College of Engineering Research Seminar
- Friday, November 6, 2015 from 3:10pm to 4:00pm
- Roberts Hall - view map
Neda Nategh, Assistant Professor in Electrical and Computer Engineering
Neuroscience, Neuro-engineering, and Neuro-innovation
Our brain is a remarkably efficient, compact and robust computational device. Vision, audition, motor control, memory, language comprehension, abstract reasoning– brain does all these and more using slow, stochastic, and inhomogeneous computing elements called neurons and yet outperforms the world’s most powerful supercomputers. Research in our lab integrates computational science and neuroscience, with the goal of solving similar problems in both fields. In this talk, using the sample biological and bio-inspired vision projects conducted in our lab, we will provide an overview on: (1) how computational science and engineering can help understand the computational principles used by the brain and how they are physically embodied in the brain; (2) how neuroengineering– translating neural activity from the brain into control signals for brain-machine interfaces– can help the design of high-performance and robust neural prostheses; and (3) how neuroscience can help develop novel computing paradigms and guide a new generation of computer technologies using the same organizing principles of the biological nervous system– neuromorphic engineering.
Stephanie McCalla, Assistant Professor in Chemical and Biological Engineering
Developing Microscale Biomedical Assays
The main focus of my research is to design assays that determine the location and identity of medically relevant molecules such as DNA, RNA, and pharmaceuticals. The ability to detect these molecules could signal the presence of a pathogen, uncover a hidden genetic disorder, or explain the mechanism behind a cellular function. For example, an increase in a specific class of circulating small RNA could indicate that a patient has cancer or heart disease. This talk will focus on microscale techniques to detect, separate, and quantify clinically relevant molecules.
Upulee Kanewala, Assistant Professor in Computer Science
Predicting Metamorphic Relations for Testing Scientific Software: A Machine Learning Approach Using Graph Kernels
Comprehensive, automated software testing requires an oracle to check whether the output produced by a test case matches the expected behavior of the program. But the challenges in creating suitable oracles limit the ability to perform automated testing in some programs including scientific software. Metamorphic testing is a method for automating the testing process for programs without test oracles. This technique operates by checking whether the program behaves according to a certain set of properties called metamorphic relations. A metamorphic relation is a relationship between multiple input and output pairs of the program. Unfortunately, finding the appropriate metamorphic relations required for use in metamorphic testing remains a labor intensive task, which is generally performed by a domain expert or a programmer. This talk describes MRpred: an automated technique for predicting metamorphic relations for a given program. MRpred applies a machine learning based approach that uses graph kernels to create predictive models. MRpred achieves a high prediction accuracy, and the predicted metamorphic relations are highly effective in identifying faults in scientific programs.
- College of Engineering