Workshop Talk
2:00 pm to 3:00 pm, FEBRUARY 22 in person at CSL 301
AbbVie’s panel presentation by the team below will focus on machine learning and deep learning as tools for signal processing. Data scientists from the Information Research will discuss how machine learning and deep learning as tools for signal processing are opening up new dimensions of analytics and quantitation than previously tractable before. As the dimensionality of data grows, so does the need for processing methods to extract knowledge and insights from the growing size and interconnected mesh of information available to science. The panel will discuss the use of 1D and 2D signal streams and imaging, 3D voxel, and n-dimensional graphs.
Coffee will be provided!
Brian Martin – Head, AI/ML, Information Research
Biography: Brian joined AbbVie in 2018 as the head of the newly formed RAIDERS team within Research & Development’s Information Research division, focused on accelerating, scaling, and amplifying the work of AbbVie’s R&D community using Artificial Intelligence technologies like machine learning, deep learning, graph computation, and cognitive computing. Brian is a part of the leadership team building and directing the AbbVie R&D Convergence Hub (ARCH) as part of the R&D Convergence initiative and a member of the ACOS Scientific Innovation Council. Brian came to AbbVie after spending five years in technology consulting across many industries, and over a decade of additional experience before that working in trading and financial markets technology. During his consulting time, Brian was the architect of the United States’ Common Securitization Platform and the technology founder of Publicis.Sapient’s AI practice. While his primary focus is AI technologies, he was also a co-founder of the QuPharm quantum computing community and the Pistoia/QED-C Quantum Community of Interest. He is a frequent presenter at conferences on topics as diverse as optical networking, quantum computing, blockchain, cognitive architecture, and other emerging technologies that are all part of digital transformation. Brian holds a B.S. degree in Computer and Cognitive Science from Alma College and a M.S in Computer Science from the University of Chicago. He is a board member for the Chicago Innovative Executives League and for the Mundelein High School Business Incubator program. He has been involved with the Creative Destruction Lab at the University of Toronto’s Rotman School and as a panelist/reviewer for the National Science Foundation Secure and Trusted Cyberspace grant division. Brian lives in Lake Bluff, Illinois with his wife and four children.
Abhishek Pandey – Principal Data Scientist, Information Research
Biography: Abhishek is the group lead for Machine Learning team called RAIDERS: Pharma Discovery in AbbVie. His team works in the field of Machine Learning in Chemistry and drug discovery, Machine Learning in imaging/multi-omics, genomics and Machine Learning. Abhishek is in-charge of the Abbvie-Calico collaboration. He is a Principal Research Scientist in Machine Learning and Deep learning. In his previous life, he was the inaugural member of precision medicine AI team and helped build and transform Tempus Labs Inc. He has his PhD in Electrical and Computer Engineering from University of Arizona.
Scott Gladstein, Manager, R&D Statistics
Biography: Scott Gladstein is a Manager, Statistics in the Discovery and Exploratory Statistics (DIVES) team at AbbVie, where he is responsible for providing biomarker analytical support for translational imaging, eye care, and precision medicine teams. As part of this role, he has developed imaging-based deep learning models for predicting neurodegenerative disease progression. Scott received his undergraduate degree in Physics from the University of Illinois at Champaign-Urbana and his PhD in Biomedical Engineering from Northwestern University.
Snehal Vadvalkar, Senior Technology Engineer, Information Research
Biography: Snehal is a Senior Data Scientist who is currently a member of the RAIDERS team at Abbvie. Her primary focus is on solving problems in drug discovery, multi-omics, and high throughput screening space using Machine Learning in imaging. With a Master’s degree in Electrical and Computer Engineering specializing in signal processing from the University of California at Santa Barbara, she has honed her skills and expertise over the years. Snehal has made significant contributions during her 6-year stint at AbbVie. Her prior experience includes working at Intel’s 3D Scene Perception group for RealSense camera and Technical Marketing at Mentor Graphics. When Snehal is not busy providing solutions to complex data-related problems, she spends quality time with her family, residing in the scenic northern Chicago suburbs.
Majdi Hassan, Associate Data Scientist, Information Research
Biography: Majdi graduated from UIUC in 2019 with a B.S in Computer Engineering. He began his career at AbbVie as a student through the AbbVie Innovation Center, joining AbbVie full-time in January 2020 as an Associate Data Scientist under the machine learning group RAIDERS. His work focuses on applying graph/geometric deep learning to accelerate drug discovery in areas like molecular property prediction, ligand-protein interaction, and de novo generation of molecules.