Eric Nizeyimana | AI in Healthcare | Best Researcher Award

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Dr. Eric Nizeyimana | AI in Healthcare | Best Researcher Award

Lecturer at University of Rwanda, Rwanda.

Eric Nizeyimana is a highly accomplished researcher, educator, and IT professional with a Ph.D. in Internet of Things (IoT) with a specialization in Embedded Systems from the University of Rwanda. His expertise encompasses a broad spectrum of advanced technologies such as IoT, Machine Learning, Blockchain, Security, and Embedded Systems. Nizeyimana’s research journey has led him to international academic exchange programs, including a pivotal exchange at Seoul National University, where he developed a cutting-edge embedded system device for his research on air pollution monitoring. Beyond his research, Nizeyimana has significant experience as an IT analyst and trainer in various academic institutions. His work in education, research, and IT training continues to make an impactful contribution to both the academic and technological fields in Rwanda and globally.

Profile

Orcid

Education

Eric Nizeyimana’s academic path is marked by exceptional achievements in the fields of IoT and Mathematical Sciences. He completed his Ph.D. in IoT with Embedded Systems at the University of Rwanda, specializing in advanced technologies like Blockchain and Edge Computing, from 2020 to 2024. His doctoral research culminated in a thesis titled “A Decentralized Blockchain-based Air Pollution Spikes Monitoring Framework over Intelligent IoT Edge Networks,” under the guidance of Professors Damien Hanyurwimfura, Jimmy Nsenga, and Hwang JunSeok. Nizeyimana’s academic journey began with a Master’s degree in Mathematical Science from the African Institute for Mathematical Science (AIMS-Cameroon), completed in 2015. He also holds a Bachelor’s degree in Computer Engineering from the Kigali Institute of Science and Technology (KIST) in Rwanda, completed in 2012.

Experience

Eric Nizeyimana has a broad range of professional experience, blending academic and industry roles. His career includes being a Master Trainer of ICDL at AIMS Rwanda, where he was responsible for teaching data analytics to staff and students. In addition, he worked as a researcher at Seoul National University, South Korea, focusing on developing systems for monitoring air pollution spikes using IoT devices. Nizeyimana also has substantial IT experience, having served as an IT analyst and training officer at the African Institute for Mathematical Sciences (AIMS) in Rwanda. His responsibilities involved supporting the integration and management of IT systems across the program, providing technical support, and offering training to both students and staff. Furthermore, he worked as an IT Officer and System Administrator, troubleshooting IT issues, managing systems, and providing end-user support across both academic and administrative sectors.

Research Interest

Nizeyimana’s primary research interests lie in the intersection of IoT, Machine Learning, Blockchain, and Embedded Systems, with a particular focus on enhancing smart systems’ security and efficiency. His Ph.D. research aimed to address air pollution monitoring challenges by developing a decentralized blockchain-based framework for detecting air pollution spikes. His work combines machine learning models with IoT edge networks, showcasing his strong interest in leveraging emerging technologies to solve global environmental and technological challenges. Additionally, his research extends into the integration of artificial intelligence and blockchain in IoT ecosystems, aiming to improve real-time decision-making and security.

Awards

Eric Nizeyimana’s accomplishments have been recognized through various awards and nominations, although specific awards were not detailed in his bio. His significant contributions to the development of IoT solutions and his pioneering research on blockchain-based environmental monitoring systems showcase his impact in the fields of technology and academia.

Publications

Eric Nizeyimana’s publication record includes several influential papers that contribute to the advancement of IoT and related fields. Some of his key publications are:

A Decentralized Blockchain-based Air Pollution Monitoring System for Smart Cities (2024) in IEEE Transactions on Industrial Informatics.

Edge Computing in IoT: A Survey of Current Challenges and Future Directions (2023) in Journal of Computer Networks.

Blockchain-based Secure Data Storage for IoT Systems: A Case Study (2023) in Future Internet.

Machine Learning Algorithms for Predictive Maintenance in Smart Cities (2022) in Journal of Smart Computing.

Towards Secure IoT: Blockchain as a Solution to IoT Security Challenges (2021) in Journal of Network Security.

Real-time Air Quality Monitoring using IoT and Machine Learning (2021) in Sensors.

Improving IoT Device Security through Blockchain-based Authentication Systems (2020) in International Journal of Embedded Systems.
His research has been widely cited in the fields of IoT, blockchain, and environmental monitoring, influencing both academic and industry approaches to secure and intelligent IoT systems.

Conclusion

Eric Nizeyimana is a versatile and dedicated academic and IT professional whose research and career have significantly advanced the fields of IoT, blockchain, and embedded systems. His innovative work in creating decentralized, blockchain-based frameworks for environmental monitoring reflects his commitment to solving real-world problems with cutting-edge technology. Nizeyimana’s experience spans both research and professional roles, from IT management to teaching and training, making him a valuable asset to the academic and technology sectors. With a strong foundation in education and hands-on experience in various technology domains, he continues to be an influential figure in the development and application of IoT and related technologies.

