Anderson Lu, from the University of Maryland, College Park with a major in Computer Science (machine learning track) and a minor in Mathematics, is the founder and CEO of Stokes AI: a human-in-the-loop AI training company focused on producing high-quality RLHF and SFT datasets for large language models. Anderson holds CompTIA Security+ and Network+ certifications, and he has used such knowledge to train AI models. In the past, he had worked extensively with Outlier AI (Scale AI) and DataAnnotation.tech (Surge AI), where he evaluated AI model outputs, ranked responses, crafted prompts to test AI model's capabilities with math reasoning, and provided STEM domain-specific feedback for AI responses. His experience in the RLHF/SFT industry inspired him to take the next step by building his own company—offering expert-crafted training data with an emphasis on STEM reasoning, precision, and diversity of feedback.
Albert Lu is a seasoned IT consultant with over 20 years of experience delivering technical solutions and operational leadership across both federal agencies and private-sector enterprises. He holds a graduate degree from the Whiting School of Engineering at Johns Hopkins University and has built a distinguished career specializing in Linux system administration, AWS cloud architecture, Cisco network management, high-availability clustering, and cybersecurity compliance frameworks such as NIST and FedRAMP.
At Stokes AI, Albert plays a critical role in transferring his real-world IT expertise into AI systems. He works closely with our data engineering and annotation teams to create high-fidelity training sets that enable AI models to learn complex tasks such as provisioning infrastructure, diagnosing network issues, managing servers, and responding to security threats—mirroring the work of skilled IT professionals. His ability to translate decades of technical know-how into structured, trainable data allows our models to not only understand but also emulate expert-level decision-making in dynamic environments.
Albert’s work ensures that the next generation of AI systems are grounded in practical, enterprise-grade knowledge and capable of supporting mission-critical operations at scale.
Adi Arora is a rising senior at UC San Diego majoring in Math–Computer Science. He is passionate about using AI and machine learning to enhance the effectiveness of real-world systems. Adi had built a computer adaptive mastery quiz that personalizes and gamifies learning using AI, showcasing his interest in applying ML to education. He aspires to become a machine learning researcher and currently leads an AI-focused club at UCSD, where students explore the history and future of artificial intelligence. Adi is very passionate about pushing the frontier of the machine learning field. Outside of academics, Adi enjoys going to the gym, playing poker with friends, and watching Netflix.
Jordan is a rising senior attending Virginia Tech majoring in Systems Engineering and minoring in Computer Science. His deep interest in financial markets—spanning stocks, crypto, and technical analysis—led him to develop a trading system that capitalizes on market inefficiencies to generate positive expectancy. Jordan currently teaches a cohort of students how to succeed in trading, but his broader vision lies in training AI models with this financial expertise. By translating trading strategies, chart patterns, financial news interpretation, and economic indicators into structured datasets, Jordan helps Stokes AI build models that reason like a seasoned trader—capable of analyzing charts, recognizing signals, and making sound market predictions.
Josh Mizrahi is the Persian Team Lead at Stokes AI, where he oversees projects involving Farsi translation, interpretation, and culturally nuanced training for large language models. He is currently majoring in Persian Language, Culture, and History at the University of Maryland, with a strong academic focus on comparative anthropology. While his work is centered on Persian anthropology and linguistics, Josh brings a global perspective to the table—drawing on his studies of Middle Eastern, South Asian, and Mediterranean civilizations to ensure AI systems are trained with a deep and balanced understanding of cultural context. With experience in both modern and classical literature, oral traditions, and ethnographic methods, Josh helps ensure that AI models reflect the richness and diversity of human communication. He is particularly passionate about preserving linguistic nuance and historical depth in multilingual AI systems, making him an essential bridge between humanistic scholarship and cutting-edge machine learning.
Behind every dataset at Stokes AI is a team of dozens of specialized contributors—each an expert in their own field. From linguists and subject matter experts to experienced annotators and QA reviewers, our network ensures that every annotation is context-aware, accurate, and relevant. This multi-disciplinary approach allows us to deliver training datasets that not only meet technical standards but also reflect real-world diversity, nuance, and depth.
