Welcome to the Inspirit AI Continuous Learning Portal!
We're thrilled to have you here as you take the next step in your AI learning journey. This portal is designed to support your growth and success by providing invaluable resources for college preparation, research and careers in AI.
- Access exclusive insights on how to strategically position your research projects in college applications, including advice from a former Harvard admissions officer.
- Gain practical strategies from our AI Careers workshop to help you confidently pursue internships, research opportunities, and professional roles.
- Additionally, dive into our AI+X Research Spotlight Talks, where Ivy League and top university instructors share how AI intersects with diverse fields and offer personal insights from their cutting-edge research.
We're excited to support your journey as you continue to learn, grow, and prepare for a future shaped by AI!
To enroll in the Continuous Learning Portal, please press "Enroll now" at the bottom of this page.
Continuous Learning Portal Content
👋 Welcome 👋
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💼 College Preparation and Careers in AI 💼
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- 📋 Positioning Research Projects in College Admissions
- 🏫 College Prep Workshop
- 📇 Careers in AI Workshop
- 💼 College Preparation and Careers in AI
- 📊 1:1 AI + X Research Gallery 📊
- 🎥 Computer Science at Stanford: My Path to Studying AI with Joel Ramirez Jr.
- 🎥 Computer Science at Princeton with Michael Garcia
- 🎥 How High Schoolers Can Prepare for STEM Programs in College with Columbia/Stanford Alum Saahil Jain
- 🎥 10 Things I Wish I Knew About AI Before Studying AI with Stanford Student, Joshua Chang
- 🎥 My Journey to AI with Caltech Alum, Sebastian
🎤 AI + X Research Spotlight Talks 🎤
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💊 AI + Medicine and Healthcare 💊
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- How AI Can Transform Dermatology with Stanford Medical Student, Joshua Tanner
- Exploring AI, Genetics, and Diversity with Stanford PhD and AI Researcher, Brianna Chrisman
- Using Computer Vision to Track Human Vision with Stanford/Harvey Mudd Alum Paul Jolly
- Chemical Language Models with Columbia Alum, Tyler Poore
- How AI Enables Drug Discovery and Can Prevent Antibiotic Resistance with Mirna Kheir Gouda
- Systems Biology: Building Biology with AI with a Williams Alum
📊 AI + Business, Finance, and Entrepreneurship 📊
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- BERT Prediction of Stock Prices with AI Researcher and Yale Alum, David McCowin
- The Stock Market, Trading, and the Economy with MIT Alum, Pat Stefanou
- Business Valuation with AI and Statistics with UPenn Alum, Jonathan Delgadillo Lorenzo
- AI + The Stock Market with Princeton Alum, Colin Reilly
- AI and Cryptocurrency with Stanford Alum, Catherine Gu
📚 AI + Humanities 📚
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🎨 AI + Art and Music 🎨
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- Artificial Intelligence in Song Recognition with Cornell Alum, Daniel Sanky
- AI + Photography: Using Generative AI Tools like Midjourney & Dall-E with Stanford Alum
- How MIT Alum Christian Cardozo Used AI to Make a Photo-A-Day Video
- How Machine Learning is Impacting Music with Aditya Chander, Yale Ph.D. Student
- AI + Poetry: Language of the Gods? with Columbia Alum
🤖 AI + Mathematics, Robotics, and Engineering 🤖
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- High School AI to MIT Quantum Computing with MIT Alum
- What You Should Know About Cryptography and Machine Learning
- Solving Linear Algebra by Program Synthesis with Columbia Alum, Emily Liang
- Machine Learning for Robotics with UPenn Ph.D. Student, Bruce Lee
- Machine Learning and Underwater Robots
- Introduction to Multidisciplinary Design Optimization with Stanford PhD, John Basbagill
- Autonomous Vehicles for Social Good with MIT Alum
🪐 AI + Space, Astrophysics, and Physics 🪐
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🧠 AI + Neuroscience 🧠
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👩⚖️ AI + Government and Law 👩⚖️
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- AI + Policy: Bridging the Gap between Technologists and Regulators with Angelo Vozza
- Applying Data Science to Public Policy Research with University of Edinburgh Ph.D. Candidate, Bhargavi Ganesh
- Data Science & Machine Learning in Politics with MIT Alum, Irene Terpstra
- Integrating AI in the Military: Legal, Moral, and Operational Issues with Princeton Alum, Zach Hammack
- Machine Learning, Bioinformatics, and Privacy with a Yale Alum
⚙️ AI + Machine Learning ⚙️
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- Machine Learning Model Selection: Interpretability vs. Accuracy
- Tricking NLP: Adversarial Examples
- Data Augmentation: How to Teach your Model Real World Invariances
- Recommender Systems
- The Limitations of Machine Learning
- Lottery Ticket Hypothesis in NLP
- Automatic Differentiation: The Magic that Powers Machine Learning