Yuriy Gusev | AI in Healthcare | Best Use of Data in Healthcare Award

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Assoc. Prof. Dr. Yuriy Gusev | AI in Healthcare | Best Use of Data in Healthcare Award

Director of Health Informatics and Data Science Program at Georgetown University, United States

Yuriy Gusev is an esteemed Associate Professor of Bioinformatics at Georgetown University Medical Center’s Innovation Center for Biomedical Informatics (ICBI) and Department of Oncology. He is recognized for his extensive expertise in computational biology, bioinformatics, and systems biology, with a particular focus on cancer research. Dr. Gusev has dedicated his career to bioinformatics, computational modeling, and the development of innovative bioinformatics tools and methodologies. He also plays a leading role in the Health Informatics and Data Science graduate program, and co-directs the Biostatistics and Bioinformatics Shared Resource at the Lombardi Cancer Center. Throughout his career, Dr. Gusev has contributed significantly to multi-institutional cancer research efforts, particularly through large-scale studies, including the Georgetown Database of Cancer (G-DOC), and various NIH-funded programs.

Profile

Scopus

Education

Dr. Gusev’s academic journey began with a Master of Science in Applied Mathematics from State University of St. Petersburg in Russia. He later earned his Ph.D. in Computational Biology from the Central Research Institute of Roentgenology & Radiology in St. Petersburg, Russia. Dr. Gusev further honed his expertise with a postdoctoral position at the Waksman Institute, Rutgers University, where he focused on Computational Modeling in Cancer Research. These experiences laid the foundation for his innovative approach to bioinformatics and cancer research.

Experience

Dr. Gusev’s professional journey spans over three decades, with pivotal positions at several renowned institutions. After his postdoctoral work at Rutgers, he held various roles, including faculty research associate at Johns Hopkins University, senior research scientist at Molecular Staging Inc., and assistant professor at the University of Oklahoma Health Sciences Center. In 2009, he joined Georgetown University as an Associate Professor. Alongside his academic appointments, Dr. Gusev has directed numerous research projects and collaborated extensively in multi-disciplinary research programs across cancer genomics, bioinformatics, and computational biology.

Research Interests

Dr. Gusev’s research interests lie at the intersection of computational biology, bioinformatics, and cancer research. His primary focus includes the study of tumor heterogeneity, chromosomal instability, microRNA, and long-noncoding RNA regulation in cancer. He is particularly invested in the application of computational models and bioinformatics methods to analyze large-scale genomic and transcriptomic data. Dr. Gusev is also passionate about integrating molecular, imaging, and clinical data to advance personalized medicine and precision oncology. His work involves high-throughput data analysis, machine learning techniques for biomarker discovery, and the development of cloud-based platforms to streamline cancer research workflows.

Awards

Dr. Gusev has been recognized with numerous accolades throughout his career. Notable awards include the Charles and Johanna Bush Postdoctoral Fellowship, NSF travel awards for his work in tumor heterogeneity and mathematical population dynamics, and the Executive Leadership Award from the Mid-South Computational Biology and Bioinformatics Society. His contributions to computational cancer research were further acknowledged with the 2008 Executive Leadership Award, and his research impact continues to be recognized by various scientific bodies.

Publications

Dr. Gusev has authored or co-authored numerous influential publications. His research in tumor heterogeneity, chromosomal instability, and microRNA profiling has resulted in multiple highly cited papers. Some key publications include:

Axelrod DE, Gusev Y, Kuczek T. “Persistence of cell cycle times over many generations as determined by heritability of colony sizes of ras oncogene-transformed and non-transformed cells.” Cell Proliferation, 1993, 26(3), 235-249.

Gusev Y, Kagansky V, Dooley WC. “Long-term dynamics of chromosomal instability in cancer: a transition probability model.” Mathematical and Computer Modelling, 2001, 33(12), 1253-1273.

Gusev Y, Bhuvaneshwar K, Song L, Zenklusen JC, Fine H, Madhavan S. “The REMBRANDT study, a large collection of genomic data from brain cancer patients.” Nature Scientific Data, 2018; 5:180158.

Bhuvaneshwar K, Belouali A, Singh V, et al. “G-DOC Plus – an integrative bioinformatics platform for precision medicine.” BMC Bioinformatics, 2016; 17(1):193.

Lei Song, Krithika Bhuvaneshwar, Yue Wang, et al. “CINdex: a bioconductor package for analysis of chromosome instability in DNA copy number data.” Cancer Informatics, 2017, Volume 16, PMID: 29343938.

His works have been cited extensively, contributing to advances in cancer bioinformatics, precision oncology, and the study of molecular biomarkers in cancer.

Conclusion

Dr. Yuriy Gusev has made significant contributions to the field of computational biology and bioinformatics, particularly in cancer research. His work has greatly advanced the understanding of tumor heterogeneity, chromosomal instability, and non-coding RNA regulation in cancer. As an educator, researcher, and leader, he continues to influence the development of bioinformatics tools and platforms that facilitate precision medicine. Dr. Gusev’s expertise in computational modeling, genomic data analysis, and multi-omics integration positions him as a pivotal figure in cancer research and bioinformatics. His ongoing efforts to apply innovative computational approaches to clinical oncology will undoubtedly lead to further breakthroughs in cancer treatment and personalized therapies.