stanford economics phd

Principal Investigator

Ann Hsing, PhD, MPH

Dr. Hsing is a professor of Medicine and of Epidemiology and Population Health in the Stanford School of Medicine. For over 35 years, Dr. Hsing’s research focused on cancer etiology and prevention, in particular prostate, lung, and hepatobiliary cancers. At Stanford, she leads studies on cancer prevention, the science of well-being, and Asian American health and prevention research. She is the principal investigator of Stanford WELL for Life ( WELL ) , the Asian American Cohort Study of Health and Disease Prevention ( ARISE ) , and an International Consortium on Cancer Biomarkers for Early Detection and Prevention ( INTERCEPT ).

Please access Dr. Hsing's Stanford CAP profile HERE .

Senior Research Scientist

Lisa Chu, PhD

Dr. Chu is a senior research scientist in the Hsing Lab at Stanford. She received a PhD in molecular biology/genetics and completed a postdoctoral fellowship at NCI on epidemiology and cancer prevention. Dr. Chu is interested in how genetic variations affect a person’s underlying risk for endocrine-related cancers as well as lung cancer in Asian women. She is also interested in how genetic variations might interact with lifestyle and environmental factors to influence risk for cancer and other diseases.

Visiting Scholar

Lijun Zhang, PhD

Dr. Zhang is a visiting research scholar at the Hsing Lab. He received a PhD in Economics from Nankai University in 2003. In 2015, he founded the HomeinCare Health Management Group, the first Internet and Nursing service platform in China. With the Hsing Lab, he plans to develop research on aging well and ways to provide a better life for the rapidly aging population in China and around the world.

Post-Doctoral Scholar

Peng Gao, PhD

Dr. Gao is a WELL post-doctoral research scholar at the Hsing Lab. He has a Bachelor of Science in Information and Computing Science and a Master of Science in Computational Mathematics. In 2023, he received his PhD in Nutrition and Food Hygiene from Zhejiang University. His research has focused on sarcopenia and obesity-related chronic disease prevention. With the Hsing Lab, he will focus on traditional Chinese medicine (TCM) body constitution type and its role on personalized well-being.

Xiaoyan Zhang, PhD

Dr. Zhang is a WELL post-doctoral research scholar at the Hsing Lab. She received her PhD in Human Development and Family Science from Syracuse University in 2022. Her research centers on the intersection of adversity, social relationships, resilience, aging, health, and well-being. She applies the biopsychosocial model, advanced methodological approaches, and longitudinal data to study well-being and health disparities over the life course. With the Hsing Lab, she is currently studying loneliness during the pandemic.

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Study In Usa > Colleges In Usa > Stanford University > Programs > Ph.D[Economics]

Doctor of Philosophy [Ph.D] [Economics] From Stanford University

Doctor of Philosophy [Ph.D] (Economics)

Field of Study : 

₽5,329,317 /Yr

• Doctor of Philosophy [Ph.D.] Economics is a 5-year program.
• The program is a thesis-based.
• This program is offered as full time and delivered on-campus.
• 135 units of full-tuition residency are required for PhD students. 
• Behavioral & Experimental
• Development Economics
• Econometric Methods with Causal Inference
• Econometrics
• Economic History
• Environmental, Resource and Energy Economics
• Industrial Organization
• Macroeconomics
• Development
• Experimental/Behavioral
• Public/Environmental
• Individual courses require a letter grade of B- or better to pass unless otherwise noted.
• The student-faculty ratio at Stanford University is 5:1, and the school has 69.3% of its classes with fewer than 20 students.
• 17 Nobel laureates are currently members of the Stanford community.
• According to QS Global World Ranking, Stanford University Ranked #2.
• The average salary is 98,384 USD.

Important Dates

Tuition Fees

Other Expenses

Eligibility & Entry Requirement

Academic Eligibility:

• Students must complete a master's degree from a recognized institution.
• The average cumulative high school GPA of students admitted to Stanford is 3.96 on a 4.0 scale.
• GRE general test scores optional.

Indian Student Eligibility:

Indian students are eligible to apply if they meet one of the following eligibility criteria:

• Completed a graduate degree with first-class in a relevant field from a recognized institution

Along with the minimum eligibility requirements, international students hailing from non-English speaking countries need to prove English proficiency through  IELTS / TOEFL /any equivalent test to get admission to this program.

Scores Required

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Required Document List

• Online Application:  Students have to submit an online application form at the official website.
• Application Fee: Students need to submit their application fees.
• Statement of Purpose:  Reasons or intentions for pursuing studies in this program, including a description of prior experience.
• 3 Letters of Recommendation:  Three letters of recommendation are required; one letter must come from an academic source, although Stanford prefer at least two. Recommendations must be submitted online. 
• Official GRE Test Scores: Students have to submit their GRE Test Scores.
• ELP Scores:  Students have to submit their English language proficiency scores like IELTS, TOEFL, or other test scores.

