Keynote Speakers

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Dr. Ganesh Neelakanta Iyer
​ National University of Singapore
Harnessing Signals, Transforming Data: AI's Impact on Cultural Heritage, Education and Healthcare

Abstract: This talk explores the diverse applications of Artificial Intelligence through three active projects. Firstly, we investigate the potential of TinyML for healthcare, focusing on the real-time analysis of medical signals at the network's edge for localized diagnosis and monitoring. Secondly, we'll delve into the use of Large Language Models and Generative AI for enhancing AI education and software engineering. These projects explore AI for creating customized learning expereinces and LLMs for large scale software engineering projwcts. Finaly , we'll examine how AI can preserve cultural heritage through a set of projects focused on Kathakali and Wayang Kulit. By employing techniques like deep learning, we explore how AI can analyze different aspects of Kathakali performances, ensuring the preservation of this rich art form for future.
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Dr. Hari Prabhat Gupta
​ IIT (BHU) Varanasi
Insights from Deploying and Measuring LoRa Technology in Smart Campus Environments

Abstract: This talk explores the crucial role of Low Power Wide Area Networks (LPWAN) in IoT wireless networking, and complementing both cellular networks and short-range options. In this session, we share our recent experiences in deploying and measuring LoRa, a top LPWAN technology.

We'll showcase how LoRa's commitments to reduced radio power, extended communication range, and efficient channel access are validated through real-world evaluations, with a special focus on implementations within smart campus environments.
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Prof. Michael Sheng
​ Macquarie University, Sydney, Australia
Intelligent IoT Sensing for Aging Well: Research Activities and Future Directions

Abstract: Worldwide, the population is aging due to increasing life expectancy and decreasing fertility. The significant growth in older population presents many challenges to health and aged care services. Over the past two decades, the Internet of Things (IoT) has gained significant momentum and is widely regarded as an important technology to change the world in the coming decade. Indeed, IoT will play a critical role to improve productivity, operational effectiveness, decision making, and to identify new business service models for social and economic opportunities. With the development of low-cost, unobtrusive IoT sensors, along with data analytics and artificial intelligence (AI) technologies, there is now a significant opportunity to improve the wellbeing and quality of life particularly of our older population. In this talk, we will overview some related research projects and also discuss several exciting research directions.
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Dr. Sreejith Vidhyadharan Nair
​ University of North Dakota, USA
Advancing Airspace Safety and Security using UAV Risk analysis and Acoustic Counter-UAS

Abstract: The abstract outlines the integration of Unmanned Aerial Systems (UAS) into the National Airspace System (NAS), emphasizing the need for safety and security measures to mitigate risks associated with drone operations. The presentation will discuss the critical role of risk analysis in managing UAVs, leveraging data analytics, probability, and machine learning to predict the optimal day and time for operation. It will also address the importance of regulating the number of UAVs to prevent airspace congestion and maintain traffic efficiency. Furthermore, the talk will explore counter-UAS strategies, including the implementation of distributed acoustic systems to detect, identify, and track intruder UAVs, thereby ensuring safety and privacy in sensitive areas.
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Prof. Dr. Marlene Harter
​ Offenburg University of Applied Sciences, Germany
Modern Radar Techniques for Automotive Applications

Abstract: Automotive radar is one of the leading technologies for advanced driver assistance systems (ADAS), due to its unique ability to operate independently of weather and light conditions. Millions of cars have already been equipped with radar sensors, helping to increase the drivers’ comfort and safety. Improving radar sensors for L2/L3 is of great importance to increase road safety. Heading toward Autonomous Driving (AD) requires robust and highly accurate environment sensing. For this purpose, the potential of radar for 4D imaging and the use of radar networks is further explored. Starting from a brief recap of the history of automotive radars and system architectures, followed by the market potential of automotive radars, the talk will present advanced radar modulation and MIMO signal processing techniques as of today’s automotive radar sensors. Finally, aspects of radar vehicle integration and interference mitigation will be addressed.


Tutorial Speakers

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Prof. Ponnurangam Kumaraguru
​ IIIT Hyderabad
Responsible & Safe AI

Abstract: In a world increasingly shaped by AI technologies, including influential models like ChatGPT and DALL.E, common applications of AI in natural language understanding, image generation, and more are prominent. There is an increased use of AI models in various applications, from enhancing customer service interactions to enabling creative image generation and autonomous vehicles. One can now use specialized GPTs for a variety of usecases through a AppStore-like AI marketplace, straight out-of-the-box. However, with great power comes great responsibility, and it's imperative to acknowledge the ethical, responsible, and fairness issues that accompany these powerful systems. Zooming out to the bigger picture, the talk will address the criticality of identifying ethical, responsible, and auditing requirements for AI systems. Our discussion will encompass a comprehensive exploration of AI, spanning computational, cultural, and legal dimensions. This would include exploring the potential harms arising from modern AI capabilities in experimental and real-world contexts, ongoing research projects and ideas for enhancing AI system safety, specific projects related to addressing bias in Large Language Models (LLMs) and their effective application in legal contexts, as well as methods to improve interpretability, consistency, and the removal of harmful labels and knowledge from these models.
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Dr. Arun Ashok
​ Senior Research Staff
Juelich Research Center
Novel Integrated Computing Architectures as Alternatives to the Von Neumann Paradigm

Abstract: The traditional Von Neumann architecture, while foundational to modern computing, faces significant limitations in addressing the demands of contemporary computational tasks, particularly in areas requiring high efficiency and low power consumption. Neuromorphic computing aims to replicate the neural structures and functioning of the human brain, offering significant advantages in parallel processing, learning capabilities, and energy efficiency. This requires the development of specialized hardware, such as analog circuits and neuromorphic chips, which can perform complex computations with minimal power consumption. Innovations in hardware design are crucial for creating systems that can support real-time adaptive processing, which is vital for applications in artificial intelligence, robotics, and sensory data processing. Quantum computing leverages the principles of quantum mechanics to perform computations beyond the reach of classical computers. This necessitates the creation of sophisticated quantum hardware, including qubits, quantum gates, and error-correction systems, to harness phenomena like superposition and entanglement. Developing robust and scalable quantum hardware is essential for solving complex problems in fields like large-scale simulations, cryptography, optimization, and drug discovery. The integration of quantum hardware into existing technological frameworks promises to unlock unprecedented levels of computational power and efficiency. The convergence of neuromorphic and quantum computing architectures represents a transformative approach to overcoming the limitations of the Von Neumann paradigm. Developing hybrid systems that integrate neuromorphic and quantum hardware will combine the adaptive, low-power benefits of neuromorphic computing with the unparalleled processing capabilities of quantum computing. This requires significant advancements in hardware design and manufacturing, including the creation of new materials, fabrication techniques, and system integration methods.