Mentor: Dr. Rohan Attele, Chicago State University

Dr. Attele’s research focuses on cryptography and geometric algebras. Mentees will be introduced to elliptic curves over finite fields, a computer algebra system based on python to do computations, study various cryptographic algorithms, and engaged in modifications and simulations. Fellows will be introduced and exposed to the theory of geometric algebras, how they can be efficiently used in neural network computations, the potential of geometric neural networks for a variety of real applications using multidimensional representations, such as in graphics, augmented reality, machine learning, computer vision, medical image processing, and robotics.

Mentor: Dr. Moussa Ayyash, Chicago State University

Dr. Ayyash's computing infrastructure and technologies research work focuses on coexistence strategies to meet the needs of heterogeneous networks. His work cuts across areas such as wired computing networks, Internet-of-Things (IoT), 5G/6G wireless and visible light communications, cognitive radio, information security, flying networks, and artificial intelligence. Prospective research fellows will participate in efforts related to designing new coexistence techniques toward optimizing the performance of heterogeneous networks. 

Mentor: Dr. Emily Brooms, Northeastern Illinois University

Dr. Brooms’ research interests lie at the intersection of infectious diseases, their broader environmental impacts, and using natural biotic factors to inhibit these diseases.  As a graduate student and post-doctoral fellow, Dr. Brooms became interested in applying her knowledge of enveloped viral entry to the discovery of novel inhibitory compounds and antiviral therapeutic development.  During this time, she screened several thousand natural plant extracts for antiviral activity and identified several promising lead compounds targeting H5N1 Avian Influenza and HIV.  Comparisons of the anti-HIV compounds showed inhibitory activity comparable to the current HIV drug, AZT.  In more recent years, her lab at NEIU has started to explore biotic factors, like plant oils and bacteria, as sources of natural inhibitors against pathogens like the amphibian fungus Batrachochytrium dendrobatidis.  Through these studies, they have identified several lead plant compounds that are potent inhibitors.

Mentor: Dr. Nick Brunelli, The Ohio State University

Dr. Brunelli research seeks to investigate the design of catalytic materials for the sustainable production of chemicals and fuels. These catalytic materials are prepared by coating a support material with an active site – the location responsible for converting reactants to products. Whereas the ideal depiction is that all sites contributed equally, the reality is that there are many different sites. They use detective skills to elucidate synthesis-structure-reactivity relationships that can help to improve catalyst activity and/or selectivity. Their work has focused on:

1. synthesizing mesoporous composite catalysts
2. synthesizing microporous zeolites with Lewis acid catalytic sites
3. developing scalable synthesis methods to produce these promising catalysts for commercial use. For mesoporous materials, the core of their work has focused on establishing new synthesis methods to produce well-defined materials for converting biomass into important chemicals such as surfactants that are used in soaps.

Mentor: Dr. Jan-Jo Chen, Chicago State University

Dr. Chen’s research takes advantage of quantum machine learning to improve computational speed as the size of datasets and the number of models increase beyond the power of classic computers. Their work seeks to facilitate a data science paradigm, starting with data collection/cleaning, data processing, data analysis to generate a pattern/model, and ultimately enhance accuracy and the production of suitable data for display through various visualization methods. Among all the processes, data analytics is the core for quality of the accuracy of models and the speed of processing. With algorithms in Machine Learning and Deep Learning, his team is implementing the creation of models with applications in forecasting the appropriate tree species for planting in the forest. They are building models with Random Forest machine learning classifiers to classify different forest cover types from cartographic variables.

Mentor: Dr. William Clemons, California Institute of Technology (Caltech)

The Clemons lab focuses on structurally characterizing important biological systems. Currently, the group has two main areas of interest. The first is the biogenesis of membrane proteins, where they have made important contributions to understanding the twin-arginine translocation pathway, the guided-entry of tail-anchored membrane proteins pathway, and membrane protein evolution. The second area focuses on glycobiology at the membrane where his team has worked on asparagine-linked glycosylation and peptidoglycan biosynthesis. The team uses a variety of structural methods supported by mechanistic biochemistry. The work has been recognized by a number of awards including an NIH Director’s Pioneer Award. In addition to research, the group focuses on education, mentoring, and diversity, equity and inclusion.

Mentor: Dr. Noe’ de la Sancha, DePaul University/Field Museum of Natural History

Dr. de la Sancha’s research is framed on the premise that we are currently in the middle of the 5^th mass extinction on the planet, the Anthropocene. The main driver for this mass stems from the reshaping of natural environments into urban habitats and other highly anthropogenic landscapes, such as agriculture (Palomino and Carrascal, 2007). While these changes can have negative impacts on native wildlife, new urban habitats can offer havens for exotic and non-native species (i.e. Rattus and Pigeons). While many species are negatively impacted by human-driven landscape changes, some species are thriving in urban environments. Dr. de la Sancha’s group, among many other questions, seeks to understand what makes some species better adapted to coexistence with humans in urban environments?; How did urban adaptation shape the genomes of native and invasive urban species? Are there inherent morphological functional traits that favor these species? How do native vs exotic species populations react to urbanization (ie. population numbers)? Do these species navigate these urban landscapes differently (ie. dispersal)? Can we say anything about their breeding behavior (ie. multiple paternity of pups)?

