yale phd program statistics

Yale Statistics Ph.D. Program

The Department offers a broad training program comprised of the main areas of statisical theory (with emphasis on foundations, Bayes theory, decision theory, nonparametric statistics), probability theory (stochastic processes, asymptotics, weak convergence), information theory, econometrics, classification, statistical computing, and graphical methods.

With this background, graduates of the program have found excellent positions in universities, industry, and government. Recent graduates have accepted appointments at the Duke University, University of California at Santa Barbara, The City University of New York, Yale University, Bristol-Meyers Squibb, RAND, Federal Reserve Board, New York University, Trinity University, Iowa State University, Merck, and Tulane University.

We seek exceptional students for our Ph.D. program. GRE scores for the General Test and for the Subject Test in the area of the undergraduate major should accompany an application. All applicants should have a strong mathematical background, including advanced calculus, linear algebra, elementary probability theory, and at least one course providing and introduction to mathematical statistics. An undergraduate major may be in statistics, mathematics, computer science, or in a subject in which significant statistical problems may arise. For those whose native language is not English, the Test of English as a Foreign Language (TOEFL) scores are required.

All applications for this program should be submitted directly to the Yale Graduate School Office of Admissions .

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Course of Study

Normally during the first two years, fourteen term courses in this and other departments are taken to prepare students for research and practice of statistics. These include courses devoted to case studies and practical work, for which students prepare a written report and give an oral presentation. Specific requirements .

There is no foreign language requirement.

Qualifying Examination

Course work prepares students for a Ph.D. qualifying examination, which is usually taken after three semesters of study. The qualifying examination consists of three parts: a written report on an analysis of a data set, a written paper on theoretical statistics, and an oral examination. A provision exists for one reexamination of one or more parts of the qualifying exam in the event that a student does not pass the first time. All parts of the qualifying examination must be competed before beginning the third year.

Dissertation

Dissertation research in collaboration with one member of the faculty is begun during the third year. A prospectus for the dissertation should be submitted no later than the first week of March in the third year. The prospectus must be accepted by the department before the end of the third year. Upon successful completion of the qualifying examination and the prospectus (as well as meeting the Graduate School Honors requirements), the student is admitted to candidacy. Most students complete the dissertation in the fifth year. Please see our Alumni page for a sampling of recent Dissertation topics.

Further information

Consult the Graduate School Programs and Policies for general information about Ph.D. study at Yale.

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Graduate School of Arts and Sciences Programs and Policies 2023–2024

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Statistics and Data Science

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203.432.0666 http://statistics.yale.edu M.A., M.S., Ph.D.

Chair Joseph Chang

Directors of Graduate Studies Andrew Barron (24 Hlh, [email protected] ) John Emerson (24 Hlh, [email protected] )

Professors Donald Andrews ( Economics ), Andrew Barron, Jeffrey Brock ( Mathematics ), Joseph Chang, Katarzyna Chawarska ( Child Study Center ​), Xiaohong Chen ( Economics ), Nicholas Christakis ( Sociology ​), Ronald Coifman ( Mathematics ​), James Duncan ( Radiology and Biomedical Imaging ​), John Emerson ( Adjunct ), Alan Gerber ( Political Science ​), Mark Gerstein ( Molecular Biophysics and Biochemistry ​), Anna Gilbert, John Hartigan ( Emeritus ), Edward Kaplan ( School of Management/Operations Research ​), Harlan Krumholz ( Internal Medicine ​), John Lafferty, Zongming Ma, David Pollard ( Emeritus ), Nils Rudi ( School of Management ), Jasjeet Sekhon, Donna Spiegelman ( Biostatistics ), Daniel Spielman, Hemant Tagare ( Radiology and Biomedical Engineering ​), Van Vu ( Mathematics ), Yihong Wu, Heping Zhang ( Biostatistics ), Hongyu Zhao ( Biostatistics ), Harrison Zhou, Steven Zucker ( Computer Science ​)

Associate Professors P.M. Aronow ( ​Political Science ​), Forrest Crawford ( Biostatistics ), Amin Karbasi ( Electrical Engineering ​), Vahideh Manshadi ( School of Management/Operations ), Ethan Meyers ( Visiting ), Sekhar Tatikonda 

Assistant Professors Elisa Celis, Zhou Fan, Joshua Kalla ( Political Science ), Roy Lederman, Lu Lu, Fredrik Savje ( Political Science ​), Dustin Scheinost ( Radiology and Biomedical Imaging ), Andre Wibisono ( Computer Science ), Zhuoran Yang, Ilker Yildirim ( Psychology ), Ilias Zadik

Fields of Study

Fields of study include the main areas of statistical theory (with emphasis on foundations, Bayes theory, decision theory, nonparametric statistics), probability theory (stochastic processes, asymptotics, weak conver g ence), information theory, bioinformatics and genetics, classification, data mining and machine learning, neural nets, network science, optimization, statistical computing, and graphical models and methods.

