Machine Learning for Biotech, Biopharma, and ML Safety Sessions Coming to ODSC East 2024
Machine learning has applications for a wide range of industries and fields. In particular, it underpins many cutting-edge developments in the realms of safety and security and biotech and pharma. At ODSC East this April 23–25, you’ll find a wide range of sessions exploring these different applications. Check them out below.
Get your ODSC East 2024 pass today!
In-Person and Virtual Conference
April 23rd to 25th, 2024
Join us for a deep dive into the latest data science and AI trends, tools, and techniques, from LLMs to data analytics and from machine learning to responsible AI.
Data Science in the Biotech/Pharma Research Organization
Eric Ma, PhD | Author of nxviz Package | Principal Data Scientist | Moderna
Explore the ways that data science can be leveraged by biotech and pharmaceutical research organizations. In particular, you’ll discuss:
- What the core mission of a data science team should be
- The ways a data science team can deliver value to the research organization
- Major classes of problems and methods
- The unique challenges data science organizations face in the _research_ space
Leave with an understanding of the frameworks for incorporating data science teams into biotech and pharma research.
Harnessing Machine Learning to Understand SARS-CoV-2 Variants and Hospitalization Risk
Tomasz Adamusiak, MD, PhD | Chief Scientist, Clinical Insights & Innovation Cell | MITRE
Take a deep dive into the potential ways machine learning could transform the biotech and pharma industries. You’ll discuss how ML can be harnessed to accelerate drug discovery, enhance personalized medicine, improve patient outcomes, and drive innovation. Form a practical understanding of the tools and techniques for, and challenges of integrating ML into existing bioinformatics workflows.
Introduction to Protein Language Models for Synthetic Biology
Etienne Goffinet, PhD | Senior Researcher | Technology Innovation Institute
Familiarize yourself with Protein Language Models, the ways they differ from NLP counterparts, and where they excel. In the hands-on portion, you’ll get practical experience working with a Protein Language Model by building a basic protein function multi-label classifier.
Overcoming the Limitations of LLM Safety Parameters with Human Testing and Monitoring
Josh Poduska | AI Advisor | Applause
Peter Pham | Senior Program Manager | Applause
Explore a new approach to address the concerns of ensuring safety, fairness, and responsibility. This comprehensive strategy leverages both crowd-sourced and professional testers from a wide range of locations, countries, cultures, and experiences. Learn how to scrutinize LLM and LLM application input and output spaces and guarantee responsible and safe product delivery.
Bringing Precision Medicine to the Field of Mental Healthcare through Large Language Models, AI, and Psychedelics
Gregory Ryslik, PhD | Chief Technology Officer | Compass Pathways
This session will investigate the slow pace of progress in the mental health field and its causes from lack of new drugs to lack of precision diagnostic tools. You’ll also examine new solutions in development, from psychedelic-based treatments to the diagnostic potential of generative AI and large language models.
How to Preserve Exact Attribution through Inference in AI: Get the Correct Explanations and Preserve Privacy via Instance-Based Learning
Chris Hazard, PhD | CTO and Co-founder | Howso
Recent years have seen an increased need for full data transparency and explainability to mitigate against bias, incorrect information, and hallucinations — as well as increasing demands for privacy. In this session, you’ll learn how instance-based learning (IBL) can be used to address these challenges from CS and AI expert, Dr. Chris Hazard.
I don’t always secure my ML models, but when I do…
Hailey Buckingham | Director of Data Science | HiddenLayer
There are many reasons for ML Ops teams, ML Engineers, and data scientists to be involved in security thinking and planning, from insights into user behavior to encouraging good operational habits. This talk will discuss how using security tools specifically designed for AI can precipitate a number of additional benefits that are likely already on the ML teams’ wishlist. These same tools will also help increase collaboration with security teams and improve the organization’s security posture.
Practical Considerations for Machine Learning in Fraud Prevention Programs
Kwan Lin | Principal Data Scientist | MoonPay
Join this presentation for an overview of how machine learning is used in the web3 space to detect and prevent fraud in a financial technology company. You’ll discuss how the case use of fraud shapes building and deploying machine learning models. By the end of the session, you should understand how an ML program can be used to prevent fraud.
Trojan Model Hubs: Hacking ML Supply Chains & Defending Yourself from Threats
William Armiros | Senior Software Engineer | Protect AI
Sam Washko | Software Engineer | Protect AI
Explore two strategies for mitigating the risk of Model Serialization Attacks (MSA), as well as other attacks using compromised artifacts: model scanning and cryptographic signing. You’ll learn how scanning the model before deserialization and enable you to examine its operators and layers to see if it is compromised by suspicious code. The second method enables the model to be signed on creation, making it clear which models come from trusted sources.
2024 Data Engineering Summit tickets available now!
In-Person Data Engineering Conference
April 23rd to 24th, 2024 — Boston, MA
At our second annual Data Engineering Summit, Ai+ and ODSC are partnering to bring together the leading experts in data engineering and thousands of practitioners to explore different strategies for making data actionable.
Navigating the Landscape of Responsible AI: Principles, Practices, and Real-World Applications
Rajiv Avacharmal | Corporate Vice President | New York Life Insurance
Join this session to explore the fundamental principles of Responsible AI: fairness, transparency, accountability, and privacy. These four principles are essential for creating AI systems that are free of bias, explainable, and reflect the values of society You’ll also discuss the practical strategies and tools for implementing Responsible AI, including techniques for mitigating bias in AI models, such as diverse and inclusive datasets, algorithmic fairness metrics, and continuous testing and monitoring.
Sign me up!
Take a deep dive into the world of Machine Learning for Safety and Security and Machine Learning for Biotech and Pharma at their respective tracks at ODSC East this month. Be sure to register ASAP before tickets sell out!
Originally posted on OpenDataScience.com
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