COURSE INSTRUCTORS / FACULTY

  • Regina Barzilay is a Delta Electronics Professor in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. Her research interests are in natural language processing, applications of deep learning to chemistry and oncology.

    Regina Barzilay

    Department of Electrical Engineering and Computer Science
    MIT Professor
    Machine Learning

  • Esther Duflo is the Abdul Latif Jameel Professor of Poverty Alleviation and Development Economics in the Department of Economics at the Massachusetts Institute of Technology and a co-founder and co-director of the Abdul Latif Jameel Poverty Action Lab (J-PAL). In her research, she seeks to understand the economic lives of the poor, with the aim to help design and evaluate social policies. She has worked on health, education, financial inclusion, environment, and governance.

    Esther Duflo

    Deptartment of Economics
    MIT Professor, Nobel Prize Winner
    Data Analysis in Social Science

  • Sara Fisher Ellison is a Senior Lecturer in the MIT Economics Department. She has been a fellow at both the Institute for Advanced Study and the Hoover Institute. Her recent research has investigated a number of questions in industrial organization, with a focus on the pharmaceutical industry and ecommerce.

    Sara Fisher Ellison

    Deptartment of Economics
    MIT Professor
    Data Analysis in Social Science

  • Tommi S. Jaakkola received M.Sc. in theoretical physics from Helsinki University of Technology and Ph.D. from MIT in computational neuroscience. His research covers theory, algorithms, and applications of machine learning, from statistical inference and estimation to natural language processing, computational biology, as well as recently machine learning for chemistry.

    Tommi S. Jaakkola

    Department of Electrical Engineering and Computer Science
    MIT Professor
    Machine Learning

  • Dr. Patrick Jaillet holds a joint appointment in the Operation Research and Statistics Group at MIT Sloan. Dr. Jaillet's research interests include online optimization and learning; machine learning; and decision making under uncertainty.

    Patrick Jaillet

    Department of Electrical Engineering and Computer Science
    MIT Professor
    Probability

  • Philippe Rigollet is an associate professor in the Department of Mathematics at MIT. He works at the intersection of statistics, machine learning, and optimization, focusing primarily on the design and analysis of statistical methods for high-dimensional problems.

    Philippe Rigollet

    Deptartment of Mathematics
    MIT Professor
    Fundamentals of Statistics

  • John Tsitsiklis is a Professor with the Department of Electrical Engineering and Computer Science, and a member of the National Academy of Engineering. His research focuses on the analysis and control of stochastic systems, including applications in various domains, from computer networks to finance.

    John Tsitsiklis

    Department of Electrical Engineering and Computer Science
    MIT Professor
    Probability

  • Karene Chu received her Ph.D. in mathematics from the University of Toronto in 2012. Since then she has been a postdoctoral fellow first at the University of Toronto and the Fields Institute, and then at MIT, with research focus on knot theory and quantum invariants. In 2015, She became a digital learning lab fellow at MIT, and made significant contributions to the MITx Calculus and Differential equations Massive open and online course series. She then moved to the Institute of Data, Systems, and Society, and has been leading the effort in the development and the running of the MicroMasters Program in Statistics and Data Science.

    Karene Chu

    Assistant Director of Education of MicroMasters SDS, Digital Learning Fellow, and Research Scientist.

ADMINISTRATIVE TEAM

  • Devavrat Shah is MIT Faculty Director of MicroMasters Statistics and Data Science Program, Professor of AI & Decisions within Department of Electrical Engineering and Computer Science at Massachusetts Institute of Technology since 2005. He is a member of the Laboratory for Information and Decision Sciences (LIDS) and the Institute for Data, Systems, and Society (IDSS). He directs the Statistics and Data Science Center (SDSC). He is a visiting Adjunct Professor at the Tata Institute of Fundamental Research (TIFR) since March 2018.

