- \n
- Scientific Session Day 1<\/span><\/a><\/li>\n
- Scientific Session Day 2<\/span><\/a><\/li>\n
- Scientific Session Day 3<\/span><\/a><\/li>\n Abstract Submission<\/a>\n Register for Conference<\/a>\n <\/ul>\n <\/div>\n\n\n\n\n
\n\n\n\n \n\n\n\nSession 1<\/span>Swarm robotics and multi-agent systems<\/h3>\n <\/div>\n\n
\nSwarm robotics and multi-agent systems are fields within robotics and artificial intelligence that focus on the coordination and collective behavior of multiple autonomous agents or robots working together to achieve complex tasks.
\nSwarm robotics is inspired by the collective behavior of social insects like ants, bees, or birds. It involves large groups of relatively simple robots that cooperate using local rules and decentralized control without a central leader.
\nMulti-agent systems consist of multiple intelligent agents (which can be robots, software programs, or virtual entities) that interact, cooperate, or compete to solve problems that are difficult for a single agent.<\/p>\n <\/div>\n \n <\/div>\n\n \n\n\n\nSession 2<\/span>Database management systems<\/h3>\n <\/div>\n\n
\nDatabase Management Systems (DBMS) are crucial software applications that facilitate the creation, management, and manipulation of databases. They provide an organized way to store, retrieve, and manage data, ensuring its integrity, security, and accessibility. DBMSs allow users to perform various operations on the data, such as querying, updating, and reporting, using a structured query language (SQL) or other interfaces. They support transaction processing, which ensures that data modifications are performed accurately and reliably, even in the event of system failures. By abstracting the complexities of data storage and management, DBMSs enable efficient data handling and facilitate the development of data-driven applications across various domains, from business and finance to research and healthcare.<\/p>\n <\/div>\n \n <\/div>\n\n \n
\n\n\nSession 3<\/span>Operating Systems<\/h3>\n <\/div>\n\n
\nOperating systems (OS) are fundamental software that manage computer hardware and provide essential services for application programs. They act as an intermediary between users and the computer hardware, ensuring efficient resource allocation and enabling multitasking. Key functions of an OS include managing hardware resources like the CPU, memory, and storage, as well as providing user interfaces, managing files, and coordinating input and output operations. Popular operating systems include Microsoft Windows, macOS, Linux, and Android, each designed to cater to specific needs and environments. The evolution of operating systems has been driven by advances in technology, aiming to enhance performance, security, and user experience.<\/p>\n <\/div>\n \n <\/div>\n\n \n
\n\n\nSession 4<\/span>Robotics & Autonomous Systems<\/h3>\n <\/div>\n\n
\n\n\n\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n\n\n\nRobotics and Autonomous Systems is a field of computer science and engineering focused on designing, building, and programming robots and intelligent machines capable of performing tasks without human intervention or with minimal supervision.
\nRobotics involves the development of physical machines (robots) equipped with sensors, actuators, and control systems to interact with the environment
\nAutonomous systems extend robotics by incorporating intelligent decision-making capabilities enabling machines to operate independently in complex, dynamic environments.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n <\/div>\n \n <\/div>\n\n \n\n\n\nSession 5<\/span>Quantum error correction<\/h3>\n <\/div>\n\n
\nQuantum error correction (QEC) is a set of techniques designed to protect quantum information from errors caused by decoherence, noise, and other quantum disturbances. Since quantum bits (qubits) are extremely fragile, QEC is essential for building reliable and scalable quantum computers.
\nQuantum error correction encodes a logical qubit into a larger number of physical qubits, spreading information redundantly to detect and correct errors without measuring the quantum information directly (which would collapse the quantum state).<\/p>\n <\/div>\n \n <\/div>\n\n \n\n\n\nSession 6<\/span>Quantum Computing<\/h3>\n <\/div>\n\n
\nQuantum computing is an advanced field of computing that leverages the principles of quantum mechanics to process information in fundamentally new ways, promising to solve certain problems much faster than classical computers.
\nQuantum computing represents a paradigm shift with the potential to revolutionize computing, cryptography, and scientific research. While still in early stages, ongoing advances could unlock new capabilities beyond classical limitations.<\/p>\n <\/div>\n \n <\/div>\n\n \n\n\n\nSession 7<\/span>Data mining and pattern recognition<\/h3>\n <\/div>\n\n
\n\n\n\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n\n\nData mining is the process of exploring large datasets to identify meaningful patterns, trends, and relationships. It combines techniques from statistics, machine learning, and database systems to extract knowledge from data. Pattern recognition is the science of automatically detecting patterns and regularities in data, often used to classify data into categories based on learned features.
