Summer interns earn $3,500 while working for eight weeks in a research laboratory with a faculty mentor. Interns have the opportunity to learn cutting-edge research skills from faculty, post-doctoral fellows, and current graduate students. Mentor assignments are based on the intern's interests and goals.

Eligibility:

Applicants should be US citizens or permanent residents who have completed one year of undergraduate work and are interested in obtaining a PhD in Pharmaceutical Sciences.

How To Apply:

The application deadline is April 15.

To apply, please:

1. Complete the online application, which includes two essays, each with a 500 word limit. Detail your research interests and why you are interested in participating in the Summer Research Program, and describe your research experience, if applicable.
2. Submit transcripts from all colleges attended (photocopies are allowed).
3. Submit two letters of recommendation.

Google Code Jam 2014 Registration is open and the qualification round starts on Friday, April 11, 2014. Google Code Jam is an international programming competition hosted and administered by Google. The competition began in 2003 as a means to identify top engineering talent for potential employment at Google.

The competition consists of a set of algorithmic problems which must be solved in a fixed amount of time. Competitors may use any programming language and development environment to obtain their solutions. More than 45,000 coders registered to compete last year and the winner, Ivan Miatselski won the $15,000 grand prize.

If you are interested in finding out more, see the Google Code Jam quick start guide and try some of the practice problems from past competitions. The first qualification round starts on April 11 and the finals will take place in Los Angeles on August 15.

Google Code Jam 2014 Registration is open and the qualification round starts on Friday, April 11, 2014. Google Code Jam is an international programming competition hosted and administered by Google. The competition began in 2003 as a means to identify top engineering talent for potential employment at Google.

The competition consists of a set of algorithmic problems which must be solved in a fixed amount of time. Competitors may use any programming language and development environment to obtain their solutions. More than 45,000 coders registered to compete last year and the winner, Ivan Miatselski won the $15,000 grand prize.

If you are interested in finding out more, see the Google Code Jam quick start guide and try some of the practice problems from past competitions. The first qualification round starts on April 11 and the finals will take place in Los Angeles on August 15.

Meet Sanchari. She is a Biochemistry and Molecular Biology major and current URA (Undergraduate Research Award) Scholar. She is also engaged in oncological research in a UMBC laboratory and has also worked in a lab at the Howard Hughes Medical Institute.

How did you find your mentor for year research project?
When I was looking for research mentors on campus, some upper classmen suggested that I look into Dr. Rosenberg’s lab, especially since I had some background in oncological research. I looked at her research on the UMBC website, and the research excited me; although I had conducted oncological research before, I had not explored the immunological approach to cancer before. I e-mailed Dr. Rosenberg about my interest in her lab, and it all started from there!

Is this your first independent research project?
No, I have worked in three other research labs before this, including an HHMI funded project.

How much time do you put into it?
I try to put in an average of 16-18 hours a week for research, but it varies depending on the experiment being conducted.

How did you hear about the Undergraduate Research Award (URA) program?
My PI, Dr. Rosenberg, emailed me about the URA. One of the senior undergraduates in my lab was a previous URA scholar, and she encouraged me to apply.

Was the application difficult to do?
Not at all – it was very straightforward and easily accessible.

What has been the hardest part about your research?
The hardest part about research is effectively communicating the results of the research to others, and this is something that Dr. Rosenberg, my mentor, Dr. Sinha, and others in the lab are working with me on so that I can continuously improve on this essential skill for any researcher. I am practicing this skill by presenting in lab meetings and conferences both here at UMBC, as well as other states.

What was the most unexpected thing?
The most unexpected thing about this internship was that, through my experience, I am getting to make so many valuable connections to people, who can (or have already) helped me out in many ways. It gives me immense pleasure to see that people understand and appreciate the research that I am doing, and are willing to help me to continue researching.

How does your research relate to your work in other classes?
When we research, we are expected to learn very specific details about one subtopic under a larger umbrella subject. In college classes, we start by learning those umbrella subjects in a very broad manner, and as classes go on, we learn the more specific details. Therefore, there are many times that I have light bulb moments in class or lab, when I am able to connect what I already know with what I am learning.

What is your advice to other students about getting involved in research?
Research is extremely important, no matter what subject area we are involved in, because it teaches us to think critically, a skill essential for any type of career today. I would like to encourage all students to get involved in some type of research in these undergraduate years. The experience can help you to narrow down your career choice and develop essential skills for that career as well.

Read her abstract here...

