UMBC undergraduate and graduate students interested in cybersecurity can apply for an NSF CyberCorps: Scholarship For Service scholarship by 20 November 2015.

The NSF CyberCorps: Scholarship For Service program is designed to increase and strengthen the cadre of federal information assurance professionals that protect the government’s critical information infrastructure. This program provides scholarships that may fully fund the typical costs incurred by full-time students while attending a participating institution, including tuition and education and related fees. Participants also receive stipends of $22,500 for undergraduate students and $34,000 for graduate students.

Applicants must be be full-time UMBC students within two years of graduation with a BS or MS degree; a student within three years of graduation with both the BS/MS degree; a student participating in a combined BS/MS degree program; or a research-based doctoral student within three years of graduation in an academic program focused on cybersecurity or information assurance. Recipients must also be US citizens; meet criteria for Federal employment; and be able to obtain a security clearance, if required.

For more information and instructions on how to apply see the UMBC CISA site or the OPM SFS site. Contact Dr. Alan Sherman (Sorry, you need javascript to view this email address. ) for questions not answered on those sites.

Name: Amir Salar Sepehri 

Internship, Co-op or Research Site: Johns Hopkins University, School of Medicine

Position Title: Research Program Assistant

Major(s)/Minor(s): Biological Science (B.S.) & Psychology (B.A.) 

Expected Graduation Year: May 2016

Briefly describe your internship, co-op,  or research opportunity, including your day-to-day tasks, responsibilities, and assignments.

I worked as a research program assistant at Johns Hopkins University, School of Medicine (Department of Geriatrics) during the summer. I went to the Bayview campus on Mondays, Wednesdays, and Fridays from 8:30 AM to 5:00 PM and worked with a group of 15-20 people. At Johns Hopkins Bayview Medical Center, we worked on several different research studies, and I helped the team by:

1. Assisting the unit supervisor with recruitment and screening activities

2. Assisting the unit supervisor with enrollment of participants for multiple research programs

3. Completing the data entry and filings, and checking the data and report for consistency and accuracy

4. Assisting with other research related assignments as needed

I also interacted with multidisciplinary clinical and research teams on campus and participated in team meetings every week.

Describe the process of obtaining your internship, research, or co-op opportunity.

I was always interested in interning in a research organization while being an undergraduate. While looking for different opportunities that were close to the UMBC campus, I found a research program at the Geriatrics Department at Johns Hopkins University. Fortunately, I found out that one of my professors (Dr. Hughes from the Department of Biological Sciences) used to be part of that same program and was still in contact with the principal investigator. She was kind enough to recommend me to the program coordinator, and after that, I sent my resume, got an interview, and started working as a volunteer at first, and then as an intern during the summer. I currently work as a part-time employee at Bayview on Fridays. 

What have you enjoyed the most about your position or organization/company?

As a pre-medicine student interested in doing research, working as an intern at Johns Hopkins was a great experience which helped me to have a better understanding of what I want to pursue when I start my journey in medical school. During the summer, I had a chance to be part of a real research study from the very beginning and observe the entire cycle. I helped the team with recruiting the participants, conducting interviews, taking different tests, and working on the samples in laboratory.

How do you believe you have made an impact through your work?

Although the elderly population (by definition, adults who are 65 years and older) represent almost 15% of the U.S. population, unfortunately they are usually forgotten, and their medical needs are mismanaged in our current healthcare system. I believe that by interning in a research program at the Geriatrics Department, I am a small part of a team that is trying to help this population and is working towards finding solutions for their medical needs. I believe that this rewarding experience will help the elderly population in the near future. 

What advice would you give to another student who is seeking an internship or similar experience?

In terms of the application process, I would encourage you to apply early and to many different programs (not just one or two!). But in general, I would say to try your best, be very persistent with what you want, and do NOT underestimate yourself! 

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Want to be the next Intern of the Week? Make sure to fill out this form and stay tuned. New interns are announced every Friday!

Are you a member on Zimride? If not, you should be! Zimride is a fun and easy way to share the seats in your car or catch a ride. With Zimride, you can find UMBC friends, classmates, and coworkers going the same way you are. Zimride is hosting a contest for you to win a $50 MasterCard gift card! All you have to do is sign up for Zimride, log at least one commute in your Commute Calendar between the days of November 2nd-6th and you will be entered to win! Be sure to tell your friends!

https://www.zimride.com/umbc/

CSEE Professor Tinoosh Mohsenin received a $212,000 grant from the National Science Foundation for a three-year project that will develop a heterogeneous ultra low-power accelerator for wearable biomedical computing. The work will be done in collaboration with researchers at George Mason University and students in the UMBC Energy Efficient High Performance Computing Lab.

With the rapid advances in small, low-cost wearable computing technologies, there is a tremendous opportunity to develop personal health monitoring devices capable of continuous vigilant monitoring of physiological signals. Wearable biomedical devices have the potential to reduce the morbidity, mortality, and economic cost associated with many chronic diseases by enabling early intervention and preventing costly hospitalizations. These low-power systems require to have the capacity to provide fast and accurate processing and interpretation of vast amounts of data and generate smart alarms only when warranted. The project will build the foundation of the next generation of heterogeneous biomedical signal processing platforms that can address the current and future generation energy-efficiency requirements and computational demands.

