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Title & Speakers	Event
[AI Alliance Materials] Discrete State-Space Diffusion and Flow Models 2025-05-15 · 14:00 External registration https://44725920.hs-sites.com/ai-alliance-material-chemistry-webinar-5-15 Unlocking Guidance for Discrete State-Space Diffusion and Flow Models Many scientific tasks, such as protein engineering and small-molecule drug discovery, can be formulated as conditional generation problems over discrete spaces. This talk introduces a new approach that enables tractable classifier and classifier-free guidance on discrete state-space diffusion and flow models. I will demonstrate how this method can be applied for conditional generation tasks in protein sequence, small-molecule graph, and DNA sequence design. Speaker Hunter Nisanoff recently graduated from his PhD in Computational Biology from UC Berkeley where he was advised by Professor Jennifer Listgarten. His research focuses on machine learning methods for protein engineering. Prior to his PhD, Hunter worked at D. E. Shaw Research developing machine learning and simulation-based methods for small-molecule drug discovery. Research publication https://arxiv.org/abs/2406.01572 About the AI Alliance The AI Alliance is an international community of researchers, developers and organizational leaders committed to support and enhance open innovation across the AI technology landscape to accelerate progress, improve safety, security and trust in AI, and maximize benefits to people and society everywhere. Members of the AI Alliance believe that open innovation is essential to develop and achieve safe and responsible AI that benefit society rather than benefit a select few big players.	[AI Alliance Materials] Discrete State-Space Diffusion and Flow Models
[AI Alliance Materials] Discrete State-Space Diffusion and Flow Models 2025-05-15 · 14:00 External registration https://44725920.hs-sites.com/ai-alliance-material-chemistry-webinar-5-15 Unlocking Guidance for Discrete State-Space Diffusion and Flow Models Many scientific tasks, such as protein engineering and small-molecule drug discovery, can be formulated as conditional generation problems over discrete spaces. This talk introduces a new approach that enables tractable classifier and classifier-free guidance on discrete state-space diffusion and flow models. I will demonstrate how this method can be applied for conditional generation tasks in protein sequence, small-molecule graph, and DNA sequence design. Speaker Hunter Nisanoff recently graduated from his PhD in Computational Biology from UC Berkeley where he was advised by Professor Jennifer Listgarten. His research focuses on machine learning methods for protein engineering. Prior to his PhD, Hunter worked at D. E. Shaw Research developing machine learning and simulation-based methods for small-molecule drug discovery. Research publication https://arxiv.org/abs/2406.01572 About the AI Alliance The AI Alliance is an international community of researchers, developers and organizational leaders committed to support and enhance open innovation across the AI technology landscape to accelerate progress, improve safety, security and trust in AI, and maximize benefits to people and society everywhere. Members of the AI Alliance believe that open innovation is essential to develop and achieve safe and responsible AI that benefit society rather than benefit a select few big players.	[AI Alliance Materials] Discrete State-Space Diffusion and Flow Models
[AI Alliance Materials] Discrete State-Space Diffusion and Flow Models 2025-05-15 · 14:00 External registration https://44725920.hs-sites.com/ai-alliance-material-chemistry-webinar-5-15 Unlocking Guidance for Discrete State-Space Diffusion and Flow Models Many scientific tasks, such as protein engineering and small-molecule drug discovery, can be formulated as conditional generation problems over discrete spaces. This talk introduces a new approach that enables tractable classifier and classifier-free guidance on discrete state-space diffusion and flow models. I will demonstrate how this method can be applied for conditional generation tasks in protein sequence, small-molecule graph, and DNA sequence design. Speaker Hunter Nisanoff recently graduated from his PhD in Computational Biology from UC Berkeley where he was advised by Professor Jennifer Listgarten. His research focuses on machine learning methods for protein engineering. Prior to his PhD, Hunter worked at D. E. Shaw Research developing machine learning and simulation-based methods for small-molecule drug discovery. Research publication https://arxiv.org/abs/2406.01572 About the AI Alliance The AI Alliance is an international community of researchers, developers and organizational leaders committed to support and enhance open innovation across the AI technology landscape to accelerate progress, improve safety, security and trust in AI, and maximize benefits to people and society everywhere. Members of the AI Alliance believe that open innovation is essential to develop and achieve safe and responsible AI that benefit society rather than benefit a select few big players.	[AI Alliance Materials] Discrete State-Space Diffusion and Flow Models
