Raul Salles de Padua

As artificial intelligence reshapes industries worldwide, developers grapple with an unexpected challenge: a shortage of high-quality, real-world data to train their increasingly sophisticated models. Now, a potential solution is emerging from an unlikely source—data that does not exist in reality at all.

Synthetic data, artificially generated information designed to mimic real-world scenarios, is rapidly gaining traction in AI development. It promises to overcome data bottlenecks, address privacy concerns, and reduce costs. However, as the field evolves, questions about its limitations and real-world impact arise.

I spearheaded the design of computer vision systems to enhance the quantum AI vision toolbox. These systems incorporate object detection, segmentation, and classification using convolutional neural network architectures compressed by tensor networks.

I spearheaded the development of machine learning and analytics capabilities, implementing revenue optimization algorithms and marketing data science techniques, which resulted in quarterly records for top-line growth and margins. I also developed and launched a product recommendation system based on consumer lifetime value concepts, driving a 400%+ increase in conversion rates compared to average marketing efforts. Additionally, I optimized eCommerce operations by developing and managing dynamic pricing, demand forecasting, A/B testing, and automating revenue management processes with AI, which grew gross margins by three percentage points and revenues by 23%.

AoCubo, an early-stage startup in the real estate market specializing in new homes, recently launched a website that centralized ownership under the company's architecture with a revamped tech stack. The site incorporates embedded machine learning systems to enhance the customer experience, featuring recommender systems, advanced search with information retrieval, and analytics to track lead generation.

Developed several NLP and LLM applications to demonstrate production-grade code and AI engineering skills, leveraging open-source code with OpenAI models, Hugging Face, and PyTorch libraries. Key highlights include the Kingdom of Pizza Knight Bot and the Barbie RAQA Application, which showcase retrieval-augmented generation (RAG).

Pricing a product to maximize value creation for stakeholders and effectively capture that value is a critical challenge for manufacturers and business owners. Determining the right price is essential for ensuring your business's and products' success. Several factors must be considered, such as production costs, market conditions, competitor pricing, and customer demand. Also, choosing the appropriate pricing strategy—cost-based, market-based, or value-based—is key.

Businesses can optimize pricing strategies by leveraging internal historical data in a structured manner. I implemented pricing optimization techniques in direct-to-consumer environments through my research and real-world applications.

Inspired by Robert L. Phillips' Pricing and Revenue Optimization, I developed a web app that fits linear and exponential regression curves to historical transaction data. This enables accurate demand predictions and the setting of optimal price recommendations.

Two fundamental challenges in healthcare arise from patient handoff and triage. Doctors often need to summarize complex findings to facilitate efficient communication with specialists and make decisions regarding each case's urgency. To address these issues, we present a state-of-the-art radiology report summarization model based on a bidirectional encoder representation from transformers (BERT-to-BERT) encoder-decoder architecture. Our approach introduces a novel method for augmenting medical data and a comprehensive performance analysis. The best-performing model achieved a recall-oriented F1 score of 58.75/100 for gisting evaluation, outperforming specialized checkpoints with more sophisticated attention mechanisms. Additionally, we provide a data processing pipeline for future models developed on the MIMIC Chest X-ray (MIMIC-CXR) dataset. This model significantly enhances radiology report summarization, demonstrating the potential for improving clinical workflows and patient care.

Financial analysis is a key tool for evaluating company performance. Practitioners rely on advanced quantitative analyses to answer financial questions and make profitable investment decisions. Financial question answering (QA) involves deep reasoning about numbers, yet the ability of pre-trained language models to reason effectively in the financial domain remains uncertain. The current state-of-the-art approach typically uses a retriever to gather relevant facts from the financial text and a generator to produce a valid financial program and final answer.

However, recent advancements with large language models, like GPT-3, have achieved state-of-the-art performance across a range of tasks with just a few-shot approach. Through multiple experiments, we found that while GPT-3 shows promising results, a separate retrieval model and logic engine are still crucial for achieving true state-of-the-art performance in financial QA tasks. This is especially due to the precise nature of financial questions and the complex information in financial documents. Building on this insight, our refined prompt-engineering approach using GPT-3 achieves near-state-of-the-art accuracy without any fine-tuning.