DBA in Emerging Technologies with concentration in Gen AI from GGU

Dr. Kolluru has completed his B.E. from NIT Trichy, India, and M.S. and Ph.D. from Carnegie Mellon University, USA, and has over 20 years of experience in various fields.

As a Doctor of Business Administration in Emerging Technologies, his specialised areas include ML, Optimization, Artificial Neural Networks, Deep Learning, Text Mining, Mathematical Algorithms, and Pattern Extraction.

Dr. Venkatesh Sunkad holds a Ph.D. from the University of Colorado, USA, and an M.S. from the University of Texas at Arlington, USA.

He brings over 20 years of experience and has expertise across IoT, Systems Architecture, Network Security, Machine Learning, and IT Strategy, offering advanced knowledge and practical skills in these cutting-edge fields of technology and innovation.

PhD from The University of Texas at El Paso | BE from National Institute of Technology Tiruchirappalli

24+ years of experience

Areas of interest: Statistics and Probability for Data Science, Machine Learning, Data Visualization, Storytelling with Data

PhD from Nirma University, Ahmedabad | MS Data Science from LJMU, UK | MTech from BITS Pilani

26 years of experience

Areas of interest: Machine learning, Natural language Processing, Data Mining, Mathematical modeling, ML for Space weather, Business Analytics

PhD from Univ of Illinois at Urbana-Champaign | MTech from IIT Bombay | BE from Mumbai University

13+ years of experience

Areas of interest: Deep learning, Machine learning, Natural Language Processing, Web Mining, Information Retrieval, and Data Mining

PhD from University of Missouri-Saint Louis | MSc from IIT Madras

23+ years of experience

Areas of Interest: Early Diagnosis of Neurological Disorders and Diseases, Healthcare at Scale - Telemedicine Sustainable Development

PhD from University of Melbourne | MPhil from Birla Institute Of Technology And Science

12+ years of experience

Areas of Expertise: AI in Healthcare

PhD from Goethe University Frankfurt | Masters in Computer Science from The University of Bonn

12 years of experience

Areas of Interest: Application of Deep Learning and Generative Model Research in Fields of Knowledge Discovery, Data Quality Management, Data Compliance and Governance and OCR

PhD from IIIT Bangalore | M.Tech from IIT Roorkee

20 years of experience

Areas of interest: Machine Learning, Deep Learning, Optimization, Trusted AI, Gen AI and Internet of Things

These courses provide the foundational knowledge and applied skills necessary to understand, evaluate, and implement emerging technologies in complex business environments.

This course provides a comprehensive introduction to core concepts in artificial intelligence and machine learning, with a focus on strategic business applications. It addresses foundational algorithms, modeling techniques, and data-driven decision frameworks, while examining how these technologies can be leveraged to enhance operational efficiency, drive innovation, and address complex organizational challenges. Emphasis is placed on the role of machine learning in evidence-based management and predictive analytics.

Learn the fundamentals of machine learning, from regression to ensemble models, and uncover patterns through classification, clustering, and rule mining. Master data-driven decision-making with visualization, storytelling, and no-code AI tools.

Topics Covered: Data-Driven Strategy through Statistical Modeling and Regression, Business Applications of Classification and Segmentation Models, Rule-Based Pattern Mining for Strategic Insight, Enhancing Competitive Edge through Ensemble Techniques, Platform-Based Implementation, Data Visualization and Storytelling

Participants engage with the theoretical and applied aspects of deep learning, including the structure and function of neural networks. The course explores various architectures such as convolutional and recurrent models, examining their relevance to business domains such as customer behavior prediction, automation, image and language processing, and product development. Attention is given to evaluating the performance and limitations of deep learning systems within enterprise environments.

Explore cutting-edge deep learning architectures like CNNs, RNNs, and GANs to solve real-world business challenges. Gain hands-on insights into pattern recognition, synthetic data, and advanced sequence modeling for enterprise AI applications.

Topics Covered: Extracting Meaningful Representations with Autoencoders and Embeddings, Visual Intelligence through Convolutional Neural Networks (CNNs), Temporal Pattern Recognition using Recurrent Neural Networks (RNNs), Synthetic Data Generation with Generative Adversarial Networks (GANs), Advanced Language and Sequence Modeling with Seq2Seq Architectures

This course offers a detailed exploration of generative artificial intelligence and its strategic potential across industry sectors. It covers the underlying principles of generative modeling, including transformer architectures, training requirements, and implementation strategies. Participants assess the implications of these technologies for innovation, content generation, process redesign, and organizational intelligence. Case studies and applied exercises support the development of analytical capabilities required for evaluating generative solutions.

Understand the shift to pre-trained generative AI models and their role in content creation, automation, and enterprise innovation. Learn to build, deploy, and govern GenAI and agentic AI systems using LLMs, RAG frameworks, and multimodal tools.