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Working Papers

Nested Bundling Revise & Resubmit, American Economic Review . (previously titled "The Simple Economics of Optimal Bundling") Last updated: November 2023.

Costly Multidimensional Screening Revise & Resubmit, Review of Economic Studies . Last updated: August 2022. arXiv . SSRN . -->

Comparison of Screening Devices (with Piotr Dworczak and Mohammad Akbarpour) Revise & Resubmit, Journal of Political Economy . Last updated: February 2024. SSRN . -->

Dynamic Pricing with Limited Commitment (with Martino Banchio) Last updated: December 2021. arXiv . -->

Published and Forthcoming Articles

Pricing Power in Advertising Markets: Theory and Evidence (with Matthew Gentzkow, Jesse M. Shapiro, and Ali Yurukoglu) American Economic Review (2024). SSRN . -->

Matter-Sector Lorentz Violation in Binary Pulsars (with Ross J. Jennings and Jay D. Tasson) Physical Review D (2015). arXiv . -->

Ilya Strebulaev

David s. lobel professor of private equity, web page:  http://faculty-gsb.stanford.edu/strebulaev/.

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• Research & Scholarship
• Publications

Ilya A. Strebulaev is the David S. Lobel Professor of Private Equity and Professor of Finance at the Graduate School of Business, Stanford University, where he has been a faculty member since 2004, and a Research Associate at the National Bureau of Economic Research. He also is the director of the Stanford Venture Capital Initiative. He graduated from the London Business School with a doctorate in Finance. He also holds degrees from Lomonosov Moscow State University (B.Sc. Economics) and the New Economic School, Moscow (M.A. Economics). Professor Strebulaev is an expert in corporate finance, venture and angel capital, innovation financing, corporate innovation, and financial decision-making. His work has been widely published in leading academic journals, including the Journal of Finance, the Review of Financial Studies, and the Journal of Financial Economics. Ilya has been awarded a number of prestigious academic awards, including the First Paper Prize of the Brattle Award for the best corporate paper published in the Journal of Finance, the Fama-DFA Prize for the best asset pricing paper published in the Journal of Financial Economics, and the Trefftzs Award by the Western Finance Association. His research has also been featured in a variety of media, including New York Times and Wall Street Journal. His most recent research has examined many aspects of the venture capital industry. For example, in the largest ever survey of VCs to date, he and his co-authors analyze all the aspects of decision-making by venture capitalists. As another example, in analyzing the valuation of highly valued VC-backed companies (called “unicorns”), he found that these companies on average are overvalued by 50% and that many of the so-called unicorns lose their unicorn status once their fair value is taken into consideration. Ilya teaches at the MBA, MsX, PhD, and executive education programs, and has been awarded the Stanford MBA Distinguished Teaching Award, the Sloan Teaching Excellence Award, as well as the inaugural Masters in Management Best Teacher Award at the London Business School. He developed an MBA-level course on Angel and Venture Capital that he has been teaching for the past several years. The course enables the students to study many aspects of innovation financing at various stages, including decision making, attracting venture and angel investments, negotiating contractual terms, valuing VC-backed companies, and analyzing the performance of venture capital funds. Professor Strebulaev has also led many workshops and executive sessions on new innovation trends, venture capital, the ecosystem of Silicon Valley, corporate innovation, and strategic decision making for senior business and government leaders around the world. When not teaching or doing research, Ilya enjoys spending time with his family, reading, traveling, listening to classical music, and collecting and appreciating fine wine and art.

Academic Appointments

• Professor, Finance

Administrative Appointments

• The David S. Lobel Professor of Private Equity and Professor of Finance (tenured), Graduate School of Business, Stanford University (2016 - Present)
• Professor of Finance (tenured), Graduate School of Business, Stanford University (2014 - 2016)
• Research Associate, National Bureau of Economic Research (2010 - Present)
• Associate Professor of Finance (tenured), Graduate School of Business, Stanford University (2010 - 2014)
• Visiting Associate Professor of Finance, London Business School (2009 - 2010)
• Associate Professor of Finance, Graduate School of Business, Stanford University (2008 - 2010)
• Assistant Professor of Finance, Graduate School of Business, Stanford University (2004 - 2008)

