IDSS students will address societal challenges by combining the fundamentals of statistics, data science, information and decision systems, as well as a rigorous study of social sciences with application domain areas.
Doctoral Program in Social and Engineering Systems
For the next generation of researchers and practitioners addressing complex systems of societal importance, IDSS offers its signature program, the Doctoral Program in Social and Engineering Systems (SES). Students receive a thorough preparation in information sciences and computation, statistics, systems, and decision sciences; they focus on a social science; and finally gain experience addressing a concrete societal challenge. Graduates of the program will advance the state of knowledge in theory or in practice, and will take their skills with them to transformative careers in their chosen field.
Technology and Policy Program
IDSS hosts the Technology and Policy Program (TPP), which has offered the Master of Science in Technology and Policy at MIT since 1976. TPP’s ongoing mission is to develop leaders who can create, refine, and implement responsible policies that are informed not only by an understanding of technology and its instruments, but also by their broad social contexts. Combining a core in science and engineering with studies in applied social sciences, TPP’s curriculum imparts strength in both a technical field and in the policy process. Moreover, each TPP student is required to complete a research thesis, supplying a significant research experience that equips graduates to be effective leaders in both the public and private sectors, or prepares them for further development of their research skills in a doctoral program.
Statistics and Data Science Programs
The need to analyze data in order to make informed decisions is fundamental to our society. Statistics is the science of making inferences and decisions from data under uncertainty. It is an essential tool for almost every quantitative field. As the home of MIT’s emerging statistics community, IDSS offers academic programs in statistics to MIT’s undergraduate and graduate students, and an online MicroMasters to learners around the world.
Interdisciplinary Doctoral Program in Statistics
The Interdisciplinary PhD in Statistics (IDPS) is designed for students currently enrolled in a participating MIT doctoral program who wish to develop their understanding of 21st century statistics, using concepts of computation and data analysis as well as elements of classical statistics and probability within their chosen field of study.
Undergraduate Minor in Statistics & Data Science
Through six subjects, MIT’s new Minor in Statistics and Data Science will provide students with a working knowledge base in statistics, probability, and computation, and develop their ability to perform data analysis. This program begins September 2016.
MicroMasters program in Statistics and Data Science
The online MicroMasters program in Statistics and Data Science is comprised of four online courses and a virtually proctored exam that will provide you with the foundational knowledge essential to understanding the methods and tools used in data science, and hands-on training in data analysis and machine learning. You will dive into the fundamentals of probability and statistics, as well as learn, implement, and experiment with data analysis techniques and machine learning algorithms. This program will prepare you to become an informed and effective practitioner of data science who adds value to an organization and will also accelerate your path towards an MIT PhD or a Master’s at other universities.
The mission of IDSS is to advance education and research in state-of-the-art analytical methods in information and decision systems, statistics and data science, and the social sciences, and to apply these methods to address complex societal challenges in a diverse set of areas such as finance, energy systems, urbanization, social networks, and health.
Technology advances in areas such as smart sensors, big data, communications, computing, and social networking are rapidly scaling the size and complexity of interconnected systems and networks, and at the same time are generating masses of data that can lead to new insights and understanding. Research at IDSS aims to understand and analyze data from across these systems, which present unique and substantial challenges due to scale, complexity, and the difficulties of extracting clear, actionable insights.
Our ability to understand data and develop models across complex, interconnected systems is at the core of our ability to uncover new insights and solutions.
IDSS offers academic programs, some via the Statistics and Data Science Center (SDSC):
IDSS has also developed online education programs including:
IDSS research entities include:
As part of the Schwarzman College of Computing, IDSS spans all five schools at MIT, embracing the collision and synthesis of ideas and methods from analytical disciplines including statistics, data science, information theory and inference, systems and control theory, optimization, economics, human and social behavior, and network science. These disciplines are relevant both for understanding complex systems, and for presenting design principles and architectures that allow for the systems’ quantification and management. IDSS seeks to integrate these areas—fostering new collaborations, introducing new paradigms and abstractions, and utilizing the power of data to address societal challenges.
IDSS greatly values diversity in and inclusion of our students, faculty, and staff. The range of cultures, backgrounds, perspectives, and experiences of the individuals within IDSS all contribute to our ability to live out our mission of tackling major societal problems using innovative, holistic, data-driven approaches. Likewise, as a unit focused on improving society, we care deeply about the overall well-being of our students—including their mental and physical health.
