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An Invitation to Applied Category Theory
Brendan Fong, Postdoc, Department of Mathematics
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Jan/14 | Mon | 02:00PM-03:00PM | 4-237 |
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Jan/15 | Tue | 02:00PM-03:00PM | 4-237 |
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Jan/16 | Wed | 02:00PM-03:00PM | 4-237 |
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Jan/17 | Thu | 02:00PM-03:00PM | 4-237 |
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Jan/18 | Fri | 02:00PM-03:00PM | 4-237 |
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Jan/22 | Tue | 02:00PM-03:00PM | 4-237 |
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Jan/23 | Wed | 02:00PM-03:00PM | 4-237 |
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Jan/24 | Thu | 02:00PM-03:00PM | 4-237 |
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Jan/25 | Fri | 02:00PM-03:00PM | 4-237 |
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Jan/28 | Mon | 02:00PM-03:00PM | 4-237 |
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Jan/29 | Tue | 02:00PM-03:00PM | 4-237 |
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Jan/30 | Wed | 02:00PM-03:00PM | 4-237 |
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Jan/31 | Thu | 02:00PM-03:00PM | 4-237 |
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Feb/01 | Fri | 02:00PM-03:00PM | 4-237 |
Enrollment: Unlimited: No advance sign-up
Attendance: Participants welcome at individual sessions
Prereq: None
Category theory is a relatively new branch of mathematics that has transformed much of pure math research. The technical advance is that category theory provides a framework in which to organize formal systems and by which to translate between them, allowing one to transfer knowledge from one field to another. But this same organizational framework also has many compelling examples outside of pure math.
In this course we provide an introductory tour of category theory, with a viewpoint toward modelling real-world phenomena. The course will begin with the notion of poset, and introduce central categorical ideas such as functor, natural transformation, (co)limit, adjunction, the adjoint functor theorem, and the Yoneda lemma in that context. We'll then move to enriched categories, profunctors, monoidal categories, operads, and toposes. Applications to resource theory, databases, codesign, signal flow graphs, and dynamical systems will help ground these notions, providing motivation and a touchstone for intuition. The aim of the course is to provide an overview of the breadth of research in applied category, so as to invite further study.
The course text will be An Invitation to Applied Category Theory; a preprint is freely available here. We will spend two lectures on each chapter.
MIT students may also take this course for credit as 18.S097. Further information available here.
Contact: Brendan Fong, 2-180, bfo@mit.edu
Art, Architecture and Models of Hyperbolic Energy
Erik Demaine
Enrollment: Unlimited: No advance sign-up
Attendance: Participants welcome at individual sessions
The Contemporary Geometric Beadwork (CGB) project, led by Kate McKinnon, is a global team of solvers involving hundreds of thousands of mathematical beaders around the world.
The project has published two books featuring revolutionary approaches to traditional constructions, and are about to publish a new volume demonstrating hyperbolic energy models and new approaches to growing engineering linkages.
Ten members of the GCB team will be at MIT for the entire month of January to collaborate and teach students and faculty how to create their forms and to help related their discvoeries to tother ongoing studies of topology, origami, mathematics, spiderwebs, knots, architecture and physics of energetic forms.
This will be the team's second appreance at MIT IAP.
There is no enrollment or material fee required to participate and students may drop in to any session.
Sponsor(s): Electrical Engineering and Computer Science
Contact: Kate McKinnon, 617-852-7682, kate@katemckinnon.com
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Jan/07 | Mon | 10:00AM-05:00PM | 34-101 |
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Jan/15 | Tue | 10:00AM-05:00PM | 26-322 |
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Jan/16 | Wed | 10:00AM-05:00PM | 26-322 |
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Jan/17 | Thu | 10:00AM-05:00PM | 26-322 |
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Jan/18 | Fri | 10:00AM-05:00PM | 26-322 |
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Jan/22 | Tue | 10:00AM-05:00PM | 26-322 |
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Jan/23 | Wed | 10:00AM-05:00PM | 26-322 |
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Jan/24 | Thu | 10:00AM-05:00PM | 26-322 |
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Jan/25 | Fri | 10:00AM-05:00PM | 26-322 |
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Jan/29 | Tue | 10:00AM-05:00PM | 26-168 |
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Jan/30 | Wed | 10:00AM-05:00PM | 26-322 |
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Jan/31 | Thu | 10:00AM-05:00PM | 26-322 |
Date: 01/08/19 - 01/25/19 (Tuesday Friday only)
Time: 10:00a - 5:00p
Classroom: 26-322
CELESTIAL NAVIGATION LITE: THEORY AND PRACTICE
Max Mulhern, MIT Bluewater Captain
Enrollment: Limited: First come, first served (no advance sign-up)
Attendance: Participants must attend all sessions
Prereq: N/A
A SHORT INTRODUCTION TO CELESTIAL NAVIGATION INCLUDING THE PRACTICE OF THE "NOON SIGHT" TO DETERMINE LONGITUDE AND LATITUDE.
