MIT: Independent Activities Period: IAP

IAP 2017 Activities by Category - Mathematics

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Computing in Optimization and Statistics

Phil Chodrow, Joey Huchette, Brad Sturt, Dimitris Bertsimas, Professor

Enrollment: Unlimited: Advance sign-up required
Sign-up by 01/01
Attendance: Participants welcome at individual sessions

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. More than ever, there is a need not
only to develop techniques, but also to implement and use them in computational practice.

This course (formerly “Software Tools for Operations Research”) is a multi-session workshop
on software tools for informing decision-making using data, with a focus on 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.

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.

Sponsor(s): Operations Research Center
Contact: Brad Sturt,

Module 1

Jan/10 Tue 09:00AM-12:00PM E51-151

Motivation, Terminal, Github

Jackie Baek, Brad Sturt

Module 2

Jan/12 Thu 09:00AM-12:00PM E51-151

Data Wrangling & Visualization in R

Steven Morse, Alex Weinstein

Module 3

Jan/17 Tue 09:00AM-12:00PM E51-151

Statistical Modeling and Machine Learning in R

Colin Pawlowski, Clark Pixton

Module 4

Jan/19 Thu 09:00AM-12:00PM E51-151

Advanced Techniques for Data Science in R

Phil Chodrow

Module 5

Jan/19 Thu 09:00AM-12:00PM E51-151

Introduction to Julia and JuMP, Linear Optimization, and Engaging

Joey Huchette, Sebastien Martin

Module 6

Jan/26 Thu 09:00AM-12:00PM E51-151

Nonlinear and Integer Optimization in JuMP

Miles Lubin, Yee Sian Ng

Module 7

Jan/31 Tue 09:00AM-12:00PM E51-151

Excel for Operations Research

Charles Thraves

Module 8

Feb/02 Thu 09:00AM-12:00PM E51-151

Deep Learning in TensorFlow, Python

Eli Gutin, Brad Sturt

Directed Reading Program in Mathematics

Slava Gerovitch

Date TBD Time TBD Location TBD

Enrollment: Limited: Advance sign-up required
Sign-up by 11/04
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 2017 IAP is: FRIDAY, NOVEMBER 4, 2016.

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:

Sponsor(s): Mathematics
Contact: Slava Gerovitch, 2-231C, 4-1459,

Formal Methods in Epistemology

Kevin Dorst

Jan/23 Mon 11:00AM-12:30PM 32-D461
Jan/24 Tue 11:00AM-12:30PM 32-D461

Enrollment: Unlimited: No advance sign-up
Attendance: Participants must attend all sessions

A crash course in two of the most prominent formal systems used in epistemology: probability theory and epistemic logic. The first part introduces the basics of probability theory as a tool for studying rational belief, with an eye towards examples and intuitive understanding. The second part introduces models of epistemic logic as a tool for studying agents' beliefs about themselves and others, with an eye towards extensions and applications. Throughout, emphasis will be placed on the presuppositions, scope, and limitations of formalism as a tool for philosophy.

Sponsor(s): Linguistics and Philosophy
Contact: Kevin Dorst,

Future Trainwreck: Mine or Modern Physics

Douglas Sweetser '84

Jan/26 Thu 03:00PM-05:00PM 3-270

Enrollment: Unlimited: Advance sign-up required

Disaster is more interesting than success. I will limit myself to an hour of tales of research failures. Modern physics as more than 42 inverse femtobarns of data from the LHC saying all the work on super symmetry has no value, none, zero. My current research rejects using tensors for any calculations in physics. In its place is a more careful consideration of numbers for space-time events that can be added, subtracted, multiplied, and divided. The physics is found by using equivalence classes for pairs of observers. If the square of the difference between two events as seen by a pair of observers is the same, that is the equivalence class of inertial observers which is at the core of special relativity. If the three space-times-times are the same for a pair of observers, that leads to an equivalence class of non-inertial observers. The space-times-time equivalence class is the basis of my new proposal for gravity. There is no graviton in my proposal. The great hunt for quantum gravity would be over. For those that last until the end, a poll will be conducted to gauge if the audience thinks my current research is headed for a flameout, or a huge shift for physics is in store. One random person will win with a free t-shirt. Register today!

Sponsor(s): Alumni Association
Contact: Elena Byrne, W98-206C, 617 252-1143, EBYRNE@MIT.EDU

Mathematics of Big Data

Jeremy Kepner, Fellow & Head MIT Supercomputing Center

Enrollment: Limited: Advance sign-up required
Sign-up by 01/06
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.  Many technologies (e.g., spreadsheets, databases, graphs, linear algebra, ...) 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 leading to rapid solutions to Big Data volume, velocity, and variety problems.  Understanding these mathematical foundations allows the student to see past the differences that lie on the surface of Big Data applications and technologies and leverage their core mathematical similarities to solve the hardest Big Data challenges.