[AI Alliance Materials] Discrete State-Space Diffusion and Flow Models 2025-05-15 · 14:00 External registration https://44725920.hs-sites.com/ai-alliance-material-chemistry-webinar-5-15 Unlocking Guidance for Discrete State-Space Diffusion and Flow Models Many scientific tasks, such as protein engineering and small-molecule drug discovery, can be formulated as conditional generation problems over discrete spaces. This talk introduces a new approach that enables tractable classifier and classifier-free guidance on discrete state-space diffusion and flow models. I will demonstrate how this method can be applied for conditional generation tasks in protein sequence, small-molecule graph, and DNA sequence design. Speaker Hunter Nisanoff recently graduated from his PhD in Computational Biology from UC Berkeley where he was advised by Professor Jennifer Listgarten. His research focuses on machine learning methods for protein engineering. Prior to his PhD, Hunter worked at D. E. Shaw Research developing machine learning and simulation-based methods for small-molecule drug discovery. Research publication https://arxiv.org/abs/2406.01572 About the AI Alliance The AI Alliance is an international community of researchers, developers and organizational leaders committed to support and enhance open innovation across the AI technology landscape to accelerate progress, improve safety, security and trust in AI, and maximize benefits to people and society everywhere. Members of the AI Alliance believe that open innovation is essential to develop and achieve safe and responsible AI that benefit society rather than benefit a select few big players.	[AI Alliance Materials] Discrete State-Space Diffusion and Flow Models
[AI Alliance Materials] Discrete State-Space Diffusion and Flow Models 2025-05-15 · 14:00 External registration https://44725920.hs-sites.com/ai-alliance-material-chemistry-webinar-5-15 Unlocking Guidance for Discrete State-Space Diffusion and Flow Models Many scientific tasks, such as protein engineering and small-molecule drug discovery, can be formulated as conditional generation problems over discrete spaces. This talk introduces a new approach that enables tractable classifier and classifier-free guidance on discrete state-space diffusion and flow models. I will demonstrate how this method can be applied for conditional generation tasks in protein sequence, small-molecule graph, and DNA sequence design. Speaker Hunter Nisanoff recently graduated from his PhD in Computational Biology from UC Berkeley where he was advised by Professor Jennifer Listgarten. His research focuses on machine learning methods for protein engineering. Prior to his PhD, Hunter worked at D. E. Shaw Research developing machine learning and simulation-based methods for small-molecule drug discovery. Research publication https://arxiv.org/abs/2406.01572 About the AI Alliance The AI Alliance is an international community of researchers, developers and organizational leaders committed to support and enhance open innovation across the AI technology landscape to accelerate progress, improve safety, security and trust in AI, and maximize benefits to people and society everywhere. Members of the AI Alliance believe that open innovation is essential to develop and achieve safe and responsible AI that benefit society rather than benefit a select few big players.	[AI Alliance Materials] Discrete State-Space Diffusion and Flow Models
[AI Alliance Materials] Discrete State-Space Diffusion and Flow Models 2025-05-15 · 14:00 External registration https://44725920.hs-sites.com/ai-alliance-material-chemistry-webinar-5-15 Unlocking Guidance for Discrete State-Space Diffusion and Flow Models Many scientific tasks, such as protein engineering and small-molecule drug discovery, can be formulated as conditional generation problems over discrete spaces. This talk introduces a new approach that enables tractable classifier and classifier-free guidance on discrete state-space diffusion and flow models. I will demonstrate how this method can be applied for conditional generation tasks in protein sequence, small-molecule graph, and DNA sequence design. Speaker Hunter Nisanoff recently graduated from his PhD in Computational Biology from UC Berkeley where he was advised by Professor Jennifer Listgarten. His research focuses on machine learning methods for protein engineering. Prior to his PhD, Hunter worked at D. E. Shaw Research developing machine learning and simulation-based methods for small-molecule drug discovery. Research publication https://arxiv.org/abs/2406.01572 About the AI Alliance The AI Alliance is an international community of researchers, developers and organizational leaders committed to support and enhance open innovation across the AI technology landscape to accelerate progress, improve safety, security and trust in AI, and maximize benefits to people and society everywhere. Members of the AI Alliance believe that open innovation is essential to develop and achieve safe and responsible AI that benefit society rather than benefit a select few big players.	[AI Alliance Materials] Discrete State-Space Diffusion and Flow Models