Topics Covered: From In-House to Pre-Trained Models, Large Language Models and Generative Architectures, Building GenAI Applications with RAG, Orchestration Tools, and Agentic AI, Operationalizing GenAI: LLMOps, Ethics, and Compliance, Multimodal GenAI and Research-Driven Innovation

These modules focus on the organizational, ethical, and strategic implications of digital technologies, preparing participants to lead responsible innovation and manage change at scale.

This course addresses the methodological and managerial aspects of initiating and implementing artificial intelligence projects. It includes frameworks for defining project scope, evaluating feasibility, aligning stakeholders, and assessing long-term impact on business functions and society. Participants examine sector-specific use cases, resource constraints, and performance metrics, with particular attention to responsible deployment and organizational readiness for AI integration.

Learn to plan, execute, and scale AI and GenAI projects using structured frameworks and design thinking. Explore real-world use cases, ethical deployment, and strategies for long-term business impact.

Topics Covered: Emerging Technology Landscape and Industry Synergies, Design Thinking and Planning for AI and GenAI Projects, Execution Frameworks, Tools, and Post-Deployment Management, Ethical, Organizational, and Operational Impact of AI Initiatives Project Proposal Development and Real-World Deployment Strategies

The curriculum examines the ethical, legal, and societal dimensions of AI development and deployment. It introduces conceptual and regulatory frameworks concerning fairness, accountability, transparency, and inclusivity. Through case analysis and policy review, the course encourages critical reflection on the risks and responsibilities associated with AI systems. Participants develop competencies to evaluate governance mechanisms and ensure compliance with international norms and evolving standards.

Explore key business topics like AI ethics and explainability, addressing whether AI is an opportunity or a threat. Explore key business topics like AI ethics and explainability, addressing whether AI is an opportunity or a threat.

Topics Covered: Foundations of Responsible and Ethical AI, Bias, Failure Cases, and Algorithmic Accountability, Privacy, Security, and Regulatory Compliance in AI, Explainable AI and Model Transparency Frameworks

This course investigates key technologies that are reshaping the global business landscape, including blockchain, the Internet of Things (IoT), edge computing, and quantum systems. Participants evaluate the strategic potential of these technologies, considering both the opportunities and barriers to adoption. The course fosters an interdisciplinary understanding of how these innovations influence business models, competitive dynamics, and organizational transformation.

Examine transformative technologies like IoT, quantum computing, blockchain, and digital twins shaping the future of business. Learn how to assess, adopt, and apply these innovations to drive strategic growth and operational excellence.

Topics Covered Data Collection and Intelligence with IoT and Cloud Infrastructure, Advanced Computing with HPC, Quantum, and Neuromorphic Systems, Automation and Process Optimization using RPA and AI Integration, Trust, Security, and Transparency through Cybersecurity and Blockchain, Strategic Simulation and Planning with Digital Twin and Immersive Technologies

This component builds doctoral-level research capabilities through a structured approach to qualitative and quantitative methodologies, data analysis, and scholarly communication.

The research component provides doctoral candidates with the methodological tools required for conducting rigorous academic inquiry. The course covers both qualitative and quantitative research methods, focusing on research design, data collection, statistical analysis, and ethical research practices. Participants are trained to formulate research questions, engage critically with literature, interpret complex datasets, and communicate findings to academic and professional audiences. The objective is to ensure research outputs are not only methodologically sound but also relevant to contemporary business and technological challenges.

Develop the research skills needed to design, conduct, and communicate rigorous doctoral-level studies in business and emerging technologies. Gain proficiency in qualitative, quantitative, and mixed methods to address complex, real-world problems with scholarly precision.

Topics Covered: Foundations of Research Design and Methodological Approaches, Quantitative and Qualitative Techniques in Business and Technology Research, Research Methodology for AI and AI Innovation Projects, Ethics, Compliance, and Responsible Research in Digital Contexts, Literature Synthesis, Mixed Methods, and Doctoral Proposal Development

A structured, milestone-driven research journey guided by faculty mentors and industry experts.

Traditional Thesis

Business Plan

Book

Identify relevant, high-impact research opportunities aligned with GenAI or emerging technologies. Present early-stage ideas to receive feedback and validate direction.

Submit a clearly articulated research topic supported by a preliminary literature review, demonstrating relevance, originality, and alignment with current academic and industry discourse.

Defend your proposed research design, methodology, and scope before a review panel. Incorporate insights to refine your approach.

Submit and present your completed dissertation. Demonstrate theoretical contribution, applied relevance, and strategic business impact before a review panel.

Three In-Person Immersions strategically designed to foster deep academic, peer, and industry connections.

Engage directly with faculty, fellow doctoral candidates, and global thought leaders through interactive sessions and workshops.

Learn from senior executives and C-suite leaders who have successfully led AI-driven transformations across diverse industries, bridging theory with practical, high-impact business insights.