Euler's and Fermat's Last Theorems, the Simpsons and CDC6600
Explore Fermat's last theorem, Euler's conjecture, and their connections to pop culture and computing history. Delve into mathematical proofs and historical insights.
Visualising Pythagoras - Ultimate Proofs and Crazy Contortions
Explore visually stunning Pythagorean proofs and related theorems, including unique variations and lesser-known facts, in this engaging mathematical journey.
The Truth About 1+2+3+...=-1/12
Explores the controversial sum 1+2+3+...=-1/12, explaining mathematical concepts behind it, addressing misconceptions, and connecting it to the Riemann zeta function and Ramanujan's work.
Win a Small Fortune With Counting Cards - The Math of Blackjack and Co.
Explore the mathematics behind casino games like blackjack and roulette, including card counting techniques and strategies for gaining an edge over the house.
Ramanujan - Making Sense of 1+2+3+... = -1/12 and Co.
Explore the mathematical paradox of infinite sums equaling unexpected values, delving into Ramanujan's work, summation rules, and connections to the Riemann Zeta function.
The Futurama Theorem
Explore the mathematical solution to mind-switching chaos in Futurama, with clear explanations and bonus Stargate connections. Unravel the intricacies of body swapping dilemmas.
Causal Inference for Earth System Sciences - Jakob Runge
Exploring causal inference techniques for Earth system sciences, focusing on advanced methods to uncover complex relationships in climate data and improve understanding of multi-scale processes.
Generalized Riemann Hypothesis and Brownian Motion - Giuseppe Mussardo
Explore the fascinating connection between number theory and physics, focusing on the Riemann Hypothesis and its relation to Brownian motion through statistical physics perspectives.
Nonparametric Bayesian Methods - Models, Algorithms, and Applications IV
Explore probabilistic models for graphs, edge exchangeability, and graph paintbox representations in this advanced machine learning lecture on nonparametric Bayesian methods.
LP, SOCP, and Optimization-Free Approaches to Polynomial Optimization
Explore advanced techniques in polynomial optimization, including LP, SOCP, and optimization-free methods, with a focus on hierarchies and matrix-analytic approaches.
Learning Uncertainty-Aware Models of Defect Kinetics at Scale - IPAM at UCLA
Explore advanced techniques for modeling metal properties, including energy landscape exploration, coarse-graining with uncertainty quantification, and descriptor-based approaches for capturing diverse material characteristics.
Machine Learning Inside MIP Solvers - IPAM at UCLA
Explore machine learning applications in MIP solvers, focusing on cutting planes and numerical stability. Gain insights into enhancing solver performance for general MIP benchmarks.
Integer Optimization for Predictive & Prescriptive Analytics in High Stakes Domains
Explore integer optimization techniques for developing interpretable, robust, and fair decision-tree models to assist high-stakes decision-making in social services and healthcare domains.
Learning for Decision-Making Under Uncertainty - IPAM at UCLA
Explore data-driven methods for uncertainty in decision-making, including mean robust optimization and techniques to reshape uncertainty sets, with applications in portfolio optimization and inventory management.
Algorithm and Abstraction in Formal Mathematics - IPAM at UCLA
Explore the paradox of formalized proofs, blending abstraction and computation in mathematics. Discover a new aesthetic that embraces algorithms and cross-referencing in formal mathematical discourse.
How to Prove a Calculation Correct? - IPAM at UCLA
Explore alternative methods for proving calculation correctness, focusing on real algebraic geometry and using intermediate information as hints for proof engines.
Computing with or Despite the Computer - IPAM at UCLA
Explore the interplay between manual, computer-assisted, and formally verified computations in number theory, examining challenges and opportunities in mathematical research.
What Can the Working Mathematician Expect From Deep Learning? - IPAM at UCLA
Explore deep learning's impact on mathematics, its potential applications, and insights from a pure mathematician's perspective on this emerging field's role in mathematical research.
Learning-Based Low-Rank Approximations - IPAM at UCLA
Explores learning-based algorithms for low-rank matrix approximation, focusing on optimizing sketching matrices using training data to significantly reduce approximation errors in efficient computational methods.
Recovering Small Molecular Structures Using Cryo-EM - IPAM at UCLA
Explores innovative computational frameworks for recovering small molecular structures using cryo-EM, challenging conventional limitations and bypassing particle picking for enhanced structural determination.
Using Maths and Physics to Treat Parkinson’s With a Vibrating Glove
Innovative vibrotactile therapy for Parkinson's disease using mathematical models to design stimulation patterns, showing promising results in reducing symptoms and abnormal brain activity through non-invasive fingertip vibrations.