Affiliated Faculty

The Bahnson lab is exploring the fascinating catalytic power of enzymes with a focus on two related areas of interest: (i) x-ray crystallography is used to solve structures of substrate complexes and intermediates of enzyme catalyzed reactions and (ii) understanding the role of evolved ordered enzyme motions in catalysis.

Rhizosphere engineering to enhance plant-microbe interactions for soil and human health.
Bais lab follows a systems approach in understanding plant-benign microbe interaction in the rhizosphere. To this end, we approach the rhizosphere (a few mm space around the roots), as the most dynamic place for plant root-microbe interactions. The goal of Bais lab is to mechanistically understand these plant-microbe, plant-plant and microbe-microbe interactions and harness the potential of benign microbes to develop technologies and enhance our fundamental understanding of how plants recruit its microbiome.

Roghayeh (Leila) Barmaki is an Assistant Professor at the Computer & Information Sciences Department and affiliated with the Data Science Institute at the University of Delaware. Dr. Barmaki leads the Human-Computer Interaction Lab at the University (HCI@UD).
Her research interests span Multimodal Data Analytics, Human-Computer Interaction, Virtual and Augmented Realities with applications in Education and Healthcare.

A trained cell biologist with experience in Single Molecule RNA imaging for 15 years. Interested in exploring the role of RNA in regulating gene expression in the cell and in maintaining cell-cell communication.

Rahmat Beheshti is an assistant professor in the Data Science Institute and also the Department of Computer & Information Sciences at the University of Delaware. He has a unique interdisciplinary background by finishing his postdoctoral training in Public Health and his PhD and MSc in Computer Science. He has been working in the area of Health Data Analytics and Computational Epidemiology for the past eight years. Specifically, he has worked extensively on two major public health epidemics: smoking and obesity, and has focused on very different aspects of these two, including the social, economic, environmental, and lately biological factors that affect these epidemics.

Regulation of gene expression is essential for development, response to environmental signals, and prevention of disease states. Transcription is the first, and most highly regulated step in gene expression. Transcription consists of three distinct phases: initiation, elongation, and termination. The first phase, initiation is the most highly regulated as many factors including DNA structure, binding of transcription factors and availability of substrate all contribute to the decision to begin transcribing RNA. Most cellular transcription is performed by multisubunit RNA polymerases (RNAPs), which are conserved in sequence, structure, and function from bacteria to humans. In addition, organelles such as the mitochondria rely on single subunit RNAPs that are also highly conserved across eukaryotic organisms. Biological functions of the cell such as metabolism and gene expression are inextricably linked. My lab uses both traditional genetic and biochemical analysis as well as novel next generation sequencing methods to address fundamental questions about the interplay of metabolic state of the cell and transcription as the first step in gene expression.

My primary research focus involves unraveling the mechanisms underlying carcinogenesis induced by certain variants of the Human papillomavirus (HPV) family. Specifically, I have elucidated the molecular pathways through which high-risk HPV strains exert their carcinogenic effects and have devised a sequence-based classification system for HPV types of unknown risk. Through meticulous analysis of HPV-encoded E2 proteins’ interactions with the replication origin of HPV (LCR) at specific binding sites, I have uncovered how these proteins trigger DNA replication. Utilizing DNA sequence analysis coupled with gel-binding assays (EMSA), my team and I have pinpointed specific mutations within the LCR that precisely correlate with the carcinogenic potential of various HPV types. Notably, high-risk HPV types harbor mutations within the binding sites, while low-risk types consistently exhibit a normal sequence (ACCGNNNNCGGT), providing a reliable sequence-based identifier for HPV risk classification. As the principal investigator, I have spearheaded these groundbreaking studies, advancing our understanding of HPV-related carcinogenesis.
As a collaborative project we investigate the ABCA4 gene that has been linked through genetic studies to a broad spectrum of blinding retinal degenerations, including Stargardt macular dystrophy and cone-rod dystrophy. Over 1000 disease associated mutations have been identified to date. The ABCA4 gene encodes a retina specific ATP binding cassette (ABC) transporter localized primarily to the rod and cone outer segment disc membranes. Each half of the bipartite ABCA4 protein contains a transmembrane domain (TMD) followed by a cytoplasmic or soluble domain (NBD), which harbors the Walker nucleotide binding motifs. Our laboratory has developed a bioinformatics platform for assessment of individual functional domains of ABCA4 as well as the consequences of disease associated genetic variations

Biological systems have been used for the production of value-added compounds for centuries; however, our ability to read and write DNA make it possible to engineer biology to far exceed its natural capabilities. My research group addresses big problems in sustainability, human health, national defense, and space exploration – using synthetic biology, metabolic engineering, genomics & systems biology, and protein engineering. We work mostly in eukaryotic systems (non-model yeast and mammalian cells) as well as bacteria. We are increasingly interested in the use of systems-scale data for better informing biological design decisions.

