As a dynamic and growth-oriented Data Scientist, I have extensive experience in data analytics, research and development, and engineering, with a particular focus on cognitive science and psychophysiology. Throughout my career, I have consistently demonstrated a goal-directed approach, leveraging advanced statistical techniques, machine learning algorithms, and data visualization tools to extract meaningful insights from complex datasets.
Being a forward-thinking professional, I led and managed end-to-end cognitive science experiments, from participant recruitment to the execution of data acquisition processes, ensuring the integrity and efficiency of research initiatives. My expertise extends to conducting advanced generative modeling and sophisticated graph theoretical analyses, providing unparalleled insights into dynamic fluctuations within large-scale networks. I am known for my ability to streamline data processing workflows through the implementation of automated pipelines in MATLAB and Python, resulting in a significant increase in team productivity. As a compassionate and motivational team member, I coordinate with multidisciplinary teams, including neuroscientists, clinicians, and engineers, to synthesize findings and integrate perspectives. Additionally, I have facilitated multimodal neuroimaging studies by creating efficient experimental protocols for the simultaneous use of electroencephalography and transcranial electrical stimulation, navigating complex regulatory processes while always adhering to ethical practices. Effective communication and collaboration are at the core of my approach, allowing me to work seamlessly with colleagues from diverse backgrounds. My problem-solving abilities have been instrumental in overcoming complex challenges, and my commitment to data ethics and privacy ensures that research initiatives are conducted responsibly and with the utmost integrity. Some of my skills are listed below: Data Visualization \| Statistical Data Analysis \| Experimental Design & Research \| Computational Modeling \| Machine Learning & AI \| Forecasting & Benchmarking

Kyle Curham

Data Scientist | Cognitive Scientist | Engineer | Transforming Complex Data into Profound Insights for Data-Driven Decision-Making

Orange City, Florida, United States of America

Research Expertise

Data Science

Computational Cognitive Neuroscience

About

As a dynamic and growth-oriented Data Scientist, I have extensive experience in data analytics, research and development, and engineering, with a particular focus on cognitive science and psychophysiology. Throughout my career, I have consistently demonstrated a goal-directed approach, leveraging advanced statistical techniques, machine learning algorithms, and data visualization tools to extract meaningful insights from complex datasets. <br> Being a forward-thinking professional, I led and managed end-to-end cognitive science experiments, from participant recruitment to the execution of data acquisition processes, ensuring the integrity and efficiency of research initiatives. My expertise extends to conducting advanced generative modeling and sophisticated graph theoretical analyses, providing unparalleled insights into dynamic fluctuations within large-scale networks. I am known for my ability to streamline data processing workflows through the implementation of automated pipelines in MATLAB and Python, resulting in a significant increase in team productivity. As a compassionate and motivational team member, I coordinate with multidisciplinary teams, including neuroscientists, clinicians, and engineers, to synthesize findings and integrate perspectives. Additionally, I have facilitated multimodal neuroimaging studies by creating efficient experimental protocols for the simultaneous use of electroencephalography and transcranial electrical stimulation, navigating complex regulatory processes while always adhering to ethical practices. Effective communication and collaboration are at the core of my approach, allowing me to work seamlessly with colleagues from diverse backgrounds. My problem-solving abilities have been instrumental in overcoming complex challenges, and my commitment to data ethics and privacy ensures that research initiatives are conducted responsibly and with the utmost integrity. Some of my skills are listed below: Data Visualization \| Statistical Data Analysis \| Experimental Design & Research \| Computational Modeling \| Machine Learning & AI \| Forecasting & Benchmarking

Publications

Logic Behind EEG Frequency Analysis

The Oxford Handbook of EEG Frequency / Sep 21, 2022

Curham, K. J., & Allen, J. J. B. (2022). Logic Behind EEG Frequency Analysis: Basic Electricity and Assumptions. In The Oxford Handbook of EEG Frequency (pp. 15–39). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780192898340.013.2

Wrist-mounted smartphone-based navigation device for visually impaired people using ultrasonic sensing

2016 Fifth ICT International Student Project Conference (ICT-ISPC) / May 01, 2016

Khampachua, C., Wongrajit, C., Waranusast, R., & Pattanathaburt, P. (2016, May). Wrist-mounted smartphone-based navigation device for visually impaired people using ultrasonic sensing. 2016 Fifth ICT International Student Project Conference (ICT-ISPC). https://doi.org/10.1109/ict-ispc.2016.7519244

Difference in Quality of Life during the Bone Healing Process in Patients with Upper or Lower Extremity Problems

Annual Global Healthcare Conference / Aug 27, 2012

Schmid, D., Lustenberger, J., & Hanson, B. (2012, August 27). Difference in Quality of Life during the Bone Healing Process in Patients with Upper or Lower Extremity Problems. Annual Global Healthcare Conference. https://doi.org/10.5176/2251-3833_ghc12.41

Brain activity diagnostics system for exoskeleton control

2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA) / Sep 14, 2022

Khorev, V. (2022, September 14). Brain activity diagnostics system for exoskeleton control. 2022 6th Scientific School Dynamics of Complex Networks and Their Applications (DCNA). https://doi.org/10.1109/dcna56428.2022.9923241

Repetitive Transcranial Magnetic Stimulation (rTMS): A Large-Scale Retrospective Clinical Data Analysis Indicating rTMS as Effective Treatment for Generalized Anxiety Disorder (GAD)