    Devavrat Shah

    Department of Electrical Engineering and Computer Science. Institute for Data, Systems, and Society. MIT Faculty Director, MicroMasters, Statistics and Data Science

  • Karene Chu received her Ph.D. in mathematics from the University of Toronto in 2012. Since then she has been a postdoctoral fellow first at the University of Toronto and the Fields Institute, and then at MIT, with research focus on knot theory and quantum invariants. In 2015, She became a digital learning lab fellow at MIT, and made significant contributions to the MITx Calculus and Differential equations Massive open and online course series. She then moved to the Institute of Data, Systems, and Society, and has been leading the effort in the development and the running of the MicroMasters Program in Statistics and Data Science.

    Karene Chu

    Assistant Director of Education of MicroMasters SDS, Digital Learning Fellow and Research Scientist.

  • Susana Kevorkova is the Program Manager with MicroMaster Program in Statistics and Data Science at the MIT Institute for Data, Systems, and Society.

    Susana Kevorkova

    MicroMasters Statistics and Data Science, Program Manager

  • Jeremy Rossen is the Senior Program Assistant with MicroMasters Statistics and Data Science at and the Institute for Data, Systems, and Society (IDSS), MIT.

    Jeremy Rossen

    MicroMasters Statistics and Data Science, Senior Program Assistant

COURSE CONTRIBUTORS / GUEST LECTURERS

  • Dimitri Bertsekas is a Professor with the Department of Electrical Engineering and Computer Science, and a member of the National Academy of Engineering. His research focuses on optimization theory and algorithms, with an emphasis on stochastic systems and their applications in various domains, such as data networks, transportation, and power systems.

    Dimitri Bertsekas

    Department of Electrical Engineering and Computer Science
    MIT Professor
    Probability

  • Ravichandra is a third-year PhD student in CSAIL, MIT under the supervision of Prof Mohammad Alizadeh. His research is in the field machine learning, and most recenty in the intersection of reinforcement learning and neural networks. Ravichandra has made major and significant contributions to the course content.

    Ravichandra Addanki
  • Yuheng Bu is a Postdoctoral Associate at the MIT Institute of Data, Systems, and Society (IDSS). He obtained his Ph.D. degree in electrical and computer engineering from the University of Illinois at Urbana-Champaign in 2019. Prior to that, he received the B.S. (Hons.) degree in electrical engineering from Tsinghua University in 2014. His research interests include machine learning, information theory and statistical signal processing. More specifically, his previous research is to develop an information-theoretic understanding of machine learning algorithms and model compression.

    Yuheng Bu
  • Wangzhi Winston Dai is currently a Ph.D. student in Department of Electrical Engineering and Computer Science at MIT. He is primarily interested in developing computational tools involving signal processing and machine learning to help clinical decision making. Before coming to MIT, He earned his Bachelor’s from Peking University in 2017. Wangzhi will be the lead TA answering your questions on the forum and made major and significant contributions to 6.86x Machine Learning with Python: from Linear Models to Deep Learning.

    Wangzhi Winston Dai
  • Qing He received her PhD in the MIT Department of Electrical Engineering & Computer Science. Her research interests include inference, signal processing, and wireless communications -- all of which rely on the fundamental concepts taught in 6.041x/6.431x. Qing has taken several probability classes at MIT, and has been a teaching assistant for this course for two semesters. Jeremy Rossen is the Senior Program Assistant with MicroMasters Statistics and Data Science at and the Institute for Data, Systems and Society (IDSS), MIT.

    Qing He
  • Jan-Christian Huetter received his PhD in the Mathematics department at MIT. His research in Mathematical Statistics is about shape constrained estimation and causal discovery. He was a teaching assistant for High-Dimensional Statistics (18.657) in 2017. You will see him in many recitation videos in this course.

    Jan-Christian Huetter
  • Yan Jin is a PhD student in the Social and Engineering Systems program at the MIT Institute for Data, Systems and Society. Her research interests include robust distributed learning, corruption detection on networks, mechanism design, and graph signal processing. She received B.A. degrees in mathematics and sociology from Macalester College in 2016.

