DR. M.G.R. EDUCATIONAL AND RESEARCH INSTITUTE (DEEMED TO BE UNIVERSITY), OFF CAMPUS - ARNI | NAAC A+ ACCREDITED | UNIVERSITY STATUS | CONTACT: NO. 191, ARCOT ROAD, ACS NAGAR, IRUMBEDU ARNI - 632317
ABOUT THE CONFERENCE
(ARNI OFF CAMPUS)
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The 3-Day International Conference on "SYNERGIZING ARTIFICIAL INTELLIGENCE AND CLASSICAL RESEARCH IN BIOSCIENCES, ENGINEERING AND MATERIAL SCIENCE" - ELEVATE-2026 is conceived as a premier global forum to bring together researchers, academicians, scientists, industry professionals, and young innovators to exchange knowledge and showcase cutting-edge research outcomes.
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It is a multi-disciplinary forum designed to bridge the gap between traditional scientific methodologies and rapidly evolving field of AI.
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The 2026 edition of this conference is typically structured to move beyond simple AI applications but instead focus on trustworthy discoveries.
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The primary goal is to foster an ecosystem where the rigor of classical research meets the predictive power of AI.
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The interdisciplinary theme is believed to break silos between biologists, material scientists and software engineers, spanning fundamental research to practical applications.
ABOUT THE UNIVERSITY
(ARNI OFF CAMPUS)
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Dr. M.G.R. Educational & Research Institute (Deemed to be University), accredited with NAAC A+ grade, has established its Arni Off-Campus in Tiruvannamalai District, Tamil Nadu.
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The campus spans 250,000 sq. ft. with modern infrastructure, library, hostels, and student amenities.
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We offer UG, PG, and research programs across Engineering, Arts & Science, Commerce,Management, Physiotherapy and Allied Health Sciences.
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The campus focuses on holistic education, innovation, and research to meet industry needs.
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Located at ACS Nagar, Irumbedu, Arni, it provides excellent academic and professional opportunities.
EXPECTED OUTCOMES
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Publication opportunities: Selected high quality papers will be published in Scopus indexed journals or conference proceedings (Springer, IEEE, Elsevier)
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Collaborative network among research community.
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Establishing open science networks where data from classical experiments is shared to train more robust Al models between researchers and engineers.
