As a Senior Machine Learning Engineer at LinkedIn, I build large language models and multimodal machine learning for the Feed AI team, applying my skills and knowledge in online and offline machine learning, representation learning, information retrieval, and natural language techniques. I also collaborate with other engineers and researchers to source data, build prototypes, create data ingestion pipelines, develop machine learning infrastructure, and track feature metrics.

Before joining LinkedIn in September 2022, I worked as a Machine Learning Engineer at Facebook and Apple, where I contributed to cross-platform ML on Instagram and other family of apps, and multimodal and natural language processing R&D for Siri Natural Language, respectively. I also demonstrated and presented my work at various internal and external events, such as the Apple ML Summit, the Natural Language Understanding workshop, and the ICASSP conference. Prior to my industry experience, I obtained my Master’s degree in Computer Science from Georgia Institute of Technology, where I studied machine learning and interactive intelligent systems, and worked on computer vision and optimization problems in the Robotics Lab. I am a Tata Scholar, a K C Mahindra Scholar, and a Stanford Summer School attendee. I have also published a paper in the International Journal of Computer Applications, and obtained multiple certifications from Coursera and NVIDIA.

Sanket is a Senior Machine Learning Engineer who is an expert in building and deploying production grade ML systems. He currently works at Spotify on a team that builds recommendation algorithms for the Home page. He has expertise in data, MLOps, system and scale considerations for ML products. He has been recently focussed on recommendation systems, LLMs as well as powering diverse and calibrated recommendations.

Nitin is the Technical Director — AI Research and Senior Research Scientist at the AI research group in PayPal Risk Sciences, where he focuses on end-to-end design and development of AI algorithms, for large-scale real-time payments fraud detection. His research involves the next generation of fraud detection capabilities by designing novel fraud problem formulations, utilizing the exhaustive PayPal data assets so as to improve fraud detection accuracy while continuing to enhance the experience of good users. As a researcher, he also collaborates with Credit Risk and Marketing teams for different ideation and proof-of-concept development initiatives. Prior to his current role in the AI research team, he built large-scale machine learning and feature engineering frameworks for stolen identity and stolen financial instruments fraud detection at PayPal. With several years of research & teaching experience in machine learning and mathematical optimization, he takes keen interest in conducting AI workshops/bootcamps, talks and seminars for a wide array of audiences.

Dr. Paul McLachlan is AI Research Leader at Ericsson Research, Silicon Valley. At Ericsson, he develops new Artificial Intelligence technology that uses 5G and edge computing to support augmented and virtual reality. He is particularly interested in data privacy, trustworthy network architecture, and hardware security. His research focuses on integrating differential privacy into environmental understanding for purposes of spatial mapping. He also leads a research initiative that seeks to understand how best to deploy Trustworthy AI systems into real-time workloads.

In addition to research, Paul also works on AI commercialization as a member of Ericsson’s North American AI/ML Steering Board. In that role, he collaborates with multiple internal organizations to develop Ericsson’s AI go-to-market strategy. In recognition of his data science expertise, he has been invited to serve as an advisor and mentor to various startups, both in the Bay Area and abroad.

Prior to joining Ericsson, Paul was Director of Data Science and Artificial Intelligence Practice Lead at Epsilon, which was recently acquired by Publicis Groupe for $4.4 billion. At Epsilon, he led development of machine learning approaches for marketing data.

Before entering the corporate world, he was an academic at Emory University. His PhD is from the University of California, San Diego.

Isabel is a data scientist with a focus on AR/VR and addressing algorithmic bias in computer vision and machine learning models. She is passionate about the ethics of technology and the power of AI for social good. Her background includes research in psychology and moral philosophy, and she received an M.S. in Business Analytics from the College of William & Mary.