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.

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.

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.

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.

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.

Professor Jegelka is an X-Consortium Career Development Associate Professor at MIT EECS, and a member of CSAIL, IDSS, the Center for Statistics and Machine Learning at MIT. She is also affiliated with the ORC. Before that, she was a postdoc in the AMPlab and computer vision group at UC Berkeley, and a PhD student at the Max Planck Institutes in Tuebingen and at ETH Zurich. Her research is in algorithmic machine learning, and spans modeling, optimization algorithms, theory and applications. In particular, she has been working on exploiting mathematical structure for discrete and combinatorial machine learning problems, for robustness and for scaling machine learning algorithms. Her research is supported by a Sloan Research Fellowship, an NSF CAREER Award, a DARPA Young Faculty Award, an NSF BIGDATA, an Adobe Research award, an STL award and other awards by NSF and DARPA. Previously, she was also supported by a Google Research Award and an MIT RSC award.​

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.

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.

Caroline Uhler joined the MIT faculty in 2015 and am currently the Henry L. and Grace Doherty associate professor in EECS (Electrical Engineering & Computer Science) and IDSS (Institute for Data, Systems and Society). I am a member of LIDS (Laboratory for Information and Decision Systems), the Center for Statistics, Machine Learning at MIT, and the ORC (Operations Research Center). Caroline Uhler holds an MSc in mathematics, a BSc in biology, and an MEd in mathematics education from the University of Zurich, and a PhD in statistics from UC Berkeley. Before joining MIT, Caroline spent a semester in the "Big Data" program at the Simons Institute at UC Berkeley, postdoctoral positions at the IMA and at ETH Zurich, and 3 years as an assistant professor at IST Austria. Caroline Uhler is an elected member of the International Statistical Institute and a recipient of a Simons Investigator Award, a Sloan Research Fellowship, an NSF Career Award , a Sofja Kovalevskaja Award from the Humboldt Foundation, and a START Award from the Austrian Science Foundation. Caroline Uhler's research focuses on statistics, machine learning and computational biology, in particular on graphical models, causal inference, algebraic statistics and applications to genomics, for example on linking the spatial organization of the DNA with gene regulation. Thanks to NSF, ONR, DARPA, IBM, the Sloan Foundation and the Simons Foundation for supporting my research!

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.

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.

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.

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

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

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.

Youssef Medhat Aboutaleb is a PhD candidate at MIT working on developing new methodologies for augmenting economic models with machine learnable structures. The resulting models combine the best available theory with big data enabling superior prediction and generalization capabilities. He earned dual masters in EECS and transportation science in 2019 from MIT and bachelors in Operations Research in 2017 at the Korea Advanced Institute of Science and Technology. Youssef enjoys teaching and served as a teaching assistant for various courses at MIT. On the industry side, Youssef has done various internship in machine learning. Outside of work, He enjoys fine dining and travel.

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.

Mohamed AlHajri is a Graduate Research Assistant pursuing his Ph.D. in Electrical Engineering and Computer Science at MIT Research Laboratory of Electronics (RLE), specifically at the Claude E. Shannon Communication and Network Group. His research interests come at the intersection of machine learning, information theory, and wireless communications, where he focuses at the analysis and design of optimal low-rank message passing algorithms. AlHajri holds a B.Sc. degree in Electrical Engineering from Khalifa University, UAE in 2015 and an S.M. degree in Electrical Engineering and Computer Science from MIT in 2018. He is recipient of several awards including IEEE EESC’2014 Contest First Place Award, IEEE ICCSPA'2015 Conference Best Student Poster Award, and the 2020 IEEE UAE Section Award of the Year.

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.

Ari i Bronsoler 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.

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.

Marie grew up in Burgundy, France and studied engineering in Paris. She moved to the US to study Computational and Mathematical Engineering at Stanford University (’16). She is passionate about statistics for education and healthcare. After graduation, she joined Microsoft as a data scientist focusing on education technology. There, she built models to better understand online collaboration, studied the impact of technology usage at school and organized STEM workshops for high school girls. She is currently a second-year graduate student in the interdisciplinary PhD program in Statistics at MIT Institute for Data, Systems and Society. Her work on Electronic Health Records combines mathematical modeling, data analysis and policy.

Gabriel Collin is a postdoctoral associate at the Institute for Data, Systems and Society (IDSS) and the Department of Physics at MIT. Gabriel received a PhD in physics from MIT, and a PhB in physics from the Australian National University. Gabriel’s research interests lie in the development and application of statistical and computational methods to problems in physics and astronomy.

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.

Andreas Haupt is a doctoral student in the Social and Engineering Systems Program, researching the Data Economy, tools and systems for data pricing, and strategic human-machine interaction. He holds Master's degrees in Economics and Mathematics, and Bachelor's degrees in Mathematics and Computer Science.

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.

Agnes Hu 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.

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.

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.

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.

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.

Nicholas Klugman is a PhD student in Electrical Engineering and Computer Science at MIT. He received his Master’s of Engineering from MIT in 2020 and B.S in both Electrical Engineering and Mathematics in 2019, also at MIT. His research interests include modeling and design of physical systems from electromagnetics to robotics.

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.

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.

Jeffery Li is a fourth-year undergraduate at MIT currently majoring in both Mathematics and Computer Science. His main academic interests include combinatorics, theoretical computer science, and probability, with slight emphasis on optimization. Jeffery helped as a TA for 18.6501x during the summer of 2021 and helped with the reorganization of content for 18.6501x.

