DISTINGUISHED SPEAKERS

Prof. Keeley

Prof. (Dr.) Keeley A Crockett

Professor
Manchester Metropolitan University
UK
Dr. Sean McLoone

Prof. (Dr.) Seán McLoone

Director
Centre for Intelligent and Autonomous Manufacturing Systems
Queen's University Belfast
UK
Dr. Christian Blum

Prof. (Dr.) Christian Blum

Senior Research Scientist
Artificial Intelligence Research Institute (IIIA)
Spanish National Research Council (CSIC)
Spain
Dr. Marita Turpin

Prof. (Dr.) Marita
Turpin

Professor
University of Pretoria
South Africa
Bridge

Prof. (Dr.) Roger B. Dannenberg

Emeritus Professor of
Computer Science
Art & Music
Carnegie Mellon University
USA

Dr. Ashish Seth

Prof. (Dr.) Ashish Seth

Professor
Inha University Tashkent
Uzbekistan
Bridge

Prof. (Dr.) Björn W. Schuller

Professor
Imperial College
London, UK
Bridge

Prof.(Dr.)Keith Sherringham

PMO Specialist
New South Wales
Australia


Bridge

Prof. (Dr.) Jan Treur

Professor
Vrije University
Amsterdam, Netherlands
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Prof. (Dr.) Sandhya Samarasinghe

Professor
AI and Complex Systems Modelling
Lincoln University
New Zealand
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Prof. Sardar M. N. Islam (Naz)

Professor
Victoria University
Melbourne, Australia
Bridge

Dr.(Smt.) N. Ranjana

Director
Directorate of Systems & Technology
Defence Researh & Decvelopment organization(DSTA)

Bridge

Prof. (Dr.) Avdesh Mishra

Texas A&M University
Kingsville
USA

Bridge

Prof. (Dr.) Arvinder Kaur

Professor in USICT and Dean in University School of Automation & Robotics
Guru Gobind Singh Indraprastha University
Bridge

Dr. Vishnu Pendyala

Professor,
San Jose State University,
California,
United States

Bridge

Mr.Ganesh Sahai

Chief Technology Officer
Nagarro
Bridge

Prof.(Dr.) Ankit Agrawal

Research Professor
Department of ECE, Northwestern University
USA
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Prof.(Dr.) Takaaki GOTO

Associate Professor
Department of Information Sciences and Arts
Toyo University, Japan
Bridge

Mr.Sapan Goel

Director
Product Management
Palo Alto Networks
Bridge

Mr.Prateek Sikka

Sr.Group Manager
STMicroelectronics
Bridge

Dr. Pantelis Sopasakis

Assistant Professor
School of Electronics, Electrical Engineering and Computer Science
Queen's University, Belfast
Bridge

Mr. Nikhil Arora

Director
Data Intelligence
S&P Global
Bridge

Ms. Priyanka Aeron

Founder & CEO
Thrive Global AI
Bridge

Mr. Mohammed Asif

Senior Vice President
Fidelity Information Services
Bridge

Ms. Kamiya Khatter

Senior Editor
Springer
Bridge

Mr. James Finlay

Vice President of Applied Sciences and Engineering
Springer
Bridge

Ms. Astha Jagia

Vice President
Genpact
Bridge

Ms. Abhilasha Tyagi

Project Manager- Data Integration
YMSLI
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Ms. Akansha Jain

Sr. Lead Software Engineer
Adobe
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Mr. Shivam Gupta

Dy. Director
Cbyer Forencis, Indian Navy
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Mr. Harshit Gupta

Marketing Director
Lapcare
Bridge

Mr. Mohit Budhiraja

Project Manager
Nagarro
Bridge

Ms. Roli Rajwanshi

Compensation Analyst for APAC
Bechtel Corporation
Bridge

Mr. Siddharth Gupta

Principal Security Consultant
Kroll Cyber Risk
Bridge

Mr. Viplov Madan

Cloud Application Architect
EPAM Systems
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Mr. Mukul Aggrawal

Manager
National Gases
Bridge

Ms. Geetika Grover

Associate Consultant
Project Management
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Ms. Srishti Singh

Senior Consultant
EY
Bridge

Mr. Subarno Chatterjee

Senior Fullstack Engineer
TO THE NEW
Bridge

Mr. Shubham Chhabra

Senior Software Engineer
HCL
Bridge

Mr. Aakarsh Shrivastava

Senior Technology Consultant
ZS

Title of the Talk: Integrative Methodology for Agentic AI Development: Component-Based Conceptual Design, Network-Oriented Detailed Design, and Risk Assessment via Probabilistic What-If Analysis for Human-AI Decision Making.

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Abstract : This talk presents an integrative methodology for agentic AI design and analysis that can be applied to develop collaborative human-AI interaction. For design it integrates component-based conceptual design methods with net-work-oriented detailed design methods. This is combined with an analysis ap-proach that integrates What-If analysis methods with probabilistic risk assess-ment methods. The approach is illustrated by application to an AI Coach support-ing cybsersecurity, thereby sharing mental models for secure decision making and by context-sensitive interactions supporting joint decision making, organisational learning and adaptation.

Title of the Talk: Data Intelligence 2.0: The AI Transformation - Moving from Insights to Autonomous Action.

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Abstract : The year 2024-25 was defined by the pillars of Connecting, Learning, Scaling, and Consuming data to drive better human decisions. However, as we move into 2026, the paradigm is shifting from human-led analytics to AI-partnered autonomous workflows. In this session, we will explore the rise of AI, where models transition from simple chatbots to sophisticated agents capable of multi-step reasoning and direct tool access.

Title of the Talk: Enhancing Intrusion Detection with CNN and CTGAN Synthetic Oversampling.

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Abstract : Network intrusion detection has become a critical component in maintaining secure systems, but effective classification of malicious traffic remains challenging due to heavily imbalanced datasets. Datasets widely used for intrusion detection research suffer from severe class imbalance, which impacts the performance of machine learning models in past research. In this article, a novel approach is proposed to tackle these challenges by utilizing a Convolutional Neural Network (CNN) for 5-class classification of network intrusion types. Unlike prior methods that predominantly rely on tabular data, this approach converts the NSL-KDD dataset into image representations to leverage the spatial learning capabilities of CNNs. Furthermore, the heavy class imbalance issue is handled by generating synthetic data using Conditional Tabular Generative Adversarial Networks (CTGAN), which allows for more equitable learning across all classes. By combining CNN-based image classification with CTGAN-generated synthetic data, the model aims to improve classification performance and provide a robust framework for network intrusion detection. The proposed model is benchmarked against contemporary state-of-the-art methods on real-world data and achieves average gains of 6.61%, 5.36%, 7.74%, and 12.42% in accuracy, precision, recall, and F1-score, respectively.

Title of the Talk: Revisiting Dynamic Programming for Music Score Alignment and Computer Accompaniment.

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Abstract : Matching a music performance to a music score has many applications. In the off-line version, matching is used as a first step in the study of expressive timing and to label performance data for various machine learning tasks. In the on-line version, matching with low-latency enables real-time computer accompaniment, in which the computer adapts it's performance timing to that of a live performer. Score matching, or score alignment, is also used for automatic page-turning and other applications where actions must be synchronized to a live performance. The author introduced several score alignment algorithms in the 1980's. Recently, others have pursued Hidden Markov Model approaches, but there have been no careful comparisons to earlier techniques. Having returned to this research to create a robust, live performance system, I will show a recent system in operation and describe some enhancements to handle non-deterministic score elements including grace notes and trills.

Title of the Talk: Projects Within the AI Business Regime.

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Abstract : The demands and complexities of projects (programs, portfolios, and project management offices) for business transformation around AI continue to increase. Having greater capacities and capabilities for project management, with skills and expertise being embedded within business areas, and for a diversity of roles is needed. Successful project delivery places a premium on professional skills, in having the necessary business prerequisites, as well as bringing revised approaches to areas including stakeholder management, testing regimes, readiness, and cybersecurity, The project impacts are applied along the logistics chain and across the value stack. The integration of AI types (whether GenerativeAI and AgenticAI, or Robotics, or IVR and ChatBots, or ImagingAI and Facial Recognition) with resolution of the data regimes having required security practices is assumed from projects. The need for a whole of business approach for AI incorporation within operations and services is expected from projects, along with the capacity and capability building of the business prerequisites necessary for delivery, enabling the integration, and support for ongoing servicing. These and other business responses from project management for the emerging AI regimes are considered in this keynote address.

Title of the Talk: From Principles to Practice: Governing AI in a Rapidly Evolving World.

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Abstract : Artificial Intelligence is transforming economies and societies, yet its ethical, social, and legal implications are evolving just as quickly. Organisations now face a critical challenge: moving beyond high‑level ethical principles to practical, accountable implementation within an increasingly complex global regulatory landscape. As risk‑based AI governance becomes the norm, AI introduces new layers of organisational accountability and responsibility. The talk argues that compliance alone is insufficient to build trust; instead, trust is strengthened through participatory approaches that harness community insight, stakeholder engagement, and co‑design to secure social licence. Looking ahead, the session also considers how increasingly agentic, goal‑driven AI systems will disrupt traditional models of control, redefining human–AI relationships and creating both new opportunities and emerging risks for organisations operating globally.