Contact: Max Mulhern, maxmulhern@hotmail.com
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Jan/08 | Tue | 11:30AM-01:30PM | 4-158 |
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Jan/10 | Thu | 11:30AM-01:30PM | 4-158 |
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Jan/15 | Tue | 11:30AM-01:30PM | 4-158 |
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Jan/17 | Thu | 11:30AM-01:30PM | 4-158 |
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Jan/22 | Tue | 11:30AM-01:30PM | 4-158 |
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Jan/24 | Thu | 11:30AM-01:30PM | 4-158 |
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Jan/29 | Tue | 11:30AM-01:30PM | 4-158 |
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Jan/31 | Thu | 11:30AM-01:30PM | 4-158 |
Some materials need to be purchased by the student i.e. 1. a Practice Chart (16$) 2. Plotting Tools (25$) 3. A Nautical Almanac (30$).
The class is limited to 10 people.
Please send an email to Max and confirm your registration.
Max Mulhern - MIT Bluewater Captain
Computational Approaches for Political Redistricting
Daryl DeFord, Postdoctoral Associate
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Jan/08 | Tue | 08:00AM-09:00AM | 34-301 |
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Jan/10 | Thu | 08:00AM-09:00AM | 34-301 |
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Jan/22 | Tue | 08:00AM-09:00AM | 34-301 |
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Jan/29 | Tue | 08:00AM-09:00AM | 34-301 |
Enrollment: Unlimited: Advance sign-up required
Sign-up by 01/05
Attendance: Repeating event, participants welcome at any session
Prereq: Basic Python experience, Linear Algebra
In the last 3 years, computational methods have become increasingly important for analyzing legislative districting plans. The MIT based MGGG group has developed the first open source software for Markov chain analysis of districting plans (github.com/mggg/GerryChain) and is preparing to provide data (github.com/mggg-states) and software tools (github.com/gerrymandr) to the public in advance of the redistricting based on the upcoming 2020 census.
Attendees will get experience with geospatial software and data as well as cutting-edge methods for computational redistricting. Each student will select a state to take responsibility for, specifically collecting the relevant data and generating an ensemble of comparison plans. Students will also have the opportunity to develop their own methods for generating districting plans and engage with related mathematical problems. Successful approaches will have the opportunity to be integrated with the MGGG codebase.
Please email <ddeford@mit.edu> to register.
Sponsor(s): Electrical Engineering and Computer Science, Computer Science and Artificial Intelligence Lab
Contact: Daryl DeFord, 32-D475A, ddeford@mit.edu
Computational Thinking for Modeling and Simulation
Dr. Ali Talebinejad, Lecturer of MIT Mechanical Engineering Department, Prof. Daniel Frey, Professor of MIT Mechanical Engineering Department
Enrollment: Limited: Advance sign-up required
Sign-up by 01/11
Limited to 25 participants
Attendance: Participants must attend all sessions
Prereq: College Mathematics
Computational thinking is becoming widely recognized as a skill necessary for every educated person in a technologically advanced society and that is why MIT is trying to make it a General Institute Requirement course. You can get a leg up in courses such as 2.086, no to mention getting 3 credits by registering under 2.S989.
Our fully-online material and software will help students to develop the thought processes involved in formulating a problem in such a way that a computer can effectively carry out that solution. This course focuses on a subset of computational thinking for modeling of the physical world and predicting their behavior – something that engineers and scientists frequently need to do. We cover many topics normally viewed as within the domain of mathematics such as algebra and calculus, but the solution procedures are algorithmic rather than symbolic.
The major themes are:
Representation. How to encode information about the world in a computer? Decomposition. How to break a large and diverse problem into many simpler parts? Discretization. How to break up space and time into a large number of relatively small pieces? Verification. How to build confidence in the results of a model?
By completing this course, you will be able to select and implement numerical methods for interpolation, integration, differentiation, solving linear and nonlinear system of equations, and finally using random variables for solving Engineering and Science problems.
