Student Projects

DNA Motif Search

Applying the Automata Processor to accelerate DNA motifs finding.
This project is mentored by Chunkun Bo and Jack Wadden.

Project Partner

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Bryce Aidukaitis
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Bryce Aidukaitis
Bryce is a fourth year student studying biomedical engineering. He works in Dr. Papin's Computational and Systems Biology Laboratory, developing algorithms to automate microplate reader data analysis. When not at a computer, he enjoys raising tropical fish and studying Russian with his wife.
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Catherine Pollack
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Catherine Pollack
Catherine is a third year student pursuing a double major in biomedical engineering and statistics. She is the President of the Rodman Scholars Program, the Corporate Relations Chair for the Society of Women Engineers, and principal clarinetist of the UVA Wind Ensemble. You can also find her either working on crossword puzzles or attempting to play racquetball.
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Mark Panetti
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Mark Panetti
Mark is a second year student in the College of Arts and Sciences. He is a double major in computer Science and mathematics with a concentration in statistics. Additionally, he is a percussionist in the UVA percussion ensemble and Charlottesville Symphony Orchestra.

Cellular Automata On The AP

Accelerating cellular automata for GIS land use modeling using the AP.
This project is mentored by Nathan Brunelle and Mateja Putic.

Project Partner

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Torry Yang
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Torry Yang
Torry is a third year computer science major interested in data analytics and machine learning. He is also an avid snowboarder, even though he grew up in Singapore and Taipei, where there's no snow.
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Adam Guo
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Adam Guo
Adam is a third year student studying statistics and computer science. He is interested in doing mobile app development, sports analysis, and anything outdoors. On grounds he has been involved in class council, Hackcville, and Asian student Union.
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Emma Dillon
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Emma Dillon
Emma Dillon is a fourth year in the Frank Batten School for Leadership and Public Policy. As a public policy student, she views learning about the processes used to analyze vast collections of data as vitally important to understanding its applicability to solving complex social problems. Whether she pursues a career in social entrepreneurship, policy analysis or impact investing, she believes that understanding Big Data will be an invaluable resource.

Phish Detection

Using the AP to analyze internet data for phishing detection.
This project is mentored by Tommy Tracy and Elaheh Sadredini.

Project Partner

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Colleen Kohout
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Colleen Kohout
Colleen is a fourth year student double majoring in Systems Engineering and Financial Mathematics. She is interested in machine learning and hopes to continue using what she learns as an AP Ambassador after graduation, especially in combination with finance related problems.
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Cameron Springer
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Cameron Springer
Cameron is a third year computer and electrical engineering major. His extracurriculars include involvement Army ROTC, Undergraduate Research Network, Undergraduate Researcher with the Center for Electrochemical Science and Engineering, and chairmanship of the College Libertarians and the UVA Chapter of the Gary Johnson campaign.
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Andrew Ton
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Andrew Ton
Andrew is a second year Rodman Scholar pursuing a double major in computer engineering and economics. He hopes to find work that merges information technology and economics. Over Summer 2016, he traveled to South Africa to set up SMS-Internet communication systems for charities and communities.

Sport Activities Analysis

Pattern mining of wearable sensors for activity analysis.
This project is mentored by Kevin Angstadt and Tiffany Ly.

Project Partner

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Emma Fass
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Emma Fass
Emma is a third year in the engineering school majoring in computer science and minoring in business. She is interested in front-end development and data analytics. Around grounds, she is involved in Unsung People, Women in Computer Science and the Virginia Riding Team.
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Luke Merrick
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Luke Merrick
Luke is a third year systems engineering major with a minor in computer science. In addition to his involvement with the Center for Automata Processing, he is a member of the Rodman Scholars, HackCville, and the Virginia Glee Club. His research interests, while nascent, center around the methodologies and applications of machine learning. Outside of academics pursuits, I also love SCUBA diving.
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Joe Tidwell
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Joe Tidwell
Joe is a third year majoring in Kinesiology. He lived in four different countries growing up, including attending high school in Santiago, Chile. Here at UVA, he works with UVA Sports Medicine and enjoys hiking and exploring the beautiful Shenandoah Valley. His passions lie in the applications of exercise for increasing athletic performance and overall well-being.

Pattern Mining In Public Health

Machine learning to mine pattern in public health.
This project is mentored by Tommy Tracy.

