Nicholas Otumi | Neuroscience Data Analysis | Best Researcher Award

Mr. Nicholas Otumi | Neuroscience Data Analysis | Best Researcher Award

University of Rochester | United States

Nicholas Otumi is a biomedical imaging researcher with a strong foundation in diagnostic imaging and deep learning. He holds a BSc in Diagnostic Imaging Technology from the University of Cape Coast (Ghana) and an MS in Diagnostic Imaging (Bioengineering & Biomedical Engineering) from the University of Rochester (USA), where his coursework included deep learning, inverse problems in imaging, clinical imaging, and signal processing. He has held research and technical roles at multiple institutions: as a remote Research Assistant at Indiana University working on pharmaceutical pricing and market analytics, and at the University of Rochester contributing to segmentation (with UNet-R) and image analysis in brain CT. Previously, as a Research and Teaching Assistant at UCC, he supported imaging equipment courses, data analysis, and undergraduate research. His research interests center on medical image analysis, especially applying deep learning to brain MRI and CT segmentation, classification of disease from imaging, and imaging in resource-constrained settings. He has several manuscripts under review or published, including a multicentre survey on advanced brain MRI in low-resource settings and case reports in radiology.  He is a recipient of honors such as the Harry W. Fischer Research Funds (2025) and the HSEAS Dean’s Fellowship (2024) at Rochester, as well as campus leadership awards in Ghana. Nicholas is committed to advancing AI-driven neuroimaging and translational imaging research across low- and middle-income settings, aiming to bridge technology gaps and enhance equitable access to advanced diagnostic tools.

Profiles : Orcid | Google Scholar

Featured Publications

Piersson, A. D., Nunoo, G., Dzefi-Tettey, K., & Otumi, N. (2025). Utility of advanced brain MRI techniques for clinical and research purposes in a low-resource setting: A multicentre survey. Next Research.

Fiagbedzi, E., Arkorful, J., Brobbey, I. F., Appiah, E., Nyarko, S., Otumi, N., Piersson, A. D., Nimo, O., Ofori, I. N., & Gorleku, P. N. (2025). A case of ruptured infrapatellar bursa sac with Baker’s cyst. Radiology Case Reports, 20(1).

Fiagbedzi, E., Arkorful, J., Appiah, E., Otumi, N., Ofori, I., & Gorleku, P. N. (2024). A rare case of intussusception in a 6-month-old baby. Radiology Case Reports, 19(10).

Ch Rajyalakshmi | Deep learning using medical image data | Best Researcher Award

Mrs Ch Rajyalakshmi - B V Raju Institute of Technology - Deep learning using medical image data

Mrs Ch Rajyalakshmi : Deep learning using medical image data

Professional profile:

Early Academic Pursuits:

Mrs. Ch. Rajyalakshmi's academic journey commenced with a Bachelor's degree in Computer Science and Information Technology from NEC, Narsaraopeta, in 2004. This laid the foundation for her pursuit of a Master's degree in Computer Science and Engineering at AVIET, Narsipatnam, in 2011. Building on her academic prowess, she is currently pursuing a Ph.D. at JNTU Kakinada, with her thesis focusing on "HIGH SPECTRAL SATELLITE IMAGE SEGMENTATION WITH DEEP LEARNING UNET BASED TECHNIQUES FOR GENERATING FLOOD INUNDATION."

Professional Endeavors:

With a teaching experience spanning 18 years, Dr. Rajyalakshmi has played pivotal roles in various academic institutions. She has been an Associate Professor at BVRIT, Narsapur, since 2011, following roles as an Assistant Professor at CMRIT Engg College, Hyderabad, and a Lecturer at Narsaraopeta Engineering College, Narsaraopeta, earlier in her career.

Contributions and Teaching Experience:

Mrs. Rajyalakshmi's expertise in computer science is evident through the diverse range of courses she has taught. From Artificial Intelligence and Machine Learning to Database Design and Operating Systems, she has contributed significantly to the education of students. Her teaching extends to areas such as Computer Networks, Software Engineering, Multimedia and Design, and Human-Computer Interaction.

Research Focus and Publications:

Mrs. Rajyalakshmi's research interests primarily revolve around the application of Deep Learning architectures for spatial data, particularly in processing high spectral satellite images for water body identification. Her publications in renowned journals and conferences reflect the depth of her contributions to the field. Some notable publications include works on satellite image data extraction using classification techniques and the detection of active attacks in Mobile Ad Hoc Networks (MANETs).

Accolades and Recognition:

Mrs. Rajyalakshmi's academic achievements include being FET Qualified in 2011 and GATE Qualified in 2006. Her commitment to excellence is also evident in her completion of various NPTEL courses and Coursera certifications, covering topics such as Machine Learning, Database Management Systems, Deep Learning, and Artificial Intelligence.

Impact and Influence:

Through her research and teaching endeavors, Mrs. Rajyalakshmi has made a significant impact on the academic community. Her publications and patents attest to her dedication to advancing knowledge in computer science and related domains.

Legacy and Future Contributions:

As a seasoned educator and researcher, Mrs. Rajyalakshmi continues to shape the academic landscape. Her administrative responsibilities, including course coordination, involvement in accreditation processes, and leadership roles in various committees, showcase her commitment to the holistic development of educational institutions. With an ongoing Ph.D. pursuit and an impressive list of publications and patents, she is poised to leave a lasting legacy in the realms of computer science, education, and research. Her multifaceted contributions highlight a career marked by continuous growth, learning, and impactful contributions to academia.

Notable Publications: