Alexandr Kalinin, Ph.D.
About Alexandr Kalinin, Ph.D.
Alexandr Kalinin, Ph.D., is a Postdoctoral Research Associate at the Broad Institute of MIT and Harvard, focusing on machine learning and biomedical image analysis. He has a diverse academic background and extensive experience in research roles across multiple institutions, including the University of Michigan and Shenzhen Research Institute of Big Data.
Work at Broad Institute
Alexandr Kalinin has been serving as a Postdoctoral Research Associate at the Broad Institute of MIT and Harvard since 2022. His role involves designing and developing tools for interactive visual analytics, particularly in the domain of biomedical image analysis. The Broad Institute is known for its collaborative research environment, and Kalinin's work contributes to advancements in computational biology through the application of machine learning techniques.
Previous Experience
Prior to his current position, Kalinin worked at several prestigious institutions. He was a Postdoctoral Research Scientist at the University of Michigan for 7 months in 2019. He also held the position of International Postdoctoral Research Scientist at the Shenzhen Research Institute of Big Data from 2019 to 2021. Additionally, he served as a Fulbright Visiting Researcher at UCLA for 11 months from 2012 to 2013. His diverse experience spans multiple countries and research environments.
Education and Expertise
Kalinin holds a Doctor of Philosophy (Ph.D.) in Bioinformatics from the University of Michigan, where he studied from 2015 to 2018. He also earned a Master's degree in Bioinformatics from the same institution, completing his studies from 2013 to 2016. His foundational education includes a Bachelor's and Master's degree in Applied Mathematics and Computer Science from Novosibirsk State Technical University, achieved between 2004 and 2010. This educational background supports his focus on machine learning and biomedical image analysis.
Research Focus
Kalinin's research primarily focuses on the intersection of machine learning and biomedical image analysis. He is dedicated to developing tools that enhance interactive visual analytics in the field of computational biology. His strong interest in applying machine learning techniques aims to advance the understanding and analysis of biomedical data, contributing to the broader field of computational biology.
Professional Development
Throughout his career, Kalinin has engaged in various roles that enhance his professional development. He worked as a Graduate Research Assistant at the University of Michigan from 2013 to 2018, contributing to research projects while gaining valuable experience. He also participated as a Stanford US-Russia Forum (SURF) Delegate from 2017 to 2018, which provided him with opportunities to collaborate and network with peers in his field.