is comprised of three components: (a) completion of three appropriate graduate courses, (b) a relevant research contribution, and (c) a research seminar.  More details on each of these are below.  If you have additional questions, please contact [email protected]

Take for credit and receive an average GPA of B+ (3.3) or better in three courses from the approved list that has three categories: core machine learning, core statistics and probabilistic modeling, and electives. One course must be selected from each category. With the permission of the certificate director, the elective course can be selected from a core category provided it does not significantly overlap with the other course selected from that category. At least one of the three courses must be outside the student’s home department and at most one course can be below the 500 level. 

The core curriculum is intended to provide training in the foundations of statistics and machine learning while ensuring that certificate students have some breadth across the core of statistics and machine learning. A list of approved core courses in the two areas is included below. In addition, a certificate student selects the third course from a listed set of elective courses that expands on the core courses.  These electives delve more deeply into supporting material (e.g., optimization) or focus on applications in a specific domain.

Students may not count courses that are used to satisfy core requirements in their home department concentration toward this certificate, however they may count up to two electives that were taken for their degree requirements.

Please note: all coursework, including SML 510 must be completed before a student enters into DCE status.

Research Component

For students completing a thesis or dissertation as part of their degree, the thesis or dissertation (or a publishable research paper) should include a significant component making contributions to statistics or machine learning, or rigorous use of such methods in an application domain. See below for additional details. For non-thesis master’s degree students, this requirement can be satisfied by a technical presentation on a topic relevant to the program.

To ensure that an important component of a Ph.D. student’s dissertation involves either rigorous data analysis, and/or mathematical or computational modeling of data or machine learning problems, one of the dissertation readers or FPO committee members must be a participating graduate certificate faculty member (see list below). This reader will be required to either send a letter, or their reader’s report, to the program director to verify that the dissertation satisfies this requirement. Master’s students who complete a thesis follow the same requirement. For non-thesis master’s students, their technical presentation will be reviewed by the certificate director. 

Graduate Research Seminar

Prior to graduation, students must enroll in and complete the requirements of the CSML graduate seminar series (SML 510) for at least one semester. 

The CSML graduate seminar, SML 510 serves as a venue for discussing current methods and results and the integration of different research approaches to data analysis. Attendance and participation in the CSML graduate seminar for at least one semester is required.  It helps teach students how to communicate technical ideas to a broad audience and encourages the development of skills for interacting with other students, postdoctoral fellows, and faculty who are investigating data analysis problems. It also serves to build a supporting community of young scholars with shared interests.

Core Machine Learning – one of the following courses

• COS 402 Machine Learning and Artificial Intelligence
• COS 424 / COS 524 Fundamentals of Machine Learning
• COS 485 Neural Networks: Theory and Applications
• COS 511 Theoretical Machine Learning
• ECE 535 Machine Learning and Pattern Recognition

Core Statistics and Probabilistic Modeling – one of the following courses

• COS 513 Foundations of Probabilistic Modeling
• ECO 513 Time Series Econometrics
• ECO 519 Advanced Econometrics: Nonlinear Models
• ECE 530 Estimation and Detection
• ORF 524 Statistical Theory and Methods
• POL 572 Quantitative Analysis II
• QCB 508 Foundations of Statistical Genomics
• SML 505 Modern Statistics

Electives – one of the following courses (including those above, with permission)

• APC 527 Random Graphs and Networks
• APC/ORF 550  Topics in Probability  Probability in High Dimension
• COS 534  Fairness in Machine Learning
• ECO 515 Econometric Modeling
• ECE 538B Sparsity, Structure, and Inference
• ECE 522 LargeScale Optimization for Data Science
• FIN 580 Quantitative Data Analysis in Finance
• MAT585/APC520 Mathematical Analysis of Massive Data Sets
• NEU 560  Statistical Modeling and Analysis of Neural Data
• ORF 505 Statistical Analysis of Financial Data
• ORF 522 Linear and Nonlinear Optimization
• ORF 523 Convex and Conic Optimization
• ORF 525 Statistical Learning & Nonparametric Estimation
• ORF 526 Probability Theory
• POL 573 Quantitative Analysis III
• POL 574 Quantitative Analysis IV
• POP 507 Generalized Linear Statistical Models
• SOC 504 Advanced Social Statistics

Office of Population Research

Graduate Students

Princeton University PhD in Mathematics & Statistics

Featured programs, how much does a doctorate in mathematics & statistics from princeton cost, princeton graduate tuition and fees.

Does Princeton Offer an Online PhD in Mathematics & Statistics?

Princeton doctorate student diversity for mathematics & statistics, male-to-female ratio.