Honors & Awards

• Best Paper, Private Equity Research Consortium Best Paper Award (2017)
• Highly commended paper, Best Private Equity paper 2017, Savvy Investor Awards (2017)
• Dhirubhai Ambani Faculty Fellow in Entrepreneurship, Stanford GSB (2014-2015)
• Finalist, 2014 TIAA-CREF Paul A. Samuelson Award (2014)
• Shanahan Family Faculty Scholar, Stanford GSB (2013-2014)
• The Sloan Teaching Excellence Award, Stanford GSB (2013)
• First Place, Fama–DFA Prize, Journal of Financial Economics (2011)
• Spence Faculty Scholar, Stanford GSB (2010-2011)
• The Masters in Management Inaugural Best Teacher Award, London Business School (2010)
• The MBA Distinguished Teacher Award, Stanford GSB (2009)
• The MBA Distinguished Teacher Award (shortlisted), Stanford GSB (2008)
• Distinguished Alumni Award, New Economic School (2007)
• First Paper Prize, Brattle Award, Journal of Finance (2007)
• The Best Paper Award, 13th Mitsui Symposium on Finance, U. of Michigan (2007)
• The MBA Distinguished Teacher Award (shortlisted), Stanford GSB (2007)
• Fletcher Jones Faculty Scholar, Stanford GSB (2006–2007)
• Moody’s Grant for $30,000, (with S. Schaefer) (2005)
• Award for the best paper, The Dimitris N. Chorafas Foundation (2004)
• The Trefftzs Award, Western Finance Association (2004)
• Inquire Europe research grant for euro 10,000, (with S. Schaefer) (2003-2004)
• Inquire UK research grant for GBP10,000, (with S. Schaefer) (2003-2004)
• Kaplanis Fellowship, London Business School (2002-2004)
• Citigroup Ph.D. Scholar, London Business School (2001-2002)

Boards, Advisory Committees, Professional Organizations

• Member, Board of Directors, Yandex N.V. (Nasdaq: YNDX) (2018 - Present)

Professional Education

• Ph.D., London Business School, Finance (2004)
• M.Phil, London Business School, Finance (2002)
• M.A., New Economic School, Moscow, Economics (1999)
• B.A., Lomonosov Moscow State University, Economics (1997)
• Academic [email protected] University - Faculty Department: Graduate School of Business - Faculty Position: Professor

Additional Info

• Mail Code: 4800

Current Research and Scholarly Interests

Professor Strebulaev is an expert in corporate finance, venture and angel capital, innovation financing, corporate innovation, and financial decision-making. His recent work has examined the valuation of VC-backed companies, decision making by startup investors, returns to VC investors, and impact of venture capital investments. Ilya's work has been widely published in leading academic journals and has been awarded a number of prestigious academic awards. His research has also been featured in a variety of media, including New York Times and Wall Street Journal.

2023-24 Courses

• Angel and Venture Capital Financing for Entrepreneurs and Investors FINANCE 385 (Aut)
• Economics of the Private Equity Industry FINANCE 334 (Aut)
• Curricular Practical Training for PhD Students GSBGEN 199 (Sum)
• Doctoral Practicum in Research FINANCE 699 (Aut, Win, Spr, Sum)
• Doctoral Practicum in Teaching FINANCE 698 (Aut, Win, Spr, Sum)
• Honors Thesis Research ECON 199D (Aut, Win, Spr)
• Individual Research GSBGEN 390 (Aut, Win, Spr)
• PhD Directed Reading ACCT 691, FINANCE 691, MGTECON 691, MKTG 691, OB 691, OIT 691, POLECON 691 (Aut, Win, Spr, Sum)

2022-23 Courses

• Advanced Corporate Finance FINANCE 626 (Aut)

2021-22 Courses

• Advanced Corporate Finance Theory FINANCE 634 (Aut)
• Research Fellows Practicum GSBGEN 697 (Aut, Win, Spr)

2020-21 Courses

• Venture Capital and Finance of Innovation FINANCE 627 (Aut)

