Trained at the University of Michigan, Danny is an experienced data scientist and machine learning (ML) engineer with a specialization in time-series analyses, signal processing, and computer vision. More often than not, he has had to lead projects where their full potential of their data had been underutilized. Due to this, he excels in identifying data and modeling needs, and then developing solutions for teams without existing frameworks. His passion for software engineering allows him to deliver mature, reliable, and maintainable solutions. As an example at the University of Michigan, he designed and implemented custom infrastructure capable of processing more than 1TB a day of data. He currently works for a leading FinTech company in Tokyo, enhancing their product offerings with in-house document Al models. Education Danny received his PhD in Behavioral and Computational Neuroscience from the University of Michigan, Ann Arbor, where his research focused on how electrical brain activity relates to behavior and diseases. These topics spanned from addiction, spatial navigation, and epilepsy. He also graduated from the University of California, Irvine with a B.S. in Biological Sciences and Computer Science.
Our Team
At Residual Insights, our team offers over 15 years in research and data science experience. We have experience in academia, insurance, neuroscience, e-commerce and much more!
Michael Demidenko is a co-partner and consultant at Residual Insights.Trained at the University of Michigan and Stanford University, Michael is an experienced data scientist, data engineer and quantitative research manager specializing in visualization, advanced statistical analytics, rigorous research, experimentation and hypothesis testing. He has authored over 30 research products, employing a wide range of research methods across clinical, behavioral, cognitive and biological domains. His passion lies in understanding unique data, analytic, research and business challenges, enabling him to address key inquiries and enhance data quality for more precise inference. Michael has built data workflows and analytic pipelines managing over 100TB of data. He has used A/B testing, data reduction techniques, predictive modeling and more. He has experience in deploying python packages and generating reproducible analytic workflows in R and Python. Education Michael graduated from the University of Michigan with a degree in Psychology, with an emphasis on Neurodevelopment and Quantitative Methods, and was a National Institute of Drug Abuse-funded Research Science Fellow at Stanford University focusing on data science, data engineer and computational neuroscience. He also received a data science certification from the University of Michigan's Institute for Data & AI in Society.