Title of the Talk: Tomorrow's AI Funky Five: Attention, Engagement, Feedback, Sleep— just add Emotion.

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Abstract : AI plans, reasons, and acts in the real world. Yet, even our best models still learn in ways that feel oddly unhuman: data-hungry, brittle under distribution shift, and prone to superficial shortcuts rather than robust, compositional knowledge. This keynote proposes a deliberately “funky” remedy: treat Artificial Emotion (AE) and Artificial Emotional Intelligence (AEI) not as performative smiles, but as the missing control groove that binds together a more human learning recipe. Inspired by Dehaene’s four pillars—attention, active engagement, error feedback, and consolidation—I argue that today’s AI implements fragments of each pillar, but rarely the closed-loop orchestration that makes them sing. Our systems can attend, yet struggle to decide what matters as goals shift; they can be trained, yet seldom self-engage through curiosity and hypothesis-testing; they optimize loss, yet often without causal error attribution; and they store parameters, yet lack a true analogue of sleep-like consolidation that compresses experience into transferable skills. Here is where emotion enters: emotion-inspired internal state for regulation—urgency, uncertainty, frustration, curiosity, confidence—as computational signals that (i) allocate attention, (ii) switch between exploration and safe fallback, (iii) demand informative feedback instead of reward hacking, and (iv) schedule consolidation and belief revision over time. The goal is a blueprint for affect-aware agentic systems that learn more like humans—selective, engaged, self-correcting, and stable—while remaining transparent, non-manipulative, and measurable through calibration, robustness, and real-world utility. We will close with the current state-of-play in AE/AEI and the concrete gaps that still block “human-like” learning in machines.

Title of the Talk: GenAI Governance Framework: Challenges & Mitigation.

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Abstract : The rapid evolution of Generative AI necessitates a robust governance framework to address critical challenges such as algorithmic hallucinations, data privacy leaks, and the complex requirements of global regulatory mandates like the EU AI Act. Unlike traditional software, GenAI requires a dynamic oversight model that balances the "black box" nature of Large Language Models with the need for transparency and accountability. The talk discusses a multi-layered mitigation strategy to ensure ethical and secure deployment.

Title of the Talk: Recent Advances in Computational Engineering & Computer-Aided Engineering: Emerging Technologies and Applications.

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Abstract : Computational Engineering and Computer-Aided Engineering (CAE) are rapidly evolving into intelligent, autonomous, and connected engineering ecosystems. The integration of AI, embedded intelligence, agentic programming, quantum computing, and digital twins is reshaping how complex engineering systems are designed, simulated, and operated across industry and healthcare. CAE is no longer static modelling; it is becoming intelligent, autonomous, and operational. These advances are transforming engineering practice across robotics, fire safety, water systems, materials, buildings, and power & energy, creating powerful opportunities for innovation, collaboration, and real-world impact.

Title of the Talk: AI through its inflection points

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Abstract : Drawing from the speaker's experience and research, AI through its inflection points traces the defining transitions that have repeatedly reshaped artificial intelligence, moving it from rule-based reasoning systems to statistical learning, from narrow task-specific models to large-scale foundation and multimodal systems. Each inflection point marked not just a technical breakthrough, but a shift in how intelligence is conceptualized, engineered, and deployed at scale. This keynote examines the forces behind these transitions and how they have reconfigured research priorities, economic value, and power structures. Looking ahead, the talk explores emerging inflection points around autonomy, alignment, regulation, and human centered AI collaboration, offering a framework for understanding what may define the next phase of AI and how organizations and societies can prepare for changes that are nonlinear, fast-moving, and consequential.

Title of the Talk: AI for Science and Engineering: Leveraging GNNs, LLMs, XAI, Nanocombinatorics, and More.

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Abstract : The increasing availability of data from the first three paradigms of science (experiments, theory, and simulations), along with advances in artificial intelligence and machine learning (AI/ML) techniques has offered unprecedented opportunities for data-driven science and discovery, which is the fourth paradigm of science. Within the arena of AI/ML, deep learning (DL) has emerged as a game-changing technique over the last decade with its ability to effectively work on raw big data, bypassing the (otherwise crucial) manual feature engineering step traditionally required for building accurate ML models, thus enabling numerous real-world applications, such as autonomous driving. In this talk, I will present some of the ongoing AI/ML/DL research in our group with illustrative applications in materials science and engineering, by leveraging graph neural networks (GNNs), large language models (LLMs), explainable AI (XAI), and generative AI (GenAI). We will also see how AI can be used to accelerate nanocombinatorics workflows to facilitate rapid structure characterization of megalibraries with millions of nanoparticles on a chip.

