We are a community of undergraduates aimed at promoting and fostering the growing interest around machine intelligence on campus. We currently hold weekly discussions on the latest papers in the field, and also plan to organize crash courses, host speakers, and arrange competitions around machine intelligence at MIT.
My main interests right now are in the area of performance engineering and generally machine
learning. In past I've worked on projects related to sound recognition, face recognition and ad-hoc
approaches to video indexing.
Also had some fun in my classes at MIT, especially in a robotics class (6.141).
My interests are in deep learning and broad questions of intelligence, such as building smarter
agents that can perceive, plan, and learn robustly.
I currently works in Joshua Tenenbaum's Computational Cognitive Science group at MIT and in the past I've worked on machine learning at Facebook and Microsoft Research. After college, I will be researching in the Google Brain Residency program and plan to both continue research and find ways to apply technologies in AI to social issues.
Michael Chang is interested in recursive self-improvement, learning compositional programs,
curiosity, and theory of mind.
In the summer of 2015 he worked with Professor Honglak Lee at the University of Michigan on object tracking in real videos. Since the fall of 2015 he has worked in the Computational Cognitive Science Group at MIT, researching with Professors Joshua Tenenbaum and Antonio Torralba on learning models of an intelligent agent's environment. More information here.
My interest in machine learning started with an internship at SpaceX, where I got to experiment with
various anomaly detection algorithms for rocket telemetry.
At MIT, I'm currently working on a few different research projects mainly in the field of computer vision, including Connectomics (building a graphical map of the brain from EM images) and more recently, scene/video understanding.
I graduated from MIT in Fall 2016, majoring in CS and Math, and am currently working as a researcher
at OpenAI. I'm interested in any technique that could help us come closer to building an AGI. As of
now, that interests me in RL, transfer learning, variants of neural networks with memory,
improvements to SGD etc.
Outside of work, I love playing cricket and badminton, going on random road trips, and having long late night conversations about society, technology and our future. :)
Simanta's current research interest is around building more adaptable reinforcement learning agents.
He works at Harvard Computational Cognitive Neuroscience Lab on combining the strengths of
model-based and model-free agents using transferable representations.
Outside of this research, Simanta enjoys working on applied machine learning projects. He has an interst in computer vision, drones, and human-computer interaction. He plans to pursue entrepreneurship after graduation.
I am a member of the class of 2017 and currently study Computer Science & Engineering. I was born and
raised in Côte d'Ivoire before moving to the us at age 17.
I am currently interested in the industrial applications of artificial intelligence to build more intelligent transport, healthcare and education systems. I also enjoy building strong communities and have been involved in the Muslim community on campus for 4 years and currently serve as the Vice President of the MIT African Students Association.
Efe is a senior at MIT, majoring in EECS and minoring in Political Science. He has extensive
experience in AI, Computer Vision, Natural Language Processing, algorithms and full stack
He has interned at Google in the summer of 2016, building a neural net model for the purpose of spam video detection on Youtube that performed 150% better than their existing model. He has been doing research at the MIT Vision Lab, building predictive models for health statistics.
Enes is a junior at MIT majoring in Computer Science. He works at MIT Computer Vision Lab,
researching on Crossmodal Transfer Learning in Video Classification.
In past, he worked on several projects, including efficient keypoint detection on human face, and Body Mass Index inference from profile pictures.
Andrew is a junior at MIT majoring in computer science and minoring in brain and cognitive
sciences. His current focus is in applied machine learning in computer vision.
In the summer of 2016 he worked on designing a drug repurposing model combining FDA adverse event reports and compound properties at a MIT startup.
I'm a junior at MIT from New Jersey and since coming to this school, I've discovered how much I
love Machine Learning!
The project I'm currently working on is attempting to perform Chemical Retrosynthesis Planning via Reinforcement Learning.
Parth is an Electrical Engineering and Computer Science student at MIT. Parth is the co-founder of Polimorphic (polimorphic.com) and Flux @ MIT. Parth's interests in machine learning span natural language processing, transfer learning, meta-learning, and few shot learning. Parth is primarily interested in the development of radically different machine intelligence approaches, the application of machine intelligence to industry, and the policy surrounding the creation of safer and more effect aware A.I.
Ajay is a computer science student at MIT. He focuses on computer vision, particularly for video and real-time inference, and compilers. In the past, Ajay also worked on robotic navigation and recommender systems. On campus, Ajay heads MIT's Machine Intelligence Community, and organized the HackMIT and Blueprint hackathons for two years.
Raised in Duisburg, Germany, Rose E. Wang is studying Mathematics and Computer Science at MIT.
She currently does research at MIT’s Computer Science and Artificial Intelligence Lab which involves creating mobile applications that use image recognition. Her interests include deep learning, especially in natural language processing and computer vision.
I'm a second year master's student at Boston University studying computer science and am extremely
passionate about deep learning.
Currently, I am researching the application of deep learning for analysis of abstract syntax trees, the application of deep learning for boolean satisfiability, and developing scalable approaches for action recognition, and working on low-cost distributed processing.