General Overview: You're hardworking, you're fun and you get the job done! You have specific interest in marketing & promotions and have strong people skills. Learn marketing from the ground-up at a growing consumer product / technology company. 

Start date: Immediately

Completion date: There is no completion date. Successful applicants will work special events throughout the year!

Hours: Part Time. Some evenings and weekends.

Rate of pay: Hourly - exact hourly wage TBD

Primary Responsibilities:

• Guerilla Marketing - hitting the streets, passing out promo items (pens, flyers, door hangers, etc) around Baltimore (mainly focused in Canton, Fed Hill, Fells Pt, Little Italy, Harbor East, Inner Harbor, & Mount Vernon)
• Attending EatBmore.com events to create/collect leads (may require transport of materials (ie: banners, flyers, stickers, etc)
• Inform people about EatBmore via social media
• Take photos of your efforts

Qualifications:

• Reliable
• Out-going
• Organized
• Fun and respectful
• Conscientious
• Problem solver
• Vehicle necessary
• Knowledge of Baltimore City

You will work in a team environment. You must be committed to the position and willing to work flexible hours.

Help high school students prepare for the HSA exam in the spring by tutoring them in the Fall.

• Receive Two Year Membership to ASM
• Earn up to $4000 in student stipend
• Participate in the ASM Fellows Presentation Institute prior to presenting research at the ASM General Meeting
• Receive up to $1000 in travel support to the ASM General Meeting (if abstract is accepted)
• Recognition in ASM publications

Masters Thesis Defense

Approximation of Nonintegral Frequency Moments

Brian Pilz

10:00am 30 November 2011, ITE325b

Let a data stream have length m over an alphabet of n letters, with letter i occurring m_i times for i = 1,…,n. For any k, define the frequency moments F_k as F_k = \sum_{i=1}^n m_i^k. Alon, Matias, and Szegedy showed how to estimate F_k for integers k>0 with a one-pass algorithm using O(n^{1-1/k}log n) space for given length m, accuracy, and confidence. Here we extend those results to non-integral k obtaining bounds on the variance giving accuracy and confidence estimates, and giving quantitative results on the algorithm’s space requirements with particular interest to when k is near 1. We also give some performance statistics of the algorithm for these cases and consider an application to entropy estimation. This algorithm is known as a sketching algorithm. Sketching algorithms are probabilistic algorithms generally requiring sublinear space vs. a "classical" O(n) (linear) space requirement, and may have applications for anomaly detection of systems or networks.

Committee:

• Drs. Samuel Lomonaco
• Brooke Stephens
• Kostas Kalpakis (chair)
• Larry Wagoner