Title of the Talk: Challenges and Future Directions of Knowledge Graphs and Ontologies adopting Large Language Model.

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Abstract : Recently, substantial research has been conducted utilizing large language models (LLMs) as AI technology advances. An increasing number of users are employing applications that utilize Large Language Models, such as ChatGPT and Google Gemini. Retrieval-Augmented Generation (RAG) is garnering significant attention as a subject pertinent to Large Language Models. RAG use knowledge graphs, and there is significant interest in subjects pertaining to the integration of ontologies, which similarly utilize graph structures. In this presentation, I will discuss the most recent research and the future prospects for developing knowledge graphs and ontologies with large-scale language models.

Title of the Talk: Large Language Models as Assistants in Optimization.

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Abstract : Large Language Models (LLMs) are artificial intelligence systems trained on vast corpora of text, allowing them to understand and generate both natural language and programming code. Based on transformer architectures—a class of deep learning models—LLMs take input prompts and generate contextually relevant responses. They have demonstrated strong performance across a wide range of tasks, including question answering, text summarization, code generation, and, more recently, progress in mathematical problem solving. As interest in LLMs has grown, their potential has been explored in numerous application domains. Consequently, researchers in optimization, particularly those working on metaheuristic methods, have begun to examine how LLMs can be used to support and enhance their approaches. In this presentation, I will discuss our recent work on employing LLMs as assistants in optimization research, including the automated refinement of existing optimization algorithms and the comparative evaluation of algorithmic performance.

Title of the Talk: Nurturing the responsible use of GenAI by Information Technology students.

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Abstract : Since the public release of ChatGPT at the end of 2022, Generative Artificial Intelligence (GenAI) has taken the world by storm. At Higher Education Institutions, students are increasingly leaning on GenAI to assist them with completing their assessments. Universities face the challenge of having to ensure that students have sufficiently mastered learning outcomes without a dependency on GenAI. At the same time, universities have to ensure that their graduates are AI literate and that they are prepared for an AI-dominated future world of work. This talk explores initiatives taken by the University of Pretoria to guide Information Technology students in the responsible use of GenAI. To date, some successes have been experienced in facilitating students to use GenAI in a self-aware, well-planned, critical and reflective manner. Examples will be shared, as well as lessons learned.

Title of the Talk: Auto-Associative Neural Networks for modelling complex systems

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Abstract : Complex systems are characterised by the presence of a large number of elements interacting in complex ways to produce nonlinear behaviours that are challenging to predict. Some of these systems display periodic behaviour producing oscillations that can be rhythmic like limit cycles attractors or arrhythmic chaotic attractors. Although mathematical modelling methods such as Ordinary Differential Equations (ODE) have been used to model these systems, ODE are challenged by nonlinearity and the scale of systems. Neural Networks have great potential to contribute to this domain but they have been largely unexplored. This talk proposes Auto-Associative Neural Networks (AANN) with an application to the complex system of biological cell cycle. Cell cycle is the mechanism by which biological organisms develop and regenerate through repeated cell division which is a tightly controlled complex system of many interacting elements (proteins). Black box architecture of traditional Neural Networks cannot capture the essence of these systems and provide insights. AANN is a Hopfield type neural network that helps model a complex system with specific and unique element interactions and explore its temporal response and potential oscillatory system responses. This talk presents the high accuracy of AANN in depicting the complex nonlinear behaviour of cell cycle which is a limit cycle oscillator.

Title of the Talk: Collision-free autonomous driving with nonlinear model predictive control.

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Abstract : Autonomous vehicles must navigate obstructed environments, avoid obstacles, and reach target poses with high precision. This talk will present a novel nonlinear hierarchical model predictive control (MPC) approach for collision-free autonomous driving. The architecture consists of a high-level nonlinear MPC that generates optimal, obstacle-aware trajectories and a low-level nonlinear MPC that tracks these paths at a frequency ten times higher than the planner. To guarantee safety, the high-level controller employs a convex set separation condition for collision avoidance. Both optimization problems are solved using Optimization Engine (OpEn), a high-performance solver for nonconvex embedded optimization implemented in Rust. We demonstrate the effectiveness of this approach through both simulations and real-world experiments on a heavy skid-steer vehicle from EquipmentShare.