Building the future with enterprise-grade AI

Brock is an expert in statistical inference, machine learning and software architecture. At Strong, he leads projects that combine each of these to build data applications at scale. Brock holds a Ph.D. in Cognitive Science from Northwestern University, in which he studied universal learning mechanisms underlying language acquisition. Prior to co-founding Strong, he built and led several software companies to successful acquisitions.

Jacob is an expert in deep learning, reinforcement learning, and leveraging artificial intelligence to automate processes in complex, dynamic systems. Jacob holds a Ph.D. in Neuroscience from Northwestern University, where he developed novel deep-learning based tools for decoding speech from electrophysiologic signals. Prior to co-founding Strong, he worked as a consultant with clients such as LG, 3M, Verizon, Duracell, Motorola, and Nokia.

Jacob is an expert in applying statistical approaches to understanding customer lifecycles; for example, predicting when and how churn happens and how to prevent it, or forecasting lifetime value and how to increase it. Jacob is also an expert in probabilistic modeling and leveraging big data to model and forecast risk. Jacob did his doctoral studies in Psychology, an M.S. in Statistics, and holds a certificate in management from the Kellogg School of Management at Northwestern University.

Noah is an expert in statistical causal inference, predictive modeling, and applied optimization techniques. Before joining Strong Analytics, he worked in the pharmaceutical and insurance industries. Noah attended Purdue University and the University of Illinois, where he received his M.S. in Statistics.

Juan specializes in researching and developing machine learning models, leveraging deep learning and natural language processing. Prior to joining Strong, Juan worked on text classification models for e-commerce transactions, generative models for synthetic time series generation, and 3D virtual environments for enhancing situational awareness in people with visual impairments. Juan attended Florida State University, where he received his M.S. in Computational Science and B.S. in Mathematics.

Mariela is an expert in statistical methods from traditional modeling to deep learning, natural language processing, and spatial analysis. Before joining the team, she developed machine learning-based approaches to automatically describe geologic and hydrologic environmental conditions from high-resolution remote sensing imagery with computer vision and integrated spatial clustering. Mariela holds a Ph.D. in Geological Sciences from the University of Colorado Boulder, a Master’s degree in Geological Sciences from MIT, and was a postdoctoral fellow at The University of Texas, Austin.

Joe brings a deep expertise in building and deploying data science and analytics projects in both the Financial and Advertising industries to his role as a data scientist at Strong Analytics. He has extensive experience in both traditional and deep learning based computer vision, and has built systems to extract text from advertisements in complex dynamic environments. Joe holds a Bachelor’s degree in Mathematics, Statistics, and Economics from the University of Chicago.

Francisco leverages his expertise in high-performance computing, computer vision, and deep learning as a data scientist as Strong. Prior to joining Strong, he developed vehicle collision claims automation tools in the insurance industry using both image and telematics data. He has also built deep learning based systems to learn and infer the low-dimensional feature dynamics of complex fluid systems. Francisco holds a Master’s degree in Aerospace Engineering from the University of Illinois at Urbana-Champaign and worked as a Computational Physics Fellow at Los Alamos National Laboratory.

Kuang specializes in deep learning, computer vision, and classical statistical inference. Prior to joining Strong, Kuang worked in the insurance space, where he optimized and enhanced a computer vision model for automobile total-loss determination. In addition, Kuang brings in unique expertise in improving the interpretability for deep learning models. Kuang holds a M.S. degree in Physics from the University of Chicago, where he was a Nambu Fellow and specialized in large-scale astronomical surveys.

Cody is an expert in working with large, heterogeneous sets of data, with experience in ingesting, transforming, and analyzing data from myriad disparate sources. Prior to joining Strong, Cody holds an M.S. and Ph.D. in Physics & Astronomy from Northwestern University, where he worked with space-based telescopes such as the Hubble Space Telescope, Planck, and Gaia, to perform broad, multi-wavelength studies of regions of interstellar space that could potentially host new star formation.

Cory is an expert in Bayesian modeling techniques with a specialty in handling missing and partially observed data. He has experience with a broad array of machine-learning techniques as well as traditional statistical methods. Prior to joining Strong, he was a post-doctoral researcher in cognitive science in Paris and Vancouver, focusing on how humans use their visual systems to make rapid inferences about quantity and on how to make inferences from sparse, but complex datasets that come from infants and young children. He holds an M.A. and Ph.D. in Brain and Cognitive Sciences from the University of Rochester. Cory is also a pianist and music teacher, earning degrees in performance and music theory from the Eastman School of Music.