Speranza: Usable, privacy-friendly software signing
Software repositories, used for wide-scale open software distribu- tion, are a significant vector for security attacks. Software signing provides authenticity, mitigating many such attacks. Developer- managed signing keys ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)How Can Large Language Models Help Humans in Design And Manufacturing?
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Counterfactual Explanations and Predictive Models to Enhance Clinical Decision-Making in Schizophrenia using Digital Phenotyping
Clinical practice in psychiatry is burdened with the increased demand for healthcare services and the scarce resources available. New paradigms of health data powered with machine learning techniques could open the possibility ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Automated Exposure Notification for COVID-19
Private Automated Contact Tracing (PACT) was a collaborative team and effort formed during the beginning of the Coronavirus Disease 2019 (COVID-19) pandemic. PACT’s mission was to enhance contact tracing in pandemic response ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Neurosymbolic Programming for Science
Neurosymbolic Programming (NP) techniques have the potential to accelerate scientific discovery across fields. These models combine neural and symbolic components to learn complex patterns and representations from data, ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Universal Motion Generator: Trajectory Autocompletion by Motion Prompts
Foundation models, which are large neural networks trained on massive datasets, have shown impressive generalization in both the language and the vision domain. While fine-tuning foundation models for new tasks at test-time ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Active Loop Detection for Applications that Access Databases
We present Shear, a new system that observes and manipulates the interaction between an application and its surrounding environment to learn a model of the behavior of the application. Shear implements active loop detection ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Active Loop Detection for Applications that Access Databases
We present Shear, a new system that observes and manipulates the interaction between an application and its surrounding environment to learn a model of the behavior of the application. Shear implements active loop detection ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Active Loop Detection for Applications that Access Databases
We present Shear, a new system that observes and manipulates the interaction between an application and its surrounding environment to learn a model of the behavior of the application. Shear implements active loop detection ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Bucket Elimination Algorithm for Dynamic Controllability Checking of Simple Temporal Networks with Uncertainty
Simple Temporal Networks with Uncertainty (STNU) can represent temporal problems where duration between events may be uncontrollable, e.g. when the event is caused by nature. An STNU is dynamically controllable (DC) if it ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Lower Bounds on the Column Sparsity of Compressed Sensing Matrices
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Bucket Elimination Algorithm for Dynamic Controllability Checking of Simple Temporal Networks with Uncertainty
Simple Temporal Networks with Uncertainty (STNU) can represent temporal problems where duration between events may be uncontrollable, e.g. when the event is caused by nature. An STNU is dynamically controllable (DC) if it ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Precise and Comprehensive Provenance Tracking for Android Devices
Detailed information about the paths that data take through a system is invaluable for understanding sources and behaviors of complex exfiltration malware. We present a new system, ClearScope, that tracks, at the level of ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Comprehensive Java Metadata Tracking for Attack Detection and Repair
We present ClearTrack, a system that tracks 32 bits of metadata for each primitive value in Java programs to detect and nullify a range of vulnerabilities such as integer overflow and underflow vulnerabilities, SQL injection ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Faster Dynamic Controllability Checking in Temporal Networks with Integer Bounds
Simple Temporal Networks with Uncertainty (STNUs) provide a useful formalism with which to reason about events and the temporal constraints that apply to them. STNUs are in particular notable because they facilitate reasoning ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Automatic Exploitation of Fully Randomized Executables
We present Marten, a new end to end system for automatically discovering, exploiting, and combining information leakage and buffer overflow vulnerabilities to derandomize and exploit remote, fully randomized processes. ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Gen: A General-Purpose Probabilistic Programming System with Programmable Inference
Probabilistic modeling and inference are central to many fields. A key challenge for wider adoption of probabilistic programming languages is designing systems that are both flexible and performant. This paper introduces ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Towards Understanding Generalization via Analytical Learning Theory
This paper introduces a novel measure-theoretic theory for machine learning that does not require statistical assumptions. Based on this theory, a new regularization method in deep learning is derived and shown to ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Using Dynamic Monitoring to Synthesize Models of Applications That Access Databases