Richard Braun is an applied mathematician whose recent research focuses on dynamics of the tear film, ocular surface and blinking. He uses mathematical modeling, perturbation methods and computation methods to get results of interest to the ocular surface community.

Austin J. Brockmeier is an assistant professor in the Department of Electrical and Computer Engineering and the Department of Computer and Information Sciences, and is a resident faculty of the DSI. His research interests center on designing algorithms and models for gaining insights into complex data sets, with applications focused on biomedical signals and text mining. He has worked on machine learning methods to search and organize large collections of scientific references for evidence-based research. He has also worked on new approaches for analyzing brain waves and neural recordings for brain-machine interfaces.

He received a BS in computer engineering from the University of Nebraska–Lincoln in 2009 and a Ph.D. in electrical and computer engineering from the University of Florida in 2014. He then worked as a postdoctoral researcher at the University of Liverpool and the University of Manchester. He is a member of the IEEE and IEEE Engineering in Medicine and Biology Society.

Dr. Roxana Burciu is a neuroscientist and an Assistant Professor in the Department of Kinesiology and Applied Physiology. Her research seeks to advance our understanding of the neural control of movement in healthy and disease. The lab she directs uses a variety of non-invasive functional and structural brain imaging techniques coupled with behavioral and genetic measures to investigate the mechanisms contributing to motor impairment in movement disorders such as Parkinson’s disease.

Dr. Chandrasekaran’s research interests include exploring high-level directive-based parallel programming models for heterogeneous HPC and embedded systems, exploring strategies to migrate scientific applications to current and future platforms, creating benchmark suites representing real-world applications to measure performance of computing systems, building validation and verification suites for validating parallel programming models and its conformance to the standard.

With a background in cognitive neuroscience, psycholinguistics and engineering, I have been developing and using cutting edge neuroimaging techniques and machine learning approaches to study the neurobiology of human communication. My current research focuses on understanding genetic factors underlying functional and structural anomalies associated with persistent stuttering and neural re-organization leading to recovery from childhood stuttering. The overall goal of my research program is to identify early markers of persistent stuttering and develop effective therapeutic interventions to treat the disorder. My current projects aim at understanding the neural processes of speech production and how these processes are disrupted in developmental stuttering using multimodal neuroimaging techniques, genetic sequencing and genetically modified mice.

Research in Coyne’s lab is broadly focused on the molecular ecology of phytoplankton, and harmful algal bloom species in particular. We are interested in phytoplankton response to changes in the environment over varying time scales, using biochemical and molecular markers to investigate these responses at different taxonomic levels.

Dr. Decker works on problems in bioinformatics that require data to be gathered autonomously from distributed sources and integrated with local data to produce new information for life scientists. He has been involved with projects to automate repetitive analyses and apply artificial intelligence reasoning techniques to represent biological processes and propose new hypotheses for testing. He is also involved in agent-based modeling of cellular and sub-cellular behavior.

Yixiang Deng is currently an assistant professor at the University of Delaware in the Department of Computer and Information Sciences. She has worked as a Postdoctoral Fellow at the Ragon Institute of Mass General, MIT, and Harvard, with a joint appointment at the MIT Department of Biological Engineering, advised by Prof. Douglas A. Lauffenburger. Her research focuses on integrating multiscale modeling and machine learning in bioengineering applications. Before joining the Ragon Institute, she completed her Ph.D. in the School of Engineering, advised by Prof. George Em Karniadakis at Brown University in 2021. She received a B.Eng. degree in Engineering Mechanics at Shanghai Jiao Tong University in 2015.

My research focuses on phylogenetics and applied probability/statistics, with an emphasis on the development of next-generation Markov chain Monte Carlo (MCMC) methods for phylogenetic inference. I am also interested in computational methods for experimental design and control of biological systems, as well as machine learning algorithms and their applications in applied sciences.

The Duncan lab uses anatomical, genetic, molecular and cell biology methods to investigate the molecular basis of blinding eye conditions, most notably cataracts. Diverse bioinformatic methods are used in this research as well. A current focus of the lab and possible topic for a research MS thesis in Bioinformatics and Computational Biology is the bioinformatic analysis of RNA-seq data generated from tissues with highly non-normal distributions of gene expression.

Pak-Wing Fok is an Applied Mathematician whose research centers on atherogenesis and plaque development. He is interested in the physical and biological processes that drive plaques to grow and later rupture, using a combination of mathematical modeling and computation to understand these complex phenomena.

