MEAM Seminar: “Machine Learning for Robotics: Achieving Safety, Performance and Reliability by Combining Models and Data in a Closed-Loop System Architecture”
The ultimate promise of robotics is to design devices that can physically interact with the world. To date, robots have been primarily deployed in highly structured and predictable environments. However, […]
Penn Engineering Undergraduate Commencement Ceremony
Doors open for guest seating at 1:30 p.m. Access livestream here.
Penn Engineering Master’s Commencement Ceremony
Doors open for guest seating at 3:00 p.m. Access livestream here.
CIS Seminar: “Towards a New Synthesis of Reasoning and Learning”
This talk discusses the role of logical reasoning in statistical machine learning. While their unification has been a long-standing and crucial open problem, automated reasoning and machine learning are still […]
CIS Seminar: ” Deep Learning for Network Biomedicine”
Abstract: Large datasets are being generated that can transform biology and medicine. New machine learning methods are necessary to unlock these data and open doors for scientific discoveries. In this […]
CIS Seminar: “Making Parallelism Pervasive with the Swarm Architecture”
Abstract: Parallelism is critical to achieve high performance in modern computer systems. Unfortunately, most programs scale poorly beyond a few cores, and those that scale well often require heroic implementation […]
CIS Seminar: “Towards Human-Level Recognition via Contextual, Dynamic, and Predictive Representations”
Abstract: Existing state-of-the-art computer vision models usually specialize in single domains or tasks, while human-level recognition can be contextual for diverse scales and tasks. This specialization isolates different vision tasks […]
The Joy of Being Faculty: How to Apply for a Faculty Position
This professional development workshop is designed to provide Penn Engineering graduate students and postdocs with a richer understanding of what it is like to pursue a career in academia from […]
PRiML Seminar: “Optimizing probability distributions for learning: sampling meets optimization”
Optimization and sampling are both of central importance in large-scale machine learning problems, but they are typically viewed as very different problems. This talk presents recent results that exploit the interplay between them. […]