Job Opening: Health Informatics Data Scientist

#machinelearning #datascience #informatics #healthIT #datamining

Where? Bethesda, MD

Description:

Incumbent will serve as the Data Scientist for the National Intrepid Center of Excellence (NICoE) at Walter Reed National Military Medical Center (WRNMMC) in Bethesda MD. NICoE is a DoD institute dedicated to providing cutting-edge evaluation, treatment, and research for service members dealing with complex interactions of mild traumatic brain injury (mTBI) and psychological health (PH) conditions. The Clinical and Research Informatics group at the NICoE maintains a large-scale database with millions of longitudinal clinical data points.

A PhD or a Master’s degree in computer science or related fields is required. Knowledge of fundamentals of machine learning, data mining and statistical modeling are required. Proficiency in standard data analytics toolkits such as WEKA, R, Python, Matlab, MALLET, SciPy, RapidMinder, and/or Orange is required. Experience in manipulating and analyzing large data sets using existing technologies such as Hadoop, MapReduce, or GPUs is a valuable skill set.

Successful candidates will be expected to conduct research and development of state-of-the-art machine learning techniques that can be used to better understand the complex effects of concussion and mTBI. Applicants are expected to have a strong background in machine learning, data mining, and statistical modeling for predictive analytics. In addition, candidates are expected to have expertise in different data types including categorical, temporal, spatial, imaging, semi-structured, and unstructured data.

Requirements? Candidate must be a US Citizen.

Interest? Contact Jesus J. Caban, PhD at   jesus.j.caban.civ at mail.mil

Job Opening: Health Informatics Data Scientist

#machinelearning #datascience #informatics #healthIT #datamining

Where? Bethesda, MD

Description:

Incumbent will serve as the Data Scientist for the National Intrepid Center of Excellence (NICoE) at Walter Reed National Military Medical Center (WRNMMC) in Bethesda MD. NICoE is a DoD institute dedicated to providing cutting-edge evaluation, treatment, and research for service members dealing with complex interactions of mild traumatic brain injury (mTBI) and psychological health (PH) conditions. The Clinical and Research Informatics group at the NICoE maintains a large-scale database with millions of longitudinal clinical data points.

A PhD or a Master’s degree in computer science or related fields is required. Knowledge of fundamentals of machine learning, data mining and statistical modeling are required. Proficiency in standard data analytics toolkits such as WEKA, R, Python, Matlab, MALLET, SciPy, RapidMinder, and/or Orange is required. Experience in manipulating and analyzing large data sets using existing technologies such as Hadoop, MapReduce, or GPUs is a valuable skill set.

Successful candidates will be expected to conduct research and development of state-of-the-art machine learning techniques that can be used to better understand the complex effects of concussion and mTBI. Applicants are expected to have a strong background in machine learning, data mining, and statistical modeling for predictive analytics. In addition, candidates are expected to have expertise in different data types including categorical, temporal, spatial, imaging, semi-structured, and unstructured data.

Requirements? Candidate must be a US Citizen.

Interest? Contact Jesus J. Caban, PhD at   jesus.j.caban.civ at mail.mil

Senior Amazon staff members will give two technical talks on next week on Tuesday, March 3, in the UC Ballroom on topics of great practical interest and utility.

• Lydia Fitzpatrick, Senior Technical Program Manager for Amazon Mobile Business will give a talk on “Web Performance Optimization” from 4:00pm to 5:00pm.

• Leo Zhadanovsky, Senior Solutions Architect for Amazon Web Services will present an “Introduction to Amazon Web Services (AWS)” from 5:30pm to 6:30pm. The talk with introduce cloud computing and  discuss the various Networking, Compute, Database, Storage, Application, Deployment and Management services that AWS offers. It will demonstrate how to launch a full three tier LAMP stack in minutes, as well as how to setup a simple web server on AWS.  The presentation will also discuss several use-cases, demonstrating how customers such as Enterprises, Startups, and Government Agencies are using AWS to power their computing needs.

The talks will be preceded and followed by an open networking opportunity with Amazon Human Resource representatives. Amazon is interested in students for internships and full-time position who are majoring in Information Systems, Business Technology Administration, Computer Engineering, Computer Science, and Cybersecurity.

Senior Amazon staff members will give two technical talks on next week on Tuesday, March 3, in the UC Ballroom on topics of great practical interest and utility.

• Lydia Fitzpatrick, Senior Technical Program Manager for Amazon Mobile Business will give a talk on “Web Performance Optimization” from 4:00pm to 5:00pm.

• Leo Zhadanovsky, Senior Solutions Architect for Amazon Web Services will present an “Introduction to Amazon Web Services (AWS)” from 5:30pm to 6:30pm. The talk with introduce cloud computing and  discuss the various Networking, Compute, Database, Storage, Application, Deployment and Management services that AWS offers. It will demonstrate how to launch a full three tier LAMP stack in minutes, as well as how to setup a simple web server on AWS.  The presentation will also discuss several use-cases, demonstrating how customers such as Enterprises, Startups, and Government Agencies are using AWS to power their computing needs.

