Aggregate Research Projects
The introduction of impedance control in 1985 by Neville Hogan paved the way for a safe, gentle and effective interaction between humans and machines. This interaction is ideal for rehabilitation and is epitomized in the design of manipulanda that pioneered clinical and neurological applications, the most prominent being the MIT-MANUS, developed by Hermano Igo Krebs. Following this line of research, we have recently introduced the MIT's pediatric Anklebot, an adaptive robotic device that provides an “assist-as-needed” therapy and targets ankle movements in children with neurological disorders (Michmizos et al. 2015). Since 2012, the rehabilitation robot has been used successfully in pilot studies in Pediatric Hospitals in the USA and Europe.
One of the biggest challenges in neurodevelopmental disorders today is that they remain behaviorally diagnosed disorders. Despite the large amounts of data from the autistic brain and an abundance of experimental evidences for its pathophysiology, no measures robustly depict the observed differences between an autistic and a typically developing brain. Recently, we measured functional connectivity in the somatosensory cortex in typically developing and autistic
Neurosurgeons have used electrical stimulation since the 60's to locate and distinguish specific brain areas. They soon discovered that stimulation of certain brain nuclei suppresses the symptoms of some neurological disorders. Recent efforts on patient-specific therapeutic approaches revealed the importance of computational methods in guiding deep brain stimulation (DBS), a neuromodulation treatment initially
With the continuous shifting of human activities from offline to online, the Web is no longer just a platform for information sharing and transmission, but a huge online economy where various products or services are distributed from producers to consumers.
Every tens of years the fundemental philosophy that drives the reserach of machine intelligence shifts between empirism and rationalism.
Explainable Recommendation and Search refers to the personalized recommendation and search algorithms that not only provide the user with the search and recommendation results, but also let the user know why such results are provided, i.e., they try to address the problem of "why" in recommendati
Among the many techniques that compose an intelligent Web, a Conversational System (such as Google Now, Apple Siri, and Microsoft Cortana) is one that serves as the direct interactive portal for end-users, which is expected to revolutionize human-computer interaction in the coming years.
Real-time human travel pattern modeling is essential to various applications, from mobile networking, to transportation, urban planning and epidemiology.
Data-driven modeling usually suffers from data sparsity, especially for large-scale modeling for urban phenomena based on single-source urban infrastructure data under fine-grained spatial-temporal contexts.
This project spans three components of Cyber-Physical Systems, i.e., communication, computation and control.
Under the Smart Cities Initiative from the White House, this project on urban cyber-physical-systems is aimed to address emerging urban mobility challenges by big-data-driven analytics.
Carpooling has long held the promise of reducing gas consumption by decreasing mileage to deliver co-riders. Although ad hoc carpools already exist in the real world through private arrangements, little research on the topic has been done. In this paper, we present the first systematic work to design, implement, and evaluate a carpool service, called coRide, in a large-scale taxicab network intended to reduce total mileage for less gas consumption.
The first video demonstrats detection of a wh-question non-manual marker. Tracked face and head is shown on left, while the right image shows the extracted spatial pyramid features. Red bars indicate detection of the wh non-manual marker, while blue bars indicate that the system detects no wh non-manual marker.
Biomedical Image Analysis Projects.
Organ shape plays an important role in various clinical practices such as segmentation.
We consider the minimization of a smooth convex function regularized by the composite prior models.
Accurate face tracking and 3D head pose prediction
Facial Expression Recognition
We develop models for simulating realistic, believable crowds.
In order to rigorously evaluate and compare crowd models, we have developed tools for quantifying the coverage, quality, and failure of crowd simulators, and its ability to emula
We develop algorithms for multi-agent motion planning in real-time dynamic environments.
Our research aims to develop integrated solutions for full-body character animation, planning based control, behavior authoring, and statistical analysis of autonomous virtual hu
We develop computational tools to assist end users to create and experience compelling, interactive, digital stories.
The primary focus of my research is computer security.
We study optimal multi-robot path planning on graphs (MPP) over four minimization objectives: the makespan (last arrival time), the maximum (single-robot tr
This paper introduces a new mobile sensor scheduling problem involving a single robot tasked to monitor several
The overreaching goal of CometCloud is to enable highly heterogeneous, dynamically federated computing and data platforms that can support end-to-end application workflows with diverse and dynamic changing application requirements. This is achieved through (a) autonomic on-demand federation of geographically distributed compute and data resources as needed by the application workflow, and (b) exposing the resulting software-defined federated cyberinfrastructure using elastic cloud abstractions and science-as-a-service platforms. As a result, CometCloud is able to create a nimble and dynamically programmable environment that autonomously evolves over time, adapting to changes in both the federated infrastructure and the application requirements.
DataSpaces is a programming system targeted at current large-scale systems and designed to support dynamic interaction and coordination patterns between scientific applications. DataSpaces essentially provides a semantically specialized shared-space abstraction using a set of staging nodes. This abstraction derives from the tuple-space model and can be associatively accessed by the interacting applications of a simulation workflow. DataSpaces also provides services including distributed in-memory associative object store, scalable messaging, as well as runtime mapping and scheduling of online data analysis operations.
The GreenHPC initiative at Rutgers is a research and educational initiative aiming at addressing several efforts in the intersection of energy efficiency, scalable computing and high performance computing. Key focus areas include (1) Energy efficiency of scientific data analysis pipelines at scale, (2) In-situ data analytics and co-processing at extreme scales and (3) Application-aware cross-layer power management for High Performance Computing systems .GreenHPC also acts as a forum for researchers and the educational community to exchange ideas and experiences on energy efficiency by disseminating research results, educational activities at different levels (PhD, MS, undergraduate - REU, K12 - GSET) and organizing events and editorial activities of related topics
Parasol is a green micro-datacenter partially powered by solar energy and partially cooled by "free-cooling". It comprises a small container, a set of solar panels, and batteries. The container lies on a steel structure placed on the roof of our building. The solar panels are mounted on top of the steel structure and shade the container from the sun. The container hosts two racks of energy-efficient servers (up to 160 of them) and networking equipment. The container uses free cooling whenever possible, and direct-exchange air conditioning otherwise.
Current advances in computer science and other disciplines rely on the massive computation horsepower of data parallel architectures, such as GPUs. Programming data parallel architecture is not easy, as it requires the efficient handling of data movements across the memory hierarchy of thousands of processing cores.
The goal of this activity is to understand the interplay between information and complexity. The current focus centers on computational complexity classes. Complexity classes provide the best tool currently available for understanding the computational complexity of real-world computational problems. Some of these problems are notoriously difficult, but recent progress justifies some optimism that additional useful insight about these complexity classes can be obtained.
Humans can help computers find better answers faster