Andrew Hansen is a researcher, instructor, and doctoral candidate in cognitive neuroscience in the Department of Cognitive Sciences at the University of California Irvine (UCI). He performs research with Megan Peters in the Cognitive and Neural Computation Laboratory (CNC Lab), where he maintains and manages a team of five research assistants. Andrew teaches undergraduate courses on topics in the neurosciences, cognitive sciences, and computational statistics; he also assists in teaching graduate courses in neuromorphic engineering and spiking neural networks. Additionally, he works as a technical writing consultant and graduate student mentor.
Andrew’s current work has largely been dedicated to that of two parallel, though distinct lines of research. First, he has continued to develop a theory of Enzymatic Reaction-Diffusion Networks (ERDNs), establishing the theoretical, mathematical, and computational foundations for dynamical modeling and comprehensive characterization of learning in complex biochemical systems. His approach is uniformly applicable to arbitrary single- and multi-cellular organisms and systems, regardless of their type or architecture. To these ends, he has been developing an open-source Python library to provide a robust framework for engineering, modeling, simulating, and analysis of ERDNs. Second, Andrew has been developing CoGraph, a joint (open) data- and knowledge base comprised of four decades of publication content and metadata from proceedings of seventy leading conferences in the neurosciences, cognitive sciences, and artificial intelligence. He has developed a Python library of machine learning pipelines to extract scientific concepts, their semantic relationships and co-occurrences, as well as author and conference metadata from publication content; to generate time series of conceptual prevalence, and construct a large temporal knowledge graph therefrom. He is applying analytical techniques from statistics, information theory, dynamical systems, and graph theory to characterize implicit organizational features in the collective temporal dynamics of scientific concepts; using a systematic and combinatoric approach toward a comprehensive understanding of the connections between the local environments and historical patterns on the emergence, propagation, adaptation, and decay of concepts. Andrew is particularly interested in the presence of topological structures in the temporal knowledge graph which are of universal importance, and in latent features within the dynamics of its evolving topology which are causal drivers of the paradigmatic shifts within the scientific ontology. Additionally, he is training generative graph neural networks on the temporal knowledge graph to forecast future developments in the continued progression of scientific knowledge.
Prior to his current position, Andrew was a faculty member in the Department of Cognitive Sciences, during which time he earned an M.S. in Cognitive Neuroscience from UCI, and worked with Emre Neftci in the Neuromorphic Machine Intelligence Laboratory (NMI Lab). Here Andrew performed machine learning research with biophysically realistic spiking neural networks in neuromorphic processors, as well as neuromorphic sensors. He developed a Python library which allows for the asynchronous, concurrent, and simultaneous design, deployment, and training of spiking neural networks in real-time on the DYNAP-SE1 mixed-signal neuromorphic chip. Previously, Andrew received an M.S. in Neuroscience from Brandeis University in 2016, where he worked in the Neural Circuits Laboratory (VH Lab) with Stephen Van Hooser on a platform-independent MATLAB library for running a variety of neurophysiology and imaging protocols on a wide array of instruments. Additionally, Andrew worked on single neuron models as well as neurophysiologically realistic spiking neural networks. In 2015, Andrew received a B.S. in Biophysics from St. Mary’s University (StMU) in San Antonio, Texas. While at StMU, Andrew performed research at multiple institutions on a range of topics in mathematics, computer science, neurobiochemistry, epigenetics, and the chemical engineering of biofuels and self-healing materials.
Further details are available for viewing on his curriculum vitae.