Publications

The MIT/IEEE/Amazon GraphChallenge.org encourages community approaches to developing new solutions for analyzing graphs and sparse data. Sparse AI analytics present unique scalability difficulties. The proposed Sparse Deep Neural Network (DNN) Challenge draws upon prior challenges from machine learning, high performance computing, and visual analytics to create a challenge that is...

Monitoring and managing High Performance Computing (HPC) systems and environments generate an ever growing amount of data. Making sense of this data and generating a platform where the data can be visualized for system administrators and management to proactively identify system failures or understand the state of the system requires...

This tutorial explains a systematic approach of co-designing functionality and security into mission-criticalembedded systems. The tutorial starts by reviewing common issues in embedded applications to define mission objectives,threat models, and security/resilience goals. We then introduce an overview of security technologies toachieve goals of confidentiality, integrity, and availability given design criteria...

The Internet is transforming our society, necessitating a quantitative understanding of Internet traffic. Our team collects and curates the largest publicly available Internet traffic data containing 50 billion packets. Utilizing a novel hypersparse neural network analysis of "video" streams of this traffic using 10,000 processors in the MIT SuperCloud reveals...

With recent trends in computation and communication architecture, it is becoming possible to simulate complex networked dynamical systems by employing high-fidelity models. The inherent spatial and temporal complexity of these systems, however, still acts as a roadblock. It is thus desirable to have adaptive platform design facilitating zooming-in and out...

This paper formulates the efficient and feasible participation of distributed energy resources (DERs) in complex electricity services as a centralized nonlinear optimization problem first. This problem is then re-stated using the novel energy/power transformed state space. It is shown that the DER dynamics in closed-loop can be made linear in...

Advances in multicore processors and accelerators have opened the flood gates to greater exploration and application of machine learning techniques to a variety of applications. These advances, along with breakdowns of several trends including Moore's Law, have prompted an explosion of processors and accelerators that promise even greater computational and...

This work investigates methods for improving a pronunciation proficiency recognition system, both in terms of phonetic level posterior probability calibration, and in ordinal utterance level classification, for Modern Standard Arabic (MSA), Spanish and Russian. To support this work, utterance level labels were obtained by crowd-sourcing the annotation of language learners'...

A challenge for second language learners, educators, and test creators is the identification of authentic materials at the right level of difficulty. In this work, we present an approach to automatically measure text difficulty, integrated into Auto-ILR, a web-based system that helps find text material at the right level for...

Many control-flow-hijacking attacks rely on information leakage to disclose the location of gadgets. To address this, several leakage-resilient defenses, have been proposed that fundamentally limit the power of information leakage. Examples of such defenses include address-space re-randomization, destructive code reads, and execute-only code memory. Underlying all of these defenses is...