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Date: Wednesday, September 27, 2023
Time: 11:30 a.m. – 2:00 p.m. PT
Duration: 2.5 Hours

As practitioners, data scientists, engineers, and researchers building effective modern recommendation systems, we understand that simple collaborative filtering models are not enough to accelerate recommender workloads. There is a gap between a few simple models and a production system that serves up relevant recommendations at any scale, including workloads that have the potential to surpass the biggest large language models in use today. Our daily work includes addressing the gap and its challenges. While adjacent domains are under spotlight for end-user accountability for their outputs or retrieved responses on a massive corpus of data, we already know as recommender system builders, how relevant recommendations engage with a person multiple times a day and have business impact on companies within media, retail, finance, and more. Our work on recommender systems continues to drive the digital economy.

Join us online with fellow data scientists, engineers, and researchers to learn, discuss, and iterate on best practices, methods, and techniques to build effective modern recommendation systems at any scale.

At this event you will learn:
  • best practices from experts, and proven techniques and methods within industry
  • learnings on leveraging session-based recommenders on a multilingual ecommerce dataset
  • accelerate performance on massive datasets and recommender workloads
Download NVIDIA Merlin on Github or contact NVIDIA for free access to Next Item Prediction Workflow.

Event Registration


You will receive an email with instructions on how to join the Event shortly.

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DGX Station Datasheet

Get a quick low-down and technical specs for the DGX Station.
DGX Station Whitepaper

Dive deeper into the DGX Station and learn more about the architecture, NVLink, frameworks, tools and more.
DGX Station Whitepaper

Dive deeper into the DGX Station and learn more about the architecture, NVLink, frameworks, tools and more.
DGX Station Whitepaper

Dive deeper into the DGX Station and learn more about the architecture, NVLink, frameworks, tools and more.


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Ding Tong
Senior Research Scientist, Netflix
Ding Tong is a Senior Research Scientist at Netflix. Her current focus is on the research and development of Netflix Homepage Recommender Systems. Prior to Netflix, she was a Senior Applied Scientist at Uber, tech leading the real-time Uber Eats Promotion Personalization Service.
Surya Kallumadi
Director of Applied Research , Lowe's
Surya Kallumadi is a Director of Applied Research at Lowe's, where he leads the machine learning initiatives in core search, recommendations, and personalization. Prior to that he was with the Search Science team at The Home Depot leading the search ranking initiatives. In the past he has worked with the Data Science teams at Flipkart and eBay in the fields of Search and Query understanding and has a PhD from Kansas State University.
Bernard Kleynhans
Director Fidelity
Bernard is a Director in the AI Center of Excellence at Fidelity Investments working on personalization and recommender systems. His work is primarily concentrated in recommender systems and optimization, and he regularly presents on these topics, most recently at IJCAI'21 and CPAIOR'21 conferences. He is the lead developer of the open-source libraries Selective, MABWiser and Mab2Rec. He holds a MS in Computational Science and Engineering from Harvard University.
Jacopo Tagliabue
Founder, Bauplan
Jacopo Tagliabue is the founder of Bauplan. Educated in several acronyms across the globe (UNISR, SFI, MIT), Jacopo was co-founder of Tooso, a startup acquired by Coveo. He led Coveo’s AI roadmap from scale-up to IPO, and built out Coveo Labs, an R&D practice rooted in open source and open science, with works that appeared in venues such as NAACL, RecSys, WWW, SIGIR. While building his new company, Bauplan, he is teaching ML Systems at NYU, which is mostly notable because it is the only job he ever had that his parents understand.
Ronay Ak
Senior Data Scientist , NVIDIA
Ronay Ak is a senior data scientist at NVIDIA working on deep learning-based recommender systems. Previously, she worked as a research associate at the National Institute of Standards and Technology (NIST). She received her PhD in energy and power systems (engineering) discipline from CentraleSupelec in Paris, France. She was part of the teams that won the WSDM WebTour Workshop Challenge 2021 by and the SIGIR eCommerce Workshop Data Challenge 2021 by Coveo.
Benedikt Schifferer
Manager, Deep Learning , NVIDIA
Benedikt Schifferer is a manager of a deep learning team at NVIDIA working on recommender systems. Prior to his work at NVIDIA, he graduated with a master of science in data science from Columbia University, New York, and developed recommender systems for a German ecommerce company. He was part of the NVIDIA AI team that won the WSDM WebTour Workshop Challenge 2021 by and KDD Cup 2023 competitions.
Even Oldridge
Director, NVIDIA
Even Oldridge is the director of the NVIDIA Merlin team at NVIDIA and is passionate about enabling recommender systems on the GPU and making RecSys accessible to a broader range of people. He was a co-chair of the industry track for ACM RecSys 2021-2022 and the author of a number of papers and patents in the space. He has a PhD in computer vision from the University of British Columbia and a masters from the same in hardware systems design

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Date & Time: Wednesday, April 22, 2018