The Gleghorn Lab is an interdisciplinary research group that studies lung and placenta development to treat congenital birth defects, conditions associated with preterm birth, and maternal-fetal health complications. Or focus relies on deciphering physicochemical intercellular communication, spatial gene regulation, cellular and microbial ecology and interactions with mammalian cells and viruses using developing organ models, microfabricated 3D organotypic culture models, quantitative analysis, and computational methods.

Dr. Hadden-Perilla uses all-atom molecular dynamics simulations — often referred to as “the computational microscope” — to study biological machines, such as viruses and molecular motors. Prior to joining the University of Delaware, she held a postdoctoral position at the Beckman Institute for Advanced Science and Technology and served as the Technology Training Organizer for the NIH Center for Macromolecular Modeling and Bioinformatics. Dr. Hadden-Perilla’s research extends beyond elucidation of the mechanisms of biological machines to developing tools and approaches that make the “computational microscope” accessible to blind and vision-impaired researchers.

Daniel Harris, PhD, MPH is a geriatric pharmacoepidemiologist and health services researcher. Dr Harris’ research aims to optimize medication and vaccine use for older adults living in the community and in long-term care. He applies a range of descriptive, causal, and predictive methodological approaches to understand how medications and vaccines are distributed throughout populations and to document their real-world safety and effectiveness. Dr Harris’ work also explores the effects of health policies on medication and vaccine use. His research has been funded by the Alzheimer Society of Canada, Canadian Institutes for Health Research, National Institute on Aging, and pharmaceutical industry partners.

Dr. Johnson uses magnetic resonance imaging (MRI) to study the mechanics of tissues in the body and how they can be used to understand the structure, function, and health of various organs, with a specific focus on the human brain. In particular, Dr. Johnson uses the magnetic resonance elastography (MRE) technique, which noninvasively probes tissue viscoelasticity through the imaging of shear waves generated in the body. His research includes the development of high-resolution MRE imaging protocols through MRI pulse sequence design, image reconstruction schemes for accelerated MRE, and incorporation of advanced tissue models such as anisotropy and poroelasticity. Dr. Johnson explores using the MRE technique for a variety of applications in neuroscience, neurology, and neurosurgery, such as better understanding the structure-function relationship of the hippocampus and evaluating meningiomas prior to surgical resection.

Dr. Jungck is primarily interested in four different areas of bioinformatics that are reflected in recent publications: 1)”Genetic Codes as Codes: Towards a Theoretical Basis for Bioinformatics;” 2)”Ka-me: A Voronoi Image Analyzer” that allows users to analyze biological images of polygonal tessellations such as dividing epithelia with computational geometry, graph theory, and spatial statistics; 3)”Evolutionary Bioinformatics: Making Meaning of Molecular Messages” with a focus on molecular phylogenetics; and “Bioinformatics education dissemination with an evolutionary problem solving perspective.” Please visit http://bioquest.org/bedrock/ for bioinformatics education modules.

Atul Kakrana works at the intersection of AI, ML, computer science, and bio- and clinical informatics to build autonomous, intelligent systems for Health and Life Science applications. Atul led global data science teams at Elsevier to build Machine Learning-based information extraction platforms, computer vision & clinical decision support systems. His work contributed to enterprise solutions, including ClinicalPath, ClinicalKey, Embase, & Pharmapendium. Atul’s current work at Zifo focuses on in silico modeling (digital biology), generative biologics, and clinical operations, applying emerging technologies to accelerate molecule discovery and generation and optimize operational efficiencies for clinical processes.

Atul completed his PhD from the University of Delaware in Bioinformatics and Systems Biology. His research focused on computational genomics for discovering disease genes, underlying pathways, and new RNA molecules.

His future interests lie in Multimodal AI for clinical support, Quantum AI for target and drug discovery, Edge AI for robotics, and Quantitative AI for financial technologies. 

Austin Keeler is a developmental neurobiologist whose research focuses on the maturation and formation of peripheral pain and touch systems. Utilizing mass cytometry, a high throughput, single-cell, protein-based technique adapted by Austin and colleagues to be compatible with neural tissues, we investigate the protein signaling pathways that regulate acquisition of distinct neuron responsiveness and function. Further, we assess perturbations in pain and touch systems to understand the complex protein signaling and neural activity that result in aberrant pain sensation.

The April Kloxin Group seeks to understand important biological signals in tissue regeneration and disease using both a materials- and engineering-based approach. They design materials to mimic soft tissues, such as brain, muscle, and connective tissue, and whose properties can be modified at any location and time. These novel biomaterials are used as a flexible platform for cell culture to ask fundamental questions about how the environment surrounding a cell influences cell function and fate for tissue regeneration or disease progression. These findings are utilized to develop better strategies for tissue repair or disease treatment towards improving human health.

