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Part 2: Estimating Individual Treatment Effects with the T-Learner Estimating the average effectiveness of a treatment across a patient population is an important first step in evidence-based medicine. However, physicians treat individual patients rather than populations, and the expected benefit of the same treatment may differ substantially from one patient to another. This article introduces … Continue reading Why Personalized Treatment Effects Matter: Building AI-Assisted Clinical Decision Support with a T-Learner →

Part 1: Understanding Average Treatment Effects Artificial Intelligence has become increasingly effective at predicting clinical outcomes, but prediction alone cannot determine whether a treatment actually caused an observed benefit. This distinction between prediction and causation lies at the heart of modern Clinical Decision Support (CDS). This article introduces the fundamental concepts of causal inference using … Continue reading Why Prediction Is Not Enough: A Practical Introduction to Causal Inference for Clinical Decision Support →

Consciousness remains one of the most elusive frontiers in science and philosophy. Despite extraordinary advances in artificial intelligence, modern systems still lack what humans intuitively recognize as awareness, the subjective sense of “I.” This essay proposes a framework I call Informational Panpsychism, in which consciousness is not an emergent byproduct of biological complexity but a fundamental … Continue reading Informational Panpsychism: A Framework for AGI and Consciousness →

I am pleased to share that my article on applying Business Machine Learning (BML) to pharmaceutical cost estimation has been published in Chemical Engineering Progress (CEP), the flagship magazine of AIChE. The article explores how BML can uncover hidden efficiencies in pharmaceutical manufacturing economics. You can read the article online: Business ML for Predicting Chemical … Continue reading Business ML (BML) in Pharma: Featured in CEP →

This case study demonstrates how machine learning can be applied to model and forecast process-level economics in semiconductor manufacturing. A simplified CMOS wafer fabrication line consisting of ten distinct steps was used to simulate time and cost parameters, forming the basis for synthetic training data. A neural network was developed to predict total wafer processing … Continue reading Business ML in Action: Predicting CMOS Process Cost with Neural Networks →

Semiconductor fabrication demands precision, consistency, and speed. In plasma etching and thin film processes, nanometer-level control directly impacts yield and device performance. Yet many fabs still rely on manual tuning and trial-based experimentation to reach optimal results. Each wafer run generates valuable process data such as chamber pressure, RF power, gas flows, temperature, and time, … Continue reading Smarter Semiconductors: How ML Neural Networks Optimize Plasma Etching in Real Time →

Can AI really predict tomorrow’s stock price?In this hands-on case study, I put a lightweight neural network to the test using none other than NVDA, the tech titan at the heart of the AI revolution. With just five core inputs and zero fluff, this model analyzes years of stock data to forecast next-day prices — delivering … Continue reading AI/ML in Finance: How a Lightweight Neural Network Forecasts NVDA’s Next Stock Price Move →

In every factory, industrial operation, and chemical plant, vast amounts of process data are continuously recorded. Yet most of it remains unused, buried in digital archives. What if we could bring this hidden goldmine to life and transform it into a powerful tool for process optimization, cost reduction, and predictive decision-making? AI and machine learning … Continue reading Bringing Historical Process Data to Life: Unlocking AI’s Goldmine with Neural Networks for Smarter Manufacturing →

Medical misdiagnoses continue to be a significant concern worldwide, often leading to unnecessary complications and preventable deaths. According to the World Health Organization (WHO), at least 5% of adults in the U.S. experience a diagnostic error annually. The impact on a global scale is even more alarming. Despite rapid advancements in Artificial Intelligence (AI) and … Continue reading The Power of Machine Learning in Medical Diagnosis – Breast Cancer Mini Case using Neural Networks →

Linear regression, one of the simplest and most foundational tools in machine learning, is widely used to predict outcomes based on input features. While custom coding has traditionally been the go-to method for solving linear regression problems, the emergence of Large Language Models (LLMs) like OpenAI’s ChatGPT, Google’s Gemini, and Microsoft’s Copilot has opened up … Continue reading Can you use ChatGPT, Gemini or Copilot to train Linear Regression Models with Gradient Descent? →