Contact: Dr. Ali Talebinejad, TAALEBI@MIT.EDU
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Jan/07 | Mon | 01:00PM-03:00PM | N51-310, Bring your laptop! |
Dr. Ali Talebinejad - Lecturer of MIT Mechanical Engineering Department, Prof. Daniel Frey - Professor of MIT Mechanical Engineering Department
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Jan/11 | Fri | 01:00PM-03:00PM | N51-310, Bring your laptop! |
Prof. Daniel Frey - Professor of MIT Mechanical Engineering Department, Dr. Ali Talebinejad - Lecturer of MIT Mechanical Engineering Department
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Jan/14 | Mon | 01:00PM-03:00PM | N51-310, Bring your laptop! |
Dr. Ali Talebinejad - Lecturer of MIT Mechanical Engineering Department, Prof. Daniel Frey - Professor of MIT Mechanical Engineering Department
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Jan/18 | Fri | 01:00PM-03:00PM | N51-310, Bring your laptop! |
Prof. Daniel Frey - Professor of MIT Mechanical Engineering Department, Dr. Ali Talebinejad - Lecturer of MIT Mechanical Engineering Department
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Jan/25 | Fri | 01:00PM-03:00PM | N51-310, Bring your laptop! |
Dr. Ali Talebinejad - Lecturer of MIT Mechanical Engineering Department, Prof. Daniel Frey - Professor of MIT Mechanical Engineering Department
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Jan/28 | Mon | 01:00PM-03:00PM | N51-310, Bring your laptop! |
Dr. Ali Talebinejad - Lecturer of MIT Mechanical Engineering Department, Prof. Daniel Frey - Professor of MIT Mechanical Engineering Department
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Feb/01 | Fri | 01:00PM-03:00PM | N51-310, Bring your laptop! |
Dr. Ali Talebinejad - Lecturer of MIT Mechanical Engineering Department, Prof. Daniel Frey - Professor of MIT Mechanical Engineering Department
Computing in Optimization and Statistics
Phil Chodrow, Brad Sturt, Arthur Delarue, Dimitris Bertsimas, Professor
Enrollment: Unlimited: Advance sign-up required
Sign-up by 01/07
Attendance: Participants must attend all sessions
Prereq: Instructor permission. Familiarity with programming language
The "big data revolution" has placed added emphasis on computational techniques for
decision-making with data. Large-scale optimization, data analysis and visualization are now
commonplace among researchers and practitioners alike.
15.S60 is a multi-session workshop on software tools for informing decision-making using data,
with a focus on contemporary methods in optimization and statistics. We concentrate on
teaching elementary principles of computational practice using common software and practical
methods. By the end of the course, students will possess a baseline technical knowledge for
modern research practice. Class participation and individual hands-on coding are stressed in
each session.
Days: Tue Thu (9am-12pm)
1/8/2019 – 1/31/2019
Place: E51-325
Credits: 3 Units (Pass/Fail or Listener Only)
The course is divided into 8 self-contained modules. Each module consists of a 3-hour,
interactive workshop where participants learn a specific software tool. Class participation, group
code-reviews and individual hands-on coding are stressed in each session. At the end of the
module, participants will be able to use the software and techniques learned in their own
research. Participants will also leave each workshop with code they, themselves, have authored
to use for future reference.
Required: Instructor permission. Email adelarue@mit.edu to request permission.
Required: Familiarity with a modern programming language
Helpful: Familiarity with optimization
Sponsor(s): Operations Research Center
Contact: Arthur Delarue, adelarue@mit.edu
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Jan/08 | Tue | 09:00AM-12:00PM | E51-325 |
Terminal, Github, and a Gentle Introduction to R
Galit Lukin, Arthur Delarue
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Jan/10 | Thu | 09:00AM-12:00PM | E51-325 |
Data Wrangling
Phil Chodrow, Xiaoyue Gong
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Jan/15 | Tue | 09:00AM-12:00PM | E51-325 |
Statistical Modeling and Machine Learning in R
Zachary Blanks
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Jan/17 | Thu | 09:00AM-12:00PM | E51-325 |
Advanced Techniques for Data Science in R
Phil Chodrow
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Jan/22 | Tue | 09:00AM-12:00PM | E51-325 |
Mini Project Presentations and Deep Learning in R
Zachary Blanks, Andreea Georgescu
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Jan/24 | Thu | 09:00AM-12:00PM | E51-325 |
Introduction to Julia and JuMP, Linear Optimization
Jean Pauphilet
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Jan/29 | Tue | 09:00AM-12:00PM | E51-325 |
Nonlinear and Integer Optimization in JuMP
Ryan Cory-Wright
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Jan/31 | Thu | 09:00AM-12:00PM | E51-325 |
Large-scale Computations and Research Output
Arthur Delarue
Directed Reading Program in Mathematics
Slava Gerovitch, Ju-Lee Kim
| Date TBD | Time TBD | Location TBD | ||
Enrollment: Limited: Advance sign-up required
Sign-up by 11/12
Prereq: at least two math courses at 18.100 level or higher.