The talks will be preceded and followed by an open networking opportunity with Amazon Human Resource representatives. Amazon is interested in students for internships and full-time position who are majoring in Information Systems, Business Technology Administration, Computer Engineering, Computer Science, and Cybersecurity.

Senior Amazon staff members will give two technical talks on next week on Tuesday, March 3, in the UC Ballroom on topics of great practical interest and utility.

• Lydia Fitzpatrick, Senior Technical Program Manager for Amazon Mobile Business will give a talk on “Web Performance Optimization” from 4:00pm to 5:00pm.

• Leo Zhadanovsky, Senior Solutions Architect for Amazon Web Services will present an “Introduction to Amazon Web Services (AWS)” from 5:30pm to 6:30pm. The talk with introduce cloud computing and  discuss the various Networking, Compute, Database, Storage, Application, Deployment and Management services that AWS offers. It will demonstrate how to launch a full three tier LAMP stack in minutes, as well as how to setup a simple web server on AWS.  The presentation will also discuss several use-cases, demonstrating how customers such as Enterprises, Startups, and Government Agencies are using AWS to power their computing needs.

The talks will be preceded and followed by an open networking opportunity with Amazon Human Resource representatives. Amazon is interested in students for internships and full-time position who are majoring in Information Systems, Business Technology Administration, Computer Engineering, Computer Science, and Cybersecurity.

What ethical problems might advances in brain-computer interface technology create?

That’s the question that will be debated Monday evening as part of the UMBC BioCOM Ethical Debates (B-Ethical) series co-sponsored by the Biology Council of Majors and Philosophers Anonymous.

The event will take place from 7:30pm to 9:00pm on Monday, March 2 in room 104 of the ITE building (lecture hall 7) at UMBC.

One team will be lead by Professor Richard Wilson, a member of UMBC’s Philosophy department with a focus on applied ethics. LThe other team is headed by Kavita Krishnaswamy, a Ph.D. student from UMBC’s Computer Science and Electrical Engineering department whose dissertation research is exploring how robotics can help increase autonomy in daily living for people with disabilities. Kavita, who has a severe physical disability herself, will participate via a Beam Smart Presence robot. Also on the team is CSEE Professor Tinoosh Mohsenin whose research includes deep learning to interpret high-resolution multichannel electroencephalography data.

Some details from the B-Ethical post:

“The field of Brain Computer Interface (BCI) research has led to the engineering of a device system that allows you to convert your thoughts into action by using your brain’s neural activity: think controlling a robotic arm via electrodes that are placed on a brain that controls the arm with its thoughts.This revolutionary field in neuroscience has given hope to those who are severely disabled including but not limited to those who suffer from blindness, paralysis, and other debilitative physical disabilities. Hence, computer-brain interface technology has the potential and power to do incredible good.

Some note, however, the importance of recognizing the possibility for ethical wrongdoing. One such ethical question surrounding this field is the possibility for social stratification as a result of barriers such as cost. If brain enhancement does become effective and ubiquitous, there is the possibility that pressure to enhance one’s brain in order to keep up with rising competition, might leave some unable to access this enhancement because of financial barriers. Hence, this will not only widen the gap in society between the rich and the poor, but become dangerous, creating a social strata in which an intellectual elite armed with thought-controlled weapons would government the people. One could think of an army with capabilities such as night vision eyes, fingers that can fire bullets, humans made immortal by copying their genetic material into more resilient hardware, these endless possibilities ascend into the world of sci-fi as they are scary.”

Ph.D. Dissertation Proposal

User Identification in Wireless Networks

Christopher Swartz

9:00-11:00pm Friday, 27 February 2015, ITE 325B

Wireless communication using the 802.11 specifications is almost ubiquitous in daily life through an increasing variety of platforms. Traditional identification and authentication mechanisms employed for wireless communication commonly mimic physically connected devices and do not account for the broadcast nature of the medium. Both stationary and mobile devices that users interact with are regularly authenticated using a passphrase, pre-shared key, or an authentication server. Current research requires unfettered access to the user’s platform or information that is not normally volunteered.

We propose a mechanism to verify and validate the identity of 802.11 device users by applying machine learning algorithms. Existing work substantiates the application of machine learning for device identification using Commercial Off-The-Shelf (COTS) hardware and algorithms. This research seeks the refinement of and investigation of features relevant to identifying users. The approach is segmented into three main areas: a data ingest platform, processing, and classification.

Initial research proved that we can properly classify target devices with high precision, recall, and ROC using a sufficiently large real-world data set and a limited set of features. The primary contribution of this work is exploring the development of user identification through data observation. A combination of identifying new features, creating an online system, and limiting user interaction is the objective. We will create a prototype system and test the effectiveness and accuracy of it’s ability to properly identify users.

