Virtual International Faculty Development Programme (FDP)
Organised by
Department of Electronics and Communication Engineering SRM Institute of Science and Technology
Ramapuram Campus
Event Name: Faculty Development Program
Title: AI Technologies for Next-Generation Communication Systems
Date: 1st June 2026 to 5th June 2026
Time: 6.30pm – 7.30pm
No. of Participants: 241
Mode: Online
Convener: Dr. N.V.S. Sree Rathna Lakshmi, Prof & Head -ECE
Co-Conveners:
Dr. R.Vani , Prof/ECE
Dr. M.Shunmugathammal, Asso.Prof/ECE
Dr. Lalitha K, Asst.Prof/ECE
Dr. S. Nagarajan, Asst.Prof/ECE
Speakers Details and Profile:
Day 1 – 01.06.2026
Dr. S. Umamaheswari is serving as an Associate Professor at Anna University. She has made significant contributions to teaching, research, and academic development in the field of Engineering and Technology. She has published numerous research articles in reputed journals and conference proceedings. Her scholarly contributions include 15 journal publications, 20 conference papers, 3 book chapters. She is actively involved in guiding students, conducting research, and promoting innovation in emerging technologies. She is also associated with various professional and academic bodies, contributing to the advancement of engineering education and research.
As a resource person, Dr. Umamaheswari has delivered expert lectures and technical sessions in Faculty Development Programmes, workshops, and conferences. Her vast academic experience, research excellence, and commitment to knowledge dissemination make her a respected educator and an inspiring speaker in the academic community.
Day 2 – 02.06.2026
Dr. Jyothi A. P. is a distinguished academician, researcher, and Senior IEEE Member with over 18 years of teaching and research experience. She is currently serving as Associate Professor and Head of the Mathematics and Computing Program in the Department of Computer Science and Engineering at M. S. Ramaiah University of Applied Sciences, Bengaluru. She obtained her Ph.D. in Computer Science and Engineering from Visvesvaraya Technological University (VTU), Karnataka. Her research interests include Artificial Intelligence, Machine Learning, Internet of Things (IoT), Wireless Sensor Networks (WSN), Mobile Ad Hoc Networks (MANET), Cloud Computing, Embedded Systems, RTOS, and Computer Networks.
Dr. Jyothi has published numerous research papers in reputed SCI/SCIE, Web of Science, and Scopus-indexed journals and has presented her work at several national and international conferences. She has authored book chapters with leading publishers such as Springer Nature, CRC Press, and Wiley. She serves as a reviewer for reputed international journals and conferences and is an Editorial Board Member of international journals in the field of computing. She has received several prestigious recognitions, including the Research Excellence Award, National Faculty Research Excellence Award, and Women Researcher Award.
Day 3 – 03.06.2026
Mr. Kasirajan Kasipandian (B.E. (Electrical and Electronics Engineering), M.E. (Power Electronics and Drives), working as Senior Lecturer in Faculty of Engineering, Built Environment, and IT, MAHSA University, Malaysia. He is an academician with over twenty 20 International Research publications. He received 6 international patents. His main research interests are Solar power plant, wind power plants and Electric Vehicles. He organized number of conferences, number of Workshops, faculty development programmes and symposium. He got sponsorship of Rs30,000 from DRDO and Rs.30,000 from CSIR for conducting the National conference on “RAPCED”. He gave number of webinars related with Solar power plants and Electric Vehicle charging techniques. Mr. Kasipandian Kasirajan working in Design of mini Solar power plant to generate power for Hydroponics and Aquaponics projects. He received MAHSA Research Grant for a Research project “Enhanced Hydrogen production for electricity generation using effective microbial consortia from vegetable wastes” during April, 2021. He received another MAHSA Research internal grant for in the year 2023.
His Project grant title is “ Optimizing the solar insolation onto solar panel using a solar tracking device-RP202-0423”. His Project amount is RM22,886.(Indian money equivalent Rs4,07,370). He received patent “A Robotic System to Check Battery Quality in Manufacturing Industries Using Artificial Intelligence” on 27th April, 2021. One more patent on “RADIATION SHIELDING CHAMBER” in the year 2023. He received another patent on “OCEAN OIL SPILLAGE CLEANUP DEVICE” during 2024. He received another patent on “Smart safety helmet with locator” during 2024.
