IDEAS/STAT Optimization Seminar: “ML for an Interactive World: From Learning to Unlearning”
The remarkable recent success of Machine Learning (ML) is driven by our ability to develop and deploy interactive models that can solve complicated tasks by understanding and adapting to the […]
IDEAS/STAT Optimization Seminar: “Theoretical foundations for multi-agent learning”
As learning algorithms become increasingly capable of acting autonomously, it is important to better understand the behavior that results from their interactions. For example, a pervasive challenge in multi-agent learning […]
IDEAS/STAT Optimization Seminar: “Foundations of Deep Learning: Optimization and Representation Learning”
Deep learning’s success stems from the ability of neural networks to automatically discover meaningful representations from raw data. In this talk, I will describe some recent insights into how optimization […]
IDEAS/STAT Optimization Seminar
ESE Spring Seminar – “AI as a Lens: Expanding Vision for Scientific Discovery”
Conventional approaches to scientific discovery often prioritize building larger sensors, gathering more data, and scaling up computational power. In this talk, I will present a complementary perspective: extracting insights hidden […]
ESE Spring Seminar – “Machine Learning: Algorithmic and Economic Perspectives”
Algorithms are increasingly integrated into various societal applications, often directly interacting with people and communities. This highlights the importance of understanding the interplay between algorithmic decisions and economic incentives when […]
IDEAS Seminar: “Equivariant Neural Inertial Odometry”
Abstract: In this talk, we introduce a new class of problems related to integrating inertial measurements obtained from an IMU that play a significant role in navigation combined with visual […]
ESE Spring Seminar – “Solving Inverse Problems with Generative Priors: From Low-rank to Diffusion Models”
: Generative priors are effective countermeasures to combat the curse of dimensionality, and enable efficient learning and inversion that otherwise are ill-posed, in data science. This talk begins with the […]
BE Seminar: “Using Computers to Derive Protein Structure from Sparse Data – A Case Study for Mass Spectrometry” (Steffen Lindert, Ohio State)
Mass spectrometry-based methods such as covalent labeling, surface induced dissociation (SID) or ion mobility (IM) are increasingly used to obtain information about protein structure. However, in contrast to other high-resolution […]