Stanford Advisees

• Doctoral (Program) Ege Ercan

All Publications

View details for DOI 10.1287/mnsc.2020.3593

View details for Web of Science ID 000651625200005

View details for Web of Science ID 000621561600018

View details for DOI 10.1016/j.jfineco.2019.08.006

View details for Web of Science ID 000517670100001

View details for DOI 10.1016/j.jfineco.2018.04.015

View details for Web of Science ID 000504520000006

View details for DOI 10.1561/104.00000069

View details for Web of Science ID 000624572800001

View details for DOI 10.1016/j.jfineco.2019.06.011

View details for Web of Science ID 000504520000008

View details for DOI 10.1111/jofi.12862

View details for Web of Science ID 000503426200001

View details for DOI 10.1093/rfs/hhy066

View details for Web of Science ID 000469820000009

View details for DOI 10.1016/j.jfineco.2018.05.008

View details for Web of Science ID 000441371500005

View details for DOI 10.1142/S2010139215500081

View details for Web of Science ID 000216885900002

View details for DOI 10.1016/j.jfineco.2013.10.014

View details for Web of Science ID 000330486100002

View details for DOI 10.1016/j.jfineco.2013.09.002

View details for Web of Science ID 000328233100007

View details for DOI 10.1016/j.jfineco.2013.09.001

View details for Web of Science ID 000328233100004

View details for DOI 10.1016/j.jfineco.2013.02.001

View details for Web of Science ID 000320218300001

View details for DOI 10.1561/104.000000011

View details for Web of Science ID 000433801100006

View details for DOI 10.1093/rfs/hhs106

View details for Web of Science ID 000311306200004

View details for DOI 10.1093/rfs/hhs091

View details for Web of Science ID 000309129700002

View details for DOI 10.1016/j.jfineco.2011.01.011

View details for Web of Science ID 000295393600001

View details for DOI 10.1093/rfs/hhq075

View details for Web of Science ID 000284432200001

View details for DOI 10.1093/rfs/hhq039

View details for Web of Science ID 000280098300001

View details for DOI 10.1257/aer.100.2.547

View details for Web of Science ID 000278389300106

View details for DOI 10.1093/rfs/hhp082

View details for Web of Science ID 000273892100007

View details for Web of Science ID 000269875400005

View details for DOI 10.1016/j.jfineco.2007.10.006

View details for Web of Science ID 000260727700001

View details for Web of Science ID 000251251600003

View details for Web of Science ID 000248726300007

Explore Graduate Programs

Predoctoral Research Fellowship Opportunities

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The SIEPR/Economics Predoctoral Research Fellows program offers a full-time one to two-year immersive research experience for post-baccalaureate individuals interested in pursuing advanced degrees and careers in economics, public policy, and related fields. Predoctoral research fellows (“predocs”) work with one or two SIEPR faculty mentors on research projects and have the opportunity to enroll in Stanford undergraduate and graduate level courses. Predocs additionally participate in a variety of professional development and social activities together.  

SIEPR/Economics Predoctoral Research Fellows Program

Fellows are appointed as non-matriculated graduate students and have the opportunity to fully engage in the intellectual life at Stanford University. They dedicate a significant portion of their time to an empirical research project and can take courses at Stanford University for credit (up to one 3-unit course per quarter). The fellowship offers tuition, health insurance, and a living stipend.

Meet our current Predoctoral Research Fellows

Hear from some SIEPR students

Diversity and Inclusivity

The SIEPR Predoctoral Research Fellows program is committed to creating inclusive research spaces for all. We value diverse backgrounds and perspectives and are a proud participant of  Pathways to Research and Doctoral Careers (PREDOC) . The goal of our program is to help foster the growth of the next generation of researchers in economics and public policy.

We seek applicants who have an interest in pursuing research related to economic policy and in learning cutting-edge research methods. Prior background is not strictly necessary; candidates interested in gaining exposure to the project areas of research are very welcome to apply. 

Qualifications

• Completion of a 4-year undergraduate degree by June 30, 2025. The degree should be a bachelor's, or foreign equivalent, in economics, statistics, applied mathematics, or a related field. 
• Strong quantitative background and interest in learning cutting-edge research methods.
• Creative and independent problem-solving skills.
• An interest in pursuing research related to economics and/or the project field of interest. However, prior background in these areas is not strictly necessary; candidates interested in gaining exposure to the project areas of research are very welcome to apply. 
• The programming experience required varies and will be outlined in the posting for each research project.

Apply to be a Fellow

Faculty Mentors

The program enables our faculty to expand their research capacity on policy-relevant topics. By expanding the talent pool from which future contributions to the economic policy will be made, we directly contribute to SIEPR’s mission of engaging future scholars in high-quality economic policy research.

See the current faculty mentors

Stanford Faculty interested in mentoring students through the program should consult our  Information for Faculty.

Please review our  frequently asked questions . If you have additional questions, email  [email protected] .

PhD Research Specializations

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Learn more about research specializations below. Then, rank your top two research areas of interest (1 being the most interested) in your application.

Computational Social Science

Decision and risk analysis, operations research.

Organizations, Technology and Entrepreneurship

Policy and Strategy

Quantitative finance.

Characterized by its social science depth, state-of-the-art methods, and field-based understanding of technology firms and markets.

Focuses on: - Analysis and design of public policy - Crowdsourcing - Coordination in online labor markets - Casual inference and experimentation

Courses to Take Statistics, computer science, optimization, economics, sociology, and possibly other social sciences

Recent Dissertations Recent PhD dissertations include: - Design and analysis of a peer-to-peer credit network and reputation system - Analysis of dynamic online markets - Design and analysis of flash teams - Fast algorithms for large scale personalized recommendations

Program and Center Affiliations Social Algorithms Lab (SOAL) Center for Work, Technology & Organization (WTO)

Focuses on applying engineering systems analysis and probability to complex economic and technical design or management problems, in the private and public sectors.

