Razor Labs’ Post

Learn from the leading experts in the field about AI/Ml for drug discovery at the summer school by IVADO, Valence Labs and Mila - Quebec Artificial Intelligence Institute in Montreal this June!

🚀 Introducing: Black Hole Dynamics through Scientific Machine Learning In the field of research, an emerging interdisciplinary area known as Scientific Machine Learning (SciML) is blending the principles of physics-based models with machine learning approaches to create a complementary relationship. Physics-based models are built on mathematical equations derived from scientific principles and physical laws. We combine the best of both worlds, allowing us to preserve the key physics while using the predictive power of ML. We want to give you a peek into this new world through the example of black holes! 🔍 What You Will Learn: (1) Introduction to the field of Scientific Machine Learning (SciML) (2) The problems which can be solved using SciML. (3) How to integrate domain knowledge in ML projects? (4) Hands on project: Black hole modeling using SciML. - Live coding session - How to integrate physics with ML? - How to discover missing equations? - Generating impactful results - We will share code files at the end (5) How to convert project results into research? (6) You can add the live project shown to your resume/CV. Here is the link to register for the webinar: https://buff.ly/4c5I8Nj You will learn a pathway to integrate domain specific knowledge with ML, and do impactful projects and research. These 90 mins may just change your life and introduce you to the immense potential of machine learning. See you on the call.

Wow, I can't believe the semester is almost halfway done! Before I jump into the midterm madness, I wanted to reflect on some of the work I’ve been doing in Alabama Astrobotics. A little about what the team does: • Design, build, and write software for a fully-autonomous regolith-mining robot • Test the robot in a simulated moon environment–I still can’t believe we have one of those on campus! • Compete annually at the national level I’m on the software sub-team, which focuses on building the codebase that enables the robot to run autonomously. Being part of this group has given me exposure to critical software development best practices and cool applications of CS and math that I wouldn’t have learned in the classroom. Since I joined in Fall 2022, I’ve learned: • How to work with a large codebase • How to leverage version control for feature development • How to use statistics to help the robot detect and avoid obstacles • How linear algebra is related to the way the robot “sees” the world around it I’m also currently working on a project that uses computer vision and geometry to improve the efficiency of our obstacle detection system. In that project alone, I’ve learned basic MATLAB, worked with depth images and point clouds, and engaged with academic literature that I never would have encountered in another context. Thanks to all my Astro teammates and team leads for making this such an amazing learning experience!

"Space Complexity Unveiled: Navigating the Memory Landscape of Algorithms 🌐💾 Join me on an exploration of space complexity, where algorithms reveal their memory footprints. From constant to linear space, we'll unravel the impact of efficient memory usage on algorithmic performance. 🚀💡 Understanding how algorithms scale in terms of space is key to optimizing code. Ready to navigate the memory landscape and enhance your coding journey? Let's dive into the world of space complexity! 💻📊 #SpaceComplexity #Algorithms #MemoryManagement #CodeOptimization"

Mathematical Foundations For Cryptopraphy

I started my ML journey a bit late, while I was pursuing my PhD at Purdue. Gradually I came to the realization that the subject relies heavily on fundamental mathematics. Since I was already pursuing theoretical research in the field of fluid mechanics, I could grasp the concepts easily. Post this experience, I firmly believe that students or working professionals from any field can transition to the field of machine learning (ML). Raj Abhijit Dandekar and Sreedath Panat also have similar journeys and have done brilliant forays into ML during their PhDs. Since there is too much information and noise regarding ML, we see that it overwhelms people, and they are too scared or confused to make this transition. To help people in this transition process, we are conducting a webinar. The webinar is titled: "ML for all engineers: How to transition to ML and publish ML research" The webinar will cover the following aspects: (1) Our stories and how we transitioned to ML. (2) The problems with current ML courses for engineers. (3) How you can transition to ML from any engineering field. (4) Introduction to the field of Scientific Machine Learning (SciML) (5) How to start doing ML projects in your domain and the importance of domain knowledge. (6) Converting projects into research: How to do independent research in ML and publish your first paper? (7) Scientific ML bootcamp and it’s details: - Is it right for you? - What all will you learn from the bootcamp? - How the bootcamp can help you completely learn Scientific ML and publish papers. Seats are limited! Do sign up here: https://lnkd.in/dzhQ6Ejw This one hour may just change your life and introduce you to the huge possibilities in the ML field!

