Director of research
Glenn received a B.S. in mathematics from Universidad Lisandro Alvarado in Venezuela. He then earned an M.S. in applied mathematics from Universidad Simon Bolivar, Venezuela, where later he worked as an assistant professor. He also earned an M.S. degree and a Ph. D. degree in computer sciences and statistics from the University of Wisconsin-Madison in 2003. His main interests are optimization approaches to machine learning and AI, with emphasis in kernel methods. For twelve years he has worked in industry (Siemens, Amazon, American family insurance) developing and applying novel machine learning techniques to solve challenging industry-related problems. He is an author of 100+ scientific publications and 30+ patents. Outside of work, he attempts to make fun music while playing the bass and enjoys collecting and playing trading card games.
Senior Data Scientist
Joe has over 15 years of experience extending and applying machine learning methods to important problems in industry and society. He has a PhD from the University of Wisconsin-Madison in Computer Sciences (2005) and did postdoc work in the Machine Learning and Applied Statistics group at Microsoft Research. Specific interests at the moment are natural language understanding and probabilistic representations of uncertainty for rational decision making (by humans or AI systems). Outside of work he enjoys spending time with his two children, partner and two cats, golfing (poorly), and playing cards, especially Sheepshead.
Devin joined the machine learning research team at American Family Insurance as an intern in the summer of 2016 and came on full-time the following year. He has undergraduate degrees in mathematics and philosophy from Cornell University and a masters in electrical engineering with a focus on optimization and active learning research from the University of Wisconsin-Madison. For work he enjoys developing full-stack solutions that use state-of-the-art machine learning algorithms for industry-quality applications and research. His most recent work includes RoCKET, a crowd-sourcing, active-learning framework for training text classification models, and the design and implementation of convolutional neural network text classifiers and a graph-based conversation service for Abby, American Family's chatbot solution. In his free time, he enjoys riding his bike, playing music, and reading sci-fi novels.
Maleeha received a B.A. in mathematics from the University of Virginia and an M.S. in computer science from the University of Wisconsin–Madison. For 10 years she worked at Siemens Medical Solutions in Malvern, PA doing research in the medical domain & helping build analytics into their EHR system. She has been a Data Scientist at American Family Insurance for the past 4 years. Her research interests include Statistical Relational Learning, Bayesian Networks, bioinformatics, and the application of machine learning techniques to the medical domain. Her personal interests include reading, traveling, dancing, crafts, cooking, and finding the best trees between which to string her hammock.
Eric joined AmFam's Machine Learning research group in 2018, and is interested in natural language processing, deep learning, and topological data analysis. Before joining AmFam, he worked at a FinTech startup and focused on customer targeting and segmentation. Having earned a Phd in mathematics, Eric is always excited to learn about novel techniques in machine learning that utilize traditionally more abstract mathematics. Outside of work, Eric enjoys spending time with his wife and son, playing the guitar, reading, and cooking.
Senior Data Scientist
Dan received a B.A. in Mathematics from the University of Chicago and an M.S. in Mathematics from the University of Oregon. He joined American Family in 2013, where he has worked on retention, customer lifetime value, and storm loss models. Specific interests include topological data analysis, non-IID learning, as well as spreading the joy of Python. Before joining American Family, he worked at Aon where he provided catastrophe modeling and consulting services. Outside of work Dan enjoys traveling and spending time outdoors.
Data Science Manager
Qian (Jane) You received her B.S. in Computer Science from Chu Kochen Honor’s College, Zhejiang University. She earned PhD in Computer Science from Purdue University. She has been a tech lead and a senior data scientist at the Snapchat (Snap Inc.) Ads Ranking Team. Before Snapchat, Jane was a machine learning scientist in Amazon. She has extensive research, engineering and product development experience in delivering machine learning and deep learning solutions for large scale online retailers and advertisers. She is one of the early participants of Amazon open-source deep learning engine DSSTNE. She also co-authored the book “Data Science Interview Exposed”. Outside work she enjoys gym, reading and fine arts.
Teja graduated with a Master’s degree in Computer Science from UNC Charlotte. He started his career as a Software Engineer where he was primarily focused on architecture design & development and currently, he is working as a Data Scientist for American Family Insurance since May 2017, primarily focused on research & delivery. He is very passionate about machine learning algorithms and his research includes Recommender systems and Natural Language Processing. Outside of work he loves to play soccer, travel or play video games.
Shailesh Acharya has a Master’s Degree in computer Science from University of North Texas. His research interest includes Natural Language Processing and Computer vision. He has worked for more than 4 years in the field of machine learning. Shailesh joined American Family Insurance as a machine learning intern in the summer of 2017 and came back as a Data Scientist the following year. Outside of work he loves to go out with his friends and travel to new places.
Jeff has extensive experience in feature detection, noise removal, convex optimization, and anomaly detection. He earned his PhD in Mathematics from the University of Wisconsin–Madison in 2010. After graduating, Jeff worked as a postdoc for the LIGO Scientific Collaboration. In 2013, Jeff joined a Madison-based startup aimed at detecting fraud in online publishing and advertising, and he continued to work in that industry until fall 2019, when he joined American Family Insurance. Jeff has published on many topics including linear algebra, discrete optimization, Internet measurement, and he is co-author on several patents. Jeff and his wife enjoy skiing with their two children and very large puppy.
Associated researcher. Professor at UW Madison
Vikas leads the image analysis service of the ADRC imaging core. He is an Associate Professor in the Biostatistics & Med. Informatics and Computer Sciences departments at University of Wisconsin-Madison. He received a Ph.D in 2007 in Computer Science at State University of New York at Buffalo.
His research is in biomedical image analysis, computer vision, and some aspects of machine learning. In particular, He is interested in problems motivated from image data with a distinct optimization and/or geometric flavor.