Summer Research Institute | 2016 Research

The University of Rochester Laboratory for Laser Energetics (LLE) is one of the largest laboratories in the world for studying inertial confinement fusion.     At LLE, sixty extremely high-powered lasers deposit a large amount of energy into a small pellet of nuclear fuel, triggering a nuclear fusion explosion that releases energy and radiation.  One component of this radiation is an extremely short and intense burst of neutrons.   Houghton students Katelyn Cook and Micah Coates will be working with physics professor Mark Yuly and a collaboration of scientists from SUNY Geneseo and LLE to develop novel ways to use these neutrons to carry out nuclear physics measurements that are unfeasible using previously available  techniques.  In particular, the intensity and short duration of the pulse makes these neutrons ideal for measuring the half-life of extremely short-lived radioisotopes.  Our goal for this summer is to carry out the preliminary experimental design and feasibly tests for a full-scale experiment next summer.

Kurt Aikens, Assistant Professor of Physics, and Houghton College students Dan Eager and Tim Powers will be working this summer with researchers from the NASA Glenn Research Center in Cleveland, Ohio. They will be collaborating with NASA researcher Jim DeBonis and the LTN/Inlets and Nozzles branch to help test and validate a new computational fluid dynamics (CFD) software called gFR.  The code is based on the cutting edge flux reconstruction methods of H.T. Huynh which achieve high-order accuracy while using unstructured grids.  Practically, this allows the code to achieve high simulation accuracy for flows around and through geometrically complex objects. Long-term, the software will be used to predict flowfields for advanced aeropropulsion systems and help NASA researchers meet the Transformational Tools and Technologies Project's challenge to "Identify and down-select critical turbulence, transition, and numerical method technologies for 40% reduction in predictive error against standard test cases for turbulent separated flows, evolution of free shear flows, and shock boundary layer interactions on state-of-the-art high performance computing hardware."

This summer, Dr. Rebecca Williams, Assistant Professor of Biology, will be working with 4 students from the Biology department:  Seema Johnson, Teri Koetsier, Theresa Taggart and Kayla Miller.  Together, we will be working towards estimating local pollution levels based on metabolic enzyme quantification in catfish (specifically the brown bullhead).   Our goal is to capture fish from clean and contaminated across western New York and measure levels of CYP1A protein, which is prevalent in liver tissue of these fish.   CYP1A is a useful bioindicator of pollution levels.   Its expression is induced when exposed to pro-carcinogenic agents such as the polycyclic aromatic hydrocarbon benzoapyrene (BaP), a common toxin.   Levels of CYP1A protein expression can be correlated to the level of pollution in the area.  Toxins such as BaP are hydrophobic and settle into the sediment of aquatic systems, where bullhead reside, making them a useful bioindicator species.  In fish from contaminated sites, we propose that CYP1A expression will be high, with the opposite effect in fish from clean sites. 

Deep learning provides computational models with multiple processing layers to learn representations of data with multiple levels of abstraction, which is particularly useful in processing high-dimensional data in machine learning. It was listed as one of the top 10 breakthrough technologies in 2013 by MIT technology review.  Situated between supervised and unsupervised learning, reinforcement learning is another kind of learning that has found wide applications in different areas including engineering such as robotics.  In reinforcement learning an agent or a robot can be taught to perform tasks from its own trial and error, while being rewarded for its performance.

Finding ways to let agents learn directly from high-dimensional sensory inputs like vision and speech is one of the long-standing challenges of reinforcement learning.  Deep learning is a right solution to this kind of problems as it provides rich representations that can enable reinforcement learning algorithms to perform effectively.  Q-learning is a well-known algorithm in reinforcement learning.  In a recent paper published in Nature and featured on the cover of Nature demonstrated how a computer learned to play Atari 2600 video games by observing just the screen pixels and receiving a reward when the game score increased. Its main technique is designing a deep neural network to train a variant of Q-learning and this network is named deep Q network and has been patented by Google.  In this summer research, Dr. Wei Hu .Professor of Math and Computer Science, will work with two students, Michael Ganger and Ethan Duryea, on creating new deep reinforcement learning algorithms.

