Humza Iqbal
About Humza Iqbal
Humza Iqbal is an Applied Research Scientist with a background in machine learning and data science. He has worked at various organizations, including SECURITI.ai and UC Berkeley, and currently holds a position at Snorkel AI, focusing on enhancing machine learning model robustness.
Current Role at Snorkel AI
Humza Iqbal serves as an Applied Research Scientist at Snorkel AI, a position he has held since 2021. In this role, he focuses on advancing the understanding of machine learning principles and their practical applications. His work contributes to the development of innovative solutions that leverage machine learning technologies.
Previous Experience at SECURITI.ai
Prior to his current role, Iqbal worked as a Data Scientist at SECURITI.ai from 2018 to 2021 in San Jose, California. During his tenure, he contributed to projects aimed at helping enterprises understand customer data and comply with privacy regulations through machine learning techniques.
Educational Background in Computer Science
Iqbal studied Computer Science at the University of California, Berkeley, where he earned his Bachelor's Degree from 2014 to 2018. His education provided a strong foundation in computer science principles, which he applies in his research and professional roles.
Research and Internships in Machine Learning
Iqbal has extensive experience in machine learning research and internships. He worked as a Research Assistant in the Bajcsy Group at UC Berkeley for four months in 2015. He also held positions as a Researcher and Project Advisor at Machine Learning at Berkeley, and completed internships at NVIDIA and HealthPals, where he focused on deep learning and data science applications.
Contributions to Machine Learning Techniques
Throughout his career, Iqbal has developed techniques to enhance the robustness of machine learning models against distribution shifts and adversarial examples. His research focuses on understanding the fundamental principles of machine learning and why these models work effectively.