Javier Carpinteyro is a biologist with an overall interest on evolutionary biology, specifically at the molecular level. Currently he is a graduate student on the BISI-BEES program working on Drosophila evolutionary/population genomics. Here at the COMBINE program he aims to integrate population genomics and network theory for studying biological speciation, a fundamental phenomenon in evolutionary biology. He uses as a model organism fruit flies of the Drosophila pseudoobscura subgroup. This group is composed by three species and what makes it interesting is the fact that they can still partially hybridize (generating only fertile females), therefore it is assumed that this group has recently initiated the process of speciation. Comparing the genomic differences that have been accumulated between this species group will help understand what genomic changes have to occur to initiate the process of species formation and, by comparing the differences with other more distant species, determine how many changes are necessary to produce different species.
Jessica Chopyk is a PhD student at the University of Maryland School of Public Health in the Toxicology and Environmental Health program under the advisement of Dr. Amy Sapkota. Previously, she obtained a Master’s degree in Plant and Soil Sciences at the University of Delaware exploring the commensal microbial communities that exist along with Shiga toxin-producing E. coli (STEC) on cattle hide during beef processing. Her current research is aimed at understanding the microbiome of human saliva and the habits, like tobacco use, that can alter it. The human oral cavity is home to a complex microbial ecosystem comprised of thousands of bacterial and viral species that are essential in maintaining health, but can also be associated with diseases, like periodontitis. Specifically, in her research she uses next generation sequencing technologies to broadly survey microbial diversity and explore the dynamic interactions between bacteriophage, viruses that infect bacteria, and their hosts in saliva samples collected from tobacco users and non-users. From these data she can compare bacterial community composition and diversity among tobacco users and non-users, as well as, characterize the transcription of the bacterial defenses system, CRISPR. As a result of this on-going work, she has begun to unravel the complex and dynamic microbial ecosystem of the mouth, with the ultimate goal of predicting disease states and improving human health.
Jacob Isbell grew up in nearby Annapolis and studied Physics at Brown University. He’s now in the Electrical Engineering Department at UMD. His lab studies the relationship between sensory information and the neural response to location recognition, especially in the bat. Predicting location is reliant upon odometry and place recognition. In flying, echolocating bats, estimating self‐motion with sonar is difficult and error‐prone, creating a higher reliance upon place recognition to correct the odometry system. He uses robotics equipped with sonar to mimic echolocation and sample the environment. These sampled fingerprints are processed by a specific algorithm to recognize place fields which mimic the response of bat place cells. Jacob shows it is possible to generate place cell like responses using only sonar.
Yu Jin is a PhD student in the Department of Electrical and Computer Engineering supervised by Professor Joseph F. JaJa. His research focuses on developing novel and scalable methods to explore the large-scale human brain networks. Current Magnetic Resonance Imaging (MRI) technology provides a way to non-invasively visualize the complex neuronal connections within the human brain at a high resolution. However, there are effective ways to draw insights from the the large-scale brain networks given its high dimensionality and often the limited sample size. The general goal of Yu’s research is to better understand the complex patterns of the neural interconnections in human brains from diffusion MRI data which could potentially be used in many medical applications, such as computer-aided diagnosis. His current research interest is to use spectral methods and high performance techniques to generate high-resolution connectivity-based brain parcellations and apply to the discriminative analysis between schizophrenic subjects and normal controls.
James Komianos is a PhD student in the Biophysics program (in the Institute for Physical Science and Technology), in the lab of Dr. Garegin Papoian. Previously studying computational physics at Carnegie Mellon University, he became interested in molecular simulations of biological systems and networks. At the University of Maryland, he is working towards developing advanced computational models to simulate biochemical networks and, in particular, the cellular cytoskeleton, which is of great importance for the understanding of many cellular processes and diseases. Through COMBINE support, James’ current work involves investigating the dynamics of motor proteins in the cytoskeleton, which drive shape changes and mechanical phase transitions while imbedded in a protein filament meshwork. Through his simulations, which rely on high-performance computer resources at UMD, he hopes to capture the emergent behavior of these large systems while retaining detail of nanoscale molecular components.