\nBoth data mining and pattern recognition are essential for making sense of vast data, enabling automated systems to discover insights, make predictions, and support intelligent decisions in many fields like finance, healthcare, and image processing.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n <\/div>\n \n <\/div>\n\n \n\n\n\nSession 8<\/span>Data Science & Big Data<\/h3>\n <\/div>\n\n
\nData Science and Big Data are interconnected fields that focus on extracting meaningful insights from large and complex datasets using a combination of statistics, machine learning, and computational tools
\nBig Data refers to datasets that are too large, fast, or complex to be processed using traditional tools
\nData Science provides the techniques to derive actionable insights, while Big Data provides the scale and complexity of modern data challenges. Together, they power innovations across sectors\u2014from business intelligence to healthcare and smart cities<\/p>\n <\/div>\n \n <\/div>\n\n \n\n\n\nSession 9<\/span>Cybersecurity & Privacy<\/h3>\n <\/div>\n\n
\nCybersecurity involves the strategies, technologies, and practices used to protect digital assets\u2014including software, hardware, and data\u2014from cyber threats such as hacking, viruses, phishing, ransomware, and denial-of-service (DoS) attacks.
\nPrivacy focuses on the responsible collection, use, and sharing of personal data. It ensures individuals have control over how their information is used and that organizations comply with data protection laws.
\nCybersecurity and privacy are closely related fields focused on protecting digital systems, networks, and information from unauthorized access, misuse, and damage, while ensuring that individuals’ personal data is handled responsibly and lawfully.<\/p>\n <\/div>\n \n <\/div>\n\n \n\n\n\nSession 10<\/span>Artificial Intelligence (AI) and Machine Learning (ML)<\/h3>\n <\/div>\n\n
\nAI refers to the broader concept of machines being able to carry out tasks in a way that we would consider “smart” \u2014 such as reasoning, problem-solving, understanding language, and perception.
\nML is a subset of AI where systems learn from data and improve their performance over time without being explicitly programmed.<\/strong><\/p>\n <\/div>\n \n <\/div>\n\n <\/div>\n <\/div>\n <\/div>\n\n\n\n \n\n\n\nSession 11<\/span>Multimodal learning<\/h3>\n <\/div>\n\n
\n\n\n\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n\n\nMultimodal learning is an area of machine learning and artificial intelligence that focuses on processing and integrating multiple types of data (modalities)\u2014such as text, images, audio, video, and sensor data\u2014to improve understanding, reasoning, and prediction.
\nMultimodal learning is crucial for creating intelligent systems that understand the world like humans do, through the integration of multiple sensory inputs. It is at the forefront of research in areas like AI-driven perception, robotics, healthcare, and multimedia analysis.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n <\/div>\n \n <\/div>\n\n \n\n\n\nSession 12<\/span>Network protocols and simulation<\/h3>\n <\/div>\n\n
\n\n\n\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n\n\nNetwork protocols and simulation are key areas in computer networking that deal with how data is transmitted across networks and how network behavior can be analyzed and tested under various conditions.
\nNetwork protocols are a set of standardized rules that define how devices communicate over a network. They ensure that data is sent, received, and interpreted correctly across diverse hardware and software systems.
\nNetwork simulation is the process of using software tools to model and test network behavior in a virtual environment before actual deployment. It helps researchers and engineers evaluate performance, scalability, reliability, and security of networks.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n <\/div>\n \n <\/div>\n\n \n\n\n\nSession 13<\/span>5G\/6G and next-generation networking<\/h3>\n <\/div>\n\n
\n\n\n\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n\n\n5G, 6G, and next-generation networking represent the evolving landscape of wireless communication technologies, aiming to deliver ultra-fast, low-latency, and highly reliable connectivity to support emerging applications like autonomous systems, immersive experiences, and massive-scale IoT.
\n5G is the current generation of mobile networks, offering significant improvements over 4G in terms of speed, latency, and capacity.
\n6G is the next wave of wireless technology, currently in research and expected to launch commercially in the 2030s. It builds on 5G with even more ambitious goals.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n <\/div>\n \n <\/div>\n\n \n\n\n\nSession 14<\/span>Internet of Things (IoT)<\/h3>\n <\/div>\n\n
\nThe Internet of Things (IoT) refers to a network of interconnected physical devices\u2014such as sensors, appliances, vehicles, and machines\u2014that collect, exchange, and act on data using the internet, often without human intervention.<\/p>\n
IoT is transforming how we interact with the physical world by enabling smarter environments, automated systems, and data-driven decision-making across industries. As it evolves, it will play a central role in future technologies like smart cities, autonomous vehicles, and AI-powered environments.<\/p>\n <\/div>\n \n <\/div>\n\n \n
\n\n\nSession 15<\/span>Cloud computing and edge computing<\/h3>\n <\/div>\n\n
\nCloud computing and edge computing are modern computing paradigms that address how data is processed, stored, and accessed across networks\u2014but with different approaches to location, latency, and scalability.<\/p>\n
Cloud computing delivers computing services\u2014such as servers, storage, databases, networking, software, and analytics\u2014over the internet (“the cloud”). It allows users to access powerful infrastructure without managing physical hardware.
\nEdge computing pushes computation and data storage closer to the data source (i.e., at the “edge” of the network), rather than relying solely on centralized cloud servers.<\/strong><\/p>\n <\/div>\n \n <\/div>\n\n \n\n\n\nSession 16<\/span>Networking & Distributed Systems<\/h3>\n <\/div>\n\n
\nNetworking and Distributed Systems are areas of computer science that focus on how multiple computers communicate and collaborate to perform tasks, share resources, and maintain system reliability\u2014often across geographic distances.