For undergraduates wanting to learn mathematical topics through guided self-study. Application deadline for Jan 2019 IAP is: MONDAY, NOVEMBER 12, 2018.
After you get admitted, we'll pair you up with a graduate student mentor with similar interests. You two will agree on a topic to explore, and find a suitable textbook. The math department pays for copies of the book (a good deal, since advanced math textbooks can be pretty expensive).
During IAP, you and your mentor will meet on campus at least 3 times per week to discuss the material. This is *directed reading* - don't expect to be taught! Instead, you have the opportunity to ask in-depth questions, discuss your impressions, and receive feedback. There's no credit for taking it, and you won't get paid.
Instructions for applying, and more information, can be found here:
http://math.mit.edu/research/undergraduate/drp
Sponsor(s): Mathematics
Contact: Slava Gerovitch, 2-231C, 4-1459, slava@mit.edu
Introduction to Laue Diffraction
Charles Settens, Research Specialist
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Jan/31 | Thu | 01:00PM-04:00PM | 13-4027, Bring single crystals (>0.5mm) |
Enrollment: Unlimited: No advance sign-up
Over a century ago, the initial X-ray scattering experiment by Walter Friedrich, Paul Knipping, and Max von Laue was performed. They emitted Bremstrahlung radiation into a copper sulfate hydrate crystal to collect what is now called a Laue diffraction pattern.
In this class, we will learn the fundamentals of Laue diffraction to orient single crystals and large grained polycrystals utilizing the Multiwire Laboratories MWL-120 Laue Diffractometer in the Materials Research Laboratory X-ray Shared Experimental Facility.
Feel free to bring single crystals for the demonstration!
Contact: Charles Settens, 13-4027, SETTENS@MIT.EDU
Introduction to operads and Koszul duality
Svetlana Makarova, Yu Zhao
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Jan/08 | Tue | 01:00PM-02:30PM | 2-135, Trees, operads, examples of operads |
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Jan/10 | Thu | 01:00PM-02:30PM | 2-135, Algebras over an operad, modules over an algebra |
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Jan/15 | Tue | 01:00PM-02:30PM | 2-135, Quadratic operads |
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Jan/17 | Thu | 01:00PM-02:30PM | 2-135, Duality for DG operads |
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Jan/22 | Tue | 01:00PM-02:30PM | 2-135, Koszul operads |
Enrollment: Unlimited: No advance sign-up
Attendance: not required to attend each, but recommended to attend in order
Prereq: linear algebra, abstract algebra and category theory
The aim of this course is to give an introduction to operads and Koszul duality for operads. Operads can be thought of as a collection of operations which formalizes the notion of “algebra structure”. For example, associative, Lie and commutative algebras can be described as algebras over certain operads.
We plan to follow the exposition of Ginzburg, Kapranov in their paper “Koszul duality for operads”.
Students are not required to pre-register, but sending an email murmuno@mit.edu to express interest would be helpful.
The course should be accessible to undergrads.
Contact: Svetlana Makarova, 2-231A, murmuno@mit.edu
Mathematics Lecture Series
Alan Edelman
Enrollment: Unlimited: No advance sign-up
Attendance:
Ten lectures by Mathematics faculty members on interesting topics from both classical and modern Mathematics. All lectures accessible to students with a Calculus background and an interest in Mathematics. These lectures are open to the public and you may attend as many or as few as you wish. Students wishing to receive course credit must register for 18.095, attend all lectures. and complete problem sets.
