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Shashank
Kapadia
Staff Machine Learning Engineer
Walmart Global Tech
Shashank Kapadia is a machine learning engineering leader specializing in large-scale AI solutions that drive measurable improvements in user engagement and business outcomes. With over a decade of hands-on experience at global organizations like Walmart, Randstad, and Monster Worldwide, he has pioneered cutting-edge ML solutions—optimizing revenue, boosting engagement, and streamlining decision-making. Shashank’s approach balances technical rigor with ethical responsibility. He champions fairness, transparency, and real-world relevance, ensuring solutions serve both the enterprise and the broader community. An active mentor and thought leader, he has spoken at global conferences, judged and mentored hackathons, authored widely-read articles on NLP, and co-authored published research—guiding teams to award-winning results. A valedictorian graduate in Operations Research from Northeastern University, Shashank continues to push the boundaries of ML innovation. His work exemplifies a seamless fusion of cutting-edge techniques, high-level strategy, and values-driven execution—advancing technology that’s as impactful as it is responsible.
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29 April 2025 16:20 - 16:40
Panel discussion: Explainable AI & industry's ethical challenges
The final interactive session of the day: bring your questions and join the discussion, as our panel examines the role of explainability in addressing ethical challenges and building trust in AI systems across various industries.
29 April 2025 14:30 - 15:00
Panel discussion: Building LLM alignment pipelines - from fine-tuning to real user feedback loops
Fine-tuning is just the beginning. As LLMs move from labs into real-world applications, achieving true alignment requires continuous learning from user interactions. In this panel, industry leaders from Google DeepMind, Walmart, Meta, and Feedback Intelligence will explore how to design end-to-end alignment pipelines - combining fine-tuning, reinforcement learning, and user feedback loops - to build safer, more useful AI systems at scale.