Mentor: Dr. David Delaine, The Ohio State University

Dr. Delaine’ research focuses on producing new knowledge that improves the extent to which community-based learning in engineering (CBL - service-learning, outreach, and volunteerism) positively impacts students, participating stakeholders, and community members. His work is contributing towards transforming engineering education in producing empirically generated knowledge highlighting the limitations and opportunities of reciprocal and equitable CBL pedagogy. Since engineering is often considered primarily technical, our research has revealed the critical nature of socio-technical considerations required for more equitable relationships among CBL and outcomes for CBL projects. The ultimate goal of his research is to provide engineering educators and community members who lead CBL programs with evidence-based approaches to promote community outcomes and social justice alongside student learning outcomes.

Mentor: Dr. Mark Erhart, Chicago State University

The Erhart Laboratory uses classical, molecular, and bioinformatic approaches to examine the evolution of the mouse genome. Starting with the premise that mouse inbred strains represent well-characterized samples of genotypic diversity within a species, we created several unique congenic and recombinant inbred mouse strains and have maintained these in our mouse colony for over 20 years. Bringing together parts of a genome which have been evolving independently in wild mouse populations for 10³ – 10⁶ years can result in chromosomal, genetic, or biochemical incompatibilities which are manifested as phenotypic anomalies in a recombinant inbred or congenic strain. We have identified four such phenotypic deviants which we have been studying in detail. We employ a variety of molecular tools to create molecular markers (variable microsatellites), measure gene expression (qRT-PCR) and map novel insertion sites (inverse PCR) of endogenous retrovirus sequences which may be responsible for some mutant phenotypes. Both undergraduate and graduate students contribute substantially to these studies.

Mentor: Dr. Narayanan “Bobby” Kasthuri, University of Chicago/Argonne National Laboratory

The Kasthuri lab at the University of Chicago and Argonne National Laboratory is pioneering new techniques for mapping the fine structure of the nervous system at industrial scale. These include large volume automated electron microscopy for mapping neuronal connections at the nanoscale – ‘connectomics’, synchrotron source X-ray microscopy for mapping the cellular composition of entire brains, and genetic labeling of specific cell types for x-rays and electrons. The hallmark of brains, unlike other organs, is that the pattern by which brain cells communicate and connect with each other (neural circuits) determines a brain’s capabilities, its ‘personality’, and its memories. Dr. Kasthuri and his team are working to provide brain maps where, for the first time, the neural circuits that underlie behaviors from unconscious breathing to attention and decision making are revealed.  Such maps will help us understand how brains perform remarkable computations, sites where diseases have changed this network, and the first blueprints for reverse engineering the capabilities of brains in our own computers and robots.

Mentor: Dr. Zhiqiang Lin, The Ohio State University

Dr. Zhiqiang Lin is interested in most of the cybersecurity problems (e.g., vulnerability identification, authentication, authorization, introspection, deception, applied cryptography, and side channel analysis), with a key focus on advancing or using program analysis to solve the security problems. More specifically, he has been working on developing new or using existing program analysis and reverse engineering techniques for vulnerability discovery with native binary code in the past decade, and recently also on byte code, script code, or even source code, covering the entire software stack from firmware to applications, from web and mobile to IoT and 5G. In addition to finding the vulnerabilities, he also works on hardening the software against various attacks (e.g., control flow hijacking and memory corruptions), particularly on improving or using binary code rewriting, virtual machine introspection, and trusted execution environment (TEE) towards this goal.

Mentor: Dr. Raghu Machiraju, The Ohio State University (OSU)

Dr. Machiraju and his team are involved in interdisciplinary and cross-departmental research projects, including the NSF ICICLE AI Institute - Engaged in foundational and applied AI research and translating results into application domains of animal ecology, digital agriculture, and smart foodsheds. They are also involved in a Proctor and Gamble funded effort with Mechanical and Aerospace Engineering, and Microbiology to detect extraneous bacteria in small-abundance fluids (e.g., to check if Mr. Clean is clean). They are also involved in Pathology/Medical Oncology with OSU Pathology Dept to create uncertainty machine learning methods for diagnostic grading of both rare (and less data) and prevalent cancers (relatively more data), and many other projects in Pathomics, Smart Automation, and Radiation Oncology.