[AI Alliance Materials] Discrete State-Space Diffusion and Flow Models 2025-05-15 · 14:00 External registration https://44725920.hs-sites.com/ai-alliance-material-chemistry-webinar-5-15 Unlocking Guidance for Discrete State-Space Diffusion and Flow Models Many scientific tasks, such as protein engineering and small-molecule drug discovery, can be formulated as conditional generation problems over discrete spaces. This talk introduces a new approach that enables tractable classifier and classifier-free guidance on discrete state-space diffusion and flow models. I will demonstrate how this method can be applied for conditional generation tasks in protein sequence, small-molecule graph, and DNA sequence design. Speaker Hunter Nisanoff recently graduated from his PhD in Computational Biology from UC Berkeley where he was advised by Professor Jennifer Listgarten. His research focuses on machine learning methods for protein engineering. Prior to his PhD, Hunter worked at D. E. Shaw Research developing machine learning and simulation-based methods for small-molecule drug discovery. Research publication https://arxiv.org/abs/2406.01572 About the AI Alliance The AI Alliance is an international community of researchers, developers and organizational leaders committed to support and enhance open innovation across the AI technology landscape to accelerate progress, improve safety, security and trust in AI, and maximize benefits to people and society everywhere. Members of the AI Alliance believe that open innovation is essential to develop and achieve safe and responsible AI that benefit society rather than benefit a select few big players.	[AI Alliance Materials] Discrete State-Space Diffusion and Flow Models
Event The Analytics Engineering Podcast 2022-06-17
Making Sense of the Last 2 Years in Data 2022-06-17 · 07:00 Jennifer Li – guest , Matt Bornstein – guest @ a16z , Tristan Handy – CEO @ dbt Labs , Martin Casado – guest @ a16z Matt Bornstein and Jennifer Li (and their co-author Martin Casado) of a16z have compiled arguably the most nuanced diagram of the data ecosystem ever made. They recently refreshed their classic 2020 post, "Emerging Architectures for Modern Data Infrastructure" and in this conversation, Tristan attempts to pin down: what does all of this innovation in tooling mean for data people + the work we're capable of doing? When will the glorious future come to our laptops? For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs. Analytics Analytics Engineering dbt	Listen
[COALESCE] Peeking Into the Future of Data Analytics w/ Julia 2021-12-09 · 22:46 Jennifer Li – guest , Sarah Catanzaro – guest , Astasia Myers – Investor @ Redpoint Ventures , Julia Schottenstein – Product Manager @ dbt Labs How is the data landscape evolving, what trends should you pay attention to and which should you ignore? In this panel, Julia Schottenstein (our fearless co-host and dbt Labs product manager) catches up with Sarah Catanzaro, Jennifer Li and Astasia Myers to dive into the trends playing out in our work. Register to catch the rest of Coalesce, the Analytics Engineering Conference, at https://coalesce.getdbt.com. The Analytics Engineering Podcast is brought to you by dbt Labs. Analytics Analytics Engineering Data Analytics dbt	Listen

The Future of the Data Warehouse 2020-12-10 · 22:45 Boris Jabes – CEO @ Census , Jeremy Levy – CEO @ Indicative , Jennifer Li – Partner @ a16z , Arjun Narayan – CEO @ Materialize Almost all of us are using our data warehouse to power our business intelligence, what if we could use data warehouses do even more? What if we could use data warehouses to power internal tooling, machine learning, behavioral analytics, or even customer-facing products? Is this a future we're heading for, and if so, how do we get there? In this video, you'll join a discussion with speakers: - Boris Jabes, CEO of Census - Jeremy Levy, CEO of Indicative - Arjun Narayan, CEO of Materialize - Jennifer Li, Partner at a16z as moderator Learn more about the speakers and their companies at: https://www.getcensus.com/ https://www.indicative.com/ https://materialize.com/ https://a16z.com/ Learn more about dbt at: https://getdbt.com https://twitter.com/getdbt Learn more about Fishtown Analytics at: https://fishtownanalytics.com https://twitter.com/fishtowndata https://www.linkedin.com/company/fishtown-analytics/ AI/ML Analytics BI dbt DWH	dbt Coalesce 2020 YouTube

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