Day 4 – 04.06.2026 (session-1)
Ts. Dr. Alawi Alqushaibi is an accomplished academician, researcher, and educator specializing in Artificial Intelligence, Machine Learning, Data Science, and Computer Networks. He is currently serving as a Lecturer in Artificial Intelligence and has extensive experience in higher education, research, and industry. He earned his Bachelor’s degree in Computer Networks and Security from Universiti Teknologi Malaysia and pursued his postgraduate studies at Universiti Teknologi PETRONAS.He has authored and co-authored numerous high-impact research papers published in internationally reputed journals, including IEEE Access and other Scopus-indexed publications.
His research contributions have addressed important applications in healthcare, smart grids, computer vision, cybersecurity, and next-generation communication systems. He is actively involved in interdisciplinary research projects and has collaborated with researchers from leading international institutions. As a passionate educator and researcher, Dr. Alqushaibi is committed to advancing AI-driven solutions for real-world challenges. His expertise, research excellence, and dedication to knowledge dissemination have made him a sought-after speaker and resource person for conferences, workshops, and Faculty Development Programmes.
Day 4 – 04.06.2026 (session-2)
Mr.Seshathiri Dhanasekaran is a technology leader and researcher with 10+ years of expertise in AI-driven analytics, data science, and digital transformation. Currently serving as a Tech Lead at first, Tromsø, Norway, he specializes in employee engagement analytics, predictive modeling, and AI-powered business intelligence solutions. As a researcher at UiT The Arctic University of Norway, Dhanasekaran has authored more than 34 peer-reviewed publications on behavioral analytics, machine learning applications in healthcare, and smart nudging systems using wearable technology. His research bridges advanced data science with practical applications in health informatics and organizational optimization. He works as a consultant for various companies, developing enterprise AI solutions for major Norwegian companies, including Hydro and Gjensidige. His career spans international institutions, including Academia Sinica (Taiwan), and Inspirisys Solutions (India) and Vision Analytics (India). A Norwegian Indian fluent in English, Norwegian, and Tamil, Seshathiri combines technical excellence with cross-cultural communication skills, making him an effective educator and consultant in emerging technologies like AI for journalism and media applications.
Day 5 – 05.06.2026
Profile of Dr. Malaya Kumar Nath
Dr. Malaya Kumar Nath is an accomplished academician and researcher currently serving as Assistant Professor in the Department of Electronics and Communication Engineering at the National Institute of Technology (NIT) Puducherry, Karaikal. He has over 12 years of teaching and research experience in the field of Electronics and Communication Engineering. Dr. Nath obtained his Ph.D. in Electrical Engineering with specialization in Signal Processing from the Indian Institute of Technology (IIT) Guwahati. He also earned his M.Tech. in Electronics and Communication Engineering (Signal Processing) from IIT Guwahati and his Bachelor’s degree in Electronics and Telecommunication Engineering from Biju Patnaik University of Technology, Odisha. His research interests include Signal Processing, Communication Systems, and related emerging technologies. He has actively contributed to academic and research activities through teaching, mentoring, and scholarly publications. Dr. Nath also served as a Visiting Assistant Professor at the Asian Institute of Technology, Thailand, gaining valuable international academic exposure.
Recognized for his expertise in research, leadership, and public speaking, Dr. Nath has been actively involved in knowledge dissemination through conferences, workshops, and Faculty Development Programmes. His dedication to academic excellence and innovative research has made him a respected educator and an inspiring resource person in the field of Electronics and Communication Engineering.