Kunal Kundu is a Ph.D. candidate from the Moult Lab (http://moult.ibbr.umd.edu/) with particular interests in Genetic Variants, Disease Mechanism and Network Biology. He is affiliated to Biological Sciences Graduate Program (BISI) with Computational Biology, Bioinformatics and Genomics (CBBG) as the concentration area. Prior to joining UMD, Kunal worked as Bioinformatics Research Engineer for five years at Innovation Labs, Tata Consultancy Services where he developed tools and pipelines for analyzing human genome/exome sequencing data in order to identify putative causative genetic variant(s). His current research focuses on building a computable mechanism representation system that can describe genetic variant – disease phenotype connection in genetic diseases. The mechanism representation is built on the idea of causal graphs where nodes represent the perturbations at a DNA stage through Phenotype stage and edges are labeled with the type of mechanism driving the state change. The approach here involves developing ontologies, network visualization web application and network algorithms to study epistatic interactions.
Dustin Moraczewski is a graduate student in the Neuroscience and Cognitive Science program and a member of Elizabeth Redcay’s Developmental Social Cognitive Neuroscience lab. He received his B.A. in Psychology and a B.F.A. in Music Performance from Marshall University. Prior to graduate school he worked as a group home manager for adults and a behavior therapist for foster children with autism. In addition, he also worked in the Developmental Cognitive Neuroscience lab at Florida International University. His research uses functional magnetic resonance imaging (fMRI) to focus on the development of functional brain networks involved in social processing in neurotypical individuals and those with autism. In addition, experiments in his lab seek to understand brain organization during real-world social interaction (e.g., text message conversation) and social perception (e.g., watching a movie), and how this organization changes throughout development.
Simona Patange is a Ph.D. Candidate in Biophysics at the University of Maryland interested in network biology at the cellular level—in particular how master regulator transcription factors act upon human gene networks, and how misregulation of these factors leads to cancer. Her project focuses on the c-MYC oncogenic transcription factor, arguably the most important and least understood oncogene in cancer. MYC was one of the first discovered oncogenes over thirty years ago, and is found to be upregulated in the majority of human cancers and associated with aggressive tumor progression. Several models have recently emerged that cast doubt on the traditional view of MYC as a gene-specific transcription factor—an ‘on/off’ switch for gene expression—and instead envision that it functions as a global amplifier—a ‘volume knob’—to elevate the existing gene expression program in a cell. Her goal is to understand how MYC acts on gene expression networks from a single, living cell perspective using fluorescence microscopy and single molecule analysis techniques.
Welles Robinson graduated from Georgetown in 2014 with a B.S. in computer science and is currently in the computer science PhD program with Eytan Ruppin as his adviser. One of the reasons he applied to become a COMBINE fellow is because he wanted to gain proficiency in biology and statistics. His current research is focused on different problems related to cancer, including the identification of new drug targets and genetic interactions.
Peng Zan is a Ph.D. student from Electrical and Computer Engineering department, now working with Prof. Jonathan Z. Simon on neural data processing. His research interest is neural source localization with MEG data and schizophrenia (SZ) patient steady-state response with EEG data. His first project aims at finding the ‘averaged’ locations of neural sources when human brain is listening to a speech. The ‘averaged’ response, which is usually called temporal response function (TRF), is in the sense that it is phase locked to speech stimuli and with a period of less than one second. The TRF turns out to be sparse in time with significant components at 50ms (M50), 100 ms(M100) and 200ms(M200). At these peak times, neural source locations are computed using a sparsity-based greedy approach, and the results are being polished. His second project, and temporal EEG response patterns between both SZ and controls, and subgroups within SZ are compared, in order to explore to what extent the EEG response representations reflects the symptoms of this disease. The symptoms related to auditory perception include delusions, hallucinations, etc. It is under going using both traditional data processing technique and the cutting-edge machine learning approach.