Membership ID: SAS/FSASS/896/2025
Mr. Samson Adekola Alagbe is a doctoral candidate in Industrial and Computational Mathematics at Morgan State University, Baltimore. His research applies mathematical modeling, Markov Decision Processes, and optimization techniques to address malaria transmission and poverty dynamics. With over 20 years of academic experience, he has authored numerous peer-reviewed publications and contributes to federally funded research initiatives. Mr. Alagbe serves as President of the American Statistical Association chapter at Morgan State and as an Editor for the International Journal of Physical Sciences. He is committed to advancing public health and sustainable development through rigorous, data-driven mathematical analysis.
Membership ID: SAS/FSASS/895/2025
Ibrahim Ademola Fetuga is currently serving as a Graduate Teaching Assistant in the department of Mechanical Engineering at Clarkson University, New York, USA. My research focuses on computational fluid dynamics, thermofluid systems, biomimicry, sustainable energy technologies and Artificial Intelligence.
Over the years, He has actively contributed to the engineering and academic communities through:
- Publishing over 40 peer-reviewed journals and conference papers across the domains of CFD, heat transfer, energy optimization, and biomedical flow modeling
- Serving as amember of the editorial board for the Journal of Engineering and Exact Sciences, Brazil and as a technical reviewer for the highly reputed Elsevier journals
· Leading and collaborating on interdisciplinary research, and my efforts have been recognized with various honors, grants, and fellowships. This strengthens my position as a dynamic contributor to engineering innovation.
Membership ID: SAS/FSASS/894/2025
Seyi R. Z. Oluwadare is a Mechanical Engineer specializing in Aerodynamics, Rotor Design, and Computational Fluid Dynamics (CFD). He holds dual master’s degrees in Mechanical Engineering (Thermofluids and Aerodynamics) and is pursuing a Ph.D. at Clarkson University, focusing on aerospace manufacturing. With over five years of industry experience in fiber optics and energy systems, his research advances renewable energy technologies and aerodynamic performance. Seyi has authored peer-reviewed publications, presented at international conferences, and actively mentors in STEM education. His work bridges academic innovation and real-world engineering
Membership ID: SAS/FSASS/893/2025
Sivakumar Sivalasetti is an ETL Architect with a balanced background, combining hands-on data pipelines development expertise with strong leadership skills. Proven ability to design and implement efficient data engineering and data warehousing solutions while effectively managing and guiding cross-functional teams. Adept at analyzing business requirements, optimizing data processes, and ensuring seamless data integration across diverse systems. Strong expertise in data architecture, data modeling,Data Warehousing systems design, database design, ETL/ELT pipelines design, development, implementation and performance tuning. Proven ability to lead cross-functional teams and deliver high-quality solutions within deadlines. Experience includes tools and platforms such as SAP Data Services, Oracle, SQL Server, Salesforce, Azure, GCP, Snowflake,Python and MS-Power BI as well as cloud data warehousing and performance tuning
Membership ID: SAS/LMSASS/325/2025
Membership ID: SAS/LMSASS/324/2025
Membership ID: SAS/SEFM/607/2025
Membership ID: SAS/SEFM/606/2025
Datta Snehith Dupakuntla Naga is a Senior, Software Engineer – QA Automation with 14+ years of diverse industry experience, specializing in test automation, CI/CD integration, and framework development across banking, finance, entertainment, and healthcare industries. Datta Snehith has architected and optimized testing frameworks to drive efficiency, reduce costs, and accelerate software delivery. His expertise spans Selenium, Python, Java, Appium, TestNG, Cucumber, Jenkins, and ETL tools, enabling him to build scalable, cross-platform automation solutions. His innovations in test automation, deployment sanity checks, and API-driven validation have saved clients significant costs while enhancing software reliability.
Membership ID: SAS/SEFM/605/2025
Krishna Ganeriwal is a distinguished software engineer driving innovation in privacy infrastructure, AI systems, and distributed computing. With a proven track record at Meta and Texas Instruments, Krishna has built backend systems that now power experiences for over 3 billion users daily, embedding fairness, compliance, and transparency into products at global scale.
At Meta, Krishna has been instrumental in architecting the Ads Privacy Infrastructure designing systems for data lineage tracking, purpose enforcement, and fairness in machine learning delivery. His work has reduced compliance enforcement latency by over 60%, enabled automated privacy controls across 100+ data pipelines.
Krishna’s technical foundation spans AI, Distributed Systems, and Computing at Scale, enabling him to tackle some of the industry’s most complex engineering challenges. His contributions include multiple patents and peer-reviewed publications, reflecting both depth and originality in his solutions.
Beyond engineering delivery, Krishna is an active voice in the global tech community—judging international hackathons, mentoring rising engineers, and speaking at conferences like Conf42. He is also deeply involved in privacy-aware product development and is known for translating complex legal-compliance logic into scalable engineering systems.
Membership ID: SAS/SEFM/604/2025
Abhinav Balasubramanian is a full stack engineer and AI researcher with a strong track record of building intelligent systems and contributing to impactful projects. His expertise spans generative AI, large language models and end to end application development. He has won multiple national level hackathons for projects that combine automation, personalization and intelligent content generation. Abhinav has published and reviewed multiple peer-reviewed journal articles on machine learning, artificial intelligence and generative AI. His work demonstrates a strong focus on building scalable, user-centric AI solutions with practical, real-world applications.