The College Qualified Leaders (CQL) program offers interested students (undergraduates and graduates) the opportunity to spend the summer working on research in a Department of Defense laboratory. These are paid internships.

The Multilingual Computing Branch (MLCB) at the U.S. Army Research Laboratory (ARL), based in Adelphi, Maryland is looking for talented computer science and engineering interns this summer. Interns will work closely with ARL researchers on current projects to develop cutting-edge natural language processing and machine learning software. The MLCB has several ongoing projects in computational linguistics/natural language processing, including active projects in machine translation, human-robot communication, and social media analysis.

Interested applicants should have a strong academic record and be willing to take the initiative on projects.

Proficiency in Java, C++, Python, Scala, or similar languages is desired. Proficiency in natural language processing, speech processing, machine learning, or pattern recognition is a plus. Proficiency in multiple natural languages is a plus. Two references are required. Applicants must be U.S. Citizens.

The CQL program is not restricted to the summer months and, thus, it may be possible for the intern to continue working with ARL during the school year.

Interested students should apply via the following URL: http://www.usaeop.com/programs/apprenticeships/cql/

It is strongly recommended that interested students apply soon, as the window of opportunity for researchers to select summer interns is rapidly closing.

For more information about ARL, please visit http://www.arl.army.mil

Note that additional CQL positions may also be available for students in other disciplines and/or at other locations (e.g., Aberdeen Proving Ground).

The College Qualified Leaders (CQL) program offers interested students (undergraduates and graduates) the opportunity to spend the summer working on research in a Department of Defense laboratory. These are paid internships.

The Multilingual Computing Branch (MLCB) at the U.S. Army Research Laboratory (ARL), based in Adelphi, Maryland is looking for talented computer science and engineering interns this summer. Interns will work closely with ARL researchers on current projects to develop cutting-edge natural language processing and machine learning software. The MLCB has several ongoing projects in computational linguistics/natural language processing, including active projects in machine translation, human-robot communication, and social media analysis.

Interested applicants should have a strong academic record and be willing to take the initiative on projects.

Proficiency in Java, C++, Python, Scala, or similar languages is desired. Proficiency in natural language processing, speech processing, machine learning, or pattern recognition is a plus. Proficiency in multiple natural languages is a plus. Two references are required. Applicants must be U.S. Citizens.

The CQL program is not restricted to the summer months and, thus, it may be possible for the intern to continue working with ARL during the school year.

Interested students should apply via the following URL: http://www.usaeop.com/programs/apprenticeships/cql/

It is strongly recommended that interested students apply soon, as the window of opportunity for researchers to select summer interns is rapidly closing.

For more information about ARL, please visit http://www.arl.army.mil

Note that additional CQL positions may also be available for students in other disciplines and/or at other locations (e.g., Aberdeen Proving Ground).

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