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.

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.

Bensu Manav is a graduate student and fellow at the MIT Department of Civil and Environmental Engineering and research assistant at the MIT Concrete Sustainability Hub. Her research is based on hurricane resilience, with a focus on understanding community characteristics that go into better informed building assessments. Bensu earned her Bachelor’s in Civil Engineering from Bogazici University in Istanbul, Turkey. While studying in Istanbul, she completed internships at design and construction firms and assistantships at Bogazici University and the Kandilli Observatory and Earthquake Research Institute.

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.

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").

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Arnab Sarker is a PhD candidate in Social and Engineering Systems and Statistics at MIT, advised by Prof. Ali Jadbabaie. Prior to MIT, I graduated from the University of Pennsylvania with a B.S.E. in Networked and Social Systems Engineering and an M.S.E. in Data Science. My research interests are broadly in social networks and statistics. Currently, I am focused on understanding the impact of group interactions in complex networks.

Tomoya Sasaki is a PhD candidate interested in applied statistics and international political economy. His research interests focus on developing statistical methods for social science research, especially on causal inference and machine learning.

Jean-Baptiste Selby is a PhD student in Operations Research at MIT. He completed his undergraduate studies in Physics (University Paris VI) and Political Science (Science Po Paris), graduated from Ecole Polytechnique Paris before joining MIT as a Masters student.

Yi (Alicia) Sun is a Ph.D student working in Data to AI Lab at MIT LIDS. Her research interests are designing machine learning algorithms that are robust and reliable as well as aligned with societal values. Before coming to MIT Yi had completed her undergraduate studies in Computer Science at Washington University in St. Louis.

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.

Samuel Tenka is a PhD candidate in EECS at MIT, advised by Josh Tenenbaum. Driving my research is the question of induction: when and why can we extract global patterns from scanty data? I'm especially interested information theoretic generalization bounds in machine learning and graphical taylor series approximations thereof. When I'm working, I enjoy music (if no neighbors are near, attempting to throat sing; otherwise, composing microtonal counterpoint and listening to bach and jacob collier).

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.

Michelle Vaccaro joins the MIT Institute for Data, Systems, and Society (IDSS) as a PhD student in September 2021. Her research focuses on human-AI decision-systems and, in particular, addresses questions such as: How can we model these hybrid decision-making processes? How can algorithms and interfaces leverage people’s cognitive strengths and safeguard against people’s cognitive weaknesses? And how do they effect individual and group decisions and experiences? Prior to joining MIT, Michelle worked at Goldman Sachs in foreign exchange strategy and structuring. She earned her Bachelor’s degree in 2019 from Harvard College, where she graduated Summa Cum Laude with highest departmental honors in Computer Science.

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.

William Wang is a PhD candidate 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.

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.

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.

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.

Lei Xu is a fourth-year Ph.D. student in EECS at MIT. He received his Master’s degree from MIT in 2020 and bachelor’s degree from Tsinghua University in 2017. He conducts his research in Data to AI Lab at LIDS advised by Dr. Kalyan Veeramachaneni. His research interests lie in deep learning and natural language processing.

Shiqi Yang is a graduate student in the Department of Electrical Engineering and Computer Science at MIT. His research focuses on Bayesian inference on healthcare data.

Silun Zhang joins the MicroMasters SDS program in 2021, and is involved with teaching 6.419x. He is also a Postdoctoral Associate with IDSS, MIT, working on research projects in large-scale network control and computation. Silun holds a Ph.D. degree in Applied Mathematics specialized in Optimization and Systems Theory. During his Ph.D. period, he served as a TA for three courses and gained extensive experience in teaching theoretical materials in a highly interactive and heuristic manner

Difei Zhang is a graduate student at MIT EECS Department. His previous research was on AI hardware and neuromorphic computing; now he is interested in applied machine learning. Zhang holds a B.S. degree in Physics from Nanjing University. ​

Zijie Zhao is a Ph.D. student at MIT EECS. Previously Zhao was at Harvard MSc Biostatistics. Zijie's current research interests focus on applied machine learning.

I'm a first year Ph.D. candidate in the Institute for Data, Systems, and Society (IDSS) of MIT. I am fortunate to be advised by Prof. David Simchi-Levi. Before joining MIT, I was advised by Prof. Zaiwen Wen, Prof. Yuejie Chi and Prof. Jing Dong. I received my bachelor's degree from School of Mathematical Sciences, Peking University in July 2023.

Guangchun (Grant) Ruan is currently a postdoc with the Laboratory for Information & Decision Systems (LIDS) at MIT. Before joining MIT, he received the Ph.D. degree in electrical engineering from Tsinghua University in 2021, and worked as a postdoc with the University of Hong Kong in 2022. He visited Texas A&M University in 2020 and the University of Washington in 2019. His research interests include electricity market, energy resilience, demand response, data science and machine learning applications.

I am a Junior at MIT (6.2 and 15.1) and like building stuff. The two biggest projects I have worked on is a device I made to remediate indoor air pollution for young infants and expecting mothers which lead to a paper I co-authored, and Invictus BCI , a company I co-founded that builds noninvasive neuroprosthetic control infaces for amputees to control their prosthetics just like their real hands .