Lee laboratory research is focused on understanding the role of cell-to-cell communication through plasmodesmata in plants by taking a multidisciplinary approach employing cell and molecular biology, biochemistry, genetics, and bioinformatics. Plasmodesmata are intercellular communication channels unique to plant systems that are considered one of the fundamental changes that occurred during the evolution of land plants. Yet, our understanding of this fundamental structure is quite limited. The Lee lab is tackling this problem by working towards unmystifying their molecular composition and architecture, control mechanisms, and roles in physiological and developmental processes.

Kelvin Lee’s research team works on problems relevant to the biopharmaceutical manufacturing community and to the Alzheimer’s disease community. The group uses -omics tools and approaches to support ways to improve manufacturing of therapeutic proteins and antibodies and also uses stem cell based models of the blood-brain barrier to improve understanding and delivery of such molecules to address neurodegenerative diseases.

Dr. Mi-Ling Li is an assistant professor in the School of Marine Science and Policy at the University of Delaware. She completed her doctoral degree in the Department of Environmental Health at the Harvard University and has worked as a joint postdoctoral fellow in the Department of Earth, Ocean, and Atmospheric Sciences, Institute for the Oceans and Fisheries, Institute for Resources and Environmental Sustainability at the University of British Columbia. Dr. Li’s research focuses on the sources, transport, fate, and bioaccumulation of contaminants in ecosystems and their health impacts, with an emphasis on linking global environmental changes to ecological and human health. Her research approach is multidisciplinary, combining analytical isotope geochemistry, quantitative modeling, and ecological fieldwork. Some of Dr. Li’s current research topics include legacy pollutants (mercury, lead) and emerging contaminants (per- and polyfluoroalkyl substances).

Li’s research is focused on the high-performance computing and its applications. Specifically, we develop code optimization techniques that accelerate programs on various computer platforms including multi-core processors and Graphic Processing Units (GPU’s). Furthermore, we deliver near-peak performance for many widely used numerical routines such as FFT and BLAS. Our research propels the efficiency of computation, which ultimately caps the size and complexity of problems that can be solved by the computational approach in bioinformatics and related areas.

Our research is focused on understanding how individual components in joint tissues, including the tendon, bone, and cartilage, are formed and maintained throughout life. Cells residing in these adjacent yet distinct tissues are regulated by unique mechanisms. Furthermore, abnormalities in these musculoskeletal resident cell populations can contribute to disease such as osteoarthritis (OA) or temporomandibular joint OA (TMJ-OA) which affects over 15 million individuals in the United States. Our overarching goal is (1) to investigate mechanisms of cell differentiation and function that are essential for the development and maintenance of unique cell types that reside in the TMJ, and (2) to determine how alterations in these mechanisms contribute to TMJ-OA and temporomandibular disorders (TMDs). We will address these questions by using multi-disciplinary approaches, including state-of-the-art imaging techniques, single cell and spatial transcriptomics, and mouse genetic tools.

Our research focuses on the mechanobiology in musculoskeletal system, in particular how cartilage and bone cells sense the mechanical forces generated from our physical activities and transfer the signals into orchestrated cellular activities. Using advanced mechanical methods, microscopy techniques, nanotechnology, proteomics and computational modeling, the mechano-chemical conversation between cartilage and bone at both molecular and cellular levels are investigated to understand the etiology of osteoarthritis and osteoporosis, and to find new therapeutic interventions aimed at the mitigation or treatment of these diseases.

Using a combination of structural (X-ray crystallography) and functional analyses, our laboratory determines the mechanistic basis of critical communication events during bacterial pathogenesis. This facilitates development of a novel class of anti-infectives that do not kill bacteria but target these communication events to curb bacterial behaviors leading to disease.

Dr. Patel uses a variety of computing paradigms (HPC, GPU, supercomputer, etc) for modeling at the quantum and classical levels, materials of relevance to biochemical processes. He is also interested in application of machine learning and artificial intelligence methods for developing computaitonal models, as well as analyzing large data sets wherever they may originate. Finally, he is interested in coupling machine learning / AI methods with current understanding of stochastic processes involved in biology, biochemistry, and mathematics.

A key theme of Dr. Perilla’s research is to explore fundamental cell processes across multiple scales. Dr. Perilla’s primary technique is molecular dynamics (MD). During the past three decades, MD simulations have emerged as a “computational microscope”, which has provided a unique framework for the study of the phenomena of cell biology in atomic (or near-atomic) detail. Remarkably, due to the the ambitious nature of Dr. Perilla’s research, his lab has developed novel MD
approaches for computation, data analysis, and interface to experiments. In addition, the synergistic interplay between Dr. Perilla’s computational work and state-of-the-art experimental work performed by experimental collaborators, has resulted in a robust framework for
elucidating accurately and quantitatively the physical mechanisms of biomolecular function.