Deep learning emerged out of research on neural networks has gained popularity in recent years by its state-of-the-art performances in machine learning. As a new way of using multiple layer neural networks, deep learning like our brain can engage learning at multiple levels or scales, which makes it closer to one of its original goals: artificial intelligence. By its very nature, deep learning is particularly well suited for learning high-dimensional perceptual data such as speech, images, text, and natural language where multiple level learning is a must. The big text data generated by users on social web sites such as Google, Facebook, Twitter, Amazon all need to be analyzed to extract useful knowledge for different goals. In this summer research, professor of math and computer science Dr. Wei Hu and his two students Brian Dickinson and Michael Ganger are going to develop methods to characterize the positive and negative movie reviews using deep learning. 

Brandon Hoffman, Associate Professor of Physics and Physics Department Chair, and two Houghton College students, Heather Phillips and Yan Tang, will be collaborating with Shefford Baker at the Cornell Center for Materials Research (CCMR) at Cornell University to study thin silver films.  Today’s technology requires the use of metal films with thicknesses of only a few hundred nanometers or less.  Not much is known about the properties of materials this small and the present models do not accurately describe the films.  Therefore, thin silver films will be produced in a high vacuum deposition chamber and studied by X-ray Diffraction (XRD) and Electron Backscatter Diffraction (EBSD) in order to characterize the microstructures and transformations of the films.  The goal of these experiments will be to improve the general model that describes similar thin metal films.

Brandon Hoffman, associate professor of physics, and one Houghton College student, Jon Ballard, will be collaborating with Joe Kellogg of Kellogg’s Research Labs on a study of nitinol microstructures.  Nitinol has a very peculiar property called “shape memory.”  This means that a given shape can be programmed into a nitinol wire.  If the wire is later bent into a different shape, it will return to the programmed shape when heated.  Research is being conducted to use nitinol wires to convert daily atmospheric temperature changes into electrical power.  The present project aims to further development of these power generators by studying the nitinol microstructures with Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD), and Differential Scanning Calorimetry (DSC).

Dr. Sullivan, Associate Professor of Biology and Biology Department Chair, will work with two students, Emilia Gildemeister and Erica Barney, in a collaborative study to assess biodiversity along a 322-km section of the Pacific Crest Trail (PCT) in northern California. The PCT is a 4,265-km trail that extends from Mexico to Canada and passes through a variety of ecosystems.  This study will serve as a ‘barometer of biodiversity’ and will facilitate a long-term assessment of animal distributions as well as possible altitudinal and latitudinal shifts in response to climate change. The collaborators will include the megatransect project originator and coordinator, Mike McGrann from William Jessup University, faculty from other institutions within the CCCU (e.g., Ben Brammell, Asbury University), and members of state entities (e.g., Brett Furnas, California Department of Fish and Wildlife).  The overall goal of the project is to document, predict, and explain patterns in biological diversity as well as to inform conservation planning.  Student participation will include hiking the trail as well as project planning and logistics, data collection and analysis, and writing for scientific publications.   The researchers  will participate in the various aspects of the megatransect project during the 2016 field season (e.g., avian point surveys, habitat assessment, eDNA collection) and hope to implement a pilot project for amphibian and reptile  search and assessment for the duration of our five-week project.

Jamie Potter, Associate Professor of Biology, will be working with two students, Grace Hollenbeck and Robert Marek, in collaboration with Dr. Keith Perry, Associate Professor at Cornell University Department of Plant Pathology and Plant-Microbe Biology, on the detection of plant RNA viruses in grapevine. We will be focusing on Grapevine leaf roll-associated viruses, GLRaV, in Vitis vinifera and related Vitis species through collection and analysis of plant samples from western New York vineyards and adjacent wild cultivars using molecular macroarray diagnostic techniques and virus specific PCR detection methodology. Our preliminary results show that grapevines in WNY host a variety of RNA viruses infections. This work is supported by the Houghton College Moreland Research Fund.

Complex dynamics is an area of mathematics which, in part, studies the chaotic behavior produced by repeated iterations of functions defined over the complex plane.  Studies of certain families of these functions often produce sets which exhibit fractal behavior.   A well-known example of this is the Mandelbrot set.  In recent years, there has been increased interest in arithmetic dynamics, a field which parallels complex dynamics, but is focused upon questions related to topics in number theory.   Brandon Bate, Assistant Professor of Mathematics, and Houghton College students Kyle Craft and Jonathon Yuly, will conduct research in this area, focusing on questions related to determining when functions over the p-adic numbers have dynamics which produce sets with fractal behavior or other forms of chaotic behavior.

Back To Top