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PhD Program in Social & Engineering Systems
The Doctoral Program in Social and Engineering Systems (SES) is a unique research program focused on addressing concrete and societally significant problems by combining the analytical tools and methods of statistics and information sciences with engineering and social science tools and methods.
SES students study problems that correspond to significant societal challenges, with an emphasis on areas such as social networks, autonomous systems, energy systems, financial networks, and urban systems. This includes analytical research that can be used to inform policy making. Graduates will go on to roles in academic departments in various fields (engineering, management, operations research, and others), serve in the public sector (from research labs to regulatory agencies), as well as pursue careers in the private sector (from industry to consulting).
Interested in applying? Speak to an SES grad student or IDSS postdoc as part of the SES Graduate Application Assistance Program (SES-GAAP). This is offered to students from historically underrepresented groups in higher education. Participation is voluntary. It will not be disclosed to the SES Admission Committee and therefore will not impact admissions decisions. Sign up for SES-GAAP now! Registration for SES-GAAP closes November 15.
Applications are due December 15. The application opens around September 15.
The first part of a student’s program consists of advanced, rigorous, and challenging classes (more details below).
However, classes are just the first part of the doctoral program. After passing both the written and oral portion of the qualifying exam, the balance of a student’s academic activity will shift from classes to research. This immersion in research is the centerpiece of the SES program.
To get a sense of the style and variety of research that is carried out within IDSS please visit the web pages that describe some of IDSS’s domains of expertise as well as the web pages of individual IDSS faculty, senior research staff members, and students.
The Character of the Program
- It is driven by problems of societal interest. The focus of the program is the study of problems that correspond to significant societal challenges, with emphasis on areas such as sociotechnical systems, autonomous systems, energy systems, finance, social networks, and urban systems. This includes analytical research that can be used to inform policy making. An example of work that falls under this program would be studying systemic risk in the banking system and its impact on the overall financial system. In contrast, profit-maximizing portfolio management does not.
- It involves quantitative methods. Societal problems or policy questions can be addressed from many different angles. However, this program focuses on problems that can be addressed through tools of computing and information sciences, including mathematical modeling and analysis, data science and statistics, and other quantitative methods.
- It relies on real-world data. Research is expected to analyze data from the application domain of interest, and thus training in statistics is part of the program.
- It engages societal aspects of the problem. The research is expected to examine the societal aspects of a problem (e.g., regulations, institutions, human behavior, or economic aspects), using theories and tools from the social sciences.
The class requirements for the doctoral program follow. In some cases classes will be selected from pre-approved class lists and in other cases classes will be subject to approval by the SES-GPC.
Take 3 of the 4 following classes. With academic advisor approval, substitutions may be possible for 6.436.
- 6.436 Fundamentals of Probability
- 18.6501 Fundamentals of Statistics or
18.655 Mathematical Statistics or
IDS.160 Mathematical Statistics (for students with previous background in statistics)or
IDS.131 Statistics, Computation and Applications
- 14.121 & 14.122 Microeconomic Theory I & II or
14.320 Applied Econometrics or
14.381 New Econometric Methods or
14.386 Inference on Causal and Structural Parameters Using ML or
17.802 Quantitative Research Methods II: Causal Inference
- 21A.809 Designing Empirical Research in the Social Sciences or
21A.819 Ethnographic Research Methods or
17.850 Political Science Scope and Methods or
SOCIOL 2205 Sociological Research Design
Information, Systems, and Decision Science
5 classes. These will be rigorous classes in the areas of probabilistic modeling, statistics, optimization, and systems/control theory. Classes used to satisfy the core can be counted toward this requirement. However, the remaining classes should be at a more-advanced level. One subject must involve the statistical processing of data. One subject must have substantial mathematical content (as defined by the IDSS-GPC). Two classes must belong to a sequence that provides increasing depth on a particular topic.
4 classes. A student proposes a coherent and rigorous program-of-study in the social sciences that provides the background necessary for the student’s research. Classes used to satisfy the core can be counted toward this requirement. However, the remaining courses should be at a more-advanced level. Three classes must form a coherent collection that builds depth in a particular social science focus area.