Dr. Peterson has extensive experience in behavioral assessment and change. He is also an expert in health and media, social marketing and has developed and implemented numerous research studies related to behavior change via social marketing interventions. He has consulted with a wide variety of private, non-profit, and government agencies in the area of behavioral change and assessment. He also has been primary investigator on many grant projects that promote health in communities throughout the Mid-Atlantic region, and has created highly acclaimed cutting-edge tools for assessing health-related behavioral, psychosocial, and ecological measures.

Expertise: Genomics, Transcriptomics, Metagenomics, Phylogenetics, Genomic Technologies, High Performance Computing, Bioinformatics Programming

Dr Qi’s research goal is to understand the neurobiological organization of language in the human brain, how that organization changes from childhood through adulthood, how it is disrupted in major neurodevelopmental disorders of language, and how knowledge of that organization may enhance language learning and language intervention. Dr. Qi uses a variety of behavioral and neuroimaging techniques to study the relationship between language learning, cognitive skills, and brain development in both children and adults.

We are interested in understanding how environmental factors, such as parenting behavior and social stress, can influence the development of behavior and psychiatric disorders. Our primary interests are centered on identifying epigenetic changes (i.e. DNA methylation) associated with early-life caregiving experiences, particularly maltreatment, and understanding their causal role in behavioral outcome

Dr. Ilya Safro received his Ph.D. from the Weizmann Institute of Science under the supervision of Achi Brandt and Dorit Ron. In January 2021, he joined the Department of Computer and Information Sciences at the University of Delaware. In 2012-2020, Dr. Safro held assistant and associate professor positions in the School of Computing at Clemson University. He was also a Faculty Scholar of the Clemson University School of Health Research. Before that he was a postdoc and Argonne scholar at the Division of Mathematics and Computer Science at Argonne National Laboratory. Dr. Safro research is funded by NSF, DARPA, DOE, BMW, and Greenville Healthcare Systems. His research interests include algorithms and models for AI, machine learning, NLP, network science and graphs, quantum computing and large-scale optimization.

Dr. Manuel Schottdorf is an assistant professor in the Department of Psychological & Brain Sciences at the University of Delaware. His scholarly research explores how learned knowledge changes how we perceive the world. He conducts basic research with multiple methods of assessment: Computational and AI techniques, electrophysiology, two-photon microscopy, and virtual-reality based behavior.

Dr. Schottdorf studied physics before completing a Ph.D. at the Max Planck Institutes for Experimental Medicine and Dynamics and Self-Organization in Germany and a Postdoc at the Princeton Neuroscience Institute. He enjoys developing scientific instrumentation and won numerous awards over the years, among them a Boehringer Ingelheim Fonds PhD Fellowship, an Otto Hahn medal, and a Burroughs Wellcome Fund’s Career Award.

Research in our lab is focused on developing mathematical and computational tools for studying the stochastic dynamics of gene-protein networks at a single cell resolution. By combining these computational techniques with high-throughput experimental data we map novel regulatory mechanisms within gene-protein networks. Of particular interests are sub-cellular biochemical networks underlying various disease systems. Current research uses coupled experimental-computational approaches to characterize gene regulatory networks encoded by pathogenic viruses such as Human Immunodeficiency Virus and Herpes viruses, and design strategies to manipulate these networks for therapeutic benefit.

Microorganisms are among the most innovative chemists, catalyzing powerful chemistries at ambient conditions with high specificity. With emerging techniques from systems and synthetic biology, my lab characterizes and harnesses these capabilities to address grand challenges in sustainability, human health, and food safety. We also create novel synthetic biology tools via protein and genetic engineering to accelerate development of these platforms for industry.

The Song lab uses computational, molecular biological, systems biology approaches such as next generation sequencing and proteomic approaches to understand the regulatory roles of microRNAs in early development.

The yield potential of agricultural crops is limited by the ability of plants to support their own weight and withstand external forces. The failure of plants to stay upright, termed lodging, can have a dramatic impact on crop yields. Lodging can occur when the stem breaks (stalk lodging) or when the root system loses contact with the soil and is up-rooted (root lodging). Although stalk lodging has been the focus of much research attention, it is suggested that root lodging is more prevalent. In some crops (e.g. corn and sorghum) specialized aerial roots, called brace roots, are thought to play an important role in stability to prevent root lodging. Yet, the benefit of brace roots to the plant and what makes a good brace root is unknown. Our lab focuses on understanding the development and function of brace roots in crops. We leverage techniques from engineering, computational biology, genetics, genomics, and molecular biology to address these research questions.