    Yan Jin
  • Younhun is a current Ph.D. student at Massachusetts Institute of Technology, studying Applied Mathematics. His focus is on combinatorics and statistics, with an emphasis on mathematical frameworks used as a means of studying real-world problems, such as Cancer Biology and Population Genetics. Youn has previously worked in industry as a programmer and received a degree in Computer Science from Brown University. Younhun made major and significant contribution to the content of 18.6501x Fundamentals of Statistics.

    Younhun Kim
  • Eren Kizildag is a graduate student in the Electrical Engineering and Computer Science department at MIT, carrying out research in the Laboratory for Information and Decision Systems (LIDS) and the Research Laboratory of Electronics (RLE). His research interests include probability, signal processing and optimization. Eren made significant contribution to the content of 6.431x Probability--the Science of Uncertainty and Data, and also contributed to the content of 18.6501x Fundamentals of Statistics. Eren has also been the teaching assistant for 6.431x Probability--the Science of Uncertainty and Data.

    Eren Can Kizildag
  • Dimitris is a second year PhD student at MIT IDSS, primarily interested financial mathematics and applications of machine learning to the financial world. He holds a Bachelors in Electrical Engineering & Computer Science from the National Technical University of Athens, a Master's in Computer Science from Carnegie Mellon University and a Master's in Machine Learning from Carnegie Mellon University. Dimitris has served as a TA for classes in machine learning, deep learning and probability theory.

    Dimitris Konomis
  • Guang-he is a third-year Ph.D. student working with Professor Tommi S. Jaakkola in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at Massachusetts Institute of Technology (MIT). He received his Master/Bachelor in Science from the Department of Computer Science and Information Engineering at the National Taiwan University (NTU) in June 2017/2015, and has been honored with the NTU Presidential Awards, the Microsoft-IEEE Young Fellowship among other scholarships. Guang-he has made major and significant contribution to the course content.

    Guanghe Lee
  • Jimmy Li received his PhD from MIT’s Department of Electrical Engineering and Computer Science. His research focused on applying the tools taught in this and related courses to problems in marketing. He took 6.041x/6.431x as an undergraduate and has also been a TA for the course three times.

    Jimmy Li
  • Liang Li is a graduate student in the Technology and Policy Program and also pursuing a dual degree in EECS at MIT. Before MIT, she worked as a technology consultant in Accenture, Singapore. She currently enjoys learning in multiple areas, including statistics, biology, computer science and many more. Liang contributed to the content of 18.6501x Fundamentals of Statistics and Data Analysis for Social Scientists.

    Liang Li
  • Tyler is an Instructor of Applied Mathematics at MIT. Previously, he obtained his PhD in Mathematics and MS in Statistics at the University of Minnesota in 2018. His current research interests span statistics, machine learning, computer vision, and nonconvex optimization. In the past, he has specifically focused on the problem of robust subspace recovery.Tyler has been an instructor for the residential course 18.650 Fundamentals of Statistics. You will see him in many recitation videos in this course.

    Tyler Maunu
  • Philip Martin received his PhD from the Department of Political Science at MIT and is now an assistant professor at George Mason University. His research focuses on the legacies of conflict and political violence. He has previously worked as a teaching assistant at MIT for 17.800 ("Quantitative Methods I: Regression") and 17.571 ("Engineering Democratic Development in Africa").

    Philip Martins
  • Nicholas graduated with a Master's in Engineering from CSAIL, MIT, in 2018. He studied style transfer in natural language under the supervision of Professor Regina Barzilay. He is currently a software engineer in the New York city area, and is a cofounder of Posh Development, a software application development and consulting company. Nicholas has made major and significant contribution to the course content.

    Nicholas Matthews
  • Tiffany is a master's candidate in computer science at MIT, where she also received her B.S. in electrical engineering and computer science. She is currently conducting research in machine learning in healthcare, ranging from clinical question answering, knowledge graph embedding, and reinforcement learning with wearable technologies. Tiffany has made major and significant contribution to the course content.

