DataTalks.Club

Hugo Bowne-Anderson, an independent AI consultant and educator, shares insights from his journey from academia to advising major companies like Netflix and Meta. He discusses how to build reliable AI systems, focusing on practical tips for prompt evaluation and dataset design. Hugo emphasizes the importance of structuring teams for successful AI adoption and offers strategies to avoid common pitfalls like prompt overfitting. Listeners will learn about debugging tools and the evolution of proactive AI agents that enhance productivity in everyday workflows.

In this talk, Sebastian, a bioinformatics researcher and software engineer, shares his inspiring journey from wet lab biotechnology to computational bioinformatics. Hosted by Data Talks Club, this session explores how data science, AI, and open-source tools are transforming modern biological research — from DNA sequencing to metagenomics and protein structure prediction.You’ll learn about: - The difference between wet lab and dry lab workflows in biotechnology - How bioinformatics enables faster insights through data-driven modeling - The MCW2 Graph Project and its role in studying wastewater microbiomes - Using co-abundance networks and the CC Lasso algorithm to map microbial interactions - How AlphaFold revolutionized protein structure prediction - Building scientific knowledge graphs to integrate biological metadata - Open-source tools like VueGen and VueCore for automating reports and visualizations - The growing impact of AI and large language models (LLMs) in research and documentation - Key differences between R (BioConductor) and Python ecosystems for bioinformaticsThis talk is ideal for data scientists, bioinformaticians, biotech researchers, and AI enthusiasts who want to understand how data science, AI, and biology intersect. Whether you work in genomics, computational biology, or scientific software, you’ll gain insights into real-world tools and workflows shaping the future of bioinformatics.Links:- MicW2Graph: https://zenodo.org/records/12507444- VueGen: https://github.com/Multiomics-Analytics-Group/vuegen- Awesome-Bioinformatics: https://github.com/danielecook/Awesome-BioinformaticsTIMECODES00:00 Sebastian’s Journey into Bioinformatics06:02 From Wet Lab to Computational Biology08:23 Wet Lab vs Dry Lab Explained12:35 Bioinformatics as Data Science for Biology15:30 How DNA Sequencing Works19:29 MCW2 Graph and Wastewater Microbiomes23:10 Building Microbial Networks with CC Lasso26:54 Protein–Ligand Simulation Basics29:58 Predicting Protein Folding in 3D33:30 AlphaFold Revolution in Protein Prediction36:45 Inside the MCW2 Knowledge Graph39:54 VueGen: Automating Scientific Reports43:56 VueCore: Visualizing OMIX Data47:50 Using AI and LLMs in Bioinformatics50:25 R vs Python in Bioinformatics Tools53:17 Closing Thoughts from EcuadorConnect with SebastianTwitter - https://twitter.com/sayalaruanoLinkedin - https://linkedin.com/in/sayalaruano Github - https://github.com/sayalaruanoWebsite - https://sayalaruano.github.io/Connect with DataTalks.Club:Join the community - https://datatalks.club/slack.htmlSubscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQCheck other upcoming events - https://lu.ma/dtc-eventsGitHub: https://github.com/DataTalksClubLinkedIn - https://www.linkedin.com/company/datatalks-club/Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

Aishwarya Jadhav, a machine learning engineer with roles at Waymo and Tesla, shares her fascinating journey from finance to AI. She discusses designing an AI guide dog for the visually impaired and contributing to malaria mapping in Africa. Aishwarya also dives into the challenges of deploying safe autonomous systems, the interplay between sensor technologies like LiDAR and cameras, and the significance of gesture recognition in traffic control. Plus, she offers insights on how to break into the self-driving AI industry.

Ranjitha Kulkarni, a machine learning and NLP engineer with experience from Microsoft and Dropbox, now leads efforts at NeuBird.ai to create LLM-driven AI products. She shares fascinating insights on building reliable AI systems in the age of generative AI, emphasizing the importance of context engineering and dynamic planning. Ranjitha also discusses the evolution of agent technology, the role of retrieval in their design, and the future potential of agent marketplaces. Her practical tips on evaluating AI agents and the challenges of ensuring reliability are invaluable.

Abouzar Abbaspour, a data engineer with a rich background in machine learning and recommendation systems, shares his unique career journey from software engineering in Iran to working at Tesla. He delves into crowd modeling at Efteling, revealing how data from rides influenced visitor flow solutions. His transition to Bol.com highlights innovative brand recommendations, including a fun 'Tinder for brands' prototype. He also discusses the impact of large language models on productivity and how practical inference is revolutionizing his work at Tesla.

Dashel Ruiz, a data and machine learning practitioner with a rich background in semiconductors and software engineering, shares his fascinating career journey. He discusses transitioning from hardware to data science, revealing how he utilized machine learning to enhance semiconductor production. Dashel emphasizes the importance of practical experience in data education and contrasts it with traditional university methods. He also delves into his creative projects, like developing predictive models and APIs, while highlighting the value of community support in learning.

Micheal Lanham, an AI innovator with 20 years of experience in diverse fields like fintech and game development, shares his remarkable journey. He delves into the evolution of AI, the impactful role of evolutionary algorithms, and practical applications of AI agents. Micheal discusses how generative AI is transforming gaming and offers insights on designing minimalistic agent workflows for efficiency. He also provides valuable career advice for aspiring AI engineers and emphasizes the importance of continuous learning in a rapidly evolving tech landscape.

In this engaging discussion, Selim Nowicki, founder of Distill Labs, shares how they're making specialized LLMs faster and more accessible through knowledge distillation. Yashasvi Mishra, a data engineer at Pure Storage, emphasizes the importance of explainable AI, focusing on accountability and compliance in real-world applications. Mehdi Ouazza, a developer advocate at MotherDuck, talks about leveraging creative content and workshops to drive the adoption of open-source tools like DuckDB.

Daniel Egbo, an astrophysicist turned machine learning engineer, shares his ambitious journey from the stars to data science. He dives into the Meerkat radio telescope's incredible ability to map the galaxy and the fusion of physics and machine learning for star identification. Daniel provides practical advice for beginners in data science, highlighting mentorship and resources. He reflects on his experiences with AI internships and setting up data pipelines with cutting-edge tools. Tune in for insights on skill-building and bridging astrophysics and technology!

Kacper Łukawski, Senior Developer Advocate at Qdrant, explains the essentials of hybrid search, focusing on cost-effective models for smaller businesses. Manish Gill from ClickHouse tackles auto-scaling OLAP databases on Kubernetes. André Charton shares insights into evolving search technologies at Kleinanzeigen, transitioning from Solr to vector search. Atita Arora and Brian Goldin from Voyager Search discuss geospatial AI and the crucial role of spatial context in search retrieval. Together, they explore how AI is reshaping search functionality across industries.