Doug Szajda

University of Richmond, Computer Science, Faculty Member

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Papers by Doug Szajda

Cycle Synchronization in Cyclone Networks

Networks with time-cyclic allocation of node resources (eg SONET or a wireless network using TDMA... more Networks with time-cyclic allocation of node resources (eg SONET or a wireless network using TDMA media access scheduling) require a degree of synchronization in order to guarantee that cycle lengths and drifts remain within acceptable tolerances. Cyclone is a ...

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Challenging Disciplinary Boundaries in the First Year: A New Introductory Integrated Science Course for STEM Majors

by Doug Szajda, Lester Caudill, Kathy Hoke, and Krista Stenger

Journal of College Science Teaching, May 1, 2012

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Introducing computer science in an integrated science course

by Doug Szajda and Lewis Barnett

Proceeding of the 44th ACM technical symposium on Computer science education - SIGCSE '13, 2013

ABSTRACT This paper describes our implementation and experience of incorporating computer science... more ABSTRACT This paper describes our implementation and experience of incorporating computer science concepts into a team-taught, first-year interdisciplinary course for prospective science majors at the University of Richmond. The course integrates essential concepts from each of five STEM disciplines: biology, chemistry, computer science, mathematics, and physics. Including computer science in this course faces three primary challenges: few of the students have any CS background; the time devoted to CS instruction is reduced compared to a traditional introductory CS course; and the spirit of the course requires the CS material to be highly integrated with the other disciplines. Here we discuss our experience from three-plus years of offering the course and its impact on the major/minor pool of students in our own discipline.

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The Lure of the Dark Side

Math Horizons, Jan 1, 2011

The second reason you should take more applied courses is that you likely have an interest in tec... more The second reason you should take more applied courses is that you likely have an interest in technology, and you live in a technological society. You use a computer and cell phone, probably own an iPod (if not several), and are surrounded by devices that are controlled by ...

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Toward an optimal redundancy strategy for distributed computations

Cluster Computing, 2005. IEEE …, Jan 1, 2005

Volunteer distributed computations utilize spare processor cycles of personal computers that are ... more Volunteer distributed computations utilize spare processor cycles of personal computers that are connected to the Internet. The related computation integrity concerns are commonly addressed by assigning tasks redundantly. Aside from the additional computational costs, a significant disadvantage of redundancy is its vulnerability to colluding adversaries. This paper presents a tunable redundancybased task distribution strategy that increases resistance to collusion while significantly decreasing the associated computational costs. Specifically, our strategy guarantees a desired cheating detection probability regardless of the number of copies of a specific task controlled by the adversary. Though not the first distribution scheme with these properties, the proposed method improves upon existing strategies in that it requires fewer computational resources. More importantly, the strategy provides a practical lower bound for the number of redundantly assigned tasks required to achieve a given detection probability.

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Toward a practical data privacy scheme for a distributed implementation of the Smith-Waterman genome sequence comparison algorithm

Proceedings of the …, Jan 1, 2006

Volunteer distributed computations utilize spare processor cycles of personal computers that are ... more Volunteer distributed computations utilize spare processor cycles of personal computers that are connected to the Internet. The resulting platforms provide computational power previously available only through the use of expensive clusters or supercomputers. However, distributed computations running in untrustworthy environments raise a number of security concerns, including computation integrity and data privacy. This paper introduces a strategy for enhancing data privacy in some distributed volunteer computations, providing an important first step toward a general data privacy solution for these computations. The strategy is used to provide enhanced data privacy for the Smith-Waterman local nucleotide sequence comparison algorithm. Our modified Smith-Waterman algorithm provides reasonable performance, identifying most, and in many cases all, sequence pairs that exhibit statistically significant similarity according to the unmodified algorithm, with reasonable levels of false positives. Moreover the modified algorithm achieves a net decrease in execution time, with no increase in memory requirements. Most importantly, our scheme represents an important first step toward providing data privacy for a practical and important real-world algorithm.

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Hardening functions for large scale distributed computations

Security and Privacy, 2003. …, Jan 1, 2003

Many recent large-scale distributed computing applications utilize spare processor cycles of pers... more Many recent large-scale distributed computing applications utilize spare processor cycles of personal computers that are connected to the Internet. The resulting distributed computing platforms provide computational power that previously was available only through the use of expensive supercomputers. However, distributed computations running in untrusted environments raise a number of security concerns, including the potential for disrupting computations and for claiming credit for computing that has not been completed (i.e., cheating). This paper presents two strategies for hardening selected applications that utilize such distributed computations. Specifically, we show that carefully seeding certain tasks with precomputed data can significantly increase resistance to cheating and to disrupting the computation. We obtain similar results for sequential tasks by sharing the computation of AE tasks among Ã AE nodes. In each case, the associated cost is significantly less than the cost of assigning tasks redundantly.

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