A5087441359- Cloud Computing and Resource Management
- Sepsis Diagnosis and Treatment
- Advanced Data Storage Technologies
- Network Security and Intrusion Detection
- Distributed and Parallel Computing Systems
- Scientific Computing and Data Management
- Parallel Computing and Optimization Techniques
- Complex Network Analysis Techniques
- COVID-19 Clinical Research Studies
- Advanced Database Systems and Queries
- Graph Theory and Algorithms
- Cardiac, Anesthesia and Surgical Outcomes
- Kawasaki Disease and Coronary Complications
- Neonatal and Maternal Infections
- Data Management and Algorithms
- Respiratory Support and Mechanisms
- Anomaly Detection Techniques and Applications
- Ion Transport and Channel Regulation
- Data Visualization and Analytics
- Security in Wireless Sensor Networks
- Big Data Technologies and Applications
- Inhalation and Respiratory Drug Delivery
- IoT and Edge/Fog Computing
- Congenital Heart Disease Studies
- Human Mobility and Location-Based Analysis
Children's Hospital of Michigan
2021-2025
Central Michigan University
2021-2025
Moscow Institute of Thermal Technology
2021-2023
MIT Lincoln Laboratory
2012-2022
Michigan United
2022
Wayne State University
2021
University at Buffalo, State University of New York
2019-2021
Duke University
2021
Women & Children's Hospital of Buffalo
2020
Jacobs (United States)
2020
Interactive massively parallel computations are critical for machine learning and data analysis. These a staple of the MIT Lincoln Laboratory Supercomputing Center (LLSC) has required LLSC to develop unique interactive supercomputing capabilities. Scaling frameworks, such as TensorFlow, analysis environments, MATLAB/Octave, tens thousands cores presents many technical challenges - in particular, rapidly dispatching tasks through scheduler, Slurm, starting instances applications with...
To understand critical paediatric coronavirus disease 2019 (COVID-19) and evaluate factors associated with mortality in children from high low-middle income countries.Prospective, observational study of critically ill hospitalised for COVID-19 18 countries throughout North America, Latin Europe between April 1 December 31, 2020. Associations were evaluated using logistic regression.557 patients (median age, 8 years; 24% <2 years) enrolled 55 sites (63% American). Half had comorbidities....
A crucial element of large web companies is their ability to collect and analyze massive amounts data. Tuple store databases are a key enabling technology employed by many these (e.g., Google Big Table Amazon Dynamo). stores highly scalable run on commodity clusters, but lack interfaces support efficient development mathematically based analytics. D4M (Dynamic Distributed Dimensional Data Model) has been developed provide rich interface tuple (and structured query language "SQL" databases)....
The ability to collect and analyze large amounts of data is a growing problem within the scientific community. gap between users calls for innovative tools that address challenges faced by big volume, velocity variety. Numerous exist allow store, query index these massive quantities data. Each storage or database engine comes with promise dealing complex Scientists engineers who wish use systems often quickly find there no single technology offers panacea complexity information. When using...
The Apache Accumulo database is an open source relaxed consistency that widely used for government applications. designed to deliver high performance on unstructured data such as graphs of network data. This paper tests the using from Graph500 benchmark. Dynamic Distributed Dimensional Data Model (D4M) software implement benchmark a 216-node cluster running MIT SuperCloud stack. A peak over 100,000,000 inserts per second was achieved which 100x larger than highest previously published value...
Non-traditional, relaxed consistency, triple store databases are the backbone of many web companies (e.g., Google Big Table, Amazon Dynamo, and Facebook Cassandra). The Apache Accumulo database is a high performance open source consistency that widely used for government applications. Obtaining full benefits requires using novel schemas. Dynamic Distributed Dimensional Data Model (D4M)[http://www.mit.edu/~kepner/D4M] provides uniform mathematical framework based on associative arrays...
Production high-performance computing (HPC) systems are adopting and integrating GPUs into their design to accommodate artificial intelligence (AI), machine learning, data visualization workloads. To aid with the operations of new existing GPU-based large-scale systems, we provide a detailed characterization system operations, job characteristics, user behavior, trends on contemporary GPU-accelerated production HPC system. Our insights indicate that pre-mature phases in modern AI workflow...
