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Big Impact from Small Samples:

Harnessing High-Precision Tunable Laser

Spectroscopy for Complex Bioprocessing Analysis

Presenter:

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Larissa Miropolsky
senior director of data science,
Nirrin Technologies
 

Larissa Miropolsky is a visionary data scientist and senior leader with a wealth of experience in developing and deploying advanced analytical solutions across diverse industries. Currently as senior director of data science at Nirrin Technologies, she leverages her expertise to spearhead transformative innovations in biopharmaceutical analysis. Larissa's professional journey reflects her passion for combining statistical rigor with practical applications to drive actionable insights and advance cutting-edge technologies.

Her academic foundation includes a master's degree in applied statistics from Haifa University in Israel equipping her with a deep understanding of data-driven methodologies. Prior to her role at Nirrin, Larissa held pivotal positions as director of data science at WCG Clinical, where she led the development of the groundbreaking Study Insight Analytics platform. This innovative tool integrated statistical process control with real-time analytics to optimize clinical trial outcomes, ensuring data integrity and alignment with regulatory standards.

During her tenure at the Harvard School of Public Health, she played an integral role in developing platforms like ARepA (Automated Repository Acquisition) and LEfSe (Linear Discriminant Analysis Effect Size), along with contributing to the creation of the R package SKAT. Her efforts have consistently propelled advancements in data science, bioinformatics, and clinical research.

Known for her hands-on style and collaborative approach, Larissa excels at fostering innovation and delivering tangible results. Her current work focuses on pioneering a tunable laser scanning system for rapid biopharmaceutical formulation analysis, underscoring her commitment to revolutionizing the field through interdisciplinary advancement of the collective understanding of high-quality data in this critical domain.