Project Partner

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Bryson Lockett
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Bryson Lockett
Bryson is a third year systems engineering and computer science major. He has experience interning as a web and mobile developer at a political marketing company and as a data architect at a startup in Silicon Valley. During the year, he volunteers with Young Life at Madison County High School. In his free time he enjoys lifting heavy objects and cooking (especially waffles).
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Sara Ho
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Sara Ho
Sarah is a fourth year studying economics and statistics. In her free time, she enjoys listening to music. She volunteers for WXTJ 100.1 and WTJU 91.1.
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Will Rinaldi
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Will Rinaldi
Will Rinaldi is a fourth year mechanical engineering and economics double major. He is from Cincinnati, Ohio, and will be working for Rolls-Royce after he graduates. He is a fourth year Trustee, works at Madison House, and is an intramural floor hockey champion.
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Machine Learning Application: Tree Kernels

 

There are many real world applications such as XML data, parse trees in natural language processing, and protein sequences in bioinformatics that can be represented by tree structures. The conventional methods used for tree structured data classification are based on dot products on the feature vectors; this can be very expensive since those vectors can be extremely large.

Tree Kernel methods have proved to be a state of the art technique for many real world problems and they are able to process tree-based information without using an explicit representation of inputs. The main bottleneck of the current solutions of the tree kernels is the processing time and because the tree structure is complex, the timing complexity is the limiting factor for the tree kernel methods. In this project, we are going to propose a novel automata solution for convolution-based tree kernels on the AP. Our method can be applied to the applications that can be represented by the ordered and unordered labelled trees such as sentiment analysis in natural language processing.

By Elaheh Sadredini

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Performance Evaluation of Regular Expression Engines Across Different Computer Architectures

 

This project focuses on regular expression matching which is playing an important role in a variety of applications, like genome sequence analysis, data mining, network inspection, etc. Different kinds of architectures such as CPU, XeonPhi, GPU, FPGA can perform regular expression matching. However, it is difficult on Von-Neumann architectures since it requires high irregular parallelism, high memory bandwidth as well as low latency.

Micron’s Automata Processor (AP) is designed for this kind of problem, using DRAM as a highly parallel reconfigurable fabric to implement NFAs. In this work we investigate for a fair comparison of best effort regular expression processing engines across all these aforementioned architectures which involves analyzing their performances with different types of regular expressions and exploring the design spaces of each of these architectures.

Our preliminary results indicate that CPU, XeonPhi and GPU are most likely bottlenecked by memory latency for rule lookup and AP and FPGA outperform other architectures due to their high capacity, their massively parallel execution and their capabilities of processing new input symbol every clock cycle. Furthermore, unlike FPGA, AP’s throughput is immune to complexity of NFA topologies and rulesets (i.e. large number of transitions, active states).

In the future work, we continue to explore the performance evaluation on more dimensions: “complexity” of regular expressions, the number of regular expressions, and multiple packets (streams) processing capability. We also extend the work to other benchmark suites that are not natural fits for regular expression, such as association rule mining, Markov chains, String kernel etc.

By Tommy Tracy

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Entity Resolution Acceleration Using AP

Entity Resolution (ER), the process of finding identical entities across different databases, is critical to many information integration applications. As sizes of databases explode in the big-data era, it becomes computationally expensive to recognize identical entities for all records with variations allowed across multiple databases. Profiling results show that approximate matching is the primary bottleneck.

Micron’s Automata Processor (AP), an efficient and scalable semiconductor architecture for parallel automata processing, provides a new opportunity for hardware acceleration for ER. We propose an AP-accelerated ER solution, which accelerates the performance bottleneck of fuzzy matching for similar but potentially inexactly-matched names, and use a real-world application to illustrate its effectiveness. Results show promising speedups for matching one record, with better accuracy over the existing CPU method.

By Dang, Vinh Quang

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Sequence alignment in Bioinformatics using Micron’s Automata processor:

Sequence alignment refers to arranging sequences of DNA, RNA, or protein against reference sequences to identify regions of similarity in Bioinformatics. The major challenge is that the reads do not always perfectly match with references, and approximate matching is needed. The process is computationally expensive to compare large number of different reads against long references when fuzziness is allowed.

Micron’s Automata Processor (AP) is an efficient and scalable semiconductor architecture for parallel automata processing.  The AP is based on an adaption of memory array architecture, exploiting the inherent bit-parallelism of traditional SDRAM. This new in-memory processing hardware architecture provides a new opportunity for sequence alignment. We use the new hardware to accelerate DNA alignment and compare with other famous sequence alignment tools (Bowtie2, Bowtie and PatMaN).

Results show at least $10$x speedup is achieved and more than 10000x speedup could be achieved when more variations are needed.

By Chunkun Bo

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