    So Yeon Tiffany Min
  • Yaroslav is a postdoctoral associate at the Institute for Data, Systems, and Society (IDSS) at MIT. He obtained a PhD in Economics and Statistics from MIT Economics in 2019. His current research interests are in the intersection of semiparametric efficiency, information geometry, optimal transport and counterfactual analysis of econometric models. Yaroslav will be making significant contribution to the content of 18.6501x Fundamentals of Statistics. He was also the main instructor who answered learners questions on the discussion forum.

    Yaroslav Muhkin
  • Uyiosa is a master’s student in the Technology and Policy program in the MIT Institute for Data, Systems, and Society. His research involves the quantification of uncertainty in the life cycle emissions and associated greenhouse gas abatement costs of renewable aviation fuels. Uyiosa has taken probability and machine learning classes at MIT and has great interest in using applied mathematics to help better our world. Uyiosa will answer your questions on the forum and will be making significant contributions to the course content.

    Uyiosa Mark Oriakhi
  • Hanzhang Qin is a Ph.D. candidate in Computational Science and Engineering from MIT, under supervision of Professor David Simchi-Levi. He has been conducting research connecting the field of management science and statistical learning, and has a special interest in studying the sample and computational complexity of various multistage stochastic systems with application to supply chain and revenue management, e.g., inventory control, dynamic pricing and online matching. Hanzhang had made significant contributions to the 6.86x Machine Learning with Python and the Capstone Exams.

    Hanzhang Qin
  • Victor Quach graduated from Ecole polytechnique in France, where he received his B.Sc and his M.Sc in Mathematics and Computer Science. He is currently a second-year PhD student at CSAIL, MIT, under Prof. Regina Barzilay’s supervision. His research interests lie between Natural Language Processing and Programming Languages.Victor has made major and significant contribution to the course content.

    Victor Quach
  • Soumya is a junior at MIT, studying Computer Science and Engineering. She's passionate about machine learning and teaching. She's done various machine learning internships in academia and in the industry, and is excited to share her knowledge. Soumya has been TA for the first run of 6.86x.

    Soumya P. Ram
  • Jagdish Ramakrishnan received his PhD from MIT’s Department of Electrical Engineering and Computer Science. His dissertation focused on optimizing the delivery of radiation therapy cancer treatments dynamically over time. His general research interests include systems modeling, optimization, and resource allocation. He was a teaching assistant for this course twice while at MIT.

    Jagdish Ramakrishnan
  • Sudarsan is a postdoctoral associate at the Institute for Data, Systems, and Society (IDSS) at MIT. He received his PhD in Electrical and Computer Engineering from UCLA Henry Samueli School of Engineering and Applied Science in 2018. Sudarsan's main research interests are algorithmic and combinatorial problems in coding and information theory, data science, and machine learning. At IDSS, Sudarsan is focused on problems in these areas and is also actively involved in course development for the IDSS Micromasters program in Data Science and Statistics. Sudarsan made significant contribution to the content of 18.6501x Fundamentals of Statistics, and was one of your instructors on the discussion forum.

    Sudarsan V S Ranganathan
  • Saeyoung Rho is a Master's candidate studying Technology Policy and Computer Science at MIT. She conducts her research at MIT Computer Science and Artificial Intelligence Lab, and her research focuses on leveraging statistical analytics to address real-world problems.

    Saeyoung Rho
  • Katie Szeto received her Bachelor and Master of Engineering degrees from MIT. Her Master’s thesis explored applications of probabilistic rank aggregation algorithms. Katie took 6.041x/6.431x with Professor Tsitsiklis when she was a sophomore at MIT. Later, as a graduate student, she was a teaching assistant for the class.

    Katie Szeto
  • Paxton Turner is currently working on his PhD in mathematics at MIT studying probability and statistics, advised by Philippe Rigollet. In particular, he is interested in discrete models as well as high-dimensional probability. He is from Baton Rouge, Louisiana and earned a B.S. in mathematics from Louisiana State University in 2015. Paxton made major and significant contribution to the content of 18.6501x Fundamentals of Statistics.

