Determinant Data

Jed Crosby

Jed was the first person to hold the title of Data Scientist at Salesforce.com. Educated in Applied Mathematics at Harvard and the University of Washington and in Scientific Computing at Stanford, Jed worked as a software engineer at a startup and for Microsoft before joining Salesforce. Jed founded Determinant Data in 2020.

Jed holds the following AI-related Salesforce certifications:

• Salesforce Certified AI Associate
• Salesforce Certified Data Cloud Consultant
• Salesforce Certified Agentforce Specialist

Jeff Davis

Jeff joined Determinant Data in 2021 and has led our efforts in machine learning automation. Before joining Determinant Data, Jeff worked developing commercial PV+Battery storage systems for Sharp Electronics for 4 years. He rearchitected Sharp's SmartStorage EMS communication infrastructure, greatly increasing its reliability in production. Jeff is an expert in integrated system development and testing, highspeed simulation for algorithm evaluation, and software quality engineering. Before Sharp, Jeff worked at AdECN (web advertising realtime bidding auction service), Microsoft (web advertising services), and Salesforce (machine learning infrastructure engineering).

Monica Isgut, PhD - Strategic AI & Data Advisor for Biotech and Digital Health

Dr. Monica Isgut combines deep expertise in biology and artificial intelligence with a proven entrepreneurial track record. She is a recurring guest faculty for the American Board of Artificial Intelligence in Medicine (ABAIM), where she teaches clinicians the technical foundations of machine learning. She holds a Ph.D. in Bioinformatics from Georgia Institute of Technology and a B.S. in Biology from Emory University, with industry experience at GlaxoSmithKline, giving her a unique, cross-disciplinary perspective on health technology. Monica has co-authored 10+ biomedical research publications in high impact journals such as Genome Medicine, Scientific Reports, IEEE Reviews in Biomedical Engineering and Medicinal Research Reviews, and specializes in integrating complex multi-modality biological and medical data in bespoke custom-designed machine learning algorithms suited to achieving biomedical research & development objectives. She has worked with diverse biological data types including: large-scale electronic health records data, multi-omics, chemical structures, biomedical time series and waveform data, genomic data, and image datasets. She has worked extensively with diverse neural network architectures (transformers, autoencoders, CNNs, etc.) and a wide array of both supervised and unsupervised machine learning algorithms - with specific expertise in adapting these models to suit the requirements of biological and biomedical datasets. She prioritizes data quality as a foundation for effective machine learning modeling, and is highly comfortable advising on how to manage and process "big" biomedical data - whether it is "omics", EHRs, high content imaging, or genotypes, for example.

Karan Samel, PhD - Strategic Advisor for Multi-Modal and Knowledge-Based AI

Dr. Karan Samel is an expert in machine learning, with a proven track record of developing impactful AI systems across industry and academia. Karan holds a Ph.D. in Machine Learning from Georgia Tech, where his research involved integrating knowledge graphs and multi-modal data within large language models (LLMs). Karan's experience includes roles at Google DeepMind to improve search relevance using LLMs in a scalable and cost-effective fashion. At Amazon, Karan improved product relevance recommendations by integrating e-commerce knowledge graphs and taxonomies within LLMs. At IBM Research, Karan applied neuro-symbolic methods to extract logical patterns from complex time-series data, facilitating actionable insights in both healthcare and video analytics. Earlier in his career, Karan led engineering efforts in feature extraction pipelines and human-in-the-loop data corrections systems to boost real-world AI model performance.