Summary of the Event:
DAY 1 – 01.06.2026 – MONDAY
The Faculty Development Program commenced with the soulful invocation of Tamil Thai Vazhthu, setting a reverent tone. Following this, Dr. N.V.S. Sree Rathna Lakshmi, Head of the Department, welcomed all the faculty members and participants for the session. Dr. R.Vani, Professor ECE, has delivered felicitation address. Resource Person Dr S Uma Maheswari ,Department of Information Technology, Madras Institute of Technology , the session by delivering a talk about Optimized Deep Learning Architecture for Multi-Crop Disease Identification. The Multi-crop disease identification is an important application of deep learning in modern agriculture. It involves automatically detecting and classifying diseases affecting different crop species using images of leaves, stems, fruits, or entire plants. Traditional disease diagnosis relies on manual inspection by experts, which can be time-consuming, expensive, and prone to human error. Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have significantly improved the accuracy of plant disease detection. An optimized deep learning architecture is designed to extract relevant features from agricultural images while reducing computational complexity. Such architectures can efficiently handle large datasets containing multiple crop varieties and disease classes. Data augmentation, transfer learning, and feature fusion techniques further enhance model performance. The integration of these methods helps achieve high classification accuracy even under varying environmental conditions. As a result, farmers can receive timely and reliable disease diagnosis, leading to improved crop health and productivity.
DAY 2 – 02.06.2026 – TUESDAY
The session on day 2 was engaged by Dr Jyothi A.P, M.S. Ramaiah University of Applied Sciences, Bangalore, has delivered a talk on “Demystifying Explainable AI: From Black-Box Models to Transparent Decision” . Explainable Artificial Intelligence (XAI) is a field of AI that focuses on making machine learning and deep learning models understandable to humans. Many modern AI systems, especially deep neural networks, are considered “black-box” models because their internal decision-making processes are difficult to interpret. While these models often achieve high accuracy, users may not understand why a particular prediction or decision was made. XAI aims to bridge this gap by providing clear explanations for AI-generated outcomes. It helps users, developers, and stakeholders gain confidence in AI systems. Explainability is particularly important in critical domains such as healthcare, finance, education, and autonomous vehicles, where incorrect decisions can have significant consequences. Techniques such as feature importance analysis, decision trees, SHAP values, and LIME are commonly used to explain model behavior. By revealing how different input features influence predictions, XAI enhances transparency and accountability. As AI adoption continues to grow, explainability has become a key requirement for building trustworthy and responsible AI systems.
DAY 3 – 03.06.2026 – WEDNESDAY
Dr. Kasirajan Kasipandian, Built Environment, and IT, MAHSA University, Malaysia has given an impactful talk on “AI Beyond Boundaries: Applications in various fields”. The session was highly informative and though provoking. The main focus was on to Identify key AI applications across diverse domains such as healthcare, education, agriculture, finance, manufacturing, transportation, cybersecurity, and entertainment. rtificial Intelligence (AI) has emerged as one of the most transformative technologies of the 21st century, extending its influence across numerous disciplines and industries. The phrase “AI Beyond Boundaries” reflects the ability of AI to transcend traditional limits and solve complex real-world problems. AI systems can learn from data, recognize patterns, make predictions, and support intelligent decision-making with minimal human intervention. In healthcare, AI assists in disease diagnosis, medical image analysis, and drug discovery. In agriculture, it enables precision farming, crop monitoring, and disease detection. The education sector benefits from personalized learning platforms and intelligent tutoring systems. AI is also widely used in finance for fraud detection, risk assessment, and algorithmic trading. In transportation, AI powers autonomous vehicles and intelligent traffic management systems. These applications demonstrate how AI is revolutionizing multiple sectors and improving efficiency, accuracy, and productivity.