Special Requirements for the Ph.D. Degree in Statistics and Data Science

There is no foreign language requirement. Students take at least twelve courses, usually during the first two years. The department strongly recommends that students take S&DS 551 (Stochastic Processes), S&DS 600 (Advanced Probability), S&DS 610 (Statistical Inference), S&DS 612 (Linear Models), S&DS 625 (Statistical Case Studies),  S&DS 631 (Optimization and Computation), S&DS 632 (Advanced Optimization Techniques), and S&DS 661 (Data Analysis), and requires that students take S&DS 626 (Practical Work). Substitutions are possible with the permission of the director of graduate studies (DGS); courses from other complementary departments such as Mathematics and Computer Science are encouraged. With the permission of the DGS and under special circumstances, appropriate courses may be taken at the undergraduate level in departments outside of Statistics and Data Science to fulfill these elective requirements.

The qualifying examination consists of three parts: a written report on an analysis of a data set, one or more written examination(s), and an oral examination. The examinations are taken as scheduled by the department. All parts of the qualifying examination must be completed before the beginning of the third year. A prospectus for the dissertation should be submitted no later than the first week of March in the third year. The prospectus must be accepted by the department before the end of the third year if the student is to register for a fourth year. Upon successful completion of the qualifying examination and the prospectus (and meeting of Graduate School requirements), the student is admitted to candidacy. Students are expected to attend weekly departmental seminars.

Students normally serve as teaching fellows for several terms to acquire professional training. All students are required to be teaching fellows for a minimum of two terms, regardless of the nature of their funding. The timing of this teaching is at the discretion of the DGS. 

Combined Ph.D. Program

The Department of Statistics and Data Science also offers, in conjunction with the Department of Political Science, a combined Ph.D. in Statistics and Data Science and Political Science. For further details, see Political Science .

Master’s Degrees

M.A. in Statistics

Three different M.A. in Statistics are offered. All require completion of eight term courses approved by the DGS; of which one must be in probability, one must be in statistical theory, and one must be in data analysis. The remaining five elective courses may include courses from other departments and, with the permission of the DGS and under special circumstances, appropriate courses may be taken at the undergraduate level in departments outside of Statistics and Data Science.

M.A. in Statistics (en route to the Ph.D. in Statistics and Data Science) This degree requires an average grade of HP or higher, and two terms of residence.

M.A. in Statistics (en route to the Ph.D. in other areas of study) Pursuit of this degree requires an application process managed by the DGS of Statistics and Data Science followed by approval from the DGSs from both programs and the cognizant Graduate School dean. All eight courses for this degree must earn grades of HP or higher. Most of the courses for the M.A. in Statistics should be in addition to the requirements of the primary Ph.D. program. This degree also has an academic teaching fellow requirement, to be determined by the DGSs from both programs and the cognizant Graduate School dean.

Terminal M.A. in Statistics Students are also admitted directly to a terminal master of arts program in Statistics. Students must earn an average grade of HP or higher and receive at least one grade of Honors. Full-time students must take a minimum of four courses per term. Part-time students are also accepted into the program. All students are expected to complete two terms of full-time tuition and residence, or the equivalent, at Yale. See Degree Requirements: Terminal M.A./M.S. Degrees, under Policies and Regulations.

Terminal M.S. in Statistics and Data Science Students are also admitted directly to a terminal master of science program in Statistics and Data Science. To qualify for the M.S., the student must successfully complete an approved program of twelve term courses with an average grade of HP or higher and receive at least two grades of Honors, chosen in consultation with the DGS. With the permission of the DGS and under special circumstances, appropriate courses may be taken at the undergraduate level in departments outside of Statistics and Data Science to fulfill elective requirements. Full-time students must take a minimum of four courses per term. Part-time students are also accepted into the program. All students are expected to complete three terms of full-time tuition and residence, or the equivalent, at Yale. See Degree Requirements: Terminal M.A./M.S. Degrees, under Policies and Regulations.

Program information is available online at http://statistics.yale.edu .