Research description: The Wei lab is interested in the regulation of cell signaling, in particular Wnt and PI3K/Akt signaling, by cell-surface metalloproteinases. Perturbations of these cellular processes can lead to severe birth defects as well as other diseases, such as tumors, arthritis and neurodegenerative diseases.

I am a food chemist and food toxicologist. My research has focused on the characterization and application of bioactive compounds. Specifically, my efforts have focused on three areas: 1) Evaluation of toxicities and endocrine disruption potential for natural bioactive compounds and newly synthesized chemicals 2) improvement of human and animal health by dietary bioactive compounds 3) enhancing food safety and quality through the novel application of antimicrobials or pulsed light technology. These focus areas represent an excellent balance between basic and applied research.

The Arsenault lab studies cellular signal transduction through the use of high-throughput peptide array technology. We study animal health and infectious diseases, gut health and immunometabolism. Bioinformatics plays a role at all levels including peptide array design, data integration, data mining and data visualization. Applying peptide array technology to new species and new post-translational modifications and the development of new data tools are an important part of our ongoing research.

Mike Chen received his Masters in Bioengineering from Shaanxi University of Science and Technology. Prior to starting ACROBiosysems, Mike worked for Life Technologies and Thermo Fisher as cell culture specialist for industrial scale cell culture process and application development. MIke Chen has over 15 years of experience in the biotechnology and pharmaceutical industry leading projects for recombinant product and application development, large scale cell culture manufacturing process development and implementation, quality ptogtrams for operational excellence. Areas of particular strength include: large scale cell culture process development, cell culture medium development and optimization, organization strategy development in global wide market, team management, leadership and entrepreneurship training.

Dr. Chiam is the Director of Health Systems Optimization at the Value Institute, ChristianaCare. He directs a team with quantitative and qualitative background in industrial engineering, data science, and human factors to develop insights and approaches to optimize patient safety, flow, access to healthcare and experience. During the COVID-19 pandemic in 2020, he led multiple cross-functional teams to develop predictive models for COVID-19 and non-COVID inpatient and outpatient volumes to inform hospital resources allocation, as well as financial prediction model to provide insights on the impact of pandemic on hospital finances. Prior to Christiana Care, he was involved in applied operations research at UMass Memorial Healthcare where he created and managed the Department of Business Intelligence and Research. His research interests include multimethod approach to optimize and control complex systems. Dr. Chiam received his PhD in Industrial Engineering from Purdue University.

Anastasia Christianson received her Ph.D. in Biological Chemistry from the University of Pennsylvania followed by postdoctoral training in Cellular and Developmental Biology at Harvard University. She has over 20 years experience in the biotechnology and pharmaceutical industry working in both Discovery and Development leading projects, managing complex portfolios, driving change programs, identifying opportunities for strategic initiatives, and translating scientific and medical questions into innovative solutions. Areas of particular strength include: strategy development and implementation, translational medicine, biomedical and health informatics, evidence-based decision makings, scientific and competitive intelligence, and “Big Data” exploitation.

Dr. Devarajan is an Associate Research Professor in the Department of Biostatistics & Bioinformatics at Fox Chase Cancer Center (FCCC) and an affiliated faculty member in the Center for High-dimensional Statistics at Temple University’s Big Data Institute. His primary research interests encompass statistical machine learning & data science with applications in bioinformatics, neuroscience, medicine & natural language processing. It spans unsupervised and supervised learning, as well as survival analysis, and primarily focuses on the development of statistical and computational approaches to analyze massive data sets generated in these areas. Dr. Devarajan is a member of the Research Review Committee and serves as the Vice Chair of the Data Safety and Monitoring Board at FCCC. Prior to joining FCCC, Dr. Devarajan held research positions at Bristol-Myers Squibb Pharmaceutical Research Institute in Princeton, NJ and the Cancer Bioinformatics Group at AstraZeneca Pharmaceuticals in Boston.

Our lab uses a reductionist approach to understand behavior with a focus on memory and psychoses. Insights into the molecular and cellular basis of behavior are key to understanding the functional design of the nervous system, including complex neural conditions such as Schizophrenia and other psychoses. We are using genetic, behavioral, and imaging based approaches to unravel synaptic modulation of dopamine by DOP-2, a C. elegans D2-like dopamine auto-receptor.

The Green Lab studies RNA biology with emphasis on post-transcriptional control of gene expression at the genomic, epigenomic, molecular genetic, and biochemical levels. Our projects include genome-wide analyses of the human RNA degradome, the Arabidopsis RNA degradome in ribonuclease mutants, and noncoding RNAs, namely miRNAs and siRNAs associated with environmental and oxidative stress, aging, and tissue type. The work is carried out in model plants, food and bioenergy crop plants, marine invertebrates, and human cells.