Happening at 7pm IST today: Free Scientific Machine Learning workshop. Title of the workshop: “Scientific ML hands on workshop: Modeling black hole dynamics” The workshop will cover the following: (1) Introduction to the field of Scientific Machine Learning (SciML) (2) The problems which can be solved using SciML. (3) How to integrate domain knowledge in ML projects? (4) Hands on project: Black hole modeling using SciML. - Live coding session - How to integrate physics with ML? - How to discover missing equations? - Generating impactful results - We will share code files at the end (5) How to convert project results into research? (6) You can add the live project shown to your resume/CV. If you are looking to master the emerging field of Scientific ML, or just to learn something new, do register! You will learn a pathway to integrate domain specific knowledge with ML, and do impactful projects and research. Here is the link to register for the webinar: https://lnkd.in/gjVXAQ9u This one hour may just change your life and introduce you to the huge possibilities in the ML field!

Black holes are cool, ML is cool and physics is cool. Mixing all these 3 is even cooler.    Do you want to learn how to model black hole dynamics using Scientific Machine Learning?   We are organising a hands on, beginner friendly workshop on 12 March. The workshop will be titled: “Scientific ML hands on workshop: Modeling black hole dynamics”   It's the first time anyone has made a webinar on this topic, and we are excited to share it with you :)   The workshop will cover the following:   (1) Introduction to the field of Scientific Machine Learning (SciML) (2) The problems which can be solved using SciML. (3) How to integrate domain knowledge in ML projects? (4) Hands on project: Black hole modeling using SciML.  - Live coding session  - How to integrate physics with ML?  - How to discover missing equations?  - Generating impactful results  - We will share code files at the end (5) How to convert project results into research? (6) You can add the live project shown to your resume/CV. Here is the link to register for the webinar: https://lnkd.in/gvk-qA9A   You will learn a pathway to integrate domain specific knowledge with ML, and do impactful projects and research.   These 90 mins may just change your life and introduce you to the immense potential of machine learning.   See you on the call.

Ever stuck with the classic DSA problem of finding the nth Fibonacci number and felt like you're running in circles? 🔄 Fear not, there's a sleek solution that's as golden as it is genius—using the golden ratio, φ (Phi)! 🌟 This method isn't just about solving a problem; it's about marveling at the beauty of mathematics. By applying the golden ratio, approximately 1.6180339887, we can leap from iterative or recursive solutions to an astonishing O(1) time complexity. Yes, you read that right—constant time! Why dive into loops when mathematics offers us a bridge? This approach not only highlights the interconnectedness of mathematical concepts and nature but also opens up a realm of efficient computing. It's like finding a secret passage in a maze, leading straight to the treasure. 🗝️💰 So, to all the problem solvers, math lovers, and curious minds out there, have you explored this golden pathway in your algorithmic adventures? Or do you have another favorite mathematical hack up your sleeve? Share your insights and let's celebrate the elegance and power of mathematics in solving complex problems! 🚀 Not writing a solution or formula you can try it out . And be amazed how even this can be conquered. #Mathematics #Fibonacci #GoldenRatio #DSA #CodingChallenges #SoftwareDevelopment #Algorithms

Another step closer to AGI. AlphaProof and AlphaGeometry2 are able to solve complex math problems and achieve a silver medal in the Mathematical Olympiad competition this year. https://lnkd.in/gSk9SGrr