For more information on idividual lectures please see:
http://math.mit.edu/academics/iap.php
Sponsor(s): Mathematics
Contact: Professor Alan Edelman, edelman@mit.edu
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Jan/07 | Mon | 01:00PM-02:30PM | 2-190, Professor Vadim Gorin |
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Jan/09 | Wed | 01:00PM-02:30PM | 2-190, Professor Peter Shor |
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Jan/11 | Fri | 01:00PM-02:30PM | 2-190, Professor Kasso Okoudjou |
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Jan/14 | Mon | 01:00PM-02:30PM | 2-190, Professor Gilbert Strang |
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Jan/16 | Wed | 01:00PM-02:30PM | 2-190, Professor Steven Johnson |
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Jan/18 | Fri | 01:00PM-02:30PM | 2-190, Professor John Bush |
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Jan/23 | Wed | 01:00PM-02:30PM | 2-190, Dr. Jeremy Kepner |
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Jan/25 | Fri | 01:00PM-02:30PM | 2-190, Professor Justin Solomon |
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Jan/28 | Mon | 01:00PM-02:30PM | 2-190, Professor Scott Sheffield |
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Jan/30 | Wed | 01:00PM-02:30PM | 2-190, Dr. Christopher Rackauckas |
Session Description TBD
For more information on individual lectures please see:
http://math.mit.edu/academics/iap.php
Mathematics of Big Data & Machine Learning
Jeremy Kepner, Fellow & Head MIT Supercomputing Center
Enrollment: Limited: Advance sign-up required
Sign-up by 12/15
Limited to 30 participants
Attendance: Participants must attend all sessions
Prereq: Linear Algebra
Big Data describes a new era in the digital age where the volume, velocity, and variety of data created across a wide range of fields (e.g., internet search, healthcare, finance, social media, defense, ...) is increasing at a rate well beyond our ability to analyze the data. Machine Learning has emerged as a powerful tool for transforming this data into usable information. Many technologies (e.g., spreadsheets, databases, graphs, linear algebra, deep neural networks, ...) have been developed to address these challenges. The common theme amongst these technologies is the need to store and operate on data as whole collections instead of as individual data elements. This class describes the common mathematical foundation of these data collections (associative arrays) that apply across a wide range of applications and technologies. Associative arrays unify and simplify Big Data and Machine Learning. Understanding these mathematical foundations allows the student to see past the differences that lie on the surface of Big Data and Machine Learning applications and technologies and leverage their core mathematical similarities to solve the hardest Big Data and Machine Learning challenges.
Copies of the MIT Press book "Mathematics of Big Data" will be provided.
E-mail the instructor to sign up.
Sponsor(s): Mathematics
Contact: Jeremy Kepner, MIT Beaver Works (300 Tech Sq), 781 981-3108, KEPNER@LL.MIT.EDU
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Jan/11 | Fri | 10:30AM-12:30PM | 300 Tech Sq 2nd Flr |
Chapters 1 and 2 of
Jeremy Kepner - Fellow & Head MIT Supercomputing Center
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Jan/18 | Fri | 10:30AM-12:30PM | 300 Tech Sq 2nd Flr |
Chapter 3 and Chapter 4 of
Jeremy Kepner - Fellow & Head MIT Supercomputing Center
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Jan/25 | Fri | 10:30AM-12:30PM | 300 Tech Sq 2nd Flr |
Chapters 7 and 8 of
Jeremy Kepner - Fellow & Head MIT Supercomputing Center
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Feb/01 | Fri | 10:30AM-12:30PM | 300 Tech Sq 2nd Flor |
Chapter 5 and 6 of
Jeremy Kepner - Fellow & Head MIT Supercomputing Center
ORC IAP Seminar 2019: "Machine Learning and Operations Research"
Nicolas Guenon des Mesnards, Kevin Zhang, Jessica Zhu
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Jan/30 | Wed | 09:30AM-04:45PM | 32-141 |
Enrollment: Unlimited: No advance sign-up
Prereq: None
Date: Wednesday, January 30th, 2019
Time: 9:30am-4:45pm
Place: 32-141
Description: Machine learning techniques are only as good as the data they are built on; optimization and OR models are needed to address data issues like robustness, interpretability, and unobserved data. The Operations Research Center IAP Seminar this year will discuss how these topics are being addressed both by researchers and practitioners.
Schedule:
9:30am-10:00am
COFFEE AND REFRESHMENTS
10:00am-10:45am
Negin Golrezaei - Assistant Professor, MIT
“Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions”
11:00am-11:45am
Nathan Kallus - Assistant Professor, Cornell University
“Learning to Personalize from Observational Data Under Unobserved Confounding”
12:00pm-1:30pm
LUNCH BREAK (not provided)
1:30pm-3:00pm
PANEL DISCUSSION WITH PRACTITIONERS
Bala Chandran - Co-founder and CEO, Lumo
Virginia Goodwin - Technical Staff, Lincoln Labs
Kermit Threatte - Director, Wayfair
3:00pm-3:45pm
Caroline Uhler - Associate Professor, MIT
“Using Interventional Data for Causal Inference”
4:00pm-4:45pm
Bartolomeo Stellato - Postdoctoral Associate at the Operations Research Center, MIT
“The Voice of Optimization”
More details available on the ORC IAP Seminar website: https://orc.mit.edu/events/orc-iap-seminar-2019
Sponsor(s): Operations Research Center
Contact: ORC IAP Coordinators, orc_iapcoordinators@mit.edu
Contact Information
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