S&DS 501a / E&EB 510a, Introduction to Statistics: Life Sciences   Jonathan Reuning-Scherer

Statistical and probabilistic analysis of biological problems, presented with a unified foundation in basic statistical theory. Problems are drawn from genetics, ecology, epidemiology, and bioinformatics. TTh 1pm-2:15pm

S&DS 502a, Introduction to Statistics: Political Science   Jonathan Reuning-Scherer

Statistical analysis of politics, elections, and political psychology. Problems presented with reference to a wide array of examples: public opinion, campaign finance, racially motivated crime, and public policy. Note: S&DS 501 – 506 offer a basic introduction to statistics, including numerical and graphical summaries of data, probability, hypothesis testing, confidence intervals, and regression. Each course focuses on applications to a particular field of study and is taught jointly by two instructors, one specializing in statistics and the other in the relevant area of application. The first seven weeks are attended by all students in S&DS 501 – 506 together as general concepts and methods of statistics are developed. The course separates for the last six and a half weeks, which develop the concepts with examples and applications. Computers are used for data analysis. These courses are alternatives; they do not form a sequence, and only one may be taken for credit. TTh 1pm-2:15pm

S&DS 503a, Introduction to Statistics: Social Sciences   Jonathan Reuning-Scherer

Descriptive and inferential statistics applied to analysis of data from the social sciences. Introduction of concepts and skills for understanding and conducting quantitative research. Note: S&DS 501 – 506 offer a basic introduction to statistics, including numerical and graphical summaries of data, probability, hypothesis testing, confidence intervals, and regression. Each course focuses on applications to a particular field of study and is taught jointly by two instructors, one specializing in statistics and the other in the relevant area of application. The first seven weeks are attended by all students in S&DS 501 – 506 together as general concepts and methods of statistics are developed. The course separates for the last six and a half weeks, which develop the concepts with examples and applications. Computers are used for data analysis. These courses are alternatives; they do not form a sequence, and only one may be taken for credit. TTh 1pm-2:15pm

S&DS 505a, Introduction to Statistics: Medicine   Jay Emerson and Jonathan Reuning-Scherer

Statistical methods relied upon in medicine and medical research. Practice in reading medical literature competently and critically, as well as practical experience performing statistical analysis of medical data. Note: S&DS 501 – 506 offer a basic introduction to statistics, including numerical and graphical summaries of data, probability, hypothesis testing, confidence intervals, and regression. Each course focuses on applications to a particular field of study and is taught jointly by two instructors, one specializing in statistics and the other in the relevant area of application. The first seven weeks are attended by all students in S&DS 501 – 506 together as general concepts and methods of statistics are developed. The course separates for the last six and a half weeks, which develop the concepts with examples and applications. Computers are used for data analysis. These courses are alternatives; they do not form a sequence, and only one may be taken for credit. TTh 1pm-2:15pm

S&DS 506a, Introduction to Statistics: Data Analysis   Robert Wooster and Jonathan Reuning-Scherer

An introduction to probability and statistics with emphasis on data analysis. Note: S&DS 501 – 506 offer a basic introduction to statistics, including numerical and graphical summaries of data, probability, hypothesis testing, confidence intervals, and regression. Each course focuses on applications to a particular field of study and is taught jointly by two instructors, one specializing in statistics and the other in the relevant area of application. The first seven weeks are attended by all students in S&DS 501 – 506 together as general concepts and methods of statistics are developed. The course separates for the last six and a half weeks, which develop the concepts with examples and applications. Computers are used for data analysis. These courses are alternatives; they do not form a sequence, and only one may be taken for credit. TTh 1pm-2:15pm

S&DS 530a / PLSC 530a, Data Exploration and Analysis   Ethan Meyers

Survey of statistical methods: plots, transformations, regression, analysis of variance, clustering, principal components, contingency tables, and time series analysis. The R computing language and web data sources are used. HTBA

S&DS 538a, Probability and Statistics   Joseph Chang

Fundamental principles and techniques of probabilistic thinking, statistical modeling, and data analysis. Essentials of probability: conditional probability, random variables, distributions, law of large numbers, central limit theorem, Markov chains. Statistical inference with emphasis on the Bayesian approach: parameter estimation, likelihood, prior and posterior distributions, Bayesian inference using Markov chain Monte Carlo. Introduction to regression and linear models. Computers are used throughout for calculations, simulations, and analysis of data. Prerequisite: after or concurrently with MATH 118 or MATH 120 . TTh 1pm-2:15pm

