Alexander Aminiemail: email@example.com
resume, google scholar
I am a PhD student at the Massachusetts Institute of Technology (MIT), in the Computer Science and Artificial Intelligence Laboratory (CSAIL), with Prof. Daniela Rus. I am a NSF Fellow and completed my Bachelor of Science and Master of Science in Electrical Engineering and Computer Science at MIT, with a minor in Mathematics.
My research focuses on building machine learning algorithms for end-to-end control (i.e., perception to actuation) of autonomous systems and formulating guarantees for these algorithms. I have worked on control of autonomous vehicles, formulating confidence of deep neural networks, mathematical modeling of human mobility, as well as building complex inertial refinement systems.
In addition to research, I am also a lead organizer and lecturer for MIT 6.S191: Introduction to Deep Learning, MIT's official introductory course on deep learning. In high school, I was awarded the first place Grand Prize at the EU Content for Young Scientists and BTYSTE with my project entitled: Tennis Sensor Data Analysis: An Automated System for Macro Motion Refinement. I grew up in New York, and then moved to Dublin, Ireland, where I attended Castleknock College, and then returned to the US in 2012.
My professional resume can be found here.
|Oct 2021||Paper: Two new papers accepted to NeurIPS 2022 on the topics of causual navigation models [paper] and sparse flows [paper]!|
|Apr 2021||Our paper on co-optimizing sensor placement and policy learning is accepted to RA-L and nominated for the Best Paper Award at RoboSoft 2021! [paper] [news]|
|Mar 2021||Two new papers using our evidential uncertainty algorithm have been accepted and published for improved robustness in molecular drug discovery [paper] and end-to-end autonomous driving [paper]!|
|Feb 2021||Awarded two grants with FinTech@CSAIL and MachineLearningApplications @CSAIL to mitigate algorithmic bias and uncertainty in financial time series modeling and clinical trial outcome prediction.|
|Jan 2021||Lead organizer and lecturer for MIT 6.S191: Introduction to Deep Learning, with over 700 registered MIT students (and over 10,000 registrations globally online). [link] [video]|
|Dec 2020||Awarded the JP Morgan Fellowship for the 2021-2022 academic year with a focus on robustness and uncertainty of learning-based systems!|
|Dec 2020||Our paper on liquid time-constant neural networks is accepted to AAAI with an Oral spotlight! [paper] [news]|
|Aug 2020||Two papers focused on uncertainty and robustness of ML! (1) published in Nature Machine Intelligence and (2) accepted at NeurIPS for presentation in Dec 2020.|
|May 2020||Paper: Learning end-to-end control policies from data-driven simulation has been accepted to RA-L with an invited presentation at ICRA 2020! Code coming soon. [paper]|
|Jan 2020||Lead organizer and lecturer for MIT 6.S191: Introduction to Deep Learning, with over 300 MIT registered students. [link] [video]|
|Oct 2019||Invited Talk and contributed papers at ICML 2019 Workshop for Autonomous Driving, and Reinforcement Learning in Real Life.|
|Mar 2019||Our paper Variational End-to-End Navigation and Localization has been nominated for the Best Paper Award at ICRA 2019 (top 1% of submissions)|
|Jan 2019||Paper: Variational End-to-End Navigation and Localization has been accepted to ICRA 2019!|
|Jan 2019||Lecturer for the 2nd straight year of MIT 6.S191: Introduction to Deep Learning, MIT's official course on deep learning applications and foundations. [link] [video]|
|Dec 2018||Paper: Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure has been accepted to AAAI/ACM AIES 2019 [paper]|
|Oct 2018||Invited Talk at IROS in Madrid, Spain: Variational Autoencoder for End-to-End Control of Autonomous Driving with Novelty Detection and Training De-biasing|
|Jun 2018||Paper: Variational Autoencoder for End-to-End Control of Autonomous Driving with Novelty Detection and Training De-biasing has been accepted to IROS 2018 [paper]|
|Jun 2018||Graduation: Master of Science (MS) from MIT in EECS. Thesis: Robust Learning for End-to-End Autonomous Driving [thesis]|
|Mar 2018||Invited Talk at NVIDIA's GTC in San Jose, California: Learning Steering Bounds for Parallel Autonomy [talk]|
|Jan 2018||Lecturer for MIT 6.S191: Introduction to Deep Learning, MIT's official course on deep learning applications and foundations. [link] [video]|
|Dec 2017||Invited Talk at the NIPS workshop on Bayesian Deep Learning (12% acceptance rate).|
|Nov 2017||Travel Award to present our paper, Spatial Uncertainty Sampling for End-to-End Control, at NIPS Bayesian Deep Learning (8% acceptance rate). [paper]|
|Jun 2017||Summer Internship starting my summer internship with NVIDIA's end-to-end self driving car team.|
|Jun 2017||NSF Fellowship. Awarded the National Science Foundation Graduate Fellowship (10% acceptance rate).|
|Jun 2017||Graduation: Bachelor of Science (BS) from MIT in EECS with a minor in Mathematics and concentration in Economics.|