The Engineering Risk Research Group (ERRG) focuses on: - complex engineered systems (e.g., optimal architecture of satellites and deflection of asteroids’ trajectories) - cyber security, and risks in games against adversaries (e.g., counter-terrorism, counter-insurgency, and staying ahead of narco-traffickers).

Courses to Take Courses include the mathematical foundations of modeling dynamic environments, value and management of uncertain opportunities and risks, and public policy and strategy applications. Risk analysis requires optimization, stochastic processes, economics and game theory courses.

Recent Dissertations Recent PhD dissertations include: - Experiment sample sizes for influence diagrams - Markov process regression - Quantile function methods for decision analysis

Focuses on developing and applying analytical, computational, and economic tools to address a wide variety of problems in business, government, and society. The area is characterized by its mathematical depth, broad applicability, and interdisciplinary nature and has a particular emphasis in developing and applying models and algorithms to gain new insights and make better decisions across multiple domains.

Courses to Take

PhD students take core courses in optimization and stochastics as well as advanced courses in computer science, game theory, microeconomics, statistics, and other areas tailored to the interests of the student, e.g. Computational Social Science, Operations Management, Environmental Policy, Health Policy, etc.

Program and Center Affiliations Institute for Computational and Mathematical Engineering (ICME)

Organization, Technology and Entrepreneurship

Characterized by the study of technical work, technology’s effects on individuals and teams, the formation and growth of entrepreneurial firms, and strategy and innovation in technology-based firms.

Courses to Take Organization theory, organizational behavior, sociology, social psychology, economics, entrepreneurship, and strategy, as well as methods courses in statistics, experimental methods, inductive case studies, computational tools field research methods courses including ethnography, and social network analysis.

Recent Dissertations Recent PhD dissertations include: - Collaborations of private and public sector organizations to create breakthrough technologies - Collective innovation - Competitive interaction in the software industry - Educational reforms and their implications for entrepreneurship in China - Flash teams - Global collaboration - Occupational identities - Platform competition - Regulatory reforms and innovation in medical device industry - Social movements

Program and Center Affiliations Center for Work, Technology, and Organizations (WTO) Stanford Technology Ventures Program (STVP)

Focuses on the design and analysis of public policies and corporate strategies, especially those with technology-based issues.  Sub-areas include Energy and Environment, Health Systems Modeling and Policy, and National Security Policy.

Courses to Take It features a grounding in microeconomics and modeling approaches. Students take courses with a policy focus include such topics such as national security, energy and environment, and health care, and courses with a strategy focus cover topics such as entrepreneurship, innovation, and product development.

Recent Dissertations Recent PhD dissertations include: -Managing uncertainty in medical decision making -Resource allocation for infectious disease control -Optimizing patient treatment decisions in the presence of rapid technological advances -Economic analysis of HIV prevention and treatment portfolios.

Program and Center Affiliations Center for Health Policy/Program on Clinical Outcomes Research (CHP/PCOR) in the Medical School Energy Modeling Forum Precourt Energy Efficiency Center Systems Utilization Research for Stanford Medicine

Focuses on the quantitative and statistical study of financial risks, institutions, markets, and technology.

Courses to Take Students take courses in probability, statistics, optimization, finance, economics, computational mathematics, and computer science as well as a variety of other courses.

Recent Dissertations Recent PhD dissertations include: - Studies of machine learning methods for risk management - Systemic financial risk - Algorithmic trading - Optimal order execution - Large-scale portfolio optimization - Mortgage markets -Statistical testing of financial models

Program and Center Affiliations Advanced Financial Technologies Laboratory (AFTLab)

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Political Economics Requirements

I. preparation.

Admitted students are assumed to have some significant background in economics and/or political science. In addition, all students are required to have, or to obtain in the first year, skill in the use of the following mathematical methods:

Students are expected to have adequate computer programming skills. Knowledge of a computer language such as Fortran, C, or APL is sufficient. If students do not have adequate computer programming skills, they may learn this material on their own or take Stanford Computer Science course CS 106A: Programming Methodology by the summer following the first academic year, at the latest.

II. Course Requirements

All required courses must be taken for a grade (not pass/fail or credit/no credit). Exceptions are made if the required course is offered pass/fail or credit/no credit only. Each course must be passed with a grade of P or B- or better. Substitutions of required courses require approval from the faculty liaison. Waiving a course requirement based on similar doctoral level course completed elsewhere requires the approval of the course instructor, faculty liaison, and the PhD Program Office.

