Donald Wedding, PhD

If your datasets are poorly labeled, you’re inviting a host of issues like noise, bias, and inconsistencies that can seriously hurt your model's accuracy and reliability when it’s out in the wild. That's why data labeling has evolved from being just a preliminary step into a vital part of engineering and operations in the AI lifecycle.

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No A/B test? No problem. Learn how Regression Discontinuity Design (RDD) can uncover causal effects in observational data by exploiting sharp cutoffs. Dive into the theory and a real-world e-commerce example in Alejandro Alvarez Pérez's newest article.

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Scientific knowledge is expanding faster than ever but the data behind it remains fragmented. Genes, diseases, drugs, publications, datasets… all living in separate silos. Nextnet Ontology brings these pieces together into a unified semantic framework that connects biological entities, relationships, and evidence across the life sciences landscape. The result: 🔸 More meaningful discovery 🔸 Smarter AI reasoning 🔸 A deeper understanding of how knowledge truly connects Read the full blog to see how ontology powers the foundation of the Nextnet platform 👉 https://lnkd.in/ebr3VE_a

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Warehouses were built for humans running reports—not agents firing evolving queries at ms cadence. We break down stateful scaling limits, manual tuning traps, and how agent-native engines close the gap (with MCP + StarRocks). Dive into the details: https://hubs.la/Q03DL_7D0 #Lakehouse #DataInfrastructure #DataAnalytics #DataEngineering

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You trained the model. You trusted the output. But can you trace the data that led to the decision? 👀 AI in analytics isn’t dangerous because it’s wrong. It’s dangerous because it’s convincing, even when it’s wrong. 📰 This article breaks down the real risks of AI-powered insights and how to design for trust with traceability, governance, and human oversight. Read it here: https://lnkd.in/eKuQnv7J

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📈 Target RWE’s causalStudio™ platform is engineered to tackle tough real-world evidence (RWE) questions. Using advanced epidemiological algorithms and modern causal methods, it helps address confounding and other biases to move beyond association toward credible causal insights. This isn’t just theory, it’s software developed, validated, and used by epidemiologists and statisticians to support regulatory decision-making. With modules like causalRisk™ (for estimating cumulative risk in the presence of missing data, competing risks, and time-varying factors) and causalPHR™ (an interactive results and visualization layer), you can run complex analyses and interpret them with confidence. 💡 Dive into causalStudio™ and request a demo today: https://lnkd.in/gA2tG5Wd #RealWorldEvidence #CausalInference #DataScience #RegulatoryScience #TargetRWE

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Snowflake Intelligence: From Data to Decisions - Atrium's AI: AI projects fail when data quality is poor and siloed. At its core, the Snowflake platform solves this by giving access to unified insights with ...

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The demand for data professionals who can think critically — not just program — is at an all-time high. The University of Minnesota School of Statistics is meeting this demand with its recently added statistical practice major. Elliot Fergle, a current student, talks about why he transitioned from a traditional CS track to this new program. By combining intense statistical foundations with a liberal arts perspective, he’s building a toolkit for complex problem-solving. See how UMN is preparing the next wave of analytical talent. https://ow.ly/9XMK50YcwIK

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