The Hopper lab studies the genetics of host shifts in herbivores (genus Heliothis) and aphid parasitoids (genus Aphelinus) using laboratory experiments on host use behavior and quantitative genetics analyses of inter and intraspecific crosses to map QTL and determine genetic architecture. We also use modern methods of molecular phylogenetics to develop a robust phylogeny for species (~100) in the genus Aphelinus. To develop and genotype SNPs for QTL mapping and molecular phylogenetics, reduced-representation genomic libraries are generated. With these libraries, we are using next-generation sequencing to discover and genotype large numbers of SNP markers distributed across the genome

Md. Jobayer Hossain, PhD, is a Senior Research Scientist and the Director of the Biostatistics Program at Nemours Children’s Health. He has over 20 years of experience in the design and analysis of clinical, epidemiological, and public health studies. His research interests focus on the innovative application of statistical, data science, and machine learning methods to explore etiologic pathways and health trends in diverse pediatric populations that improves the health of children affected by a broad range of diseases, disorders, and health conditions. Besides routine modeling of data, his research focuses on tracking changes in health trajectories; recognizing hidden patterns; creating and predicting classes of distinct patterns; and identifying individual and community-level demographic, clinical and other features responsible for diverse trajectories. Dr. Hossain provides training and education on statistics and analytical software skills at Nemours and affiliated institutions. As an active research and teaching faculty, Dr. Hossain is a lead or co-author for 134 published articles and more than 250 published abstracts and presentations.

Expertise: Proteomics, Pathway/Enrichment Analysis, Biostatistics

Claudine Jurkovitz, MD, MPH, is Director of Clinical Research in the Value Institute at Christiana Care and lead of the Biostatistics Epidemiology Research Design (BERD) core of the Delaware ACCEL-Center for Translational Research (CTR). As such she helps Physicians, Residents and young Investigators at Christiana Care and other ACCEL-CTR Institutions to develop their research projects and analytical plan. She is also Director of the INBRE Centralized Research Support Network (CRSN), which goal is to develop mechanisms to leverage existing infrastructure such as the Delaware ACCEL-CTR with expertise in epidemiology, study design, biostatistics, community-based participatory research and patient engagement and to make these services available to the INBRE network’s biomedical investigators. As a Nephrologist Epidemiologist, Dr. Jurkovitz has actively developed her own research interests, mostly in the field of chronic kidney disease (CKD) and health services research.

Atul Kakrana works at the intersection of AI, ML, computer science, and bio- and clinical informatics to build autonomous, intelligent systems for Health and Life Science applications. Atul led global data science teams at Elsevier to build Machine Learning-based information extraction platforms, computer vision & clinical decision support systems. His work contributed to enterprise solutions, including ClinicalPath, ClinicalKey, Embase, & Pharmapendium. Atul’s current work at Zifo focuses on in silico modeling (digital biology), generative biologics, and clinical operations, applying emerging technologies to accelerate molecule discovery and generation and optimize operational efficiencies for clinical processes.

Atul completed his PhD from the University of Delaware in Bioinformatics and Systems Biology. His research focused on computational genomics for discovering disease genes, underlying pathways, and new RNA molecules.

His future interests lie in Multimodal AI for clinical support, Quantum AI for target and drug discovery, Edge AI for robotics, and Quantitative AI for financial technologies. 

The Kalavacharla group’s current research broadly focuses on epigenetic regulation in plants. We are particularly interested in transcriptomic and epigenomic responses of plants to biotic and abiotic stresses. Using molecular genetics, genomics, and bioinformatics, our group has developed plant resources for a better understanding of complex crop plant genomes. In the long run, we seek to identify regulatory mechanisms of control of gene expression, intersection of genes and pathways in plants that respond to abiotic and biotic stresses, and developing integrated genome wide maps that contain genome, transcriptome, and epigenomic information. Additionally, research by the Kalavacharla group focuses on identifying and elucidating histone and DNA modifying enzymes and their regulation.

The Killian Lab is a research group in the Orthopaedic Research Laboratories in the Department of Orthopaedic Surgery at the University of Michigan Medical School. We aim to identify key regulators of musculoskeletal growth that can be leveraged to improve musculoskeletal growth and healing.
We study the cell and tissue-scale mechanisms underlying pediatric and young adult orthopedic disorders (such as joint instability, contracture, and overuse) using micro-computed tomography, histology, molecular and cell biology, transgenic mouse models, and mechanical testing.

