Istvan Sleder
About Istvan Sleder
Istvan Sleder is a Staff Machine Learning Engineer at Flock Safety and Principal Machine Learning Engineer at Optimal Trust, specializing in multi-modal text and image search and unsupervised representation learning.
Current Positions
Istvan Sleder currently holds two prominent positions in the machine learning domain. He works remotely as a Staff Machine Learning Engineer at Flock Safety, contributing to the company's safety and security solutions. Additionally, he serves as a Principal Machine Learning Engineer at Optimal Trust, applying his expertise to enhance trust and security measures using advanced machine learning techniques.
Previous Experience
Istvan Sleder has a robust professional background in machine learning and software engineering. He worked at Slingshot Aerospace as a Senior Machine Learning Engineer for 11 months from 2021 to 2022, dealing with advanced aerospace technology in Concord, Massachusetts. Prior to this, he was with Swift Engineering, Inc. for five years as a Principal Machine Learning Engineer and Data Scientist from 2016 to 2021. His earlier roles include Senior Software Engineer at Skyhook in Boston, MA from 2011 to 2016, and Senior Embedded Software Engineer at Uthere LLC in Malden, MA in 2011.
Education and Expertise
Istvan Sleder holds a Bachelor's degree in Electrical and Electronics Engineering, and Computer Science from Kando Kalman University. He further pursued studies at New York University from 1997 to 2000, laying a strong foundation for his career in machine learning. His specializations include multi-modal text and image search, unsupervised representation learning, and zero-shot learning. He has significant expertise in object re-identification using contrastive learning, and utilizes embedding vector clustering for finding duplicate and similar images.
Focus Areas in Machine Learning
Istvan Sleder focuses on several cutting-edge areas in the field of machine learning. He works extensively with embedding vector clustering to enhance image similarity searches. His work also includes developing models for object pose identification and prediction through manifold learning techniques. Additionally, he is involved in model training and dataset curation, leveraging embeddings to improve the accuracy and performance of machine learning models.