S&DS 540a, An Introduction to Probability Theory   Robert Wooster

Introduction to probability theory. Topics include probability spaces, random variables, expectations and probabilities, conditional probability, independence, discrete and continuous distributions, central limit theorem, Markov chains, and probabilistic modeling. This course may be appropriate for non-S&DS graduate students. Prerequisite: MATH 115 or equivalent. MW 2:30pm-3:45pm

S&DS 541a, Probability Theory   Yihong Wu

A first course in probability theory: probability spaces, random variables, expectations and probabilities, conditional probability, independence, some discrete and continuous distributions, central limit theorem, Markov chains, probabilistic modeling. Prerequisite: calculus of functions of several variables. MW 9am-10:15am

S&DS 542a, Theory of Statistics   Andrew Barron

Principles of statistical analysis: maximum likelihood, sampling distributions, estimation, confidence intervals, tests of significance, regression, analysis of variance, and the method of least squares. Prerequisite: S&DS 541 . HTBA

S&DS 565a, Introductory Machine Learning   John Lafferty

This course covers the key ideas and techniques in machine learning without the use of advanced mathematics. Basic methodology and relevant concepts are presented in lectures, including the intuition behind the methods. Assignments give students hands-on experience with the methods on different types of data. Topics include linear regression and classification, tree-based methods, clustering, topic models, word embeddings, recurrent neural networks, dictionary learning, and deep learning. Examples come from a variety of sources including political speeches, archives of scientific articles, real estate listings, natural images, and others. Programming is central to the course and is based on the Python programming language. TTh 11:35am-12:50pm

S&DS 572a, YData: Data Science for Political Campaigns   Joshua Kalla

Political campaigns have become increasingly data driven. Data science is used to inform where campaigns compete, which messages they use, how they deliver them, and among which voters. In this course, we explore how data science is being used to design winning campaigns. Students gain an understanding of what data is available to campaigns, how campaigns use this data to identify supporters, and the use of experiments in campaigns. The course provides students with an introduction to political campaigns, an introduction to data science tools necessary for studying politics, and opportunities to practice the data science skills presented in S&DS 523 . W 1:30pm-3:20pm

S&DS 580a, Neural Data Analysis   Ethan Meyers

We discuss data analysis methods that are used in the neuroscience community. Methods include classical descriptive and inferential statistics, point process models, mutual information measures, machine learning (neural decoding) analyses, dimensionality reduction methods, and representational similarity analyses. Each week we read a research paper that uses one of these methods, and we replicate these analyses using the R or Python programming language. Emphasis is on analyzing neural spiking data, although we also discuss other imaging modalities such as magneto/electro-encephalography (EEG/MEG), two-photon imaging, and possibility functional magnetic resonance imaging data (fMRI). Data we analyze includes smaller datasets, such as single neuron recordings from songbird vocal motor system, as well as larger data sets, such as the Allen Brain observatory’s simultaneous recordings from the mouse visual system. TTh 2:30pm-3:45pm

S&DS 600a, Advanced Probability   Sekhar Tatikonda

Measure theoretic probability, conditioning, laws of large numbers, convergence in distribution, characteristic functions, central limit theorems, martingales. Some knowledge of real analysis is assumed. TTh 2:30pm-3:45pm

S&DS 610a, Statistical Inference   Harrison Zhou

A systematic development of the mathematical theory of statistical inference covering methods of estimation, hypothesis testing, and confidence intervals. An introduction to statistical decision theory. Knowledge of probability theory at the level of S&DS 541 is assumed. TTh 11:35am-12:50pm

S&DS 612a, Linear Models   Zongming Ma

The geometry of least squares; distribution theory for normal errors; regression, analysis of variance, and designed experiments; numerical algorithms (with particular reference to the R statistical language); alternatives to least squares. Prerequisites: linear algebra and some acquaintance with statistics. MW 11:35am-12:50pm

S&DS 625a, Statistical Case Studies   Brian Macdonald

Statistical analysis of a variety of statistical problems using real data. Emphasis on methods of choosing data, acquiring data, assessing data quality, and the issues posed by extremely large data sets. Extensive computations using R. Enrollment limited; requires permission of the instructor. HTBA

S&DS 627a, Statistical Consulting   Jay Emerson

Statistical consulting and collaborative research projects often require statisticians to explore new topics outside their area of expertise. This course exposes students to real problems, requiring them to draw on their expertise in probability, statistics, and data analysis. Students complete the course with individual projects supervised jointly by faculty outside the department and by one of the instructors. Students enroll for both terms ( S&DS 627 and 628 ) and receive one credit at the end of the year. Enrollment limited; requires permission of the instructor.   ½ Course cr F 2:30pm-4:30pm

