The rapid advancement in VLSI technology and system integration has enabled the design of chips with more than a billion transistors. A specialized one-month summer training program from 19th June to 18th July 2026 has been scheduled at IIIT-A. The 11th edition of STPVD 2026 is technically co-sponsored by IEEE Circuit and System Society, Chapter UP Section. This training program will cover analog and digital integrated circuit design using commercial technology kits and simulation tools, and exposure to fabrication methods and PCB design. Experts from Industry and academia shall train the registered applicants.

This comprehensive program takes you on a journey through the theoretical foundations and practical applications of Artificial Intelligence, equipping you with the skills and knowledge in high demand across industries.

The rapid advancement of Artificial Intelligence (AI) and Deep Learning (DL) has significantly transformed various sectors, including computer vision, healthcare, finance, transportation, manufacturing, and education. To harness this transformative potential, it is essential to equip young researchers and students with advanced knowledge and hands-on skills in Deep Learning.

About the workshop: Algebra and Analysis are two fundamental pillars of Mathematics, forming the foundation of numerous advanced areas with significant applications across almost all disciplines in which Mathematics is involved. The main aim of this training program is to provide students with a comprehensive understanding of fundamental concepts and applications of algebra and analysis through a series of structured lectures, interactive discussions, and collaborative problem-solving sessions, and prepare them for competitive exams such as JAM, NET, GATE, and NBHM Fellowship.

ADASIVA is an annual short-term course organized by the Computer Vision and Biometrics Lab. The course will be useful for PG and PhD Students and Young Professionals working with recent state-of-the-art methods in AI/ML/DL for a variety of applications in signal, image, and biometric processing, and will address how these approaches can be applied to real-world research problems. The course covers the essentials of machine learning and deep neural networks, as well as other models and their applications to practical problems in signal, image, and computer vision.

The Department of Applied Sciences, Indian Institute of Information Technology Allahabad (IIIT-A) is going to organise a workshop on Machine Learning in Survival Analysis with Hands-on R during July 06-11, 2026. This workshop integrates classical survival analysis with modern machine learning techniques for time-to-event data. Topics include regularized Cox models, survival trees, random survival forests, gradient boosting. Applications span healthcare, reliability engineering, finance, and social sciences. In the workshop, the participants will get enough exposure to build survival prediction models using ML methods, handle high-dimensional and non-linear survival data.

Generative Artificial Intelligence (GenAI) encompasses a class of computational models capable of producing novel content across multiple modalities, including text, images, audio, video, and synthetic data. These systems acquire representational knowledge from extensive training datasets through advanced deep learning techniques and subsequently generate outputs that exhibit statistical and structural similarity to the learned data distributions. The operational workflow of generative AI can be conceptualized in three primary phases: model training, task-specific tuning, and content generation. During the training phase, large-scale “foundation models” are developed by exposing neural architectures to diverse and voluminous datasets. The tuning phase further adapts these models to specialized tasks through approaches such as fine-tuning or reinforcement learning from human feedback. In the final phase, the model produces outputs that are iteratively evaluated and refined, often supplemented by mechanisms such as Retrieval-Augmented Generation (RAG) to enhance factual grounding and reduce error propagation.