Invited Talks
Soumith Chintala
I am an AI researcher, engineer and community builder. I am currently at Meta and NYU, jumping between Engineering, Research and Leadership as I find convenient. I currently lead PyTorch and AI Infra-related things at Meta. These days I increasingly dabble with robots, at NYU. I specialize in machine learning platforms and generative models.
Ion Stoica
My area of research is at the intersection between AI and systems, cloud computing, and distributed systems. I am equally interested in designing algorithms and systems with strong theoretical foundations, and in providing practical implementations that are deployable in the real world.
The scaling of large language models has led to impressive gains in language understanding, but at a cost of insatiable memory and bandwidth requirements. We take a principled approach of designing optimization and quantization algorithms that can reduce memory requirements without sacrificing accuracy. This includes gradient compression methods (GaLore, SignSGD) and logarithmic number system for representation. We also design fine-grained memory reduction schemes such as KV cache compression, chunking and offloading to overcome memory bottlenecks in language models, especially in the reasoning mode where current memory requirements are massive. Such principles are broadly applicable and especially relevant to physical AI where the memory and bandwidth requirements are even greater than frontier LLMs.
Animashree Anandkumar
Professor Anandkumar's research interests are in the areas of large-scale machine learning, non-convex optimization and high-dimensional statistics. In particular, she has been spearheading the development and analysis of tensor algorithms for machine learning. Tensor decomposition methods are embarrassingly parallel and scalable to enormous datasets. They are guaranteed to converge to the global optimum and yield consistent estimates for many probabilistic models such as topic models, community models, and hidden Markov models. More generally, Professor Anandkumar has been investigating efficient techniques to speed up non-convex optimization such as escaping saddle points efficiently.
The human-like generative ability of Large Language Models (LLMs) has ushered in a new era of foundational models and generative AI, unlocking new possibilities and driving cross-domain innovations. However, the transformative potential of LLMs has been seriously challenged the problematic hallucinations of LLMs, which may lead to misinformation, biases, harmful content, making responsible finetuning of LLMs a grand challenge. Safety alignment of pretrained LLMs represents an important step forward to ensure their outputs being helpful, harmless, and honest, respecting human preferences and societal values. However, recent studies have shown that many safety-aligned LLMs suffer from security/privacy/ethic risks of user finetuning: the well-aligned LLMs can easily be broken and produce harmful, helpless or untruthful content in the presence of a small amount of harmful finetuning data. In this keynote, I will discuss some potential vulnerabilities and risks of existing safety alignment and finetuning techniques, and share some of our recent research efforts towards developing a responsible framework and techniques for more robust alignment/finetuning of LLMs.
Ling Liu
Ling Liu is a Professor in the School of Computer Science at Georgia Institute of Technology. She directs the research programs in the Distributed Data Intensive Systems Lab (DiSL), examining various aspects of Internet-scale big data powered artificial intelligence (AI) systems, algorithms and analytics, including performance, reliability, privacy, security and trust. Her research in the ML systems area is mainly centered on efficient AI systems and Algorithms, as well as trustworthy AI through developing AI security and AI privacy guardrails. Prof. Ling Liu’s current research is primarily supported by National Science Foundation under CISE programs, CISCO and IBM.