Mentor: Dr. Kristy Mardis, Chicago State University/Argonne National Laboratory

Mardis’ group has focused on electronic structure calculations of polymer-based solar cells (Niklas et. al., 2020) and cobalt and nickel based hydrogen catalysts (Niklas et. al., 2012; Niklas, et. al., 2015).These studies have focused on combining density functional theory calculations with EPR measurements. Correlations between the calculated electronic structures and the metal coordination environment allow insight into structural factors underlying the observed catalysis activity. Older work focused on using molecular dynamics simulations to explain wide angle x-ray scattering data of solution structures of porphyrin arrays (Tiede et. al. 2009; Mardis, 2009). To investigate the role of structure and solvent on photocatalytic behavior, we leverage existing capabilities at Argonne National Lab running many of the larger electronic structure calculations on LCRC computing center resources. Collaborators at ANL synthesize and obtain EPR measurements on the same materials for which we perform density functional theory calculations at multiple oxidation states using Turbomole (Steffen, et. al. 2010) for optimization and ORCA (Neese, 2012)for electronic parameter calculation.  This collaborative approach of experiment and calculation provide a thorough understanding of the effects of changing ligands and solvent on the electronic structure of a catalyst that can be directly correlated with functionality. Additionally, there is limited data on the suitability of different DFT functionals in this area, although inclusion of dispersion seems critical and xc functionals appear promising (Das et, al., 2022).

Mentor: Dr. Ivan Mutis, Illinois Institute of Technology

Dr. Mutis’ research focuses on various applications of UAV. UAV uniquely capture a wide spectrum of spatial and temporal information from the construction site environment. The collected UAV data, aerial visualizations coupled with telemetry data, offer a distinctive perspective. It enables the simultaneous visualizations of in-situ construction resources, processes, and management of activities as they unfold over time. UAV data provides observers with the opportunity to develop skills that integrate spatial and temporal information by enhancing their understanding of interdependencies, interactions, and constraints among integrated and specialized engineering systems in the construction project. This research looks to demonstrate how the use of UAV technology enables CM&PE to expand her/his repertoire of actionable possibilities for contextual awareness of construction tasks to solve problems. The research aims to reveal the value of the UAV visualizations and data as a unique technological tool for learning.

Mentor: Dr. Beth Reinke, Northeastern Illinois University

Understanding biological diversity requires both identifying the mechanisms and functions that underlie it, and understanding the fitness consequences of variation. Animal coloration and patterning are ideal traits with which to study functions and consequences of variation because these highly visible phenotypes can impact every aspect of an organism’s life. Long-term field studies on wild animals are known to provide opportunities for novel insights, to record temporal heterogeneity, to lead to the possibility of establishing new model systems, and are necessary to assess functions and consequences of variation in long-lived organisms. Dr. Reinke’s group run two long-term field studies of painted turtles, Chrysemys picta, a species named because of their yellow skin stripes and their bright orange ventral shells (plastrons). Despite their name and widespread range, very little is known about their color or patterns. Their current focus is on studying two populations, one in western Illinois and one in northern Wisconsin, to document their survival, growth, color change, and population dynamics.

Mentor:Dr. Abdollah Shafieezadeh, The Ohio State University Storm-related power outages cause $20 to $55 billion in damage to the U.S. economy every year, and one of the leading causes of death in hurricane events is loss of power (Campbel, Library of Congress; Rappaport & Blanchard, 2016). Transmission lines that carry bulk electricity compared to distribution networks pose a higher risk for widespread power outages if they experience failure during extreme climate and weather events such as hurricanes. While failure of overhead structures that support transmission lines and their probability of occurrence have been investigated, aging and corrosion effects in overhead structures and their implications for the reliability of structures in the power grid are understudied. A reliable age-dependent model should account for the various types of corrosion observed in lattice towers. Dr. Shafieezadeh research seeks to develop age-dependent corrosion and loss-of-stiffness models for transmission tower components, implement those functions into existing computational models of transmission towers, conduct computational pushover analyses, and analyze the resulting time-dependent force-deformation responses for transmission towers.

Mentor: Dr. Ness Shroff, The Ohio State University

Dr. Shroff’s research focuses on the development of new online learning or Reinforcement learning techniques in order to design future XG networks. His team is also interested in the development of ML theory that in any of the areas of bandits, reinforcement learning, deep neural networks, Baysian optimization, federated learning, Meta learning, and transfer learning. There is also a focus on the development of AI techniques to combat disinformation in social networks. Finally, his group is also interested in the analysis and design of complex systems from communication Networks to cyberphysical systems, and social networks.

Mentor: Dr. David Wood, The Ohio State University

Dr. Wood’s research focuses on downstream processing of biopharmaceuticals and on applied protein engineering.  An important advance has been the development of several new self-cleaving tag modules that allow proteins and protein complexes to be purified in a variety of formats.  These advances have been commercialized in a startup company, which is now generating sales revenues from this technology.

Because Dr. Wood’s work is focused on the highly regulated biopharmaceutical industry, his research program is highly rigorous in experimental design, reproducibility and ethics.  Students working in within Dr. Wood’s group are exposed early to the concept of GMP manufacturing, and the components of processes and products that will eventually be introduced into human patients.  This training includes an examination of case studies where different types of process failures or violations have taken place, along with how the environment was created that led to them and how they could have been avoided. The rigorous training has made Dr. Wood’s students highly attractive to the biopharmaceutical industry and academia.