Sponsor(s): Mathematics
Contact: Jeremy Kepner, 2nd Floor, 300 Tech Sq, 781 981-3108, KEPNER@LL.MIT.EDU

Four Perspectives on Data

Jan/10 Tue 11:00AM-01:00PM 2nd Flr 300 Tech Sq, Bring lunch if you like

Preface and Chapter 1 of "Mathematics of Big Data" text

D4M: A New Tool for Big Data

Jan/17 Tue 11:00AM-01:00PM 2nd Flr 300 Tech Sq, Bring lunch if you like

Chapter 2 and Chapter 3 of "Mathematics of Big Data" text. Introduction to D4M (

Manipulation Big Data

Jan/24 Tue 11:00AM-01:00PM 2nd Flr 300 Tech Sq, Bring lunch if you like

Chapters 4, 5, 6, 7 of "Mathematics of Big Data" text.

Mathematical Foundations of Big Data

Jan/31 Tue 11:00AM-01:00PM 2nd Flr 300 Tech Sq, Bring lunch if you like

Student presentations

Chapters 8, 9 of "Mathematics of Big Data"

Modern Pure Mathematics for the Modern Applied Mathematician. A not-for-credit short course of 6 loosely-connected lectures

Homer Reid

Enrollment: Unlimited: No advance sign-up
Attendance: Participants welcome at individual sessions

Do you---as an engineer---sometimes consult pure-math papers or textbooks in the hope of deriving insight into a puzzling mathematical challenge, only to be stymied by an impenetrable wall of jargon such as short exact sequences and functoriality?

Do you---as a physicist---need to know what things like cohomology and p-forms are, but can't learn from pure-math textbooks because of the dense thicket of abstract terminology and concepts lying between the title page and the interesting content?

Or do you---as an applied or numerical mathematician---simply wonder what your pure-math colleagues are doing down the corridor all day?

If so, this is the course for you!

Sponsor(s): Mathematics
Contact: Homer Reid, (857) 829-1667,

Cohomology for dummies

Jan/23 Mon 02:30PM-04:00PM E25-117

Algebraic topology

Jan/25 Wed 02:30PM-04:00PM E25-117

Differential Geometry

Jan/27 Fri 02:30PM-04:00PM E25-117

Algebraic Geometry

Jan/30 Mon 02:30PM-04:00PM E25-117

Number Theory

Feb/01 Wed 02:30PM-04:00PM E25-117

Tying up Loose Ends; open-ended Q&A

Feb/03 Fri 02:30PM-04:00PM 32-144

ORC IAP Seminar 2017: "Careers in OR and Analytics"

Steven Morse, Clark Pixton, Shwetha Mariadassou

Jan/25 Wed 09:00AM-05:00PM 32-123

Enrollment: Unlimited: No advance sign-up
Prereq: None

Date: Wednesday, January 25th, 2017

Time: 9:00am-4:30pm 
Place: 32-123

Description: This year, the MIT Operations Research Center will welcome speakers from cutting-edge fields in OR and analytics — both in industry and in academia — to discuss their work, research, and careers and to discuss the future of OR.



9:00-10:00 - Iain DunningSolving Intelligence at DeepMind: Research overview, applications, and connections to OR

10:00-11:00 - Kris Ferreira - OR Academics Informing Industry…and Vice Versa

11:00-12:00 - Gina MourtzinouThe Financial Service Industry: Investment Management and Advice

12:00-1:00 - LUNCH BREAK (lunch not provided) 

1:00-2:00 - Nataly YoussefAnalytics in Healthcare

2:00-3:00 - Rama RamakrishnanFrom Data to Dollars: Confessions of an OR Entrepreneur

3:00-4:30 - Careers in OR and Analytics Panel

Brian Denton - Professor, University of Michigan, Industrial & Operations Engineering;  INFORMS President
Bill Pulleyblank - Professor of Operations Research, West Point
Rama Ramakrishnan - Senior Vice-President, Data Science, Salesforce (NYSE: CRM)
Dimitris Bertsimas - Boeing Professor of Operations Research; Codirector, Operations Research Center, MIT

More details available on the ORC IAP Seminar website:

If you have any questions you may contact the ORC IAP Seminar student coordinators by email:

Sponsor(s): Operations Research Center
Contact: Steven Morse,

Positional Games

Enrollment: Unlimited: No advance sign-up
Attendance: Participants welcome at individual sessions

Positional games is a branch of Combinatorics, studying deterministic two player zero sum games with perfect information, played usually on discrete or even finite boards. Among other, positional games include the popular games Tic-Tac-Toe and Hex as opposed to abstract games played on graphs and hypergraphs. This subject is strongly related to other branches of Combinatorics such as Ramsey Theory, Extremal Graph Theory and the Probabilistic Method. In this mini course we introduce the subject and its basic notions; learn some classical results in the field; discuss few general known tools as long as possible extensions; sketch some recent research results and talk about some interesting open problems in the field. 


Sponsor(s): Mathematics
Contact: Asaf Ferber, 2-246A,

Lecture one:

Jan/17 Tue 10:00AM-12:00PM 4-153

A brief introduction to the subject. The game of HEX, Tic-Tac-Toe, Shannon's switching game, strategy stealing, Ramsey-Type games and more.

Lecture two:

Jan/19 Thu 10:00AM-12:00PM 4-153

Weak games, the conditional expectation method (the Erdos-Selfridge Theorem), biased games and strong games.