7:00-8:00 pm: SEAS Penn iTalks presentation at Wu and Chen Hall
8:00-9:30 pm: Happy hour (open to all graduate engineering students) at Levine Lobby
8:00-9:30 pm: Happy hour (open to all graduate engineering students) at Levine Lobby
The three finalists presenting during this session are:
Session 1: March 25th
Adrian Lievano and Matthew Lisle (MEAM/Robotics)
BionUX, The New Era for Upper-Limb Prosthetic Devices
Madhur Behl (ESE)
Sometimes, Money Does Grow On Trees
The most recent decade was the nations warmest on record and experts predict that temperatures are only going to rise. Every year, heat waves in summer and polar vortexes in winter are growing longer in duration and cause massive peaks in the electricity power consumption across the electric grid. The peaks in the electricity consumption overburden, an already overstressed grid and can result in energy shortages and blackouts. For Penn's campus, which has several hundred buildings, the electricity bill for just 5 days is up to $1.5 million due to peak power pricing imposed by the utility companies. This talk will be about DR-Advisor: A Data Driven Demand Response Recommender System. Demand response (or DR) refers to intentional changes in electricity usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity, or to financial payments provided by utilities during peak hours or when electric grid reliability is jeopardized.Using just historical power consumption and weather data, we build family of regression trees to learn models for predicting the real-time power consumption of a building. These trees are used for making recommendations for the building’s facilities manager on the best electricity curtailment strategies for demand response, which minimize electricity costs. The techniques and methods that I will describe in this talk are at the intersection of large scale systems research, machine learning, control theory and statistics.Justin Thomas (MEAM)
Extending Autonomous Capabilities of Micro Aerial Vehicles for Real World Applications
Micro Aerial Vehicles (MAVs) have the potential to improve many fields such as first response, environmental monitoring, and package delivery. However, MAVs are currently restricted to aerial observation roles and cannot physically interact with their surroundings. Further, with maximum flight times around thirty minutes, mission durations are significantly limited, and the use of MAVs is not yet practical in many applications. In observation and surveillance tasks, a robot could perch and shut off the motors to decrease energy usage, and in other tasks, we can decrease the required airborne time by increasing the speed of grasping. In this presentation, I will show preliminary steps to achieve autonomous, gecko-inspired perching and the fastest-known, avian-inspired grasping using quadrotors.
Session 2: April 1st
Heather Culbertson (MEAM)
Haptics: Making the Virtual World Feel Real
Amin Rahimian (ESE)
Learning without Recall
People exchange beliefs in social networks to benefit from each other's opinions and private informationin trying to learn an unknown state of the world. Beliefs about the unknown are mathematically modelled as probability distributions over the set of finitely many possibilities, and the refinement of beliefs with new observations is therefore understood as an update from one probability distribution to another. The rational (optimal) approach to this problem of social learning is for each agent to successively apply the Bayes' rule to her entire history of observations. However, it is well known that repeated applications of Bayes' rule in networks become computationally intractable, since the rational agent would need to make very complicated inferences about the possible sources of her observations. This motivates the "Learning without Recall" model of belief propagation, which we consider in this talk. In this model, although agents behave rationally, they do not recall their history of past observations. We analyze the evolution of beliefs amongst these so-called "Rational but Memoryless" agents and show that they can still learn the truth by relying on each other’s observations; provided that as time evolves they put less and less weight on their neighbouring beliefs - eventually learning the truth, but in isolation.
Sarah Tang (MEAM)
Planning for Aggressive Maneuvering of a Quadrotor with a Cable-Suspended Load
If you have any questions/feedback about the event, please email penn.italks.seas@gmail.com