Scalable monitoring and kernel learning for energy grids

Vassilis Kekatos
Department of Electrical and Computer Engineering
University of Minnesota

12:00pm-1:00pm, Thursday, 13 March 2014, ITE 325b, UMBC

The smart grid vision urges for enhanced situational awareness, sustainability, and economics over our energy systems. While meters are being installed throughout the grid, algorithms that can effectively process this big data deluge are now demanded. Aligned to that end, this talk focuses first on scalable grid monitoring. Albeit control centers monitor their local grids independently, deregulation and renewables call for power system state estimation (PSSE) at the interconnection level. To address the complexity and communication challenges involved, a decentralized PSSE framework based on the alternating direction method of multipliers has been developed. Beyond conventional least-squares, our framework can identify outliers and circuit breaker statuses as verified on IEEE grids having thousands of nodes. Electricity market inference is the second theme of this talk. We will first demonstrate how grid topologies can be revealed using only publicly available real-time energy prices. This becomes feasible after recognizing that the price matrix can be factorized as the product of the grid Laplacian times a low-rank and sparse matrix. Leveraging the link between energy markets and the underlying physical grids, we will then cast day-ahead price forecasting as a kernel learning task. Through a novel nuclear norm-based regularization, kernels across pricing nodes and hours are systematically selected. Numerical tests using real data from the Midwest ISO market corroborate the interpretative merits of our schemes.

Dr. Vassilis Kekatos is currently a postdoctoral associate with the ECE Dept. of the University of Minnesota, Minneapolis. He obtained his Ph.D. in Computer Engineering and Science from the University of Patras, Greece, in 2007. In 2009, he received a Marie Curie fellowship. During the summer of 2012, he worked as a consultant for Windlogics Inc. His current interests lie in the areas of signal processing, optimization, and statistical learning towards modernizing our energy systems.

Host: Tulay Adali

Scalable monitoring and kernel learning for energy grids

Vassilis Kekatos
Department of Electrical and Computer Engineering
University of Minnesota

12:00pm-1:00pm, Thursday, 13 March 2014, ITE 325b, UMBC

The smart grid vision urges for enhanced situational awareness, sustainability, and economics over our energy systems. While meters are being installed throughout the grid, algorithms that can effectively process this big data deluge are now demanded. Aligned to that end, this talk focuses first on scalable grid monitoring. Albeit control centers monitor their local grids independently, deregulation and renewables call for power system state estimation (PSSE) at the interconnection level. To address the complexity and communication challenges involved, a decentralized PSSE framework based on the alternating direction method of multipliers has been developed. Beyond conventional least-squares, our framework can identify outliers and circuit breaker statuses as verified on IEEE grids having thousands of nodes. Electricity market inference is the second theme of this talk. We will first demonstrate how grid topologies can be revealed using only publicly available real-time energy prices. This becomes feasible after recognizing that the price matrix can be factorized as the product of the grid Laplacian times a low-rank and sparse matrix. Leveraging the link between energy markets and the underlying physical grids, we will then cast day-ahead price forecasting as a kernel learning task. Through a novel nuclear norm-based regularization, kernels across pricing nodes and hours are systematically selected. Numerical tests using real data from the Midwest ISO market corroborate the interpretative merits of our schemes.

Dr. Vassilis Kekatos is currently a postdoctoral associate with the ECE Dept. of the University of Minnesota, Minneapolis. He obtained his Ph.D. in Computer Engineering and Science from the University of Patras, Greece, in 2007. In 2009, he received a Marie Curie fellowship. During the summer of 2012, he worked as a consultant for Windlogics Inc. His current interests lie in the areas of signal processing, optimization, and statistical learning towards modernizing our energy systems.

Host: Tulay Adali

CSEE professor Marie desJardins was interviewed for a story, Education Key Theme for International Women’s Day, by the Voice of America on International Women’s Day (March 8).

“If you look at the statistics, well over half of all STEM jobs in the next 10 to 15 years are in computing … yet when people talk about STEM, they tend to think about biology, physics,” said University of Maryland Baltimore County Professor Marie Desjardins who teaches computer science and engineering. “Those are great areas, but those are not where the big job growth is going to be.”

DesJardins also says exposing kids at an early age is key to getting them interested in STEM.

“Let your girls try things that are not typically girly,” she said. “Make sure your kids are getting that from an early age so they think of themselves as creators of technology and new ideas not just following the rules.”

DesJardins says she emphasizes that computer science is really about helping make the world a better place.

Here is the video that accompanied the VIA story.