S&DS 631a / AMTH 631a, Optimization and Computation   Zhuoran Yang

An introduction to optimization and computation motivated by the needs of computational statistics, data analysis, and machine learning. This course provides foundations essential for research at the intersections of these areas, including the asymptotic analysis of algorithms, an understanding of condition numbers, conditions for optimality, convex optimization, gradient descent, linear and conic programming, and NP hardness. Model problems come from numerical linear algebra and constrained least squares problems. Other useful topics include data structures used to represent graphs and matrices, hashing, automatic differentiation, and randomized algorithms. Prerequisites: multivariate calculus, linear algebra, probability, and permission of the instructor. Enrollment is limited, with preference given to graduate students in Statistics and Data Science. TTh 1pm-2:15pm

S&DS 645b / CB&B 645b, Statistical Methods in Computational Biology   Hongyu Zhao

Introduction to problems, algorithms, and data analysis approaches in computational biology and bioinformatics. We discuss statistical issues arising in analyzing population genetics data, gene expression microarray data, next-generation sequencing data, microbiome data, and network data. Statistical methods include maximum likelihood, EM, Bayesian inference, Markov chain Monte Carlo, and methods of classification and clustering; models include hidden Markov models, Bayesian networks, and graphical models. Offered every other year. Prerequisite: S&DS 538 , S&DS 542 , or S&DS 661 . Prior knowledge of biology is not required, but some interest in the subject and a willingness to carry out calculations using R is assumed. Th 10am-11:50am

S&DS 665a, Intermediate Machine Learning   John Lafferty

S&DS 365 is a second course in machine learning at the advanced undergraduate or beginning graduate level. The course assumes familiarity with the basic ideas and techniques in machine learning, for example as covered in S&DS 265 . The course treats methods together with mathematical frameworks that provide intuition and justifications for how and when the methods work. Assignments give students hands-on experience with machine learning techniques, to build the skills needed to adapt approaches to new problems. Topics include nonparametric regression and classification, kernel methods, risk bounds, nonparametric Bayesian approaches, graphical models, attention and language models, generative models, sparsity and manifolds, and reinforcement learning. Programming is central to the course, and is based on the Python programming language and Jupyter notebooks. MW 1pm-2:15pm

S&DS 688a, Computational and Statistical Trade-offs in High Dimensional Statistics   Ilias Zadik

Modern statistical tasks require the use of both computationally efficient and statistically accurate methods. But, can we always find a computationally efficient method that achieves the information-theoretic optimal statistical guarantees? If not, is this an artifact of our techniques, or a potentially fundamental source of computational hardness? This course surveys a new and growing research area studying such questions on the intersection of high dimensional statistics and theoretical computer science. We discuss various tools to explain the presence of such “computational-to-statistical gaps” for several high dimensional inference models. These tools include the “low-degree polynomials” method, statistical query lower bounds, and more. We also discuss connections with other fields such as statistical physics and cryptography. Prerequisites: maturity with probability theory (equivalent of 241/541) and linear algebra and a familiarity with basic algorithms and mathematical statistics. T 4pm-5:50pm

S&DS 690a, Independent Study   Jay Emerson

By arrangement with faculty. Approval of DGS required. HTBA

S&DS 700a, Departmental Seminar   Staff

Presentations of recent breakthroughs in statistics and data science.   0 Course cr M 4pm-5:30pm

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Yale University graduates walk out on commencement over war in Gaza

Owen Decatur said he felt nervous in the minutes before he, along with his fellow Yale University graduates, planned on walking out of their own commencement ceremony. But those feelings faded as Decatur heard the chants nearby in support of the Palestinians.

“I got to see that our Dean Lewis of Yale College had to stop speaking and had to stop doing his conferrals,” Decatur said.

Decatur was one of hundreds who left the ceremony on Monday in a show of protest. The demonstration was the latest in an ongoing series of actions calling on Yale University to divest from weapons manufacturers supplying Israel with arms in its ongoing war in Gaza.

Decatur and other students said it made sense to walk out as a statement against what they say is their university’s support for Israel.

Some supportive faculty members joined the rally, which began on the university’s “Old Campus” and ended at the New Haven Green. Protesters chanted “It's our Yale” along with other slogans as other graduates and their families milled about, or walked past them.

Yale University said in a statement that "a number of graduating students chose to peacefully walk out during the ceremony" and that university staff guided the students to an area outside the event. The ceremony continued as scheduled, officials noted.