Big Data (as embodied by Hadoop clusters) and Compute MPI provide unique capabilities for storing processing large volumes of data. clusters make distributed computing readily accessible to the Java community high parallel efficiency compute intensive workloads. Bringing big data communities together is an active area research. The LLGrid team has developed deployed a number technologies that aim best both worlds. MapReduce allows map/reduce programming model be used quickly efficiently in...
The MIT SuperCloud database management system allows for rapid creation and flexible execution of a variety the latest scientific databases, including Apache Accumulo SciDB. It is designed to permit these databases run on High Performance Computing Cluster (HPCC) platform as seamlessly any other HPCC job. ensures seamless migration resources assigned by scheduler centralized storage files when not running. also permits snapshotting allow researchers experiment push limits technology without...
BACKGROUND AND OBJECTIVES: The decline in hospital mortality children hospitalized with sepsis has increased the number of survivors. These survivors are at risk for adverse long-term outcomes, including readmission and recurrent or unresolved infections. We described epidemiology 90-day readmissions after hospitalization children. tested hypothesis that a increases odds readmissions. METHODS: Retrospective cohort analysis Nationwide Readmissions Database. included index unplanned admissions...
Timely empiric antimicrobial therapy is associated with improved outcomes in pediatric sepsis, but minimal data exist to guide therapy. We sought describe the prevalence of four pathogens that are not part routine coverage (e.g., Staphylococcus aureus, Pseudomonas aeruginosa, Clostridium difficile, and fungal infections) sepsis patients a contemporary nationally representative sample.This was retrospective cohort study using administrative data.We used Nationwide Readmissions Database from...
Massive power-law graphs drive many fields: metagenomics, brain mapping, Internet-of-things, cybersecurity, and sparse machine learning. The development of novel algorithms systems to process these data requires the design, generation, validation enormous with exactly known properties. Such accelerate proper testing new are a prerequisite for success on real applications. Many random graph generators currently exist that require realizing in order know its exact properties: number vertices,...
Knights Landing (KNL) is the code name for second-generation Intel Xeon Phi product family. KNL has generated significant interest in data analysis and machine learning communities because its new many-core architecture targets both of these workloads. The vector processor design enables it to exploit much higher levels parallelism. At Lincoln Laboratory Supercomputing Center (LLSC), majority users are running applications such as MATLAB Octave. More recently, applications, UC Berkeley Caffe...
The Dynamic Distributed Dimensional Data Model (D4M) library implements associative arrays in a variety of languages (Python, Julia, and Matlab/Octave) provides lightweight in-memory database implementation hypersparse that are ideal for analyzing many types network data. D4M relies on which combine properties spreadsheets, databases, matrices, graphs, networks, while providing rigorous mathematical guarantees, such as linearity. Streaming updates put enormous pressure the memory hierarchy....
Our society has never been more dependent on computer networks. Effective utilization of networks requires a detailed understanding the normal background behaviors network traffic. Large-scale measurements are computationally challenging. Building prior work in interactive supercomputing and GraphBLAS hypersparse hierarchical traffic matrices, we have developed an efficient method for computing wide variety streaming quantities diverse time scales. Applying these methods to 100,000,000,000...
The Internet has never been more important to our society, and understanding the behavior of is essential. Center for Applied Data Analysis (CAIDA) Telescope observes a continuous stream packets from an unsolicited darkspace representing 1/256 Internet. During 2019 2020 over 40,000,000,000,000 unique were collected largest ever assembled public corpus traffic. Using combined resources Supercomputing Centers at UC San Diego, Lawrence Berkeley National Laboratory, MIT, spatial temporal...
Lemierre syndrome is defined by septic thrombophlebitis of the internal jugular vein caused Fusobacterium. Historically, these infections originate from oropharynx and typically are seen in older children, adolescents young adults. More recently, otogenic sources younger children have been described with increasing frequency. We present a case two-year old, who initially developed an otitis media perforation tympanic membrane went on to develop mastoiditis non-occlusive thrombosis venous...