PyTorch Conference

Kismat Singh is the VP of Engineering for AI Frameworks at Intel. He brings over two decades of AI experience and has also worked at companies such as Nvidia, AMD, HP and Stream Processors Inc. Kismat has made significant contributions to industry leading deep learning libraries and compilers along with improvements to hyperscale training resiliency. Kismat strongly believes that democratizing PyTorch will unlock some compelling use cases and help us realize the full value of AI advancements.

Mudhakar Srivatsa is a distinguished research staff member at the Distributed Cloud department in IBM T. J. Watson Research Center. His work is focussed on heterogeneous spatiotemporal data with applications to edge computing, AIOps and Hybrid AI Scaling. He is an IBM master inventor, authored over 200 research papers, 100 granted US patents, recipient of one IBM corporate award, one IBM Recognition Experience Honoree, nine IBM outstanding technical achievement awards and three IBM research division awards and has transitioned major software artifacts to various IBM products including: Hybrid Cloud (Cloud Pak for Data, Watson Studio, Watson Discovery, Cloud SQL Query, IBM Analytics engine), Data & AI (IBM Streams, SPSS Modeler, Db2 warehouse, Db2 BLU, Db2 event store, IBM Integrated Analytics System, Cognos and Planning Analytics), Automation (Watson AIOps, Instana SmartAlert open beta), AI Applications (Maximo) and Systems(db2 for i, Spectrum Scale). These machine learning algorithms have been used in production environment in various domains such as: CodeFlare (blogs, github), AIOps, remote sensing data, AI-assisted air traffic control (video), NASA space app challenge (github, video, blog), customer care analytics, maritime piracy, music festivals, connected vehicles, smart wildlife, and predicting asteroid encounters.

He serves as a technical area leader for Secure Hybrid Networks research in International Technology Alliance in Distributed Analytics and Information Sciences (DAIS-ITA), a research consortium formed from sixteen US and UK industrial and academic members (2016-21). He also served a principal investigator for Information Network Research in Network Science Collaborative Technology Alliance (2009-19). He serves on the editorial board for IEEE Transactions on Emerging Topics in Computing and serves as the program co-chair on IEEE SMDS 2021.

Prior to joining IBM, he received a B.Tech in Computer Science and Engineering from IIT Madras with a minor in Operations Research in 2002, and a PhD in Computer Science from Georgia Tech in 2007.

CTO @ Lightning AI, Founder (Orobix, Tensorwerk), early PyTorch core contributor, Manning Author (Deep Learning with PyTorch). PhD in Bioengineering.

Piotr joined the PyTorch team at NVIDIA in 2019 and currently manages the team. He drives NVIDIA’s effort in maintaining and advancing PyTorch’s CUDA backend and received the PyTorch SUPERHERO award in 2023 for his community contributions especially in the PyTorch discussion board. As a Core Maintainer, he is also focussed on PyTorch’s long-term vision and development.

Dr. Peng Wu is the engineering manager of the PyTorch Compiler team at Meta. Dr. Wu spent over a decade at IBM research, working on many aspects of programming systems. She then founded the Programming Technologies Lab at Huawei and led its growth for six years. At Meta, she supported the team’s search for the right compiler solutions for PyTorch over the last four years, culminating in the release of PyTorch 2.0 in 2023. She holds a PhD in Computer Science from the University of Illinois, Urbana-Champaign.

Will Constable works on PyTorch Distributed Algorithms and Infrastructure at Meta as an IC and Tech Lead. Previously, he worked at Intel and Nervana Systems on different parts of the Deep Learning SW stack including Compiler Frontends, Integrations to TensorFlow and PyTorch, Distributed Training libraries, and worked closely with hardware designers for the Intel-Nervana accelerator.