DAY 4 – 04.06.2026 – THURSDAY
Ts. Dr. Alawi Alqushaibi (Faculty , Department of IT, MAHSA University, Kuala Lumpur, Malaysia ) has delivered a clear and compelling technical first half session on the topic “ The AI-Driven Shift in Cybersecurity Education: Preparing Future-Ready Professionals”.The main focus was on to understand industry expectations and emerging career opportunities at the intersection of AI and cybersecurity Design for Testability. The integration of AI into cybersecurity education is essential for preparing future-ready professionals capable of protecting modern digital infrastructures. Organizations increasingly rely on AI-driven security solutions to defend against sophisticated attacks such as ransomware, phishing, malware, and advanced persistent threats. Therefore, students must develop expertise in both cybersecurity principles and AI technologies. Future cybersecurity professionals need skills in machine learning, data analytics, threat intelligence, ethical hacking, and security automation. AI enables faster decision-making by automating routine security tasks and allowing experts to focus on complex threats. Universities and training institutions are collaborating with industry partners to design curricula aligned with emerging workforce requirements. Ethical considerations, privacy protection, and responsible AI usage are also becoming important components of cybersecurity education. By combining technical knowledge with AI-driven capabilities, graduates can effectively address future cyber risks. This AI-driven educational shift is creating a new generation of professionals equipped to secure the increasingly interconnected digital world.
Mr. Seshathiri Dhanasekaran, Faculty of Science and Technology, UiT The Arctic University
of Norway has handled the second half session by delivering an informative talk on “TinyML & on device inference”. The discussion was mainly about the concept of on-device inference and how it differs from cloud-based AI processing . TinyML is an emerging field that enables machine learning models to run directly on low-power embedded devices such as microcontrollers, sensors, wearables, and Internet of Things (IoT) devices. Unlike traditional AI systems that depend on cloud computing, TinyML performs inference locally on the device itself. This approach reduces latency, improves privacy, and minimizes the need for continuous internet connectivity. TinyML combines machine learning, embedded systems, and edge computing to create intelligent devices capable of making real-time decisions. Optimized models are compressed and deployed on hardware with limited memory, processing power, and energy consumption. Applications of TinyML include speech recognition, gesture detection, predictive maintenance, health monitoring, and smart home automation. On-device inference allows data to be processed where it is generated, reducing communication costs and bandwidth requirements. The advancement of lightweight neural networks and efficient hardware platforms has accelerated the adoption of TinyML. As a result, intelligent capabilities can now be integrated into even the smallest electronic devices.
DAY 5 – 05.06.2026 – FRIDAY
The session on Day 5 was started with welcome address to the resource person Dr. Malaya Kumar Nath, Electronics & Communication Engineering, NIT Puducherry by Dr. Lalitha K. The topic for the discussion was “Intelligent Cardiovascular Disease Detection Using Emerging Computational Methods”. It has been highlighted the current research trends and innovations in smart healthcare and cardiovascular disease monitoring. Emerging computational methods offer significant advantages in the prevention, diagnosis, and management of cardiovascular diseases. AI-powered systems can predict the likelihood of heart attacks, arrhythmias, coronary artery disease, and other cardiac conditions by analyzing patient data in real time. Wearable devices equipped with intelligent algorithms continuously monitor vital signs and provide early warnings of potential health risks. These technologies enable personalized healthcare by tailoring treatment recommendations based on individual patient characteristics. Hospitals and research institutions are increasingly adopting AI-based diagnostic tools to improve accuracy and reduce healthcare costs. Additionally, cloud computing, big data analytics, and the Internet of Medical Things (IoMT) enhance the accessibility and scalability of intelligent healthcare solutions. Despite these advancements, challenges such as data privacy, model interpretability, and regulatory compliance must be addressed. With continuous developments in computational intelligence and medical technology, intelligent cardiovascular disease detection is expected to play a crucial role in advancing preventive healthcare and improving global cardiovascular health outcomes.
The 5 days FDP on “AI Technologies for Next-Generation Communication Systems” provided valuable exposure to evaluate emerging AI applications in communication systems, including smart cities, connected vehicles, industrial IoT, and satellite communication. FDP was concluded with valedictory function at which Dr. Shumugathammal has delivered vote of thanks.
241 participants from various institutions have attended the session, and they had the opportunity to engage in an interactive Q&A session, enhancing their understanding of cutting-edge technologies The feedback from the participants was good and session ended with vote of thanks. The event provided an excellent platform for knowledge exchange and interactive discussions.
We express our sincere thanks and gratitude to management and higher officials for their constant support and encouragement in organizing the event.
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