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Machines and Molecules - Productizing Research Code: Martin Steinegger on how to create useful and reusable Software

Productizing Research Code: Martin Steinegger on how to create useful and reusable Software

09/06/24 • 33 min

Machines and Molecules

Dr. Martin Steinegger is an expert in computational biology and bioinformatics, specializing in large-scale sequence data analysis. He earned his Ph.D. from the Technical University of Munich in collaboration with the Max Planck Institute for Biophysical Chemistry, focusing on methods to cluster and assemble metagenomic sequencing data. Currently an Associate Professor at Seoul National University, his research group develops novel computational methods to analyze microbial communities using machine learning and big data algorithms. Martin is the creator of MMseqs, a highly efficient software suite for protein sequence searches and co-author of AlphaFold2. His work in pathogen detection and metagenomics has made a significant impact on bioinformatics, with a strong commitment to open science and open-source tools.

Together with Martin we discuss the importance of productizing research code and the key factors for creating reusable software. He emphasizes the need for user-friendly interfaces, intuitive outputs, and software that doesn't crash. Martin talks about his background in software engineering and how it influenced his approach to developing tools in bioinformatics. Hence, he gives an overview about all the different tools he has developed over the years and for what they are used. Furthermore, he explains the significance of protein structure in understanding protein evolution and function, and highlights the role of his tools MMSeqs and AlphaFold in protein sequence and structure analysis. Martin shares his personal journey from starting in a lower-level school to pursuing higher education and research, driven by his passion for computers and learning.

00:00 - 01:32 Introduction

01:32 - 06:25 Productization of research code

06:25 - 08:20 Testing of software

08:20 - 12:28 Overview about the tools Martin has developed

12:28 - 15:40 The relevance of protein structure

15:40 - 18:00 Structural vs. Statistical approaches

18:00 - 21:00 AlphaFold collaboration and insights

21:00 - 31:10 Martin's personal journey and motivation

31:10 - 33:41 Machines and Molecules theme - 3rd M-Word

Season 2, episode 5 - Category Knowledge

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Dr. Martin Steinegger is an expert in computational biology and bioinformatics, specializing in large-scale sequence data analysis. He earned his Ph.D. from the Technical University of Munich in collaboration with the Max Planck Institute for Biophysical Chemistry, focusing on methods to cluster and assemble metagenomic sequencing data. Currently an Associate Professor at Seoul National University, his research group develops novel computational methods to analyze microbial communities using machine learning and big data algorithms. Martin is the creator of MMseqs, a highly efficient software suite for protein sequence searches and co-author of AlphaFold2. His work in pathogen detection and metagenomics has made a significant impact on bioinformatics, with a strong commitment to open science and open-source tools.

Together with Martin we discuss the importance of productizing research code and the key factors for creating reusable software. He emphasizes the need for user-friendly interfaces, intuitive outputs, and software that doesn't crash. Martin talks about his background in software engineering and how it influenced his approach to developing tools in bioinformatics. Hence, he gives an overview about all the different tools he has developed over the years and for what they are used. Furthermore, he explains the significance of protein structure in understanding protein evolution and function, and highlights the role of his tools MMSeqs and AlphaFold in protein sequence and structure analysis. Martin shares his personal journey from starting in a lower-level school to pursuing higher education and research, driven by his passion for computers and learning.

00:00 - 01:32 Introduction

01:32 - 06:25 Productization of research code

06:25 - 08:20 Testing of software

08:20 - 12:28 Overview about the tools Martin has developed

12:28 - 15:40 The relevance of protein structure

15:40 - 18:00 Structural vs. Statistical approaches

18:00 - 21:00 AlphaFold collaboration and insights

21:00 - 31:10 Martin's personal journey and motivation

31:10 - 33:41 Machines and Molecules theme - 3rd M-Word

Season 2, episode 5 - Category Knowledge

Previous Episode

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Driving Innovation Forward: Carlos Härtel on Deep Tech Solutions and Market Adoption

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Carlos joins us in our Ecosystem category. We dive into CO2 capturing technologies and their role in climate innovation. Key takeaways include the cost-effectiveness of capturing CO2 at industrial sources, the advanced direct air capture methods Carlos developed at Climeworks, and the challenges of ocean-based CO2 capture. Drawing from his experience, Carlos furthermore discusses the critical factors for advancing hard technical innovations in climate and deep tech. He highlights how market demand drives innovation, addresses the chicken-and-egg dilemma of reliability versus adoption, and emphasizes the role of government policies in creating market demand. His insights reveal what’s needed to overcome barriers and implement complex solutions effectively.

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undefined - Accelerating AI Innovation: Laura Möller’s Mission to Shape the Startup Ecosystem and Foster Entrepreneurship in Academia.

Accelerating AI Innovation: Laura Möller’s Mission to Shape the Startup Ecosystem and Foster Entrepreneurship in Academia.

Laura Möller is a seasoned expert in venture capital and entrepreneurship, with a focus on artificial intelligence and technology transfer. She holds leadership roles as Director of the Künstliche Intelligenz Entrepreneurship Zentrum (K.I.E.Z.) and UNITE in Berlin, and is the founder of Paola Ventures. With over a decade of experience, she has built expertise in supporting start-ups and fostering innovation in AI-driven ventures. She holds a Master’s degree in European Studies from Humboldt-Universität. Laura’s broad network and hands-on experience make her a vital asset in connecting entrepreneurs and investors, advancing Berlin’s tech ecosystem.

In this episode of Machines and Molecules, Laura Möller, Director of KIEZ Accelerator, discusses supporting AI-driven startups, particularly those rooted in scientific research. She highlights KIEZ’s individualized approach, offering startups access to a strong network of venture capitalists, grants, and expert guidance. Laura emphasizes the challenge for AI and science-based startups in turning cutting-edge technology into practical business solutions.She also shares KIEZ’s vision of uniting accelerators and networks to create interdisciplinary teams of AI and domain experts and bridge gaps between research and commercialization. Laura stresses the need to open the funnel and fully utilize the potential of all researchers in academia, not just those who choose the entrepreneurial path. Laura believes fostering entrepreneurial education early, would be a gamechanger to the European startup ecosystem.

00:00 - 03:40 Introduction to Laura Möller and KIEZ Accelerator

03:40 - 05:45 Individualized Support Offered by KIEZ to Startups

05:45 - 07:40 Long-term Vision of KIEZ

07:40 - 10:45 Common Challenges Faced by AI and Science-Based Startups

10:45 - 15:22 Strategies for Securing Funding After the KIEZ Accelerator

15:22 - 19:50 Enhancing Access to Government Funding in the EU

19:50 - 28:50 Comparison of EU and US Funding and other Factors for Startup Success

28:50 - 32:50 3rd M-Word and Laura’s Mission

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