"Yale is committed to promoting freedom of speech and expression," the statement said.

Decatur said the protest was a way to reject his bachelor's degree.

“It would symbolize a rejection of our degrees because we did not want a degree from an institution that continues to invest in weapons manufacturing that attributes to the genocide of Palestinian people in Gaza,” Decatur said.

Other students who walked out included graduate Tadea Martin Gonzalez, who wants Yale to divest from weapons manufacturers. Martin Gonzalez pushed back on criticisms of the protest and noted that it is also her special day. However, she could not ignore the fact that nearly 90% of all schools in Gaza lay in ruins.

“Who are we to graduate? When many others who we carry in our memory do not have the privilege,” Martin Gonzalez said.

Fawn Cho, who is not a student at Yale, but is supportive of her sister who is, said she liked seeing the students exercise their free speech.

“It is interesting for all the people who come here to have different perspectives,” Cho said.

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Adult Mental Health Studies Enrolling at Yale

Be part of clinical research at yale, have you experienced a life-threatening event.

If you are between the ages of 18-60 and have experienced a traumatic or life-threatening event , you may be eligible to participate in a research study examining brain activity in individuals with PTSD. Study visits involve a psychological screening over Zoom to determine eligibility, a 90-minute fMRI scan, and a 90-minute MEG scan. This is not a treatment study, but your participation will play an important role in helping to advance treatment.

Participants will receive $50 for the psychological screen, $100 for the fMRI, and $150 for the MEG, as well as travel reimbursement.

If you are interested in learning more, please email us at [email protected] or call/text (203) 936-9322.

Sponsored by Yale University School of Medicine, HIC# 2000025892

Do you have Post-Traumatic Stress Disorder (PTSD) and are interested in a new, rapid 7 day treatment?

Combining neurobiology and new learning (ketamine/midazolam combined with psychotherapy) for a potential rapid treatment for ptsd.

We are currently recruiting participants with PTSD who are 21 to 70 years old for a study that involves a week long treatment for those with PTSD.

Compensation up to $900.

Sponsored by National Institutes of Health (NIH); National Center for PTSD (NCPTSD); Yale University School of Medicine; HIC# 1509016530

Do you have trichotillomania?

Efficacy of valbenazine for the treatment of trichotillomania in adults.

If you are an adult with trichotillomania , you may be eligible to participate in a research study run by the Yale School of Medicine. This study will examine the efficacy of Valbenazine for the treatment of trichotillomania. Valbenazine is a dopamine-depleting agent that is currently FDA-approved for tardive dyskinesia and has a relatively safe side-effect profile in initial trials.

While enrolled in the trial, you will receive regular psychiatric and medical assessments of symptoms at no cost. Additionally, you will receive a stipend of up to $590 over the course of 26 weeks for your participation.

If you are interested in the trial or wish to learn more, please do not hesitate to contact us by phone at 203-737-4809 or email at [email protected] if you have any questions about the trial.

Sponsored by Neurocrine Biosciences, Inc.; HIC# 2000034993

Are you experiencing suicidal thoughts but NOT planning to act on them? Have you tried antidepressants before without much success?

Help us understand the effects of the rapid antidepressant ketamine on the brain and earn up to $435 for your time.

For more information and to see if you are eligible to participate, contact (203)692-9263 or email [email protected] .

Sponsored by American Foundation for Suicide Prevention, HIC# 2000033608

Are you Depressed?

Have you tried antidepressants before without much success? Help us understand the effects of the rapid antidepressant ketamine on the brain and earn up to $670 for your time.

Sponsored by National Institutes of Mental Health, HIC# 200021345

Concerned about eating or overeating?

If you are between 21 and 50 years old with binge-eating disorder, you may be eligible for a brain-imaging study. Study visits involve in-person screening visits to determine eligibility, a 30-minute MRI scan, and a 90-minute PET scan. This is not a treatment study.

Compensation up to $400.

If you are interested in participating, or if you have any questions, please contact our study coordinators, Adam Stryjewski ( [email protected] ; 203-535-4424) or Marcella Mignosa ( [email protected] ; 203-903-7795) and mention “binge-eating study.”

Sponsored by National Institute of Mental Health (NIMH); HIC#2000034271

Are you aged 12-30 and noticing a recent change in your thinking, behavior, or experiences?

Pro-net study.