III. Practicum

PE students are required to attend PE seminars and the PE student research lunch, and sign up for either a research or teaching practicum every quarter of enrollment.

For the first year, the research practicum involves attending the Political Economy seminar and the PE student research lunch. For years two to five, the research practicum involves a research appointment with a faculty supervisor in addition to attending the Political Economy seminar and the PE student research lunch. Students will be assigned to a faculty member each quarter. The research practicum allows the students the opportunity to interact regularly with faculty and foster their professional development. A minimum of one quarter of teaching practicum (or course assistantship) is required during the student’s time in the program. The teaching practicum involves course preparation work with a faculty member in addition to attending the Political Economy seminar.

IV. Summer Research Papers

Each student is required to write a research paper each summer following the first and second year of study, and to present these papers to the faculty in an hour-long seminar during the autumn quarter of the second and third years, respectively. Successful completion of both papers is required for admission to candidacy.

V. Field Exam

Students are required to pass a field exam consisting of three parts:

• Political economy (covering material in POLECON 680, 681, 682)
• Microeconomics (covering material in the core microeconomic sequence)
• Econometrics (covering material in the core econometrics sequence)

Students take the field exam during the summer after their first year. In rare cases students may petition the faculty to defer taking the field exam to the summer after the second year. Per Stanford GSB policy, students who fail the exam are not guaranteed an opportunity to re-take the exam. At the faculty’s discretion, a student who fails the exam may be permitted to re-take the exam.

VI. Teaching Requirement

One quarter of course assistantship or teaching practicum. Requirement must be completed prior to graduation.

VII. Candidacy

Admission to candidacy for the doctoral degree is a judgment by the faculty of the student’s potential to successfully complete the requirements of the degree program. The procedure for making this assessment centers on a presentation by the student to his or her primary advisor and two other faculty members who are likely to become committee members. Students are expected to convene this meeting in the Spring of the third year. Students are required to advance to candidacy by September 1 before the start of their fourth year in the program.

VIII. University Oral Exam

The oral examination is a defense of the dissertation work in progress. The student orally presents and defends the thesis work in progress at a stage when it is one-half to two-thirds complete. The oral examination committee tests the student on the theory and methodology underlying the research, the areas of application and portions of the major field to which the research is relevant, and the significance of the dissertation research. Students are expected to complete the oral exam by the end of winter quarter of the fourth year and are required to successfully complete the oral exam by September 1 before the start of their fifth year in the program.

IX. Dissertation

The doctoral dissertation is an original contribution to scholarship or scientific knowledge, to exemplify the highest standards of the discipline, and to be of lasting value to the intellectual community. This generally involves the completion of three articles, although the specific format of the dissertation should be discussed between the student and his/her advisors. A maximum of one article can be co-authored with a faculty member. A minimum of one article must be solo-authored.

Typical Timeline

Years one & two.

• Field Requirements
• Directed Reading & Research
• Advancement to Candidacy
• Formulation of Research Topic
• Annual Evaluation
• Continued Research

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Research Data Analyst 2

🔍 school of medicine, stanford, california, united states.

The Sergiu Pasca Lab and Kevin Kelley Lab, within the Department of Psychiatry & Behavioral Sciences at Stanford University, have an exciting and unique opportunity for a motivated computational/bioinformatic scientist interested in joining a highly collaborative environment at the forefront of human neuroscience and neurodevelopmental disease modeling. Our research couples advanced human brain cellular models with innovative computational genomics to address fundamental questions on the neurobiological mechanisms of psychiatric disease. The successful candidate will be embedded within an interdisciplinary team and will develop novel computational and statistical approaches for analyzing and interpreting large multi-modal sequencing and imaging based datasets from human-derived neural tissue. They will develop rigorous and reproducible analytic pipelines for processing and interpreting large datasets generated from single-cell RNA-seq, ATAC-seq, spatial transcriptomics, and electrophysiology data. We offer an exciting environment where the successful applicant can make significant contributions to the advancement of our understanding of neuropsychiatric disorders and grow into a leader within academia or industry.

Duties include:

• Develop lab-wide high-dimensional data initiatives from storage to preprocessing to interpretation.
• Develop reproducible processing pipelines for single cell RNA-seq, ATAC-seq, multi-omics, spatial transcriptomics, electrophysiology data or combinations of these technologies.
• Enable experimentalist colleagues (i.e. postdocs, graduate students, staff) with close collaboration, design, and interpretation of high-dimensional dataset collection and analysis.
• Presenting computational efforts at conferences and symposia. 
• Generation of peer-reviewed publications with colleagues.
• Staying up to date with computational/bioinformatics best practices and data analysis methods from recent publications, seminars, conferences, and training courses.