Committee: Drs. Joshi (Chair/Advisor), Nicholas, Younis, Finin, Pearce, Banerjee

Visual understanding of human actions

Dr. Hamed Pirsiavash

Postdoctoral Research Associate
Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology

12:00-1:00pm Friday, 27 February, 2015, ITE 325B

The aim in computer vision is to develop algorithms for computers to “see” the world as humans do. Central to this goal is understanding human behavior as an intelligent agent functioning in the visual world. For instance, in order for a robot to interact with us, it should understand our actions to produce the proper response. My work explores several directions towards computationally representing and understanding human actions.

In this talk, I will focus on detecting actions and judging their quality. First, I will describe simple grammars for modeling long-scale temporal structure in human actions. Real-world videos are typically composed of multiple action instances, where each instance is itself composed of sub-actions with variable durations and orderings. Our grammar models capture such hierarchical structure while admitting efficient, linear-time parsing algorithms for action detection. The second part of the talk will describe our algorithms for going beyond detecting actions to judging how well they are performed. Our learning-based framework provides feedback to the performer to improve the quality of his/her actions.

Host: Mohamed Younis

Ph.D. Dissertation Proposal

MLVFS: A Scalable Storage System For Managing Big Scientific Data

Navid Golpayegani

3:00-5:00pm Tuesday 24 February 2015, ITE 346

Managing peta or exabytes of data with hundreds of millions to billions of files is a necessary first step towards an effective big data computing and collaboration environment for distributed systems. Current file system designs have focused on providing better and faster data distribution. Managing the directory structure for data discovery becomes an essential element of the scalability problems for big data systems. Recent designs are addressing the challenge of exponential growth of files. Still largely unexplored is the research for dealing with the organizational aspect of managing big data systems with hundreds of millions of files. Most file systems organize data into static directory structures making data discovery, when dealing with large data sets, hard and slow.

This thesis will propose a unique Multiview Lightweight Virtual File System (MLVFS) design to primarily deal with the data organizational management problem in big data file systems. MLVFS is capable of the dynamic generation of directory structures to create multiple views of the same data set. With multiple views, the storage system is capable of organizing available data sets by differing criteria such as location or date without the need to replicate data or use symbolic links. In ad- dition, MLVFS addresses scalability issues associated with the growth of the stored files by removing the internal metadata system and replacing it with generally avail- able external metadata information (i.e. data base servers, project compute servers, remote repositories, etc.). This thesis, moreover, proposes to add, plug in capabilities not normally found in file systems that make this system highly flexible, in terms of specifying sources of meta data information, dynamic file format streaming and other file handling features.

The performance of MLVFS will be tested in both simulated environments as well as real world environments. MLVFS will be installed on the BlueWave cluster at UMBC for simulated load testing to measure the performance for various loads. Simultaneously, stable version of MLVFS will run in real world production environ- ments such as those of the NASA MODIS instrument processing system (MODAPS). The MODAPS system will be used to show examples of real world use cases for MLVFS. Additionally, there will be other systems explored for the real world use of MLVFS, such as at NIST for research into Biomedical Image Stitching.

Committee: Drs. Milton Halem (Chair, Advisor), Yelena Yesha, Charles Nicholas, John Dorband, Daniel Duffy

Understanding Social Spammers: A Data Mining Perspective
Xia “Ben” Hu

Computer Science and Engineering
Arizona State University

12:00-1:00 Tuesday, 24 February 2015

With the growing popularity of social media, social spamming has become rampant on all platforms. Many (fake) accounts, known as social spammers, are employed to overwhelm legitimate users with unwanted information. Social spammers are unique due to their coordinated efforts to launch attacks such as distributing ads to generate sales, disseminating pornography and viruses, executing phishing attacks, or simply sabotaging a system’s reputation. In this talk, I will introduce a novel and systematic analysis of social spammers from a data mining perspective to tackle the challenges raised by social media data for spammer detection. Specifically, I will formally define the problem of social spammer detection and discuss the unique properties of social media data that make this problem challenging. By analyzing the two most important types of information, network and content information, I will introduce a unified framework by collectively using heterogeneous information in social media. To tackle the labeling bottleneck in social media, I will show how we can take advantage of the existing information about spam in email, SMS, and on the web for spammer detection in microblogging. I will also present a solution for efficient online processing to handle fast-evolving social spammers.

Xia Hu is a Ph.D. candidate in Computer Science and Engineering at Arizona State University, supervised by Professor Huan Liu. His research interests include data mining, machine learning, social network analysis, etc. As a result of his research work, he has published nearly 40 papers in several major academic venues, including WWW, SIGIR, KDD, WSDM, IJCAI, AAAI, CIKM, SDM, etc. One of his papers was selected for the Best Paper Shortlist in WSDM’13. He is the recipient of IEEE “Atluri Award” Scholarship, 2014 ASU’s President’s Award for Innovation, and Faculty Emeriti Fellowship. He has served on program committees for several major conferences such as WWW, IJCAI, SDM and ICWSM, and reviewed for multiple journals, including IEEE TKDE, ACM TOIS and Neurocomputing. His research attracts wide range of external government and industry sponsors, including NSF, ONR, AFOSR, Yahoo!, and Microsoft.

– more information and directions: http://bit.ly/UMBCtalks –