Orlando Miranda, PhD
Co-Founder
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I’m a scientist who applies biochemistry, microscopy, and generative artificial intelligence to study biochemical reactions in single living cells which are important for medicine, agriculture, and AI research. I enjoy teaching and giving talks.
Kevin Flores, PhD
Co-Founder
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Dr. Kevin Flores is a Professor in the Department of Mathematics at North Carolina State University, where he also serves as the Director of the Biomathematics Graduate Program. He joined the university in 2015 as part of a special initiative focused on advancing research in precision medicine.
His research sits at the intersection of applied mathematics, machine learning, and the life sciences. Dr. Flores leads a dedicated lab that develops mathematical models and data-driven methods to better understand complex biological systems. This work includes tracking cellular movements, analyzing tissue mechanics, and modeling health data to forecast outcomes like blood glucose levels.
Before joining the faculty at NC State, Dr. Flores gained valuable experience working as a bioinformatician at the Mayo Clinic and held postdoctoral research positions at Arizona State University and NC State. He earned his bachelor’s degree in mathematics from the University of California, Santa Barbara, and completed his Ph.D. in applied mathematics at Arizona State University.
Vahid Shahrezaei, PhD
Scientific Advisor
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Vahid Shahrezaei is Professor of Biomathematics at the Department of Mathematics, Imperial College. He joined Imperial as a lecturer in 2008. Before joining Imperial he was a postdoctoral fellow in center for nonlinear dynamics at McGill University in Montreal. He did his PhD at Simon Fraser University, Vancouver. He holds a BSc and MSc in Physics from Sharif University of Technology. He spent a sabbatical year at the Crick Institute in 2023-2024. He is currently on the BBSRC pool of experts.
Prof Shahrezaei is passionate about development of early career researchers such as PhD students and postdocs and he has won President’s medal for research supervision. In addition to his role in the Department of Mathematics, he is currently Deputy Director (Academic) of Early Career Researcher Institute (ECRI) at Imperial college.
The main area of Prof Shahrezaei’s research is Computational Molecular Systems Biology. He is interested in studying design principles that enable cells to function robustly, in spite of significant inherent stochasticity and environmental noise. To this end, a combination of analytical and computational methods is used to investigate the temporal, spatial and stochastic dynamics of biochemical networks. In recent years Shahrezaei’s group has developed methods for the analysis of single cell RNA-sequencing data and simulation-based inference methods for biochemical networks.
Innovative AI Solutions
Transforming microscopy data into actionable insights for researchers.
Our platform leverages advanced AI technology to enhance model discovery and validation, streamlining biological research processes.
Cutting-Edge Technology
Utilizing state-of-the-art algorithms for data analysis.
We employ the latest AI methodologies to ensure precise and efficient analysis of microscopy data, supporting scientific advancements.
User-Friendly Interface
Designed for ease of use, catering to all skill levels.
Our intuitive GUI allows users to navigate complex data effortlessly, making research more accessible.
Our Commitment to Innovation in AI and Microscopy
At our company, we leverage cutting-edge AI technology to enhance the understanding of microscopy data. Our focus is on facilitating knowledge transfer through intuitive online interfaces, enabling researchers to discover and validate models for various biological applications. By integrating advanced data analysis techniques, we aim to support scientific research and foster innovation in the field.
Our Vision for the Future
At our company, we leverage cutting-edge AI technology to enhance the understanding of microscopy data. Our focus is on facilitating knowledge transfer through intuitive online interfaces, enabling researchers to discover and validate models for various biological applications. By integrating advanced data analysis techniques, we aim to support scientific research and foster innovation in the field.
We envision a future where AI-driven insights transform the landscape of biological research. Our team is dedicated to developing tools that empower scientists to analyze complex microscopy data effectively. Through our innovative solutions, we strive to bridge the gap between technology and biology, ensuring that researchers can make informed decisions based on robust data analysis.
Empowering Researchers with AI
Our mission is to empower researchers by providing them with the tools they need to excel in their work. We understand the challenges faced in data analysis and model discovery, which is why we focus on creating user-friendly interfaces that simplify these processes. By harnessing the power of AI, we aim to enhance the efficiency and accuracy of scientific research, ultimately leading to groundbreaking discoveries.
Our Team and Expertise
Our team comprises experts in AI, microscopy, and biological sciences, all committed to advancing research through technology. We believe that collaboration and knowledge sharing are key to driving innovation. By working together with researchers and institutions, we aim to create a community that fosters growth and exploration in the field of biological applications.
As we continue to evolve, we invite you to join us on this exciting journey. Together, we can unlock the potential of AI in microscopy and make significant strides in scientific research. Our commitment to excellence and innovation drives us to push boundaries and explore new frontiers in data analysis and model validation.
Our company stands at the forefront of AI technology, dedicated to transforming the way researchers interact with microscopy data. We are passionate about empowering scientists and facilitating groundbreaking discoveries that can change the world.