In the past 10 years we have seen an explosion of new therapies and new approaches to cancer treatments. Few of these breakthroughs have benefited children to a significant degree. It is time to consider a new paradigm; to treat childhood cancers as singular diseases worthy of the same focused investment as each adult cancer. In the Nemours Cancer Therapeutics Laboratory, we are developing new models of cancer that reflect the specific behavior of cancer in children (phenotype) and the specific and diverse molecular variants of childhood cancers (genotype). The genotype and phenotype of childhood cancers are specific and predict response to therapy and survival. They may also ultimately be used to identify new therapies.

My interests are in technical methods and practical processes for benefit-risk assessment of pharmaceuticals and other medical treatments. My work includes development and application of formal decision analytic techniques (e.g. multi-criteria decision analysis) as well as informal decision-making processes in both individual and group settings, the use of stated choice studies (e.g. conjoint analysis, best-worst scaling) to assess physician and patient preferences for benefits and harms, and the use of clear high-dimensional data visualization techniques to communicate complex assessments. My prior research dealt with organizational learning, evolutionary-based optimization, high-dimensional data visualization, and combinatorial chemistry.

Most of my current research interests fall under the broad heading of knowledge engineering. My current project involves the use of social media for understanding unmet patient needs and improving patient education and awareness within the health care system. Specifically I am looking at patient sentiment using social media analytics and sentiment analysis. This brings together many of my skills and interests in genomics, pharmacology, toxicology, text analytics and supervised learning. I am interested in the applications of text analytics to problems in bio, clinical, medical informatics including information extraction from EHRs and patient records and development of vocabularies that support the linkage of information, like a personal genome, while maintaining privacy.

The primary emphasis of the Meyers lab is the analysis of small RNAs in plants. With our many collaborators, we have pioneered genomic analysis of small RNAs and their targets, working with “next-gen” sequencing technologies nearly since their invention. The Meyers lab continues to develop and apply novel informatics approaches for the analysis of RNA function in plants.

BINF601: Introduction to Data Science course instructor
I have extensive biostatisticial experience in clinical trials in medical devices and pharmaceutical industry. Therapeutic areas of experience include cardiovascular (stents), central nervous system (sleep disorders, Alzheimers, depression and bipolar) and immunology (psoriasis, psoriatic arthritis, rheumatoid arthritis and ankylosing spondylitis). Statistical consultant for academia as well as industry.

Dr. Papas received her doctoral degree in epidemiology from Johns Hopkins University. Dr. Papas has authored over 30 peer reviewed articles investigating the built environment and adolescent overweight, growth rates among children with early growth deficiency, dietary habits of inner-city adolescent mothers, the influence of pain on adherence to mammography screening guidelines, and food insecurity in Hispanic families. Dr. Papas’ expertise includes study designs, the assessment of screening tools, the analysis of longitudinal data, and the use of GIS in understanding the effect of place on health and health behaviors.

Jae has over 20 years in the Biopharmaceutical Industry, from R&D to Clinic. Majority of experience working with expressions systems, new cell technologies, immunoassay design and development, immuno oncology targets, and antibody development. Jae has a PhD in Immunology and a MBA.

Tomasz G. Smolinski is an assistant professor in the Department of Computer and Information Sciences at Delaware State University, and the head of the Computational Intelligence and Bio(logical)informatics Laboratory (CIBiL). At CIBiL, Dr. Smolinski and his “CIBiLings” apply various computational intelligence methods (e.g., evolutionary algorithms, artificial neural networks, rough sets, fuzzy logic, etc.) to solve complex problems in the broadly defined field of biological sciences, with the use of parallel and high performance computing techniques. The projects at CIBiL range from exploration and visualization of large parameter spaces of neuronal models, through assembly and analysis of plant transcriptomes, to annotation of amino-acid sequences for protein function prediction.

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The Wisser plant breeding and genetics lab studies complex or quantitative trait variation as it applies to crop improvement. Investigations are centered on the genetics and biology of response to selection, environmental adaptation and disease resistance. Our laboratory provides a setting for students and professionals to explore biological questions relevant to plant improvement while developing fundamental skills in the fields of genetics, breeding, statistics, bioinformatics and molecular biology through a combination of field, laboratory and computational experimentation.

Dr. Yi’s research focuses on the application of statistics to heart studies, kidney studies, dementia research, health research. Dr. Yi’s expertise includes the analysis of data from large-scale observational studies and the design and analysis of clinical trials. She has extensive experience with longitudinal data analysis, missing data, survival analysis, and biostatistics.

Dr. Zhang has worked extensively in medical studies related to cardiology, cancer research, environmental health, and precision medicine focusing on the genetic risk and coronary heart disease. He conducted comprehensive researches in both clinical trials and large observational studies. The areas he has the greatest interests are: microarray data analysis, survival data analysis, longitudinal data analysis, Bayesian Analysis, and health economics, including cost-effectiveness analyses and quality of life.