About 25.0% of the students who received their PhD in mathematics and statistics in 2019-2020 were women. This is less than the nationwide number of 29.0%.

Racial-Ethnic Diversity

Around 12.5% of mathematics and statistics doctor’s degree recipients at Princeton in 2019-2020 were awarded to racial-ethnic minorities*. This is about the same as the nationwide number of 11%.

PhD in Mathematics & Statistics Focus Areas at Princeton

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Graduate School

2024 Maeder Graduate Fellows study social norms and the water-energy nexus

2024 Maeder Graduate Fellows Jinyue (Jerry) Jiang, left, and Jordana Composto stand in the Andlinger Center lobby (photo by Adena Stevens).

Two graduate students, Jordana Composto and Jinyue (Jerry) Jiang , have been awarded the Maeder Graduate Fellowship in Energy and the Environment. Composto and Jiang received the fellowship for their work, respectively, to understand how individuals and organizations respond to climate change and to analyze the role of water and wastewater treatment in catalyzing the energy transition.

The fellowship, which is awarded to one or two graduate students each year who demonstrate strong potential to develop solutions for a sustainable energy and environmental future, is supported by the Paul A. Maeder ’75 Fund for Innovation in Energy and the Environment. The fellowship will cover both students’ tuition and stipend for the 2024–2025 academic year.

Jordana Composto

 Composto (photo by Adena Stevens).

Composto, a graduate student in psychology and social policy , researches how individuals and collectives work to address climate change. She is advised by Elke Weber , the Gerhard R. Andlinger Professor for Energy and the Environment and professor of psychology and public affairs .

Composto is particularly interested in understanding how people within organizations perceive and interact with social norms, the collectively endorsed rules and expectations that guide how people should act. For instance, she has previously discovered that independent of personal attitudes and beliefs, employees at a company were more likely to act sustainably if they thought their company would support their actions.

“There is a lot of literature suggesting that norms are important in shaping attitudes, behaviors, and policy support. But there is still a lot of heterogeneity — in some contexts they work, and in others, they don’t,” Composto said. “I’m ultimately trying to examine the underlying cognitive mechanisms to understand why and when norms are effective.”

Composto also studies how trust influences the direction and speed of the energy transition. She investigates the level of trust that exists between stakeholders in the energy sector, where there are significant trust gaps, and whether behavioral science interventions can help to build trust for emerging technologies such as carbon capture and sequestration.

“Behavioral science cuts across any climate solution that we have, because people are the ones addressing climate change,” Composto said. “It helps us understand why some solutions are really hard to implement or why the best technologies might not be adopted by individuals or integrated into companies.”

The fellowship will support Composto’s progress toward her professional goal of being a behavioral scientist working alongside engineers and policymakers on multifaceted climate solutions.

“Addressing the climate crisis will take all of us working together,” Composto said. “It’s both a daunting challenge and an inspiring, collaborative issue to work on as a researcher.”

Jinyue (Jerry) Jiang

Jiang (photo by Adena Stevens).

At the beginning of his graduate career, Jiang studied new technologies for decarbonizing the water and wastewater treatment sectors. Yet as he enters his fifth year as a graduate student in civil and environmental engineering , he has shifted his focus to study how water itself will play a role in the overall decarbonization of society.

“Water and energy are strongly intertwined, and the relationship between them goes both ways,” said Jiang. “Many of the steps for extracting and treating water are surprisingly energy-intensive, and on the other hand, many energy technologies require quite a large amount of water, both as a feedstock and for cooling purposes.”

Advised by Z. Jason Ren , professor of civil and environmental engineering and the Andlinger Center for Energy and the Environment, Jiang began at Princeton working on microbial electrolysis cells, which utilize microorganisms to break down organic matter while generating clean hydrogen as a product. He studied these cells from the lab bench to the pilot scale, even spearheading a demonstration project in Illinois to convert food waste into hydrogen and jet fuel.

Now, Jiang is investigating the costs, opportunities, and feasibility of utilizing treated wastewater as a water source for the emerging hydrogen economy.

One of the most promising methods for clean hydrogen production is electrolysis, in which water is split into hydrogen and oxygen using electricity from renewable sources like solar energy. Yet Jiang said many seemingly ideal locations for such a process, such as California, with its large solar generation capacity, are already water-stressed. Consequently, deploying these technologies at scale in those regions would exacerbate existing water challenges, siphoning away potable water that could otherwise be used for human consumption.

“Currently, almost every electrolyzer is pulling water from the public supply,” Jiang said. “If we could instead integrate these technologies to use treated wastewater effluent, it could be an efficient and widely available way to deploy clean energy without significantly worsening water shortages.”

