The CEL Lab is passionately driven to delve into the intricate relationships that bind various system components, whether they belong to the realms of biological, social, or operational systems. This pursuit can be succinctly described as "connecting the dots," a process that involves not only establishing connections but also evoking a sense of curiosity and igniting a thirst for knowledge. These connections and the act of eliciting information harmoniously converge in a downstream learning pipeline, forming the bedrock of an all-encompassing and distinctive data analytics endeavor.
To bring this vision to life, the CEL Lab harnesses the power of network science, discrete optimization, and machine learning—a triad of cutting-edge methodologies that allow us to tackle a wide spectrum of contemporary interdisciplinary research inquiries.
Our recent explorations span three principal disciplines, underscoring the breadth of our commitment to cross-disciplinary discovery:
Bioinformatics
Our investigations in bioinformatics delve into the intricate molecular landscapes of living systems, unraveling hidden patterns and interactions that underlie biological phenomena. By applying our analytical methodologies to biological data, we seek to extract insights that could lead to breakthroughs in fields such as genomics, proteomics, and personalized medicine.
Research:
Developing machine learning frameworks for accurate and efficient protein function prediction [Link]
Functional annotation of hypothetical proteins using network science and machine learning
Multiplex network analysis of bacterial genomes
Predicting ADHD in children through machine learning algorithms
Protein protein interaction prediction using graph neural networks
Current Projects:
Functional Elucidation of Virulence Associated Proteins Encoded By Flavobacterium Columnare, An Important Fish Pathogen, USDA, July 2021 - June 2024
Ongoing Initiatives:
Elucidating Common Genetic Architectures in Fetal Alcohol Spectrum Disorders, Microtia, and Craniofacial Microsomia through Network Science and Machine Learning
Social Science
Within the realm of social science, we delve into the complexities of human interactions, societal dynamics, and behavioral patterns. By dissecting social networks, understanding information diffusion, and deciphering the underlying mechanisms of social phenomena, we strive contributing to a deeper comprehension of how societies function and evolve.
Research:
Comparison of Parler and Twitter (aka X these days) data using NLP: U.S. capitol incident
Social media network analytics to reveal groups of people with certain traits
Ongoing Initiatives:
Investigation of Polarization Metrics Using Graph Theory and Machine Learning
Operations Research
The discipline of operations research provides us with a toolkit to optimize systems and make informed decisions. Through rigorous mathematical modeling, simulation, and analysis, we aim to enhance efficiency, streamline processes, and unravel the underlying principles governing operational systems across various domains.
Research:
Optimization based network clustering with descriptive social network analysis
Developing mathematical programming models for bi-objective Covid-19 vaccine distribution
The CEL Lab serves as a nexus where diverse systems intertwine, curiosity is nurtured, and multidisciplinary inquiries flourish. Our collaborative efforts and cutting-edge methodologies empower us to contribute meaningfully to the ever-evolving landscape of knowledge. The CEL lab collaborates across disciplines to tackle exciting new challenges. We are always exploring innovative ways to analyze complex systems, extract insights, and further multidisciplinary discovery.
Current Projects:
Multilayer Analysis of Energy Networks (supported by AI Sustein)