Opportunity: Machine Learning Workshop

Fundamentals of Machine Learning Workshop at Stanford University

March 31, 2017

Discover the basics behind the application of modern machine learning algorithms. The workshop instructors will discuss a framework for reasoning about when to apply various machine learning techniques, emphasizing questions of over-­fitting/under-­fitting, regularization, interpretability, supervised/unsupervised methods, and handling of missing data.  

For more information, visit the Stanford ICME website: https://icme.stanford.edu/events/fundamentals-machine-learning-workshop

Attendees should have undergraduate-­level knowledge of linear algebra and statistics, and basic programming experience (R/Matlab/Python). Please note that this is not a Stanford for-credit course.

Space is limited, so register today.


Thanks to Judy Logan from the Institute for Computational and Mathematical Engineering (ICME) at Stanford University and the Women in Data Science (WiDS) Conference for this post.

Opportunity: Roundtable on data science post-secondary education

Webcast on March 20: Meeting #2 of the Roundtable on Data Science Post-Secondary Education

The National Academies of Sciences, Engineering, and Medicine invite you to attend a one-day webcast on March 20 from 9am-4pm PST on data science post-secondary education. This meeting will bring together data scientists and educators to discuss how to define and strengthen existing data science programs and how to best engage and retain data science students. For more information, visit the event website or download the preliminary program.

During the event, we encourage webcast participants to send questions for the speakers to Ben Wender at bwender@nas.edu, who will read them out if time permits.

Opportunity: Graduate Student Modeling Workshop (IMSM 2017), July 2017

 The 23rd Industrial Mathematical & Statistical Modeling (IMSM) Workshop for Graduate Students will take place at North Carolina State University, between 16-26 July 2017.  The workshop is sponsored by the Statistical and Applied Mathematical Sciences Institute (SAMSI) together with the Center for Research in Scientific Computation (CRSC) and the Department of Mathematics at North Carolina State University.
The IMSM workshop exposes graduate students in mathematics, engineering, and statistics to exciting real-world problems from industry and government. The workshop provides students with experience in a research team environment and exposure to possible career opportunities. On the first day, a Software Carpentry bootcamp will bring students up-to-date on their programming skills in Python/Matlab and R, and introduce them to version control systems and software repositories.

Local expenses and travel expenses will be covered for students at US institutions.

The application deadline is April 15, 2017.
Information is available at http://www.samsi.info/IMSM17
and questions can be directed to grad@samsi.info

With best regards,
Mansoor Haider, Ilse Ipsen, Pierre Gremaud, and Ralph Smith

Opportunity: Graduate-Level Research in Industrial Projects for Students (GRIPS)-Berlin July 3 – August 25, 2017

The Institute for Pure and Applied Mathematics (IPAM) is offering a graduate student research program in Berlin with our partners in Berlin at the research campus MODAL (Mathematical Optimization and Data Analysis Laboratories). This is in conjunction with the Free University of Berlin (FU Berlin) and the Konrad-Zuse Zentrum für Informationstechnik Berlin (ZIB). This will be an 8-week research experience with one of 3 industrial partners. Students will be in Berlin for 2 months, and work in teams of 4 students on exciting projects.

The ideal student is a second or third year graduate student, but we will also consider students who are younger or further along. Only U.S. citizens and permanent residents are eligible to apply for this program.

The partners and projects for 2017 are:

  • Project 1: Therapy Planning – 1000shapes GmbH
  • Project 2: Nanophotonics – JCMwave GmbH
  • Project 3:  Selecting an Optimization Solver: Machine Learning under Expensive Function Evaluations – Satalia

More information about the entire program and the different projects can be found at:


[Via Christian Ratsch, Associate Director, Institute for Pure and Applied Mathematics, UCLA, cratsch@ipam.ucla.edu]