Since wastewater treatment plants are ubiquitous across the U.S. and the world, Jiang said being able to pair wastewater treatment with clean energy technologies means the treatment plants could have an important role to play in accelerating the energy transition.

“I want to move past the idea that wastewater treatment is just there to get rid of society’s unwanted things,” Jiang said. “There are so many opportunities embedded in the wastewater treatment process for creating valuable products and catalyzing the energy transition.”

The Paul A. Maeder ’75 Fund for Innovation in Energy and the Environment supports the Maeder Graduate Fellowship. The Andlinger Center for Energy and the Environment administers the fund and fellowship. More information on the program and past recipients can be found   on the Maeder Graduate Fellowship page .

Global Health at Princeton

Visiting scholars pursue health-focused research at princeton.

Scholars from around the United States and the world visited Princeton University during the 2023-2024 academic year to conduct research on vector-borne diseases, child maltreatment, and other pressing global health issues.

The visits were arranged and funded through the Visiting Research Scholars Program at the Center for Health and Wellbeing (CHW), which invites researchers from leading institutions to spend either an academic year or a semester in residence at Princeton. While on campus, visitors focus on research, discussion, and scholarly collaborations concerning the determinants of physical, mental, and emotional wellbeing along with the impact of public policy on health and wellness. This often includes presenting their work in various forums, mentoring students, and advising senior theses.

2023-2024 Visiting Research Scholars

Six researchers participated in the CHW Visiting Research Scholars Program during the 2023-2024 academic year. Visiting from as far away as Brazil and South Africa, they contributed to Princeton’s academic community while acquiring valuable insights and building new partnerships. 

Jérôme Adda 

Jérôme Adda is a professor of economics at Bocconi University in Milan, Italy, where he has also served as vice-rector.

Interestingly, Adda was planning to pursue a Ph.D. in biology when a poster advertising the University of Paris’s economics program caught his eye. “I had no clue what that really was at the time, but it sounded very cool. I liked the idea of mixing math and science, with a touch of humanity, to address world problems and answer some big, very important questions,” he said.

Adda’s research spans health and labor economics, with a specialization in understanding the determinants of health behaviors and the role of public health policies in driving those behaviors. “If I were to summarize my work, I’d say that I’m interested in how society, in its broadest definition, shapes population health,” he explained. His first health-related topic focused on the European mad cow crisis in the 1990s, when beef consumption plummeted amid fears that a fatal cattle disease could be transmitted to humans through certain types of meat. This line of research led to work probing smoking, viruses, occupational health, bacteria, and other health issues.

At Princeton, Adda mainly investigated how segments of the U.S. population established beliefs about smoking and how those beliefs evolved over time. He also worked on a paper exploring antibiotic resistance.

“I had a lot of time to concentrate on my research and to exchange ideas with other health-minded scholars, making it a very productive experience,” said Adda.

N. Meltem Daysal

N. Meltem Daysal, who holds a Ph.D. in economics, is an associate professor in the Department Economics at the University of Copenhagen.

Daysal’s research examines the connections between health shocks, public policy, and socio-economic outcomes, with a primary focus on children and women. Her current work examines the impact of medical innovations and social policies on socioeconomic status and educational attainment within societies and families. 

“Most recently, I’ve been thinking about the causes and consequences of mental health disorders,” said Daysal. “That’s also an area of interest for [CHW Co-Director] Janet Currie, whose work has inspired a lot of my research. The opportunity to learn from her and to interact with other top-notch researchers brought me to Princeton.”

During her visit, Daysal partnered with Currie on research investigating whether or not someone with a physically disabled sibling is more likely to have a mental health diagnosis and how those disorders are treated. She also participated in seminars, advised Princeton students, and explored new collaborations.

“I’m a first-generation college graduate. If I were to tell my 17-year-old self that one day I would be visiting Princeton University, I’m fairly sure she’d be skeptical,” she added. “Just to be here, engaging with the academic world, is really fantastic.” 

Maria Fitzpatrick

Maria Fitzpatrick, who holds a Ph.D. in economics, is professor of economics and public policy as well as senior associate dean for academic affairs at Cornell University’s Brooks School of Public Policy. 

Over the course of her career, Fitzpatrick has used economics to understand how government policies affect education and, more broadly, the health and wellbeing of children and families. “I’ve always been incredibly interested in both policy and education,” she said. “Policy impacts everything we do, and education is such an important tool for reducing inequality and impacting children’s health.”