Kartikay Khandelwal is a software engineer in the PyTorch and AI Infra team at Meta where he leads the development of the PyTorch ecosystem for GenAI, including open-source libraries like torchtune for LLM fine-tuning and torchchat for LLM inference. Prior to PyTorch, he worked in AI Research at Meta where he focused on developing state-of-the-art models for cross-lingual and multilingual understanding like XLM-R, and the infrastructure needed to deploy these models at scale.

Mengtao (Martin) Yuan is a Tech Lead Manager in Meta’s PyTorch Edge team. With multiple years of experience in the AI industry, Mengtao is focused at building software systems to help AI researchers and engineers to deploy their models on edge devices such as mobile phones, AR/VR wearables and embedded devices. His current focus areas are on on-device generative AI for both inference and fine tuning.

Chip Huyen works to accelerate data analytics on GPUs at Voltron Data. She also advises companies on building AI platforms. Previously, she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup (acquired), and taught Machine Learning Systems Design at Stanford. She’s the author of the book Designing Machine Learning Systems, an Amazon bestseller in AI. Her new book, AI Engineering, is scheduled to come out in late 2024.

Sebastian Raschka, PhD, has been working in machine learning and AI for more than a decade. In addition to being a researcher, Sebastian has a strong passion for education. He is known for his bestselling books on machine learning with Python and his contributions to open source.

Sebastian is a Staff Research Engineer at Lightning AI, focusing on implementing and training large language models. Before his industry experience, Sebastian was an assistant professor in the Department of Statistics at the University of Wisconsin–Madison, where he focused on deep learning research. You can learn more about Sebastian at https://sebastianraschka.com.

Hanna Hajishirzi is the Torode Family Associate Professor in the Allen School of Computer Science and Engineering at the University of Washington and a Senior Director of NLP at AI2.  

Her current research delves into various domains within Natural Language Processing (NLP) and Artificial Intelligence (AI), with a particular emphasis on accelerating the science of language modeling, broadening their scope, and enhancing their applicability and usefulness for human lives. She has published over 140 scientific articles in prestigious journals and conferences across ML, AI, NLP, and Computer Vision. She is the recipient of numerous awards, including the Sloan Fellowship, NSF CAREER Award, Intel Rising Star Award, Allen Distinguished Investigator Award, Academic Achievement UIUC Alumni Award, and was a finalist for the Innovator of the Year Award by GeekWire. The work from her lab has been nominated for or has received best paper awards at various conferences and has been featured in numerous magazines and newspapers.

Ion Stoica is a Professor in the EECS Department at the University of California at Berkeley, and the Director of Sky Computing Lab (https://sky.cs.berkeley.edu/). He is currently doing research on cloud computing and AI systems. Past work includes Ray, Apache Spark, Apache Mesos, Tachyon, Chord DHT, and Dynamic Packet State (DPS). He is an Honorary Member of the Romanian Academy, an ACM Fellow and has received numerous awards, including the Mark Weiser Award (2019), SIGOPS Hall of Fame Award (2015), and several “Test of Time” awards. He also co-founded three companies, Anyscale (2019), Databricks (2013) and Conviva (2006).

Tri Dao is an Assistant Professor at Princeton University and chief scientist of Together AI. He completed his PhD in Computer Science at Stanford, co-advised by Christopher Ré and Stefano Ermon. He works at the intersection of machine learning and systems, and his research highlights include FlashAttention and Mamba.

Jerry is the co-founder/CEO of LlamaIndex, the data framework for building LLM applications. Before this, he has spent his career at the intersection of ML, research, and startups. He led the ML monitoring team at Robust Intelligence, did self-driving AI research at Uber ATG and worked on recommendation systems at Quora.

Sara Hooker leads Cohere For AI, the dedicated research arm of Cohere. Cohere For AI seeks to solve complex machine learning problems and supports fundamental research that explores the unknown. With a long track-record of impactful research at Google Brain, Sara brings a wealth of knowledge from across machine learning. Her work has focused on model efficiency training techniques and optimizing for models that fulfill multiple desired criteria — interpretable, efficient, fair and robust. Sara leads a team of researchers and engineers working on making large language models more efficient, safe and grounded. Sara is currently on Kaggle’s ML Advisory Research Board and serves on the World Economic Forum council on the Future of Artificial Intelligence.

