The challenge that motivates the ANA group is to foster a healthy future for the Internet. The interplay of private sector investment, public sector regulation and public interest advocacy, as well as the global diversity in drivers and aspirations, makes for an uncertain future.
This CoR brings together researchers at CSAIL working across a broad swath of application domains. Within these lie novel and challenging machine learning problems serving science, social science and computer science.
This CoR aims to develop AI technology that synthesizes symbolic reasoning, probabilistic reasoning for dealing with uncertainty in the world, and statistical methods for extracting and exploiting regularities in the world, into an integrated picture of intelligence that is informed by computational insights and by cognitive science.
We focus on finding novel approaches to improve the performance of modern computer systems without unduly increasing the complexity faced by application developers, compiler writers, or computer architects.
This community is interested in understanding and affecting the interaction between computing systems and society through engineering, computer science and public policy research, education, and public engagement.
The Systems CoR is focused on building and investigating large-scale software systems that power modern computers, phones, data centers, and networks, including operating systems, the Internet, wireless networks, databases, and other software infrastructure.
Our goal is to develop a model for the binding, internalization, and tumor-killing dynamics of liposome-enclosed doxorubicin targeted to cancer cells and develop design principles for creating more effective therapeutics.
Our research aims to scale hard-to-parallelize applications through new programming models and multicore architectures. Our goal is to enable
programmers to write efficient and scalable parallel programs as easily as they
write sequential programs today.
We develop, parameterize, and validate a model for tumor growth inhibition using in vivo mouse data and study the effects of modeling uncertainty and inter-individual variability on drug candidate efficacy predictions.
To enable privacy preservation in decentralized optimization, differential privacy is the most commonly used approach. However, under such scenario, the trade-off between accuracy (even efficiency) and privacy is inevitable. In this project, distributed numerical optimization scheme incorporated with lightweight cryptographic information sharing are explored. The affect on the convergence rate from network topology is considered.
We develop algorithms, systems and software architectures for automating reconstruction of accurate representations of neural tissue structures, such as nanometer-scale neurons' morphology and synaptic connections in the mammalian cortex.
This project focuses on altering the way trusted execution environments handle paging to decouple page access patterns from the control flow of the program, preventing an attacker from using these access patterns to learn information about program secrets.
This week it was announced that MIT professor Armando Solar-Lezama has received a prestigious NSF award for junior faculty, to go towards a new project that could impact scientific discovery in domains as diverse as organic chemistry, RNA splicing and cognitive science.
On January 15, 2019, the MIT Internet Policy Research Initiative (IPRI) and Quest for Intelligence (QI) hosted the first MIT AI Policy Congress. The conference brought together global policymakers, technical experts, and industry executives to discuss the impact of AI across sectors, with panels on transportation and safety, manufacturing and labor, healthcare, criminal justice and fairness, national security and defense, and international perspectives.
Last week MIT’s Institute for Foundations of Data Science (MIFODS) held an interdisciplinary workshop aimed at tackling the underlying theory behind deep learning. Led by MIT professor Aleksander Madry, the event focused on a number of research discussions at the intersection of math, statistics, and theoretical computer science.