We previously developed Konure, a tool that uses active learning to infer the functionality of database applications. An alternative approach is to observe the inputs, outputs, and database traffic from a running ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Using Active Learning to Synthesize Models of Applications That Access Databases
We present a new technique that uses active learning to infer models of applications that manipulate relational databases. This technique comprises a domain-specific language for modeling applications that access ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Best-first Enumeration Based on Bounding Conflicts, and its Application to Large-scale Hybrid Estimation
With the rise of autonomous systems, there is a need for them to have high levels of robustness and safety. This robustness can be achieved through systems that are self-repairing. Underlying this is the ability to diagnose ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Learning Models of Sequential Decision-Making without Complete State Specification using Bayesian Nonparametric Inference and Active Querying
Learning models of decision-making behavior during sequential tasks is useful across a variety of applications, including human-machine interaction. In this paper, we present an approach to learning such models within ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Generating Component-based Supervised Learning Programs From Crowdsourced Examples
We present CrowdLearn, a new system that processes an existing corpus of crowdsourced machine learning programs to learn how to generate effective pipelines for solving supervised machine learning problems. CrowdLearn uses ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)An Efficient Fill Estimation Algorithm for Sparse Matrices and Tensors in Blocked Formats
Tensors, linear-algebraic extensions of matrices in arbitrary dimensions, have numerous applications in computer science and computational science. Many tensors are sparse, containing more than 90% zero entries. Efficient ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Multi-Unit Auction Revenue with Possibilistic Beliefs
The revenue of traditional auction mechanisms is benchmarked solely against the players' own valuations, despite the fact that they may also have valuable beliefs about each other's valuations. Not much is known about ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Inference and Regeneration of Programs that Store and Retrieve Data
As modern computation platforms become increasingly complex, their programming interfaces are increasingly difficult to use. This complexity is especially inappropriate given the relatively simple core functionality that ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Collaborative Diagnosis of Over-Subscribed Temporal Plans
Over-subscription, that is, being assigned too many tasks or requirements that are too demanding, is commonly encountered in temporal planning problems. As human beings, we often want to do more than we can, ask for things ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Alpenhorn: Bootstrapping Secure Communication without Leaking Metadata
Alpenhorn is the first system for initiating an encrypted connection between two users that provides strong privacy and forward secrecy guarantees for metadata (i.e., information about which users connected to each other) ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Automatic Inference of Code Transforms and Search Spaces for Automatic Patch Generation Systems
We present a new system, Genesis, that processes sets of human patches to automatically infer code transforms and search spaces for automatic patch generation. We present results that characterize the effectiveness of the ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Modeling Network User Behavior: Various Approaches
This project involves learning to predict users' mobility within the network topology. Topological mobility, as opposed to physical mobility, can be substantial as a user switches from LTE to wifi network, while moving ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Deep Learning without Poor Local Minima
In this paper, we prove a conjecture published in 1989 and also partially address an open problem announced at the Conference on Learning Theory (COLT) 2015. For an expected loss function of a deep nonlinear neural network, ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Delphi: A Software Controller for Mobile Network Selection
This paper presents Delphi, a mobile software controller that helps applications select the best network among available choices for their data transfers. Delphi optimizes a specified objective such as transfer completion ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Outlier Detection in Heterogeneous Datasets using Automatic Tuple Expansion
Rapidly developing areas of information technology are generating massive amounts of data. Human errors, sensor failures, and other unforeseen circumstances unfortunately tend to undermine the quality and consistency of ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Jenga: Harnessing Heterogeneous Memories through Reconfigurable Cache Hierarchies
Conventional memory systems are organized as a rigid hierarchy, with multiple levels of progressively larger and slower memories. Hierarchy allows a simple, fixed design to benefit a wide range of applications, because ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Does invariant recognition predict tuning of neurons in sensory cortex?
