Projects listed here are only for students enrolled at MIT. Please do not contact us if you are not a MIT student.
See our Visitor and Membership Information if you want to join our group from outside MIT.

Student Research Opportunities for MIT Students - Fall 2013

See below for the projects we offered, if you'd like to familiarize yourself with our interests.

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Fast BigData Learning with GPUs

Recent advances in programming tools allow the programming of graphics processing units (GPUs) at a high level of abstraction, leveraging the great computational power of graphics cards for general purpose computation. These massively parallel architectures (more than a thousand cores) are in fact being used in a variety of fields such as bioinformatics, computational finance, and data science among many other scientific applications. In the field of Machine Learning, GPUs can reduce significantly the time required to learn from large-scale datasets. In particular, Evolutionary Computation approaches are great candidates for parallelization in GPU due to the fact that they can easily be parallelized. We are seeking an undergraduate, AUP, or senior who is interested in programming GPUs and implementing scalable and fast machine learning approaches for BigData scenarios with special focus on (but not limited to) evolutionary computation.

Please contact: alfa-apply@csail.mit.edu


Development of a "blended" (classroom and online) course in China on Evolutionary Processes, Systems and Computation

How do you create a novel and interesting course in Computer Science with a scalable global delivery?

In collaboration with Shantou University in China we are developing a "blended" (classroom and online) course on Evolutionary Processes, Systems and Computation. The first week will be taught in China, then 8 weeks are taught online and finally the last week is taught in China. The course aims are:

  • To extend the students understanding of evolutionary processes, systems and computation.
  • To expose the students to new or non-conventional ways of learning, given their experience.

The longer term aim is to expand and scale the course to a full MOOC. In order to further the MOOC educational experience we want to consider what data to collect in the course in order to do analytics.

The tasks in the project involve:

  • Setting up software for running the blended course
  • Develop software for the projects in course, e.g. Mobile distributed Interactive Evolutionary Computation project of co-evolving strategies
  • Assisting delivery of blended course to Chinese students
  • Designing experiments and collection of data regarding blended learning

You will be working with a team of Post Docs, PhDs, MEngs and UROPs. Knowledge of Mandarin is a bonus. Full, might open during IAP and Spring 2014 Please contact: alfa-apply@csail.mit.edu


Mining a MOOC's activity data: 6.002X explored

We are building a variety of machine learning algorithms for mining data generated while delivering educational content to hundreds and thousands of students all over the world. A very fundamental question that folks in education are attempting to answer is: "What worked?" Answering this question would require us to analyze data in novel ways, for example building models of students, balancing for confounding factors. We are looking for a talented UROP or MEng student to work with a Research Scientist and a group of scientists and fellows at the MIT EdX team. This project has possible transformative affects on the next generation education systems. Read about EdX here and here.edX_Logo_Col_RGB_FINAL.jpg: 605x403, 33k (August 21, 2012, at 02:41 PM)

Juniors, Seniors, MEng
Background: Course 6 courses in software and machine learning knowledge (6.034 and 6.867)
Please contact: alfa-apply@csail.mit.edu


Super-UROP Projects 2013-2014

ALFA Group listed 8 super-UROP projects on the EECS department super-UROP site. Five were accepted after being discussed, refined and submitted as proposals. Another has become a UROP project.

Considering Biological Factors to Improve Genetic Programming in Detecting SNP Interactions, Chau Vu.

Improving the Speed and Performance of FlexGP Using Core-sets, Elisa Castener

Knowledge Discovery and Prediction from Blood Pressure Data, Harrison Hunter

Knowledge Discovery from Data Arising from Massive Open Online Courses, Matt Susskind

Scaling Dynamic Bayesian Networks on Volunteer Computer: Course Quality for MITx, Nico Rakover

Our 2012-2013 Super UROP projects are introduced here.

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