Explaining Your AI Model's Decisions
As AI models grow in complexity, ensuring safety, trust, and reliability becomes critical. Lucidic AI provides advanced tools, such as human-interpretable semantic features, to explain and interpret complex AI models by identifying why specific decisions or outputs occur. Our platform enhances model transparency, helps detect failure modes, and ensures AI systems behave as intended, reducing risks in critical applications.
Active Founders
Andy Liang, Founder
Founder/CTO at Lucidic AI, working on AI interpretability so black-box models can explain their decisions, enhancing trust and safety. Stanford BS/MS Computer Science (AI specialization). Ex Citadel, AppLovin.
Previously built GrocerCheck, a COVID-19 web app helping shoppers social distance effectively, with over 200,000 users and endorsed by the Government of Canada.
Reach me at andy@lucidic.ai
Abhinav Sinha, Founder
Hi, I’m Abhinav the founder and CEO for Lucidic AI! I’m from Stanford where I studied Computer Science with an AI specialization (both bachelor's and master's). I’ve worked at Apple as a software engineer and at Citadel Securities and Susquehanna International Group as quant.
In my free time, I like to play basketball (recently pickleball, not very well though), lift, and rewatch Christopher Nolan movies!
Feel free to reach out, would love to chat -- abhinav@lucidic.ai
Jeremy Tian, Founder
Hi! I'm Jeremy Tian, one of the founders and the Chief Scientist at Lucidic AI, working to explain your AI models' decisions. I have a BS/MS in Computer Science with an AI specialization from Stanford University. Ex. Quantitative Trader at DRW and software engineering/machine learning engineering at Steel Dynamics. Contact me at jeremy@lucidic.ai!
