The CompGen Fellowship program awards predoctoral fellowships, funded by the NSF and the University of Illinois, to promote interdisciplinary research in computational genomics.Fellows can be either computationally focused doctoral students with a biology co-advisor, or biologically focused doctoral students with a computational co-advisor. The fellowships were initially founded to support research directly relevant to the NSF-funded CompGen instrument, a supercomputer designed exclusively for genomic biology. The fellowship program now also supports fellows in any branch of interdisciplinary computational genomics.
Examples of focus areas in biology and genomics include accurate detection of genomic variation, development of statistical methods and methodologies for metagenomics, improvements in phylogeny reconstruction, enabling drug discovery through the use of microbial genomes, genotype-to-phenotype associations, and behavioral and neurological genomic biology. Computational projects may include topics such as reduction of data volume, optimization of storage hierarchy, identification of primitives that are common across algorithms, visualization and toolkits for computational genomic tools, construction of flexible software platforms that simplify the use of important statistical tools such as HMMs, tool flow optimization, mathematical simulations of genomic problems, and performance and reliability assessment of existing software.
2015–2016 CompGen Fellows
Jennifer Arp is a graduate student in the Crop Sciences Ph.D. program. She has previously earned bachelor’s degrees in genetics, cell biology & development, and plant biology from the University of Minnesota, Twin Cities. Jennifer’s research is focused on applying gene expression networks to find candidate regulatory genes to improve nitrogen use efficiency in maize. Her advisor is Dr. Stephen Moose (Crop Sciences) and her CompGen mentor is Dr. Matthew Hudson (Crop Sciences).
Arjun P. Athreya is a Ph.D. candidate in the Department of Electrical and Computer Engineering at the Univ. of Illinois and is advised by Prof. Ravi Iyer. Arjun’s broader interests are in analytics and statistical methods for big data applications. Arjun’s work comprises two main parts, inferring the underlying biology and generating likely hypothesis that explains the observed behavior in the data and second, predicting clinical outcomes based on known biology. Arjun is currently researching analytical methods to understand the biology in drug mechanisms in the context of breast cancer and depression, and building predictive models to study disease development in the context of epigenetic impact on humans. Both Arjun and Prof. Iyer collaborate with Dr. Widman at the Univ. of Illinois and Drs. Kalari, Wang, and Weinshilboum at the Mayo Clinic.
Angela is pursuing her master’s degree in statistics. Previously, she earned a B.S. degree in statistics from the University of Illinois at Urbana-Champaign. Her CompGen research focuses on building high-performance computing software that can capture complex interactions among genes and accurately model their combined contribution to an organism’s phenotype. This software is being designed to implement a state-of-the-art statistical approach with high computational efficiency. Her work will contribute to widespread applications of genome-wide association studies. Her co-advisors are Dr. Alexander E. Lipka (assistant professor of biometry, Department of Crop Sciences) and Dr. Liudmila Sergeevna Mainzer (National Center for Supercomputing Applications and Carl R. Woese Institute of Genomic Biology).
Ben Chidester is enrolled in the Electrical and Computer Engineering (ECE) Ph.D. program. Previously, he received his B.S. in ECE from Carnegie Mellon University. Ben is interested in the application of signal and image processing and machine learning to cancer image data for the investigation of correlates between image features and genomic data. This integration of imaging and genomics will be leveraged to inform predictive models of cancer diagnosis and prognosis and to discover informative connections between genotype and phenotype for pathologists. Professor Minh Do (ECE) and Professor Jian Ma (BIOE) are his co-mentors.
Adam Hamilton is enrolled in the Ph.D. Neuroscience Program. Previous degrees are a B.S. in psychology and a B.S. in genomics and molecular genetics, both from Michigan State University. Adam is interested in how interactions between the genome and environment result in contextually specific patterns of gene expression that can organize behavior (social, aggressive, food gathering, and otherwise) in honey bees. In the framework of CompGen, this entails developing novel techniques capable of combining gene expression data (both microarray and RNAseq) from behavioral experiments with computationally derived predictions of transcription factor binding sites to generate accurate and functionally relevant predictions of the transcriptional regulatory networks that underlie behavior. The architecture of these networks can then inform us about what transcription factors might be critical for organizing specific behaviors, as well as whether a common set of transcription factors exists that can influence behavior in a more general sense. Adam is co-mentored by Professor and Director Gene Robinson (Carl R. Woese Institute for Genomic Biology) and Professor Saurabh Sinha (Computer Science). Adam served as a CompGen Fellow in 2013–2014, 2014–2015, and his fellowship was renewed for 2015–2016.
