Anil Mandloi is a highly accomplished Engineering Manager and Technical Leader with over 19 years of experience in enterprise software development, digital transformation, and large-scale banking and financial technology solutions. Currently serving as Senior Software Engineer and Technical Lead at American Express, USA, he has successfully led global onboarding and digital account service platforms across multiple international regions. His expertise includes microservices architecture, cloud-native computing, event-driven systems, AI/ML solutions, distributed systems, and enterprise security implementation. Throughout his career with organizations such as American Express, Mindtree, Cognizant, Mphasis, Zensar Technologies, and Silicus, he has consistently delivered scalable, secure, and high-performance enterprise applications. Anil has strong leadership experience in managing cross-functional teams, mentoring engineers, and aligning technology strategy with business objectives. In addition to industry contributions, he is actively involved in research and innovation, having authored multiple peer-reviewed publications in cloud computing, AI, machine learning, and scalable system architecture. He also serves as a peer reviewer for academic conferences and journals, contributing to the advancement of high-quality technical research and innovation-driven development practices.

Multimodal AI-Driven Precision Medicine: How Graph Neural Networks, Transformers, and Generative Models Enhance Bioinformatics and Computational Biology   The intersection between Artificial Intelligence, Bioinformatics, and Computational Biology is shaping up to change medicine and health in fundamental ways. In this talk, we discuss BioIntelli-Net, a cutting-edge multimodal hybrid AI model using Graph Neural Networks (GNN), Transformers, and Generative AI modules that combines genomics, transcriptomics, proteomics, medical information, and imaging data. BioIntelli-Net specifically solves the problems of sparsity, heterogeneity, and interpretation in biological data analysis, which will speed up biomarker discovery, predicting drug response (immunotherapy in particular), and personalized therapy development.BioIntelli-Net outperforms state-of-the-art uni-modular solutions on multiple cancer cohorts from databases such as TCGA or immunotherapy datasets, showing increased AUC by 0.08-0.15 when predicting immunotherapy treatment success and discovering new drug targets. Innovations in the architecture involve self-supervised pre-training module for dealing with sparse multi-modal genomic data, cross-modal attention based fusion, and explainable AI layer built with attention modules and SHAP values.Further topics covered in this talk are technical issues such as scalability in the context of multimodal data processing, ethics, data privacy, AI-assisted medicine and treatment and bias mitigation, as well as potential future developments like quantum-assisted simulation and federated learning.