Fitzpatrick felt that Princeton’s Center for Health and Wellbeing was the ideal base for her sabbatical year, noting the vibrant community of researchers studying various dimensions of health and wellness. She points to a spirit of collaboration between faculty, students, and postdocs that is enriched by a robust web of visiting scholars.

At Princeton, Fitzpatrick worked on projects pertaining to child maltreatment and schools as a potential avenue for improving child health. Primarily, she studied big data and algorithms to support the decision-making process for child welfare systems, and explored how financial incentives impact the provision of health screenings and other services in schools.

“The visit has elevated my work while giving me the time and space to tackle problems in different ways,” said Fitzpatrick. “I leave with a lot of new ideas about what we know and don’t know, and how to address these issues in future projects.”

Karen Hofman

Karen Hofman, a qualified pediatric geneticist, is a research professor and founding director of the South African Medical Research Council’s Centre for Health Economics and Decision Science/PRICELESS SA at the University of Witwatersrand (WITS) in Johannesburg, South Africa. She participated as a CHW visiting research scholar through the SPIA International Fellows Program.

Early in her career, Dr. Hofman realized that she was far more interested in population health than medicine, particularly in low- and middle-income countries. So she shifted her career toward evidence-informed policymaking, serving as policy director of the Fogarty International Center at the U.S. National Institutes of Health before returning to South Africa to join the WITS School of Public Health. There she has led multidisciplinary policy research to evaluate public health interventions that provide the greatest return on investment. 

Dr. Hofman’s work concentrates on disease prevention, commercial determinants of health, priority setting, and health equity. She explored these issues during her time at Princeton through research and a roundtable discussion with CHW affiliates Janet Currie, Alyssa Sharkey and Heather Howard, as well as various stakeholders, to address the growing obesity epidemic. “We talked about why it is taking so long to get traction on the issue and potential catalysts for change,” she noted. 

Additionally, Dr. Hofman engaged with students through lectures and one-on-one interactions, and participated in many panels and seminars. “The School of Public Health [in Johannesburg] is doing amazing things, but I don’t frequently have the opportunity to interact with sociologists, anthropologists, and other scientists with differing viewpoints,” she noted. “That was a valuable part of my Princeton experience.” 

Ted Loch-Temzelides

Ted Loch-Temzelides, who holds a Ph.D. in economics, is the George and Cynthia Mitchell Professor in Sustainable Development in the Department of Economics at Rice University, where he is also a scholar at the James A. Baker III Institute for Public Policy’s Center for Energy Studies.

Loch-Temzelides’ research lies at the intersection of economics and ecology, with a focus on public health. “Epidemiology uses mathematical models that treat people very mechanically and do not incorporate behavior,” he explained. “That’s why I became interested in economics, which emphasizes decisions and incentives, a process that exposes inequities and resulting health outcomes.” 

His current health-related research investigates ways to mitigate the risks associated with infectious diseases and climate change. At Princeton, he studied the epidemiology of zoonotic disease transmission with CHW affiliate Andrew Dobson while discussing and advancing projects with other researchers. 

“Princeton is a very special place,” he said, highlighting the university’s reputation for academic excellence as well as its distinguished staff, faculty, and students, and the breadth and quality of their research. “I interacted with some of the world’s leading scholars and met some of my scientific heroes,” he added. “My only regret is that the visit was too short.”

Daniel Villela

Daniel Villela, who holds a Ph.D. in electrical engineering, is a senior research scientist and professor at the Oswaldo Cruz Foundation (Fiocruz), Brazil’s largest research institution dedicated to public health. He is an affiliate of the epidemiology, computational biology, and parasitic biology programs, where he teaches and mentors graduate students.

While his training focused on engineering, Villela’s longstanding interests in biology and mathematical modeling -- along with an intriguing career opportunity -- led him to the field of public health. His recent work explores the dynamics of infectious diseases, particularly vector-borne diseases. 

As a visiting research scholar, Villela advanced projects on malaria, including a study on the incidence of malaria in the Brazilian Amazon region. He examined the disease’s impact on indigenous communities, inequities in health care access, and the potential consequences of such disparities. Additionally, Villela studied the seasonality of malaria, including the effects of climate, as well as strategies for controlling the Aedes aegypti mosquito population to reduce transmission of arboviruses, such as Dengue virus.

He also planted seeds for new projects through connections with CHW affiliates Jessica Metcalf, Bryan Grenfell, Simon Levin, and other researchers. “I plan to bring the evolutionary aspects of disease transmission to my research and to propose a new course on the evolution of infectious diseases at Fiocruz,” he said. “I hope that my Princeton visit will foster more collaborations in my own research and between our institutions.”