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, bsturt@mit.edu
| Jan/10 | Tue | 09:00AM-12:00PM | E51-151 |
Motivation, Terminal, Github
Jackie Baek, Brad Sturt
| Jan/12 | Thu | 09:00AM-12:00PM | E51-151 |
Data Wrangling & Visualization in R
Steven Morse, Alex Weinstein
| Jan/17 | Tue | 09:00AM-12:00PM | E51-151 |
Statistical Modeling and Machine Learning in R
Colin Pawlowski, Clark Pixton
| Jan/19 | Thu | 09:00AM-12:00PM | E51-151 |
Advanced Techniques for Data Science in R
Phil Chodrow
| Jan/19 | Thu | 09:00AM-12:00PM | E51-151 |
Introduction to Julia and JuMP, Linear Optimization, and Engaging
Joey Huchette, Sebastien Martin
| Jan/26 | Thu | 09:00AM-12:00PM | E51-151 |
Nonlinear and Integer Optimization in JuMP
Miles Lubin, Yee Sian Ng
| Jan/31 | Tue | 09:00AM-12:00PM | E51-151 |
Excel for Operations Research
Charles Thraves
| 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:
http://math.mit.edu/research/undergraduate/drp
Sponsor(s): Mathematics
Contact: Slava Gerovitch, 2-231C, 4-1459, slava@mit.edu
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, kmdorst@mit.edu
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
| 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
| 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 (http://d4m.mit.edu).
| 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.
| 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!
http://homerreid.com/teaching/MoPuMMAM
Sponsor(s): Mathematics
Contact: Homer Reid, (857) 829-1667, homereid@mit.edu
| Jan/23 | Mon | 02:30PM-04:00PM | E25-117 |
| Jan/25 | Wed | 02:30PM-04:00PM | E25-117 |
| Jan/27 | Fri | 02:30PM-04:00PM | E25-117 |
| Jan/30 | Mon | 02:30PM-04:00PM | E25-117 |
| Feb/01 | Wed | 02:30PM-04:00PM | E25-117 |
| 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.
Schedule:
8:30-9:00 - COFFEE AND REFRESHMENTS
9:00-10:00 - Iain Dunning - Solving 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 Mourtzinou - The Financial Service Industry: Investment Management and Advice
12:00-1:00 - LUNCH BREAK (lunch not provided)
1:00-2:00 - Nataly Youssef - Analytics in Healthcare
2:00-3:00 - Rama Ramakrishnan - From 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: http://orc.mit.edu/events/orc-iap-seminar-2017
If you have any questions you may contact the ORC IAP Seminar student coordinators by email: orc_iapcoordinators@mit.edu
Sponsor(s): Operations Research Center
Contact: Steven Morse, stmorse@mit.edu
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, ferbera@mit.edu
| 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.
| Jan/19 | Thu | 10:00AM-12:00PM | 4-153 |
Weak games, the conditional expectation method (the Erdos-Selfridge Theorem), biased games and strong games.
Contact Information
COPYRIGHT 2017