* - Other duties may also be assigned

DESIRED QUALIFICATIONS:

• PhD in computational or bioinformatics-related field; Alternatively, PhD in biology/neuroscience -related fields with strong computational experience.
• Strong background in bioinformatics, computational genomics, and statistics, including analysis of high-throughput sequencing data with relevant publication record.
• Proficiency with high performance computing, database management, and multiple programming languages (e.g. Python, R).
• Experience working within a UNIX/Linux environment
• Excellent communication and team skills and fluency in both spoken and written English

EDUCATION & EXPERIENCE (REQUIRED):

Bachelor's degree and three years of relevant experience or combination of education and relevant experience. Experience in a quantitative discipline such as economics, finance, statistics or engineering.

KNOWLEDGE, SKILLS AND ABILITIES (REQUIRED):

• Relevant subject matter knowledge.
• Substantial experience with high performance computing, database management, and multiple programming languages (e.g. Python, R) .
• Experience working within a UNIX/Linux environment.
• Excellent writing and analytical skills.
• Ability to prioritize workload.
• Substantial experience with MS Office and analytical programs.

PHYSICAL REQUIREMENTS*:

·          Sitting in place at computer for long periods of time with extensive keyboarding/dexterity.

·          Occasionally use a telephone.

·          Rarely writing by hand.

* - Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to perform the essential functions of his or her job.

WORKING CONDITIONS:

·          Some work may be performed in a laboratory or field setting.

·          This position is based on the Stanford main campus with the option of telecommuting/hybrid schedule subject to operational needs.

The expected pay range for this position is $86,000 to $126,000 per annum.   Stanford University provides pay ranges representing its good faith estimate of what the university reasonably expects to pay for a position.   The pay offered to a selected candidate will be determined based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, internal equity, geographic location and external market pay for comparable jobs.

• Schedule: Full-time
• Job Code: 4752
• Employee Status: Regular
• Requisition ID: 104274
• Work Arrangement : Hybrid Eligible

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From Law to Data Science: A Journey of Interdisciplinary Passion

Embarking on a journey from law to data science is not a path often traveled—and not for the weak of heart. It requires a unique blend of curiosity, determination, and a passion for understanding the world through different lenses. In this feature story, we delve into the inspiring journey of Janka Deli a legal scholar from Hungary and Stanford Data Science alumna, who ventured into data science, highlighting the motivations, challenges, and triumphs encountered along the way.

Beginnings in Law and Economics

 Janka’s journey started in Hungary, where she pursued a JD degree. Even before her legal studies, she had a profound interest in economics, recognizing it as a crucial framework alongside law in understanding how the world operates and how decisions are made. This interdisciplinary interest was sparked during her first year of law school when she was introduced to the economic analysis of law. 

Although it was a brief exposure, it left a lasting impact, igniting her fascination with the intersection of legal thinking and economics.

Discovering Empirical Legal Studies

Janka’s interest in the theoretical aspects of law and economics soon led her to the United States, where she participated in an exchange program focused on these subjects. While studying at a university in the Midwest, a serendipitous encounter with the Conference on Empirical Legal Studies broadened her horizons. Here, she realized the potential of empirical research to address real-world legal questions, moving beyond theoretical analysis to investigate the actual impact of laws through data-driven methods.

The Stanford Experience

Determined to gain the methodological skills needed for empirical research, she applied to several master's programs in the U.S. Initially, she faced the challenge of finding a program that allowed her the flexibility to take courses outside the traditional legal curriculum. It was during this search that she discovered Stanford's unique program at the Law School , focusing on interdisciplinary legal thinking and empirical research. Despite the odds, she was accepted, and this marked the beginning of her exciting journey into data science.

Building a Methodological Foundation

At Stanford, Janka’s focus was on building a strong foundation in empirical research and data science. She immersed herself in courses on statistics, econometrics, and programming, despite the steep learning curve. “The journey was challenging” admits Janka. “Courses like introductory R programming pushed me outside of my comfort zone. However, the rewarding nature of mastering these skills kept me motivated. Little did I know that this methodological foundation would position me well for the Data Science Scholars Program.”

Joining the Stanford Data Science Scholars Program

 One of the pivotal moments in Janka’s journey was being accepted into the Stanford Data Science Scholars Program . This opportunity provided her with professional credibility and a supportive community of like-minded scholars from diverse fields. 

“The program not only helped me establish my presence in the data science community but also offered a sense of belonging and shared experiences, essential for navigating the complexities of interdisciplinary research. And I made some dear friends,” shares Janka.