If you're between the ages of 12 and 30 and have recently experienced changes in your thinking, behavior, or experiences , you may qualify for this study. These changes include confusion between reality and imagination, feeling a lack of control over your thoughts or ideas, being suspicious or paranoid, experiencing unreal sensations like hearing sounds or seeing things that aren't there, and having difficulty communicating clearly.

Participants in this study will undergo an initial screening visit, 2 MRI scans, and 2 sets of blood work. Additionally, they will be asked questions about their experiences and emotional well-being.

Total compensation of up to $1000 is provided.

To inquire further or determine eligibility for participation, please contact us at 203-200-8997 or email [email protected] .

Sponsored by National Institute of Mental Health (NIMH); HIC# 2000029159

Have you experienced a traumatic event?

If you are between the ages of 18 to 45 , you can help in advancing research about trauma and its impacts. Compensation will be provided.

If you’re interested, please fill out our screening questionnaire to see if you are eligible: https://redcap.research.yale.edu/surveys/?s=4F4TNRX473PH97NX

Sponsored by the National Institute of Mental Health and the Yale University School of Medicine; HIC# 2000026215

Have you been diagnosed with a psychotic-spectrum disorder? Do you experience auditory hallucinations?

If you are between the ages of 18 and 65, are right-handed, and speak English , you could help advance psychiatric research by participating in our brain imaging study. Auditory hallucinations are among the most distressing aspects of psychotic illness, and between 10 and 30% of people with hallucinations do not respond to antipsychotic medications. Help us take the first step toward individualized treatment approaches to hallucinations.

Compensation up to $400 for your participation.

For more information, and see if you are eligible to participate, please contact us: Text or call: 203-441-7213 email: [email protected] .

Sponsored by Burroughs Wellcome Fund; National Institute of Mental Health (NIMH); Yale University School of Medicine;HIC# 2000024774

Do you act out your dreams?

Neuro-immune interactome in parkinson's disease.

If you are over 40 years of age and do act out your dreams by kicking or punching out along with talking and/or yelling , you may have REM behavior disorder. REM Sleep Behavior Disturbance (RBD) is a sleep problem which may (or may not) be an early sign of Parkinson’s Disease (PD) or other neurological problems. The purpose of this research study is to determine whether or not RBD is associated with an immune process initiated in the gut, and to identify the earliest immunological changes in people at risk for developing PD. Your participation will require one or two phone or online interviews, and two to three in-person visits to research facilities at Yale New Haven Hospital, then yearly visits thereafter.

Compensation up to $500.

To learn more or see if you are eligible to participate, contact Kara Mead, Yale Sleep Medicine and Neurology, at [email protected] or call (203) 785-2285.

Sponsored by Michael J. Fox Foundation and Yale University School of Medicine; HIC#: 2000029032

Do you have Parkinson’s disease and symptoms of depression?

Depression can have a real impact on quality of life. But right now treatments for depression in PD are inadequate. We believe that the drug ketamine could reduce depression and possibly target other symptoms of PD.

The trial will involve receiving either ketamine or placebo twice per week for 3 weeks. You will also have the option to take part in brain imaging before and after the treatment.

You will be reimbursed up to $2000 for your time.

Contact us to find out more. Call or Text: (475) 287-9521 E-mail: [email protected] . Click here for more information on this trial.

Sponsored by National Institute of Neurological Disorders and Stroke (NINDS); HIC #2000033352

Are you a first-time expectant mother or father? Help us learn about changes across the transition to parenthood.

If you are currently expecting a child for the first time, you and/or your partner may be eligible to participate in a research study focused on understanding more about emotions, the brain, and behavior in new parents before and after the baby is born.

This study involves: 1) a brief online study visit during the third trimester of pregnancy, and 2) one in- person lab visit with you and your infant around 2-4 months after delivery. You would complete surveys, tasks, and non-invasive measures of brain activity as part of the study.

Compensation for this study is $100 ($20 for pregnancy visit, $80 for after-delivery visit) in the form of gift cards. If your partner is also participating, you would each be separately compensated $100. Compensation for parking/travel is also available.

If you are interested in participating in the Emotions, Brain, and Behavior (EBB) Study for new parents, please email us at [email protected] or call/text 475-500-5828.

Sponsored by American Psychological Foundation; HIC# 2000033554

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Sarah Millholland receives 2024 Vera Rubin Early Career Award

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Sarah Millholland , an assistant professor of physics at MIT and member of the Kavli Institute for Astrophysics and Space Research, is the 2024 recipient of the Vera Rubin Early Career Award for her wide-ranging contributions to the formation and dynamics of extrasolar planetary systems. The American Astronomical Society’s Division on Dynamical Astronomy (DDA) recognized Millholland for her demonstration “that super-Earth planets within a planetary system typically have similar masses, that the statistics of compact multi-planet systems are consistent with a smooth inclination distribution, and that resonances trapping obliquities to high values may enhance the tidal evolution of planetary orbits.”

