BeyondAI
Introduction to AI and Research
Join our free international nine-week research programme for high schoolers and undergrads in their first or second year interested in getting involved in the world of AI!
Interested in AI?
From the 7th of October to the 6th of December you will embark on an exciting journey through the world of Machine Learning. Learn the basics and engage with more advanced material on Machine Learning and AI. Get introduced to the world of academic research and tackle a research project with your team! From beginners to more advanced, we offer resources for everyone! Expand your knowledge and make your first steps into the world of AI research.
Whether it is classical Machine Learning or Deep Learning, we have you covered!
The programme at a glance
The nine-week programme consists of two parts: the Course Stage and the Research Stage. In the Course Stage you'll be taught about the basic concepts of modern Machine Learning & AI via a problem-based approach. We'll combine both mathematical thinking with practical coding. You'll be also introduced to the world of research.
In the Research Stage you will be conducting a research project in a group under the guidance of an academic mentor. You'll finish with bang by presenting your research poster, which will be published in our proceedings!
Preparation
During the seven week application phase you will be working with our preparation material to build the necessary foundations in both maths and programming in Python. Your Learning Journey starts now!
Learning Time
By engaging in interactive live sessions in weeks 0-3, we will get you covered in the basics of Machine Learning & AI conceptually, theoretically and practically. There will be also sessions to build your skillset as a researcher.
Mentor-guided Research
After you have been assigned a team and an academic mentor in week 3, you will apply your learning and skills while working on a group research project in weeks 4-7!
Presentation
The research is done and your research poster is ready to be presented to the cohort! Boost your presentation skills in our internal poster presentation competition and have it published in our proceedings!
Application Requirements
- Be in high school or first-year undergraduate (gap years are also considered)
- Be curious and passionate about the world of AI (we accept all levels of expertise!)
- Be willing to work in a team and to learn from others
- Commit to the programme schedule to the best of your abilities and expect to spend 12-14h per week on this programme.
- Be ready to work on your foundations to build the necessary prerequisites using the resources linked below in the run up to the programme!
Does this sound like you? Apply now!
*Psst*... For you!
We have curated this beautiful Notion Page for you to kick-start your adventure right now and get ready for the programme!
Engaging with our resources as well as maintaining the progress tracker is required for a successful application to the BeyondAI programme!
Meet your Mentors!
Dr. Devendra Singh Dhami
Dr. Dhami currently works as an Assistant Professor in the Uncertainty in Artificial Intelligence group at Eindhoven University of Technology. He received his PhDs from the University of Texas at Dallas, and Indiana University Bloomington, in Artifical Intelligence and Computer Science respectively. He is interested in the intersection of causality and neuro-symbolic AI where the causal models inform neuro-symbolic models and vice versa in order to learn better systems.
Dr. Helena Bahrami
Full-time Artificial Intelligence and Machine Learning Team Leader at Wine-Searcher. Dr. Bahrami received her PhD at Auckland University of Technology, and has experience working as an AI Research Scientist. She is interested in brain-like and quantum-inspired hybrid deep learning models for Spiking Neural Networks.
Dr. Filip Bar
Founder and CEO of PhysicsBeyond. Dr. Bar got his PhD from Cambridge, he is a qualified teacher of maths and physics with more than six years of teaching experience, and is currently affiliated with Lund University doing research in Synthetic Differential Geometry, Infinitesimal Algebra and its applications to Classical Field Theory.
Adeyemi Damilare Adeoye
PhD student in Computer Science and Systems Engineering researching optimization at IMT Schoolf for Advanced Studies Lucca. He holds a Master degree in Machine Intelligence, and in Mathematical Sciences. He is interested in optimization for machine learning and engineering applications, as well as data-driven identification and control of complex dynamical systems.
Matthew Pugh
Machine Learning Research PhD student at University of Southampton. He is interested in applied category theory, Kan extensions and enriched category theory. He also holds a degree in mechatronic engineering.
Emilie Gregoire
PhD student in the Data Analytics Laboratory at Vrije Universiteit Brussel. She holds a Master’s degree in Physics and Astronomy, and has taught various courses in Informatics and Data Science at the Faculty of Economics at Vrije Universiteit. Her research interests include multi-task learning, theoretical aspects of deep learning, learning dynamics, and recurrent neural networks.
Barbora Barancikova
PhD student at the AI4Health Doctoral Training Centre at Imperial College London, specialising in rough path theory, a mathematical framework for extracting insights from time series data. Much of her research focuses on deep learning, generative modelling, and their applications in healthcare and finance. Recently, she has been working on diffusion models for time series generation. She also holds a degree in Mathematics and Computer Science from the University of Glasgow.
Matej Cief
PhD student at Kempelen Institute of Intelligent Technologies, researching off-policy evaluation and learning of multi-armed bandits. He is interested in reinforcement learning from human feedback and how to evaluate LLMs without collecting new preference data. He has a Master’s in software engineering and completed two science internships at Amazon.
Transcend knowledge with us