The Intersection of Law, Data Science, and Policy

The erosion of the rule of law in Hungary deeply influenced her specific research interests. She sought to understand the economic implications of the underlying legal reforms, a question that required robust empirical analysis. The tools and skills she acquired at Stanford enabled her to approach this research question systematically, highlighting the broader impact of legal institutions on economic conditions.

Advice for Aspiring Data Scientists with Legal Background

For those with a background in law aspiring to venture into data science, Janka’s advice is to focus on building a solid methodological foundation. Embrace the challenge of learning new skills and be patient with the steep learning curve. Balancing foundational knowledge with practical applications is key. Don't shy away from technical details, but also recognize the limits of how deep you need to go, especially if your primary focus is applied research rather than model development.

Balancing Work with Personal Interests

Outside of her academic pursuits, she finds joy in baking. The process of creating something tangible and sharing it with others provides a satisfying contrast to the often long and abstract nature of empirical legal research. Baking allows Janka to unwind, focus, and build community, reminding her of the importance of balance in life.

Long-Term Commitment

Janka’s journey from law to data science underscores the value of interdisciplinary thinking and the importance of empirical research in addressing complex real-world issues. It’s a testament to the power of curiosity, resilience, and the pursuit of knowledge across traditional boundaries. As she continues to navigate this path, she remains committed to using data science to explore and understand whether the economy can serve as a check on creeping authoritarianism.

• Theodore Eisenberg Poster Award, 2023 Conference on Empirical Legal Studies 
• Poster Award, 2023 Stanford Data Science Annual Conference
• Gerhard Casper Fellowship in the Rule of Law, Stanford Center on Democracy, Development, and the Rule of Law
• Gerald J. Lieberman Fellowship, Office of the Vice Provost for Graduate Education at Stanford University
• Data Science Scholarship, Stanford Data Science
• László Sólyom Scholarship (granted by the late László Sólyom, former President of Hungary and the Constitutional Court of Hungary)
• Gerald Gunther Prize, Stanford Law School
• Graduate Scholarship, Dr. Elemer and Eva Kiss Scholarship Fund
• Graduate Scholarship, Rosztoczy Foundation
• Graduate Scholarship, Hungary Initiatives Foundation[JD4] 
• International Fellowship, American Association of University Women
• Stanford Law School Scholarship, Stanford Program in International Legal Studies

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Economics (Applied Economics) - Doctor of Philosophy (PhD)

Hagey Hall Hub located on Waterloo's main campus

Get a strong foundation in economic theory and econometrics and conduct original research alongside world-renowned scho lars with the Ph D in A pplied Economics program .

You’ll receive training in the areas of public policy, labour economics, macro-economics, monetary economics, econometrics, natural resources, environmental and health economics.  

Through this rigorous academic program and applied research experience , you’ll develop your communication and analytical skills, preparing you for a career in academia or the private sector as an economist, data analyst, policy advisor, economic consultant and many others .    

Research areas and degree options

• Public Economic Policy

Program overview

Department/School : Economics Faculty : Faculty of Arts Admit term(s) : Fall (September - December) Delivery mode : On-campus Program type : Doctoral, Research Length of program : 48 months Registration option(s) : Full-time Study option(s) : Thesis

Application deadlines

• February 1 (for admission in September)

Key contacts

[email protected]   (for program inquiries)

Amanda Campbell  (for all other inquiries) 

519-888-4567, ext. 45099

Admission limitations

Due to funding restrictions, the Faculty of Arts is currently limiting the number of international students we can admit. Please contact the department's Associate Chair, Graduate Studies prior to applying to discuss your interest in this program.

Supervisors

• Review the finding a supervisor resources
• No supervisor is required to apply to this program

Admission requirements

• Master's degree in Economics.
• Minimum 80%, first class average in Master of Arts (MA) Economics degree.
• A one-page statement of Academic Interest.
• A GRE score is not required but is recommended for all international students applying to the program.

Degree requirements

• Review the degree requirements in the Graduate Studies Academic Calendar, including the courses that you can anticipate taking as part of completing the degree
• Check out Waterloo's institutional thesis repository - UWspace to see recent submissions from the department of Economics graduate students

Application materials

• The SIF contains questions specific to your program, typically about why you want to enrol and your experience in that field. Review the  application documents web page for more information about this requirement
• If a statement or letter is required by your program, review the  writing your personal statement resources  for helpful tips and tricks on completion

Writing Sample

Transcript(s)

• Three academic  references are required
• TOEFL 90 (writing 25, speaking 25), IELTS 7.0 (writing 6.5, speaking 6.5)
• The Department of Economics does not consider international applicants whose English language proficiency scores fall below the minimum requirements

Tuition and fees

• Visit the  graduate program tuition page  on the Finance website to determine the tuition and incidental fees per term for your program
• Review living costs and housing
• Review the funding graduate school resources for graduate students