Ben Clair
Data AnalystAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Principal Software Engineer at Splunk
• Strong programming skills in Python, Golang, Scala, Java, R, MATLAB • Neural network expertise from 10 years of academic research, implementation, teaching, analysis • Data engineering experience with Spark, S3, Parquet, Cassandra, Dynamo, Mongo, Postgres, Pulsar, AWS Cloud infrastructure • Passion for distributing machine learning algorithms, analytics, and complex computations • Mathematical modeling background simulating biological neurons, California wildfires, weather-adjusted solar power production, traffic flow, and the human brain • Deep knowledge of human cognition, language, and neuroscience
University of California, Merced
Doctor of Philosophy (Ph.D.), Cognitive and Information Sciences
January 1, 2009 – January 1, 2016
University of California, Merced
Bachelor of Science (B.S.), Applied Mathematics
January 1, 2005 – January 1, 2009
Splunk
Principal Software Engineer
May 1, 2024 – Present
Splunk
Senior Software Engineer
July 1, 2019 – May 1, 2024
Splunk
Software Engineer
July 1, 2018 – July 1, 2019
KryptonCloud
Machine Learning Engineer
September 1, 2017 – August 1, 2018
San Francisco Bay Area
Insight Data Science
Data Engineering Fellow
May 1, 2017 – August 1, 2017
Palo Alto, CA
University of California, Merced
Laboratory Assistant
February 1, 2017 – May 1, 2017
Merced, California Area
University of California, Merced
Lecturer
January 1, 2017 – May 1, 2017
Merced, California Area
UC Merced
Graduate Teaching Assistant
January 1, 2009 – January 1, 2015
UC Merced
Student Research Assistant
May 1, 2007 – August 1, 2007
UC Merced
Student Research Assistant
June 1, 2006 – May 1, 2007
Conceptual Spiking in Dorsolateral Prefrontal Cortex
January 1, 2015 – January 1, 2016
Neural firing patterns in the dorsolateral prefrontal cortex (DLPFC), thought to encode working memory contents, provide a great example of the variety of ways in which the polychronous neuronal groups can inform our understanding of neural information processing. In this project, we studied how DLPFC representations may constrain whole-brain dynamics in a context dependent way, illuminating interesting relationships between temporally-coded and rate-coded representational schemes.
Topological Dependence of Rate Code Stability
January 1, 2014 – January 1, 2016
Popular accounts of neural representation often invoke rate coding schemes. In this project, it is shown how only certain recurrent network types are capable of enduring tonic rate coded stimulation in a meaningful way. Particularly, networks with larger ranges in action potential propagation delay enabled the stability of more densely connected networks by increasing their modularity through sparsity of receipt (finding a "sweet spot" of metastable dynamics). Results suggest that the rate coded firing of an excitatory neuron in a densely connected cortical network cannot be assumed simply because it received rate coded synaptic stimulation.
Neural Firing Patterns Depend on Network Topology
January 1, 2014 – January 1, 2016
How do the neural firing patterns emerging from fully recurrent networks relate to changes in the pattern of network connectivity and conduction delay across and between clusters of neurons? I approached this question empirically through a large parametric study which simulated clustered network topologies of cortical excitatory neurons with inhibitory interneurons. From this massive grid search of the space of over 15,000 network structures, networks were found to be either: supercritically excited, with a vast array of explosive firing patterns (most common); subcritically quiescent, where no firing patterns were evoked despite tonic stimulation; or, metastably active, where tonic stimulation yielded likewise tonic firing within the network. Whether a particular network was found to be supercritical, subcritical, or metastable ultimately depended on its distinct combination of parameters.
Philosophical Implications of Polychronous Neuronal Groups
February 1, 2011 – January 1, 2015
There is growing empirical and theoretical neuroscientific evidence that relevant information in the brain is encoded in spatiotemporal patterns of spikes called polychronous neuronal groups (PNGs) which emerge naturally out of any spiking neural network with variable axonal conductance delays. Essentially, they are the unique chain reaction of spikes that becomes triggered in response to stimuli, almost like a special firework that looks differently depending on the stimuli. People are interested in PNGs because they represent a fundamental dynamic of information in brain-like neural networks that has only recently been discovered, so there is a lot to learn–our theories and explanations of cognition need to be adapted to take them into account. This project addresses this adaption, drawing implications from understanding the dynamics of Polychronous Neuronal Groups for theories of neural representation and philosophy of mind.
Cultural Fit Analysis
The candidate's career trajectory shows a strong focus on software engineering and machine learning within the tech industry, particularly at Splunk. Their personal projects are heavily academic and theoretical, focusing on neural networks and cognitive science, which are not directly aligned with a typical 'Data Analyst' role. While the candidate has a Ph.D. and experience in data engineering, their recent roles as Principal/Senior Software Engineer suggest a different career path than a pure Data Analyst. The lack of explicit data analysis projects or roles (beyond data engineering and ML) makes the cultural fit for a dedicated Data Analyst role less direct. The candidate's experience is more aligned with a Data Scientist, Machine Learning Engineer, or Software Engineer role.
Soft Skills & Operational Fit
The candidate's extensive experience in leading engineering efforts and building scalable solutions suggests strong problem-solving, leadership, and operational skills. Their academic background in complex systems indicates a capacity for abstract thinking and analytical rigor. However, the provided data does not offer direct insights into communication style, stress handling, or team collaboration beyond the job descriptions.