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.

MEng and UAP in Fall 2016

Data science (RA for MEng): Data science is positioned at the intersection of large scale data collection, analytics and machine learning. Domain experts want insights that are actionable from their data but extracting predictive models and other insights requires an extensive software work flow. We are trying to make that workflow flexible, fast and intelligent. This project will tackle the problem of working the student behavioral data archived from a MOOC through an open source data science pipeline. Experience in Python is essential, experience with SQL and machine learning is helpful. See for more information. Send a resume, relevant courses and grades to

Cybersecurity, AI and Law, Program verification: ALFA has 3 research projects from which UAP and MEng projects are possible. The topics come from our push to apply machine learning to problems with real impact.

Machine Learning and CyberSecurity in Software Defined Networks

The flexibility of Software Defined Networks has resulted in increasing growth and adaptation. However recently alarming hacking vulnerabilities have been revealed. The project will involve applying machine learning to replicate how topology poisoning can be used for eavesdropping and how to detect the fake links introduced into the network by the attacker. It is ideal for students planning on taking or who have taken 8.857 and/or 6.858. We need help with the modeling, elucidating the hacking schemes, figuring out defenses.

Coding the Tax Code: Regulations to Formalism

AI techniques exist that translate case law into software and that support intelligent reasoning and argumentation around it. This project focuses alternatively on the regulatory form of law. It will involve developing an automatic parsing system for translating regulations into a formalism. It is part of the larger STEALTH project, NY Times coverage) This project will appeal to students interested in programming languages and/or natural language text understanding techniques.

Coding the Tax Code: Software Verification

To date, programs that constitute AI-based law are difficult to check, i.e. there is no formal process that verifies the law is correctly transcribed. This project will involve developing a verification process. We're trying to develop a method to verify the program resulting from translating an intermediary legal formalism (the specification) to a program. This activity is part of the larger STEALTH project (NY Times coverage ). This project will appeal to student interested in the topics of two advanced courses: 6.820 and/or 6.887.

Please contact if you’d like to apply. Include a list of relevant courses and grades, why you're interested, whether it's MEng or UAP you're looking for.

Student Research Opportunities for MIT Students - IAP/Spring 2017

No time to update with 2017 projects at the moment, please check back in December!
See below for some of the projects we offered in the past, if you'd like to familiarize yourself with our interests.

Table of Contents

Coevolutionary Algorithm Design: (MEng) (Fall 2016, filled)

Implementation of a co-evolutionary algorithm to support the RIVALS and MACE systems. These systems support the study of adversarial dynamics in cyber-security. They model attackers and defenders in cyberspace paying particular attention to arms races and deception.

Network Model Simulation for Cybersecurity: (MEng, UROP) (Fall 2016, filled)

Model peer to peer and Software Defined Network configurations and traffic to support the configurability of the RIVALS and MACE systems. These systems support the study of adversarial dynamics in cyber-security. They model attackers and defenders in cyberspace paying particular attention to arms races and deception.

MOOC Data Schema Population (MEng) (Fall 2016, filled)

Contribute and support the release of open-source software that transforms the raw learner behavioral data captured during MOOC learning into a relational database.

MOOC Behavioral Variable Engineering (Super-UROP) (Fall 2016, filled)

Contribute and support the release of open-source software that transforms direct MOOC learner data stored within a relational database into module-based variables that describe learner behavior at a practical abstraction.

MOOC Student Modeling (UROP) (Fall 2016, filled)

Contribute and support the release of open-source software that models and predicts student behavior from module-based variables.

STEALTH: Tax Law as Non-Monotonic Logic (Fall 2016, filled)

Translate specific parts of the tax code into monotonic logic to evaluate the logic's capacity to express code and support inferential use. be added, time permitting. ALFA has 3 Super-UROPS, 3 UROPS and 3 MEng's.

Machine Learning for Candidate Filtering (Fall 2015, filled)

How do you make sure that you do not miss any candidates with potential when you filter out applicants for an advertised position, i.e. can you create a system that covers for you when you have a bad day? When selecting candidates from a large number of applications there is always a worry that you missed a candidate with great potential. In classification terms, you indicated a false negative. For example when there are multiple applications to an education program, how do you make sure that you do not miss students with strong potential. This project involves identifying which features to observe when selecting candidates from a large number of applications and building a machine learning system that produces a model for decision support. This system will be deployed in order to indicate candidates with great potential.

STEM (Fall 2015, filled)

How do you make computer science teaching, compelling, accessible and possible to use at a large scale? In the ALFA group we are developing Educational Technology to help improve the content, delivery and reach of computer science education, e.g. reducing the passive learning by interacting algorithms in the class room and then stepping through them online. Whether, you think you can create material which is aesthetically pleasing, intuitive to use, contain engaging exercises and/or enlarge capacity and automation, then ALFA is for you.

STEALTH (Fall 2015, filled)

Can we hack the legislation "whack-a-mole"? Every time a regulation is introduced, a new loophole is found and exploited. In the STEALTH project at the ALFA group we are developing Artificial Intelligence approaches in order to anticipate fraud in regulations, e.g. learning what transactions in the US Partnership taxation that can be non-compliant and how to audit them. In the STEALTH project you will specify, implement, test and improve legislation before releasing it in order to catch unwanted features and identify what actions might be indicative of suspicious behavior.

Fast BigData Learning with GPUs (2013)

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:

Development of a "blended" (classroom and online) course in China on Evolutionary Processes, Systems and Computation (2014, filled)

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:

Mining a MOOC's activity data: 6.002X explored (2012, filled)

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:

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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