The interdisciplinary project is expected to inspire and enable new approaches to healthcare monitoring, and can significantly impact several fields including human-centered cyber-physical systems, cyber-security, mobile communications, bioinformatics and applications that require high performance and energy efficient embedded computing from different sensors.

How to Run Sustainable and Effective
Computing Summer Camps

Note: One of the workshops will be held at UMBC on December 6, 2015.

Summer camps are a great way to increase interest in computing. They can especially be helpful for girls and underrepresented minorities. The Expanding Computing Education Pathways (ECEP) Alliance is offering a webinar and two workshops this fall on how to run financially self-sustaining and effective computing summer camps. Attendees who are from ECEP partner and associate states can apply for up to $4,999.99 in funds for equipment for the camps if they attend one of the workshops (in-person or remotely).

The webinars and workshops are free and lunch will be provided at the workshops. Attendees who attend the workshops in-person and live more than 30 miles from the workshop location can apply for up to $100 reimbursement for expenses if they are from our partner or associate states. The workshops can also be attended remotely via webinar software that only requires a browser and internet connection.

Who can attend

College and University faculty and staff, high school computing teachers, and non-profit staff that offer summer camps at colleges and universities

When and where

• 1 hour webinar – Oct 28th from 8pm – 9pm EDT – We recommend that you attend this to find out how to get started, especially if you haven’t run computing camps before.
• 1st workshop – November 21 10am – 3pm CST – At the University of Texas at Austin
• 2nd workshop – Dec 6, 12pm – 5pm EST – At the University of Maryland Baltimore County

ECEP has $45,000 in seed funds that we can use to buy equipment for these camps thanks to a generous gift from Oracle as well as funds from the National Science Foundation. Qualifying institutions will have to apply for the funds. The webinar and workshops are to help you understand how to create a great application and run effective and financially self-sustaining camps.

What you will learn

• What to consider when starting or expanding camps for 4th – 12th grade students
• The equipment and tools that we recommend you use in the camps
• Details on how to plan, advertise, run, and evaluate the camps
• How to apply for up to $4,999.99 in funds for equipment for the camps
• How the applications for equipment funds are evaluated

See the ECEP flyer and apply for the webinar and workshops here.

Lyrics Augmented Multi-modal
Music Recommendation

Abhay Kashyap

1:00pm Friday 30 October, ITE 325b

In an increasingly mobile and connected world, digital music consumption has rapidly increased. More recently, faster and cheaper mobile bandwidth has given the average mobile user the potential to access large troves of music through streaming services like Spotify and Google Music that boast catalogs with tens of millions of songs. At this scale, effective music recommendation is critical for music discovery and personalized user experience.

Recommenders that rely on collaborative information suffer from two major problems: the long tail problem, which is induced by popularity bias, and the cold start problem caused by new items with no data. In such cases, they fall back on content to compute similarity. For music, content based features can be divided into acoustic and textual domains. Acoustic features are extracted from the audio signal while textual features come from song metadata, lyrical content, collaborative tags and associated web text.

Research in content based music similarity has largely been focused in the acoustic domain while text based features have been limited to metadata, tags and shallow methods for web text and lyrics. Song lyrics house information about the sentiment and topic of a song that cannot be easily extracted from the audio. Past work has shown that even shallow lyrical features improved audio-only features and in some tasks like mood classification, outperformed audio-only features. In addition, lyrics are also easily available which make them a valuable resource and warrant a deeper analysis.

The goal of this research is to fill the lyrical gap in existing music recommender systems. The first step is to build algorithms to extract and represent the meaning and emotion contained in the song’s lyrics. The next step is to effectively combine lyrical features with acoustic and collaborative information to build a multi-modal recommendation engine.

For this work, the genre is restricted to Rap because it is a lyrics-centric genre and techniques built for Rap can be generalized to other genres. It was also the highest streamed genre in 2014, accounting for 28.5% of all music streamed. Rap lyrics are scraped from dedicated lyrics websites like ohhla.com and genius.com while the semantic knowledge base comprising artists, albums and song metadata come from the MusicBrainz project. Acoustic features are directly used from EchoNest while collaborative information like tags, plays, co-plays etc. come from Last.fm.

Preliminary work involved extraction of compositional style features like rhyme patterns and density, vocabulary size, simile and profanity usage from over 10,000 songs by over 150 artists. These features are available for users to browse and explore through interactive visualizations on Rapalytics.com. Song semantics were represented using off-the-shelf neural language based vector models (doc2vec). Future work will involve building novel language models for lyrics and latent representations for attributes that is driven by collaborative information for multi-modal recommendation.

Committee: Drs. Tim Finin (Chair), Anupam Joshi, Pranam Kolari (WalmartLabs), Cynthia Matuszek and Tim Oates