The citation noted that her work “is distinguished by thoughtful analyses of 3D dynamical processes in planetary systems and by effective use of observational data to constrain dynamical models.” Millholland is invited to give a lecture at the 56th annual DDA meeting in spring 2025.

“I am incredibly honored to receive the DDA Vera Rubin Early Career Prize, and I am especially grateful to my advisors and mentors within the dynamical astronomy community,” says Millholland. “The DDA means a lot to me, and I look forward to continuing to be a part of it for years to come.”

Millholland is a data-driven dynamicist who studies extrasolar planets, including their formation and evolution, orbital architectures, and interiors/atmospheres. She studies patterns in the observed planetary orbital architectures, referring to properties like the spacings, eccentricities, inclinations, axial tilts, and planetary size relationships. She specializes in investigating how gravitational interactions like tides, resonances, and spin dynamics sculpt observable exoplanet properties.

Millholland obtained bachelor’s degrees in physics and applied mathematics from the University of Saint Thomas in 2015. She earned her PhD in astronomy from Yale University in 2020, and was a NASA Sagan Postdoctoral Fellow at Princeton University until 2022, when she joined MIT.

The Vera Rubin Early Career Prize was established in 2016 in honor of the late Vera Rubin, a longtime DDA Member and galactic dynamicist.

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EMD student spotlight series: Emily Johnson

Emd student spotlight series.

Emily Johnson.

This series spotlights the amazing students in the YSPH Epidemiology of Microbial Diseases (EMD) program. This month focuses on students who are studying ticks and tick-borne diseases. Here, Emily Johnson, 2nd year PhD student, tells us about the program and some of her exciting research in a Q&A format.

Please tell us a little about yourself and what inspired you to pursue research in the EMD Department at the Yale School of Public Health?

Emily : I am a second year PhD student in the EMD department, working in Dr. Erol Fikrig's lab. I first completed the 5-year BS/MPH here studying Molecular Biophysics and Biochemistry and Environmental Health Sciences, before finding my niche in tick-borne diseases and the EMD department. I was inspired to join the department by the amazing faculty pursuing groundbreaking and life-saving research through a variety of fronts, from benchwork, to surveillance, to modeling. I wanted to be able to pursue my passion for research learning by such incredible faculty and researchers.

What is the current focus of your research?

I focus on the host, pathogen, and vector interactions of Lyme disease, which is currently the most common vector-borne disease in the US. I want to understand how Borrelia can survive in mice, humans, and ticks. I am also involved in anti-tick public health interventions being studied in the lab.

What are some of the most significant findings or innovations from your tick-borne disease research?

My PhD project looks at protein interactions between Borrelia and the mammalian host, and Borrelia and the tick vector. I am beginning to uncover an uncharacterized receptor in the tick that may participate in cross-phyla signaling for its own development and may be utilized by Borrelia for survival.

The lab is moving in an exciting direction in the development of anti-tick vaccines, which would revolutionize how tick-borne diseases are prevented and treated. Emily Johnson

Which emerging trends or technologies in tick-borne disease research do you find most exciting?

I am most excited about the movement from public health interventions from specific pathogens directly to targeting the tick itself. The lab is moving in an exciting direction in the development of anti-tick vaccines, which would revolutionize how tick-borne diseases are prevented and treated.

Where do you see the field of tick-borne disease research heading in the next few years, and what role do you hope your work will play in this future landscape?

I hope that the field continues to study the relationship between mammalian hosts and ticks, as well as dive deeper into understudied tick-borne diseases. With climate change on the rise, there is an ever important need to continue watching tick-borne diseases and finding new interventions on global health scales.

How has the EMD Department supported your research and academic goals?

The EMD Department has been instrumental in my development as a graduate student and researcher. Every faculty member is invested in our research interests and goals and have offered exceptional mentorship and support. I am particularly grateful to Dr. Fikrig as well as all the faculty members who have supported me in this journey.

What advice would you give to prospective students considering applying to the EMD training programs at Yale?

Explore the wide range of training opportunities in the EMD department! Between courses, seminars, training programs, and exploring the research and work being done by all the faculty members in the department, there are endless ways to expand our skills as we begin to engage in our academic goals and future public health careers.