Today’s guest believes that decoding the immune system is at the heart of improving drug efficacy. He is currently focused on this effort as the CEO and Co-founder of Immunai – a company that is building an AI model of the immune system to facilitate the development of next-generation immunomodulatory therapeutics. Noam Solomon begins our conversation by detailing his professional history and how it led to Immunai before explaining what Immunai does and why this work is vital for healthcare. Then, we discover how understanding the immune system will help to improve how drugs work in our bodies, how the team at Immunai accomplishes its goals, the major challenges of working with complex ML models, and some helpful recommendations for processing the high-dimensional nature of biological data. Noam also explains the collaborative landscape of Immunai, how the evolution of technology made his work possible, Immunai’s plans for the future, and his advice to others on a similar career path.

Key Points:

• Unpacking Noam Solomon’s professional journey that led to his founding of Immunai.
• What Immunai does and why this work is vital for the healthcare industry.
• How understanding the immune system will help to improve drug efficacy.
• Exploring how Noam and his team use AI to accomplish their goals.
• The standardization of data and other challenges of working with complex ML models.
• Techniques for handling the high-dimensional nature of biological data.
• How ML experts collaborate with other domains to inform and build Immunai’s models.
• The technical advancements that have made Noam’s work possible.
• His advice to other leaders of AI-powered startups, and imagining the future of Immunai.
• How to connect with Noam and his work.

Quotes:

“First, let’s talk about the problem, which is today, getting a drug from IND approval to FDA approval—which is the process of doing clinical trials—has less than a 10% chance of success, usually about a 5% chance, takes more than 10 years, and more than $2 billion of open immune therapy.” — Noam Solomon

“Different people respond differently to the same drug, and the reason they respond differently is because their immune system is different.” — Noam Solomon

“You first need to fall in love with the problems. Many ML people—physicists, mathematicians, computer scientists—we love building models; we love solving puzzles. In biology, you need to really fall in love with the question you are trying to answer.” — Noam Solomon

“It’s a great decade for biology.” — Noam Solomon

Links:

Noam Solomon on LinkedIn

Noam Solomon on X

Immunai

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

Imagine a world where radiology backlogs are a thing of the past, and AI seamlessly augments the expertise of radiologists. Today, I'm joined by Robert Bakos, Co-Founder and CTO of HOPPR, to discuss how his company is bringing this vision to life. HOPPR is pioneering foundation models for medical imaging that have the potential to transform healthcare. With access to over 15 million diverse imaging studies, HOPPR is developing multimodal AI models that tackle radiology’s most significant challenges: high imaging volumes, limited specialist availability, and the growing demand for rapid, accurate diagnostics.

In this episode, Robert offers insight into the rigorous process of training these models on complex data while ensuring they integrate seamlessly into medical workflows. From data partnerships to specialized clinical collaboration, HOPPR’s approach sets new standards in healthcare AI. To discover how foundation models like these are revolutionizing radiology and making healthcare more efficient, accessible, and equitable, be sure to tune in today!

Key Points:

• Robert’s background in medical imaging and tech and how it led him to create HOPPR.
• Ways that HOPPR’s AI models improve diagnostic speed and accuracy.
• The significant data and compute resources required to build a foundation model like this.
• Partnering with imaging organizations to collect diverse data across multiple modalities.
• How HOPPR differentiates itself with ISO-compliant development and multimodal training.
• The quantitative metrics and clinical review involved in validating its foundation model.
• Key challenges in building this model include data access, diversity, and secure handling.
• Reasons that proper data diversity and balance are essential to reduce model bias.
• How API integration makes HOPPR’s models easy to adopt into existing workflows.
• The real-world clinical needs and input that go into building an AI product roadmap.
• Robert’s take on what the future of foundation models for medical imaging looks like.
• Valuable lessons on the importance of strong labeling, compute scalability, and more.
• Practical, real-world advice for other leaders of AI-powered startups.
• The broader impact in healthcare that HOPPR aims to make.

Quotes:

“Having clinical collaboration is super important. At HOPPR, our clinicians are an important part of our product development team – They're absolutely vital for helping us evaluate the performance of the model.” — Robert Bakos

“Because we are training across all these different modalities, getting access to this data can be challenging. Having great partnerships is critical for finding success in this space.” — Robert Bakos

“Make sure that you're addressing real problems. There are a lot of great ideas and cool things you can implement with AI, but at the end of the day, you want to make sure you can deliver value to your customers.” — Robert Bakos

“Foundation models – trained on a breadth of data – can make a positive impact on underserved areas around the world. With the volume of images growing so rapidly, constraints on radiologists, and burnout, it's important to leverage these models to make a big impact.” — Robert Bakos

Links:

Robert Bakos

HOPPR

Robert Bakos on LinkedIn

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.

What are the unique challenges of operating mission-critical facilities, and how can reinforcement learning be applied to optimize data center operations? In this episode, I sit down with Vedavyas Panneershelvam, CTO and co-founder of Phaidra, to discuss how their cutting-edge AI technology is transforming the efficiency and reliability of data centers. Phaidra is an AI company that specializes in providing intelligent control systems for industrial facilities to optimize performance and efficiency. Vedavyas is a technology entrepreneur with a strong background in artificial intelligence and its applications in industrial and operational settings. In our conversation, we discuss how Phaidra’s closed-loop, self-learning autonomous control system optimizes cooling for data centers and why reinforcement learning is the key to creating intelligent systems that learn and adapt over time. Vedavyas also explains the intricacies of working with operational data, the importance of understanding the physics behind machine learning models, and the long-term impact of Phaidra’s technology on energy efficiency and sustainability. Join us as we explore how AI can solve complex problems in industry and learn how Phaidra is paving the way for the future of autonomous control with Vedavyas Panneershelvam.

Key Points:

• Hear how collaborating on data center optimization at Google led to the founding of Phaidra.
• How Phaidra’s AI-based autonomous control system optimizes data centers in real-time.
• Discover how reinforcement learning is leveraged to improve data center operations.
• Explore the range of data needed to continuously optimize the performance of data centers.
• The challenges of using real-world data and the advantages of redundant data sources.
• He explains how Phaidra ensures its models remain accurate even as conditions change.
• Uncover Phaidra’s approach to validation and incorporating scalability across facilities.
• Vedavyas shares why he thinks this type of technology is valuable and needed.
• Recommendations for leaders of AI-powered startups and the future impact of Phaidra.

Quotes:

“Phaidra is like a closed-loop self-learning autonomous control system that learns from its own experience.” — Vedavyas Panneershelvam

“Data centers basically generate so much heat, and they need to be cooled, and that takes a lot of energy, and also, the constraints in that use case are very, very narrow and tight.” — Vedavyas Panneershelvam

“The trick [to validation] is finding the right balance between relying on the physics and then how much do you trust the data.” — Vedavyas Panneershelvam

“[Large Language Models] have done a favor for us in helping the common public understand the potential of these, of machine learning in general.” — Vedavyas Panneershelvam

Links:

Vedavyas Panneershelvam on LinkedIn

Phaidra

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.

Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.

Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.