Cleanlab
Curtis Northcutt is a highly skilled professional with a diverse range of work experience. Curtis is currently the CEO & Co-Founder of Cleanlab, where they focus on improving machine learning model accuracy and reliability for enterprises. Prior to this, Curtis was the CTO & Co-Founder of ChipBrain, an AI company specializing in visual and audio processing. Curtis also has a strong academic background, having completed a PhD in Computer Science at the Massachusetts Institute of Technology (MIT), where they received the MIT Morris Levins Thesis Award. Their research at MIT focused on uncertainty quantification and AI for augmenting human intelligence. Curtis has also held research positions at Oculus VR, Knowledge AI, Google, Amazon, Facebook AI, Microsoft Research, and MIT Lincoln Laboratory, where they developed a wide range of skills and expertise in areas such as machine learning, AI research, and cybersecurity.
Curtis Northcutt holds a Doctor of Philosophy (Ph.D.) in Computer Science from Massachusetts Institute of Technology, which they obtained from 2017 to 2021. Prior to that, they completed a Master of Science (MS) in Electrical Engineering and Computer Science from the same institution, from 2013 to 2017. During their time as an MIT PhD student, they also engaged in cross-registration at Harvard University in 2015, focusing on Machine Learning. In 2009, Curtis earned a Bachelor of Science degree in Honors Computer Science and Mathematics from Vanderbilt University. Additionally, they have obtained certifications in educational data-mining from Carnegie Mellon University as part of the LearnLab track, and has been recognized as an MIT Global Fellow.
Cleanlab
Pioneered at MIT and proven at Fortune 500 companies, Cleanlab provides the world's most popular Data-Centric AI software. Most AI and Analytics are impaired by data issues (data entry errors, mislabeling, outliers, ambiguity, near duplicates, data drift, low-quality or unsafe content, etc); Cleanlab software helps you automatically fix them in any image/text/tabular dataset. This no-code platform can also auto-label big datasets and provide robust machine learning predictions (via models auto-trained on auto-corrected data). What can I get from Cleanlab software? 1. Automated validation of your data sources (quality assurance for your data team). Your company's data is your competitive advantage, don't let noise dilute its value. 2. Better version of your dataset. Use the cleaned dataset produced by Cleanlab in place of your original dataset to get more reliable ML/Analytics (without any change in your existing code). 3. Better ML deployment (reduced time to deployment & more reliable predictions). Let Cleanlab automatically handle the full ML stack for you! With just a few clicks, deploy more accurate models than fine-tuned OpenAI LLMs for text data and the state-of-art for tabular/image data. Turn raw data into reliable AI & Analytics, without all the manual data prep work. Most of our cutting-edge research powering Cleanlab tools is published for transparency and scientific advancement: cleanlab.ai/research/