Brain Stimulation / Nov 01, 2020

Morris, T., Engelbertson, A., & Guidice, W. (2020). Repetitive Transcranial Magnetic Stimulation (rTMS): A Large-Scale Retrospective Clinical Data Analysis Indicating rTMS as Effective Treatment for Generalized Anxiety Disorder (GAD). Brain Stimulation, 13(6), 1843. https://doi.org/10.1016/j.brs.2020.06.021

Abstract #116: Simulations of temporal interference tCS in a realistic head model

Brain Stimulation / Mar 01, 2019

Rampersad, S., Shayestehfard, K., Roig-Solvas, B., Yarossi, M., & Brooks, D. H. (2019). Abstract #116: Simulations of temporal interference tCS in a realistic head model. Brain Stimulation, 12(2), e40. https://doi.org/10.1016/j.brs.2018.12.123

On the Permanence Property in Spherical Spline Interpolation,

Nov 01, 1982

Freeden, W. (1982). On the Permanence Property in Spherical Spline Interpolation,. Defense Technical Information Center. https://doi.org/10.21236/ada126263

Brain Waves Meet Real Life: Recent Advances in Mobile EEG

Psychophysiology / Dec 01, 2018

Brain Waves Meet Real Life: Recent Advances in Mobile EEG. (2018). Psychophysiology, 55(S1). Portico. https://doi.org/10.1111/psyp.13244

Time‐Frequency, Source, and Connectivity Approaches to Study Dynamic Relations Between Monitoring, Control and Anxiety Across Development

Psychophysiology / Nov 12, 2017

Time‐Frequency, Source, and Connectivity Approaches to Study Dynamic Relations Between Monitoring, Control and Anxiety Across Development. (2017). Psychophysiology, 54(S1). Portico. https://doi.org/10.1111/psyp.12937

Multivariate Time-Series Brain Connectivity: A Sum-Up

Methods in Brain Connectivity Inference through Multivariate Time Series Analysis / Apr 19, 2016

Multivariate Time-Series Brain Connectivity: A Sum-Up. (2016). In Methods in Brain Connectivity Inference through Multivariate Time Series Analysis (pp. 261–268). CRC Press. https://doi.org/10.1201/b16550-23

Midline Frontal ERPS and Theta Phase Alignment

Psychophysiology / Aug 27, 2015

Midline Frontal ERPS and Theta Phase Alignment. (2015). Psychophysiology, 52(S1). Portico. https://doi.org/10.1111/psyp.12490

The Agony and the Ecstasy: Leveraging Physiological Indices of Reward and Threat Sensitivity to Improve Measurement of Clinical Phenomena

Psychophysiology / Jul 18, 2014

The Agony and the Ecstasy: Leveraging Physiological Indices of Reward and Threat Sensitivity to Improve Measurement of Clinical Phenomena. (2014). Psychophysiology, 51(S1). Portico. https://doi.org/10.1111/psyp.12276

Design of a quasi-passive knee exoskeleton to assist running

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems / Sep 01, 2008

Dollar, A. M., & Herr, H. (2008, September). Design of a quasi-passive knee exoskeleton to assist running. 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. https://doi.org/10.1109/iros.2008.4651202

Education

University of Arizona

Ph.D., Cognitive Neuroscience / January, 2022

Tucson, Arizona, United States of America

University of Arizona

M.A., Psychology / May, 2017

Tucson, Arizona, United States of America

Western Governors University

M.S., Data Analytics / November, 2023

Salt Lake City, Utah, United States of America

University of South Florida

B.S., Mechanical Engineering / May, 2012

Tampa, Florida, United States of America

University of South Florida

B.A., Psychology / May, 2013

Tampa, Florida, United States of America

Experience

University of Arizona

Consulting Data Scientist / November, 2022 — November, 2023

Identify and elucidate effects of transcranial magnetic stimulation on resting-state electrophysiology to maximize understanding of neural responses and open avenues for exploring new applications of neurostimulation techniques in clinical / research settings. Perform advanced statistical analysis on collected resting-state electrophysiological data by employing techniques such as time-frequency analysis and connectivity measures.

Principal Investigator / August, 2019 — May, 2021

Facilitated multimodal neuroimaging studies by creating efficient experimental protocols for simultaneous use of electroencephalography and transcranial electrical stimulation.

Graduate Assistant / August, 2017 — January, 2022

Devised specialized software and hardware solutions to meet stringent regulatory and business requirements, positioning organization at forefront of technological innovation in meeting industry standards.

Teaching Assistant / August, 2014 — January, 2022

Guided students in practical application of quantitative techniques through courses in Measurement and Statistics, Research Methods, and Neuroeconomics. Delivered courses on Drugs, Brain, and Developmental Psychology to render holistic perspectives on complex interactions between brain function, substance use, and human development.

University of South Florida

Research Assistant / August, 2012 — May, 2024

Maximized integrity of the experimental process by strategically overseeing cognitive science experiments, from participant recruitment to acquisition of electrophysiological signals.

Research Assistant / August, 2011 — May, 2012

Conducted in-depth analysis to assess prosthetic dynamics and kinematics across diverse environmental conditions to assure functional adaptation and optimal performance of prosthetic solutions.

Volusia County Schools

Teacher / August, 2023 — Present

Teaching - Probability and Statistics

DataAnnotation.tech

AI Researcher / November, 2023 — Present

Provide High-fidelity training data for AI training and development; critique AI models for A/B testing.

Links & Social Media

Research Web Site

Link

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