Tuning properties of simple cells in cortical V1 can be described in terms of a "universal shape" characterized by parameter values which hold across different species. This puzzling set of findings begs for a general ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Risk Allocation for Temporal Risk Assessment
Temporal uncertainty arises when performing any activity in the natural world. When activities are composed into temporal plans, then, there is a risk of not meeting the plan requirements. Currently, we do not have ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)continuous Relaxation to Over-constrained Temporal Plans
When humans fail to understand the capabilities of an autonomous system or its environmental limitations, they can jeopardize their objectives and the system by asking for unrealistic goals. The objective of this thesis ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Learning and recognition of hybrid manipulation tasks in variable environments using probabilistic flow tubes
Robots can act as proxies for human operators in environments where a human operator is not present or cannot directly perform a task, such as in dangerous or remote situations. Teleoperation is a common interface for ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Robust, Goal-directed Plan Execution with Bounded Risk
There is an increasing need for robust optimal plan execution for multi-agent systems in uncertain environments, while guaranteeing an acceptable probability of success. For ex- ample, a fleet of unmanned aerial vehicles ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Energy-efficient Control of a Smart Grid with Sustainable Homes based on Distributing Risk
The goal of this thesis is to develop a distributed control system for a smart grid with sustainable homes. A central challenge is how to enhance energy efficiency in the presence of uncertainty. A major source of uncertainty ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Unsupervised Learning and Recognition of Physical Activity Plans
This thesis desires to enable a new kind of interaction between humans and computational agents, such as robots or computers, by allowing the agent to anticipate and adapt to human intent. In the future, more robots may ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Learning object segmentation from video data
This memo describes the initial results of a project to create aself-supervised algorithm for learning object segmentation from videodata. Developmental psychology and computational experience havedemonstrated that the ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Investigating shape representation in area V4 with HMAX: Orientation and Grating selectivities
The question of how shape is represented is of central interest to understanding visual processing in cortex. While tuning properties of the cells in early part of the ventral visual stream, thought to be responsible for ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Fluorescence Assay for Polymerase Arrival Rates
To engineer complex synthetic biological systems will require modulardesign, assembly, and characterization strategies. The RNApolymerase arrival rate (PAR) is defined to be the rate that RNApolymerases arrive at a specified ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Permutation Tests for Classification
We introduce and explore an approach to estimating statisticalsignificance of classification accuracy, which is particularly usefulin scientific applications of machine learning where highdimensionality of the data and the ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Direction Estimation of Pedestrian from Images
The capability of estimating the walking direction of people would be useful in many applications such as those involving autonomous cars and robots.We introduce an approach for estimating the walking direction of people ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)The Theory of Timed I/O Automata
Revised version -- November 23, 2004.This paper presents the Timed Input/Output Automaton (TIOA) modeling framework, a basic mathematical framework to support description and analysis of timed systems.
MIT Computer Science and Artificial Intelligence Lab (CSAIL)On The Boolean Algebra of Shape Analysis Constraints
Shape analysis is a promising technique for statically verifyingand extracting properties of programs that manipulatecomplex data structures. We introduce a new characterizationof constraints that arise in parametric ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Dissociated Dipoles: Image representation via non-local comparisons
A fundamental question in visual neuroscience is how to represent image structure. The most common representational schemes rely on differential operators that compare adjacent image regions. While well-suited to encoding ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Near-Optimal Distributed Failure Circumscription
Small failures should only disrupt a small part of a network. One wayto do this is by marking the surrounding area as untrustworthy ---circumscribing the failure. This can be done with a distributedalgorithm using ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)A Reliable Broadcast Scheme for Sensor Networks
In this short technical report, we present a simple yet effective reliable broadcast protocol for sensor networks. This protocol disseminates packets throughout the sensor network by flooding and recovers from losses ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Representation and Detection of Shapes in Images
We present a set of techniques that can be used to represent anddetect shapes in images. Our methods revolve around a particularshape representation based on the description of objects usingtriangulated polygons. This ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Marriage, Honesty, and Stability
Many centralized two-sided markets form a matching between participantsby running a stable marriage algorithm. It is a well-knownfact that no matching mechanism based on a stable marriage algorithmcan guarantee truthfulness ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Dynamic Input/Output Automata: A Formal Model for Dynamic Systems
We present a mathematical state-machine model, the Dynamic I/O Automaton (DIOA) model, for defining and analyzing dynamic systems of interacting components. The systems we consider are dynamic in two senses: (1) components ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Secure Program Execution Via Dynamic Information Flow Tracking
We present a simple architectural mechanism called dynamicinformation flow tracking that can significantly improve thesecurity of computing systems with negligible performanceoverhead. Dynamic information flow tracking ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Selecting Refining and Evaluating Properties for Program Analysis
This research proposes and evaluates techniques for selectingpredicates for conditional program propertiesÂthatis, implications such as p ) q whose consequent must betrue whenever the predicate is true. Conditional ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)New Algorithms for Load Balancing in Peer-to-Peer Systems
Load balancing is a critical issue for the efficient operation of peer-to-peer networks. We give new protocols for several scenarios, whose provable performance guarantees are within a constant factor of optimal. First, ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)On the Max-Flow Min-Cut Ratio for Directed Multicommodity Flows
We give a pure combinatorial problem whose solution determines max-flow min-cut ratio for directed multicommodity flows. In addition, this combinatorial problem has applications in improving the approximation factor of ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Compact Representations for Fast Nonrigid Registration of Medical Images
We develop efficient techniques for the non-rigid registration of medical images by using representations that adapt to the anatomy found in such images. Images of anatomical structures typically have uniform intensity ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)Amorphous Infrastructure for Language Implementation
We propose a method for the robust implementation of simple graphical automataon an amorphous computer. This infrastructure is applied to the implementationof purely functional programming languages. Specifically, it is ...
MIT Computer Science and Artificial Intelligence Lab (CSAIL)