James is Head of Compute at Anthropic, where he is focused on ensuring that the company has the accelerator resources it needs to pursue its mission, and that the resources can be used effectively and efficiently across the organization. He joined in 2023 from Google DeepMind, where he was a member of the JAX Engagements Team focused on large-scale LLM users.

Aparna is VP Engineering at Meta, responsible for AI Infrastructure, Data Infrastructure and Developer Infrastructure. Over the last eight years at Meta, Aparna has built a world-class team that is responsible for some of the largest scale systems on the planet – to process exabyte-scale data, to train models like Llama and to serve models to enable Meta’s products. She’s also responsible for industry-leading development infrastructure and open-source frameworks, such as PyTorch, used both by Meta and by the rest of the industry to further research in AI.

Kush R. Varshney is an IBM Fellow based at the IBM T. J. Watson Research Center where he is responsible for leading innovations in AI governance. He and his team developed the well-known open-source toolkits AI Fairness 360, AI Explainability 360, and Uncertainty Quantification 360. He independently published the book Trustworthy Machine Learning in 2022.

Aleksander Mądry is a Member of Technical Staff at OpenAI. Aleksander is also a Professor of Computing at MIT (currently on leave), where he has been serving as the Director of the MIT Center for Deployable Machine Learning and a Faculty Co-Lead of the MIT AI Policy Forum.

Cormac Brick is a principal Engineer at Google working on frameworks and on device machine learning.   He has over 10 years experience in AI software, silicon and systems, with work spanning AI frameworks and ecosystems and compilers down to silicon microarchitecture.   Over that time projects have included AI Edge Torch (Google),  leading silicon & software architecture for Intel’s first 2 NPU architectures (meteorlake, lunarlake), and building a demo of googlenet running on edge silicon on a USB key at NeurIPs in 2016 while at Movidius.

Kate Rooney is a technology reporter based out of CNBC’s San Francisco bureau, covering Amazon, financial technology, payments and venture capital for the network. She also writes for CNBC’s digital platforms.

Rooney won a National Headliner Award for her Celsius coverage in 2023. She joined CNBC in 2015 as a news associate before working as a producer for CNBC’s “Squawk Box” (M-F, 6AM-9AM ET) and was most recently a markets reporter for CNBC.com.

She graduated from Boston College with a bachelor’s degree in communication and earned her master’s degree from the Medill School of Journalism at Northwestern University where she received an Eric Lund Global Reporting and Research Grant to film and produce a documentary in the Philippines. She also worked as a multimedia reporter in Buenos Aires, Argentina in 2015 with a focus on housing and politics.

I am the Society Lead at the Stanford Center for Research on Foundation Models (CRFM). I am completing my PhD at Stanford Computer Science, advised by Percy Liang and Dan Jurafsky. Funding: Lieberman Fellowship (active), NSF Graduate Research Fellowship (completed).

Prior to Stanford, I began research at Cornell (BA Math, BA CS, MS CS) under Claire Cardie. I am honored to have worked with the late Professor Arzoo Katiyar. Cornell CS holds a special place in my heart: the department wrote this about my journey. [Profile] [Profile 2]

I research the societal impact of AI, especially foundation models. My research has been featured in The Atlantic, Axios, Bloomberg, Euractiv, Fast Company, Financial Times, Fortune, The Information, MIT Technology Review, Nature, The New York Times, Politico, Quanta, Rappler, Reuters, Tech Policy Press, VentureBeat, The Verge, Vox, The Wall Street Journal and The Washington Post.

Navigating the cloud native universe with a knack for puns and a keen eye for psychology. Living in the heart of LA, I blend tech innovation with mental insights, one punny cloud at a time. Avid reader, thinker, and cloud whisperer.