Title: Thinking Geometrically in the World of Data-driven Models Abstract: As learning models grow in scale and capacity, a natural question arises: is it still relevant to think geometrically, or should we seek more data? In this talk, I will argue that geometry remains essential, not just for interpretability, but for enabling control, structure, and… Read More »Thinking Geometrically in the World of Data-driven Models by Niloy J. Mitra

Title: Dot-Product Proofs Speaker: Prahladh Harsha (TIFR Mumbai):  Details: July 28 (Monday) | 11:30 AM | Bharti 404 Abstract: A dot-product proof is a simple probabilistic proof system in which the verifier decides whether to accept an input vector based on a single linear combination of the entries of the input and a proof vector. In this… Read More »Dot-Product Proofs by Prahladh Harsha

Title: Multiturn Evals (and RL) for LLMs Details: 8th August, 12 pm, SIT001 Abstract: LLMs often fail at multi-step tasks requiring memory and strategic planning, a gap not captured by traditional single-turn evals. To address this, we've developed a suite of human and automated evals that stress test Gemini on these capabilities. This talk will cover… Read More »Multiturn Evals (and RL) for LLMs by Kartikeya Badola

Title: Explainable AI for Malware Analysis   Abstract: In recent years, explainable artificial intelligence (XAI) has become a critical component of ensuring transparency and trust in machine learning systems, particularly in high-stakes domains like cybersecurity. This talk will begin with a basic introduction to XAI, highlighting its importance in understanding model decisions, especially in the context of… Read More »Explainable AI for Malware Analysis by Mohd Saqib

Speaker: Akash Kumar (Ruhr University Bochum) Details: Tue, 12 Aug, 3:30 PM, SIT 001 Abstract: The widespread adoption of Artificial intelligence and Machine Learning (AI/ML) models across various fields, such as healthcare, autonomous vehicles, smart agriculture, and industrial automation, has led to a growing demand for efficient and scalable AI/ML solutions. However, as AI/ML algorithms… Read More »Enabling Energy-efficient AI Computing: Leveraging Application-specific Approximations by Akash Kumar

Title: A brief survey of quantum numerical algorithms Speaker: Prof. Pranav Singh Details: August 14 (Thursday) | 12(noon)-1 PM | Bharti 501 Abstract: Quantum Numerical Algorithms (QNA) encompass a broad class of techniques including quantum numerical linear algebra (QNLA), quantum optimization, quantum variational algorithms (QVA), quantum machine learning (QML), and Hamiltonian simulation (HS). These areas… Read More »A brief survey of quantum numerical algorithms by Pranav Singh