\nComputer networking involves the design and management of communication systems that allow data to be transferred between devices. It forms the backbone of the internet and most modern computing environments.
\nDistributed systems consist of multiple interconnected computers that work together as a single system. These systems aim to provide scalability, fault tolerance, and resource sharing across various nodes.<\/p>\n <\/div>\n \n <\/div>\n\n \n\n\n\nSession 17<\/span>Formal methods and logic in computing<\/h3>\n <\/div>\n\n
\n\n\n\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n\n\nFormal methods and logic in computing refer to the use of mathematically-based techniques for the specification, development, and verification of software and hardware systems. They help ensure that systems are correct, reliable, and secure, particularly in safety-critical domains.
\nFormal methods involve creating precise mathematical models of software or hardware behavior. These models allow developers to prove properties such as correctness, consistency, and absence of bugs before systems are implemented or deployed.
\nLogic forms the foundation of formal methods and is used to reason about computation, algorithms, and system behavior.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n <\/div>\n \n <\/div>\n\n \n\n\n\nSession 18<\/span>Computational complexity<\/h3>\n <\/div>\n\n
\nComputational complexity is a field in theoretical computer science that studies the inherent difficulty of computational problems and classifies them based on the resources required to solve them, such as time (how fast) and space (how much memory).
\nComputational complexity provides a deep theoretical framework for understanding the limits of computation. It tells us not only how to solve problems, but whether some problems are even solvable within practical constraints.<\/p>\n\u00a0<\/strong><\/p>\n <\/div>\n \n <\/div>\n\n \n
\n\n\nSession 19<\/span>Algorithms and data structures<\/h3>\n <\/div>\n\n
\nAlgorithms and data structures are fundamental concepts in computer science that deal with how data is organized and how computational problems are solved efficiently.
\nAn algorithm is a step-by-step procedure or set of rules designed to perform a specific task or solve a problem. Algorithms are evaluated based on their correctness, efficiency (in terms of time and space), and scalability.
\nA data structure is a way of organizing and storing data so it can be accessed and modified efficiently. The choice of data structure affects the performance of algorithms.<\/p>\n <\/div>\n \n <\/div>\n\n \n\n\n\nSession 20<\/span>Theoretical Computer Science<\/h3>\n <\/div>\n\n
\n\n\n\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n\n\nTheoretical Computer Science (TCS) is the branch of computer science that focuses on the mathematical and abstract foundations of computing. It seeks to understand the limits of what computers can do, how efficiently problems can be solved, and how to model computation itself.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n <\/div>\n \n <\/div>\n\n <\/div>\n <\/div>\n <\/div>\n
\n\n\n \n\n\n\nSession 21<\/span>Human-robot interaction<\/h3>\n <\/div>\n\n
\n\n\n\nHuman-Robot Interaction (HRI) is an interdisciplinary field focused on studying, designing, and evaluating the ways humans and robots communicate, collaborate, and coexist safely and effectively.
\nHuman-Robot Interaction is critical to making robots more accessible, trustworthy, and effective partners in various aspects of daily life and work. By improving HRI, we can foster smoother, safer, and more meaningful collaboration between humans and machines.<\/p>\n<\/div>\n<\/div>\n<\/div>\n <\/div>\n \n <\/div>\n\n \n\n\n\nSession 22<\/span>Software Engineering<\/h3>\n <\/div>\n\n
\nSoftware engineering is the systematic application of engineering principles to the design, development, testing, deployment, and maintenance of software systems. It aims to produce high-quality, reliable, and scalable software in a cost-effective and efficient manner<\/p>\n <\/div>\n \n <\/div>\n\n \n
\n\n\nSession 23<\/span>Automated testing and debugging<\/h3>\n <\/div>\n\n
\n\n\n\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n\n\nAutomated testing and debugging are essential practices in software development aimed at improving code quality, reliability, and development efficiency by using tools and scripts to detect errors and ensure correct behavior without manual intervention.
\nAutomated testing involves writing test scripts that automatically execute and verify<\/strong> whether a program behaves as expected. These tests can be run repeatedly, especially after code changes, to catch regressions and bugs early in the development process.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n <\/div>\n \n <\/div>\n\n \n\n\n\nSession 24<\/span>Code generation using AI<\/h3>\n <\/div>\n\n
\nCode generation using AI refers to the use of artificial intelligence\u2014particularly machine learning and natural language processing models\u2014to automatically write, complete, or suggest code based on human input or existing code context.<\/p>\n
AI code generation systems can take natural language prompts (e.g., \u201cwrite a function to sort a list\u201d) or partial code snippets and generate functional code in a target programming language. These tools are trained on vast code repositories and can understand patterns, syntax, and even coding best practices.<\/p>\n <\/div>\n \n <\/div>\n\n \n
\n\n\nSession 25<\/span>Human-Computer Interaction (HCI)<\/h3>\n <\/div>\n\n
\n\n\n\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n\n\n - Scientific Session Day 2<\/span><\/a><\/li>\n