2 classes. A student takes a total of two classes in the application domain of their research. One class may also be counted toward the social science requirement. Another class may be satisfied by an internship or independent study in which the student is graded on their performance of hands-on work in a particular domain.
1 class. A student serves as a teaching assistant for one subject and receives credit for 12 units of IDS.960 Teaching in Data, Systems, and Society.
Written Qualifying Exams
A student passes the written qualifying exams through strong performance in three core subjects from different areas. This is normally accomplished by the end of their third semester in the program.
Oral Qualifying Exam
Between the student’s fourth and sixth semester in the program, and after the student passes the written qualifying exams, they take the oral qualifying exam. The oral qualifying exam includes a research presentation by the student. To pass the oral qualifying exam a student must demonstrate the ability to undertake doctoral-level research and to handle questions about that research, including extensions to related problems.
They might be answered in the Frequently Asked Admissions Questions or in the SES Policies & Procedures. If not, get in touch!
Interdisciplinary PhD in Social & Engineering Systems and Statistics
Systems engineering mit and social
As of Fall 2021 I am a third year SES PhD Student at Massachusetts Institute of Technology focusing on computational social science. I’m broadly interested in using large scale data to support human rights and violence prevention efforts, as well as Internet accessibility and freedom of expression online.
Before starting at MIT I worked in Cloudflare’s DC Office as a Policy Analyst where I focused on corporate social responsibility, namely organizing Project Galileo and the Athenian Project. I graduated from Harvard in 2017 with a degree in Mechanical Engineering and a minor in Government.
In college, I was a letter winner on the Harvard-Radcliffe Lightweight Rowing Team and in my spare time I’ve coached high school rowing for Bethesda Chevy Chase High School and group fitness at [solidcore].
Social Media and Conflict in Syria
I am working on an ongoing cross-disciplinary project at MIT with Professor Fotini Christia, Political Science, and Assistant Professor Kiran Garimella, Rutgers Library and Information Science. Our work focuses on analyzing data from social media sources to understand the conflict in Syria including narratives around conflict and refugee return. The work has been presented at HiCN 2020, PaCSS 2021, MPSA 2021, and APSA 2021.
Perovskite Solar Cells with Graphene Electrodes
This project was supported by Professor Jing Kong in the Nanomaterials and Electronics Group at MIT. My work focused on fabricating perovskite-based solar cells through two-step deposition processes to achieve the best possible efficiency and reliability. The experience was made possible through the MIT Summer Research Program as well as the Center for Energy Efficient Electronics Science (E3S) REU Program.
Team Product Design
I was a member of an eight person Harvard-Hong Kong University of Science and Technology team which designed and implemented a drone delivery system featuring automated flight with rudimentary obstacle avoidance and robust tracking capabilities on a mobile application. Our team was mentored by Professor Evelyn Hu (Harvard), Professor Ling Shi (HKUST) and Professor Kei May Lau (HKUST) among others. This program was supported by Xiang Dong Wang and Nancy Wang, Harvard SEAS, HKUST and the Harvard President’s Innovation Fund for International Experiences. Website.
Final Presentation (PDF)
Work and Teaching
Policy Analyst at Cloudflare, Inc
At Cloudflare, I was a member of a four person global policy team. Along with researching current policy initiatives and speaking with lawmakers I organized, automated and ran day to day operations for their corporate social responsibility projects. More on my experience here.
Course Assisting at Harvard University
While at Harvard I taught, graded for and tutored a variety of classes including Physics 15a (Introductory Mechanics), Math 1b (Calculus, Series and Differential Equations) and Math 21a (Multivariable Calculus).
I’m a Resident Tutor in Mather House at Harvard, where I advise sophomores, assist with IMs and otherwise work with the Faculty Deans and other tutors to organize house initiatives. I’m very grateful to be part of such a wonderful and supportive community as I navigate my graduate experience.
I was the external affairs director for the MIT Policy Hackathon Fall 2020. Article on the event.
Senior reflection on my college rowing experience.
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I have the honor to speak with Mrs. Catherine. The newly-made Mistress shuddered and froze with surprise, guessing the reason for the call and for some reason anticipating the dire consequences of her antics. But there was nowhere to retreat. All that remained was to give oneself a proud and important look and condescendingly throw in response: Yes.