Jack Hou is an M.D./Ph.D. student in the Bioengineering program. Previously, he received his B.S degree in biochemistry and statistics from Iowa State University. Jack is interested in developing methods in personalized cancer genomics. Specifically, he is interested in computational detection of driver events in cancer and quantifying the interaction of the driver events with cancer drug targets to predict drug response. As a CompGen Fellow, Jack is advised by Dr. Jian Ma (Bioengineering) and Dr. Paul Hergenrother (Chemistry).
Grace Kim is an M.D./Ph.D. student in the Neuroscience Program. Previously, she earned a B.S. in Brain and Cognitive Sciences from MIT and an M.S. in Bioengineering from Illinois. Grace’s research focuses on studying sex differences in microRNA (miRNA) expression in post-traumatic stress disorder and depression. She is especially interested in studying dynamic, sex-specific patterns of miRNA expression across important developmental time periods (i.e., adolescence) in brain and peripheral tissues to understand the role of miRNAs in bidirectional crosstalk between the brain and periphery. Professor Monica Uddin and Professor Victor Jongeneel are her co-mentors.
Meggan J. Lee is a doctoral student in the Sociology department. She received a master’s degree in sociology from DePaul University and a B.A. degree in criminal justice and psychology from Clark University. She studies punishment practices within and the production of inequalities by the criminal justice system, specifically in prisons. As a CompGen Fellow, Meggan is working with her co-mentors, Professors Ruby Mendenhall, Sandra Rodriguez-Zas, and Olgica Milenkovic on identifying ways to leverage the CompGen instrument to better detect genomic variation among low-income black mothers who live in areas with high levels of violence.
Marcelo Melo is a third-year graduate student in the Center for Biophysics and Quantitative Biology, working in the Luthey-Schulten group. He has a B.S. degree in biological sciences and an M.S. degree in biophysics, and has always focused on software development for biological research. He has been carrying out systems biology studies in yeast and bacteria, analyzing the variation in fluxes through metabolic networks in response to changes in the microenvironment and varying protein copy numbers due to stochastic gene expression. This research is being applied to study the partitioning of leucine-tRNA synthetase into its canonical and noncanonical functions; that is, establishing the genetic code for protein production, and participating in signaling pathways that control cell growth.
Safa is a second-year Ph.D. student in the DEPEND group. She obtained her bachelor as well as her master of science degree in electrical engineering and information technology from the Technical University of Munich, Germany. She also has a master of science degree in computer engineering from Virginia Tech. Safa was also a one-year visiting scholar at the University of California Berkeley, where she worked on automatic synthesis of reliable aircraft electrical power system architecture. She is interested in big data analytics and hardware acceleration for genomic applications. Last summer, she interned at IBM Research Tokyo, where she worked on accelerating a SAM to BAM parser on a field-programmable gate array (FPGA).
Anand Ramachandran is enrolled in the Electrical and Computer Engineering program and is pursuing his Ph.D. studies. He is interested broadly in algorithms in genomics as well as in accelerating genomic workflows. More specifically, he is working to formulate alignment and variant-calling methods for better quality mutation calls as applicable to cancer genomics. He is also currently working on accelerating DNA error correction using FPGA and GPU devices and high-performance heterogeneous computing systems. He has been serving as a CompGen Fellow since 2013.
Zachary Stephens is enrolled in the Electrical and Computer Engineering (ECE) Ph.D. program. He received his M.S. degree in ECE from UIUC, and his B.S. degree in electrical engineering from The Pennsylvania State University. Zachary is interested in estimating accuracy bounds in genomics problems (e.g., read alignment, variant calling) as well as characterizing the repetitive structures in genome sequences. In ongoing work, he is collaborating with Mayo Clinic to improve the accuracy of structural variant detection algorithms. As a CompGen Fellow, his co-mentors are Professor Ravi Iyer (ECE) and Professor Bryan White (Animal Sciences).