Join us for an exciting kick-off event at the Penn Museum as part of the Women in Data Science (WiDS) @ Penn conference, where the past meets the future in a guided tour and storytelling workshop.
Precise RNA expression, tailored to specific brain regions, cell types, and subcellular compartments, is pivotal for orchestrating complex brain functions. In the first part of my talk, I will introduce a confocal imaging-based spatial transcriptomics platform, STARmap, that seamlessly combines in situ hybridization, hydrogel tissue chemistry, and in situ sequencing technologies. Leveraging scalable experimental and computational pipelines, […]
Large language models (LMs) such as ChatGPT have revolutionized natural language processing and artificial intelligence more broadly. In this talk, I will discuss my research on understanding and advancing these models, centered around how they use the very large text corpora they are trained on. First, I will describe our efforts to understand how these […]
The Wharton School and Penn Engineering are proud to host the fifth annual Women in Data Science (WiDS) @ Penn Conference on February 8-9, 2024, on the University of Pennsylvania’s campus. A celebrated interdisciplinary event, WiDS @ Penn welcomes academic, industry, and student speakers from across the data science landscape to celebrate its diversity, both […]
This is a hybrid event with in-person attendance in Wu and Chen and virtual attendance on Zoom. ABSTRACT Generative models, which map a latent parameter space to instances in an ambient space, enjoy various applications in 3D Vision and related domains. A standard scheme of these models is probabilistic, which aligns the induced ambient distribution […]
The predictive capabilities of turbulent flow simulations, critical for aerodynamic design and weather prediction, hinge on the choice of turbulence models. The abundance of data from experiments and simulations and the advent of machine learning have provided a boost to turbulence modeling efforts. However, simulations of turbulent flows remain hindered by the inability of heuristics […]
Combining physics with machine learning is a rapidly growing field of research. Thereby, most work focuses on leveraging machine learning methods to solve problems in physics. Here, however, we focus on the converse, i.e., physics-inspired machine learning, which can be described as incorporating structure from physical systems into machine learning methods to obtain models with […]
The Department of Bioengineering at the University of Pennsylvania and Dr. Despina Kontos are pleased to announce the Doctoral Dissertation Defense of Apurva Singh. Title: "Computational imaging and multiomic biomarkers for precision medicine: characterizing heterogeneity in lung cancer" Date: February 12, 2024 Time: 1:00 PM Location: John Morgan Building, Class of 62 auditorium Zoom link The public is […]
Computers can be programmed for superhuman pattern recognition of images and text; however, their application in biology and medicine is still in its infancy. In this talk, I will discuss our advances over the past half-decade, which are accelerating discoveries in the crucial and underinvested area of antibiotic discovery. We developed the first antibiotic designed […]
The predominant paradigm in deep learning practice treats neural networks as "black boxes". This leads to economic and environmental costs as brute-force scaling remains the performance driver, and to safety issues as robust reasoning and alignment remain challenging. My research opens up the neural network black box with mathematical and statistical analyses of how networks […]
ABSTRACT: As predictive and generative models are increasingly being deployed in various high-stakes applications in critical domains including healthcare, law, policy and finance, it becomes important to ensure that relevant stakeholders understand the behaviors and outputs of these models so that they can determine if and when to intervene. To this end, several techniques have […]
This will be a hybrid event with in-person attendance in Levine 307 and virtual attendance on Zoom. ABSTRACT With rapid evolution of sensing, communication, and computation, integrating learning and control presents significant Embodied AI opportunities. However, current decision-making frameworks lack comprehensive understanding of the tridirectional relationship among communication, learning and control, posing challenges for multi-agent […]