    Paxton Turner
  • Qiaomin Xie is a visiting assistant processor in ORIE at Cornell University. Prior to that, she spent two years as a postdoctoral researcher with LIDS at MIT, and was a research fellow at the Simons Institute during Fall 2016. Qiaomin received her Ph.D. degree in Electrical and Computing Engineering from University of Illinois Urbana Champaign in 2016, and her B.E. degree in Electronic Engineering from Tsinghua University. Her research interests lie in the fields of applied probability, stochastic networks and reinforcement learning. She is the recipient of UIUC CSL PhD Thesis Award (2017) and the best paper award from IFIP Performance Conference (2011). Qiaomin Xie made significant contribution to the content of 6.86x Machine Learning with Python.

    Qiaomin Xie
  • Kuang Xu received his PhD from MIT’s Department of Electrical Engineering and Computer Science. His research focused on the design and performance analysis of large-scale networks, such as data centers and the Internet, which involve a significant amount of uncertainties and randomness. Kuang took his first probability course in his junior year, and served as a teaching assistant for 6.041x/6.431x in 2012.

    Kuang Xu
  • Farrell Eldrian Wu is a second-year undergraduate at MIT majoring in Computer Science, Data Science, and Economics. He has a wide variety of academic interests, encompassing math, computer science, economics, and finance, tied together with a focus on modeling societal phenomena using mathematical techniques. He grew up in Philippines, where he won the country’s first gold medal at the International Mathematical Olympiad as well as a bronze medal at the International Olympiad in Informatics. Farrell made significant contribution to the content of 18.6501x Fundamentals of Statistics. Prior to joining the 18.6501x course team, he served as a TA for 6.042 (Mathematics for Computer Science) and is the founding lecturer for the January term class 6.S087 (Matrix Tricks for Statistics and Data Science), both at MIT.

    Farrell Eldrian Wu
  • Taylor Baum is a computational neuroscientist and controls engineer broadly interested in fundamentally understanding the brain and developing novel technologies. At the Massachusetts Institute of Technology she is a newly admitted Electrical Engineering and Computer Science PhD candidate and currently advised by Dr. Emery Brown and Dr. Munther Dahleh. In her current work she is developing brain state estimation algorithms for use in Closed-Loop Anesthetic Delivery (CLAD) systems and exploring mechanisms of control with the human brain through computational models. In addition to my research Taylor is a highly experienced educator and pursuing ventures in the medical and med-tech industries.

    Taylor Baum
  • Ari is a Fourth year PhD in Economics student specialized in Development Economics and Organizational Economics. His research is mainly focused on health organization in developing countries and specifically on how more efficient and simple communication can improve outcomes.

    Ari Bronsoler

Course TAs

  • Agnes is currently a PhD student in the Computational Science and Engineering program. Her research interests include machine learning, natural language processing and data-driven algorithms in operations research. She obtained her B.S in both Mathematics and Statistics from the University of Michigan - Ann Arbor and M.S in Computation for Design and Optimization from MIT.

    Agnes Hu

    Machine Learning

  • Yaroslav is a postdoctoral associate at the Institute for Data, Systems, and Society (IDSS) at MIT. He obtained a PhD in Economics and Statistics from MIT Economics in 2019. His current research interests are in the intersection of semiparametric efficiency, information geometry, optimal transport and counterfactual analysis of econometric models. Yaroslav will be making significant contribution to the content of 18.6501x Fundamentals of Statistics. He was also the main instructor who answered learners questions on the discussion forum.

    Yaroslav Muhkin

    Probability

  • Max Vigalys is a PhD student in Social and Engineering Systems in MIT’s Institute for Data, Systems, and Society. His research focuses on the economics of adaptation to climate change. Prior to MIT, he earned a BS in electrical engineering from Stanford University.

    Max Vigalys

    Probability

  • William is a 3rd year PhD student in EECS at MIT, advised by Ali Jadbabaie. His research is in the intersection of machine learning and optimization, and he seeks to understand how certain algorithms can perform better in practice than the existing theory suggests. On the industry side, William has previously worked in software engineering and quantitative trading. When he isn't working, he enjoys playing piano and cooking.

    William Wang

    Probability and Machine Learning