AIRG meets on Thursdays at 11:00 a.m. in Beckett Conf. Rm.
Dates and topics for this semester are as follows:
- Sept 3
- CS AI Faculty
- "AI Research @WPI Overview"
- VIDEO Dmitry Berenson
- Joe Beck
- Dave Brown
- Neil Heffernan
- Carolina Ruiz
- Sept 10
- CS AI Faculty
- "AI Research @WPI Overview"
- Xiangnan Kong
- Dmitry Korkin
- SLIDES
Yanhua Li
- Eugene Eberbach
- Sept 17
- Dave Brown
- "Some Issues in Computational Design Creativity"
- [slides]
Computational Creativity is still at the cutting edge of
AI research: we don't yet know how to do it in general, but a
start has been made. While
biased towards Engineering Design, much of this applies to
evaluating the creativity of a computational system in any
domain.
- Sept 24
- Xinyue Liu
- "Organizational Chart Inference"
- Advisor: Xiangnan Kong
-
Nowadays, to facilitate the communication and cooperation among
employees, a new family of online social networks has been adopted in
many companies, which are called the "enterprise social networks"
(ESNs). ESNs can provide employees with various professional services
to help them deal with daily work issues. Meanwhile, employees in
companies are usually organized into different hierarchies according
to the relative ranks of their positions. The company internal
management structure can be outlined with the organizational chart
visually, which is normally confidential to the public out of
privacy and security concerns. In this paper, we want to study the IOC
(Inference of Organizational Chart) problem to identify the company
internal organizational chart based on the heterogeneous online ESN
launched in it. IOC is very challenging to address as, to guarantee
smooth operations, the internal organizational charts of companies
need to meet certain structural requirements (about its depth and
width). To solve the IOC problem, a novel unsupervised method "Create"
(ChArT REcovEr) is proposed in this research. It consists of 3 steps:
(1) social stratification of ESN users into different social classes,
(2) supervision link inference from managers to subordinates, and (3)
consecutive social classes matching to prune the redundant supervision
links. Extensive experiments conducted on real-world online ESN
dataset demonstrate that Create can perform very well in addressing
the IOC problem.
- Oct 1
- Antonio Umali
- "Early stage work on developing a semi-autonomous robot assistant for PPE removal"
- Advisor: Dmitry Berenson
-
Compliant and cost-effective personal and industrial robots have
allowed for more opportunities for Human-Robot Collaboration (HRC). In
this work, we present a framework for using the Baxter robot in aiding
health-care workers in the removal of biologically-contaminated
clothing and equipment. Wearing this protective equipment is difficult
for humans because it traps body heat and limits dexterity. Thus our
approach seeks to minimize risk while also taking the least amount of
time possible to execute. We use Baxter as a semi-autonomous assistant
instead of a fully autonomous robot and take advantage of its
compliant actuation. We present a systematic way of decomposing
high-risk tasks into a series of human-robot actions which reduce the
human's risk of exposure to infection. We also present a few key
modifications to the Baxter robot which are likely to significantly
improve its effectiveness for such tasks.
- Oct 8
- Neil Heffernan
- Educational Data Mining
-
- Oct 15
- No Meeting: last day of A term
-
- Oct 22
- Break: no meeting
-
- Oct 29
- Ahmedul Kabir
- "Predictive Modeling of the Outcome of Patients after Stroke"
-
The outcome of a stroke patient is assessed after 90 days of stroke
onset, and is measured by the Modified Rankin Scale (mRS) score. The
objective of our research is to predict this outcome at the time of
the patients' discharge from the hospital. For this purpose, we have
built a dataset in collaboration with the UMass Medical School,
Worcester, consisting of the stroke patients' demographic information,
past medical history, treatment records and laboratory test results.
Numerous techniques for predictive analysis have been applied on the
data, including well-known classification algorithms, prediction via
clustering, instance-level complexity analysis and discriminant
analysis. This presentation discusses the application and results of
these techniques in details, and sheds lights on future directions in
this research.
- Nov 5
- John Boaz Lee
- "Deep Learning Architecture with Dynamically Programmed Layers
for Brain Connectome Prediction"
- Advisor: Xiangnan Kong
-
-
This paper explores the idea of using deep neural network
architecture with dynamically programmed layers for brain
connectome prediction problem. Understanding the brain
connectome structure is a very interesting and a challenging
problem. It is critical in the research for epilepsy and
other neuropathological diseases. We introduce a new deep
learning architecture that exploits the spatial and temporal
nature of the neuronal activation data. The architecture
consists of a combination of Convolutional layer and a Recurrent
layer for predicting the connectome of neurons based
on their time-series of activation data. The key contribution
of this paper is a dynamically programmed layer that is
critical in determining the alignment between the neuronal
activations of pair-wise combinations of neurons.
- Nov 12
- No meeting
-
- Nov 19
- Hao Wan
- What should the agent do next?
- Advisor: Joe Beck
-
Many tasks in AI can be phrased in terms of "what should the agent do
next?" One approach for approaching such tasks is to treat them as
multi-armed bandit problems. The multi-armed bandit framework looks at
the observed past payoffs for various actions, and gives strategies
for selecting which action to try next that balances obtaining
additional information vs. selecting the action currently believed to
be best. We examine an online educational platform called ASSISTments
in the context of a bandit problem. For our task, we look at which
method of presenting information to a student leads to the most
learning. We used three different bandit algorithms (epsilon-greedy,
softmax, softmax with simulated annealing) and, as a control, a simple
algorithm based on statistical hypothesis testing (t-test). We
evaluated the algorithms on two past experiments in ASSISTments. Our
findings were that all of the approaches did better than a purely
random strategy with respect to allocating students. Surprisingly, the
simple statistical approach was hard to beat, and in many cases it
performed the best. We suspect the strong performance of our control
technique is due to characteristics of our problem: namely, a small
number of decisions (2 or 3 experimental conditions) relative to other
control problems, and a relatively homogeneous set of payoffs.
- Nov 26
- Thanksgiving Break
- Dec 3
- Dmitry Korkin
- Machine learning techniques in studying complex diseases
-
- Dec 10
- tbd
- title tbd
-
- Dec 17
- Ermal Toto
- Predicting the Pulse of the City: A Real Time System for Multi-Granular
Predictions of Crowd Flow
- Advisor: Yanhua Li
-
In recent years, the fast pace of urbanization has caused the rise of
complex multi-modal transportation systems that operate under dynamic
and interrelated traffic conditions. Accurate forecasts of traffic
dynamics throughout the transportation system would enable smart
services to mitigate urban challenges. In this presentation we
explore 21 days of data from the Shenzhen subway network and discuss
methods for dynamic feature selection based on network properties that
are learned from data. Preliminary results show that temporal and
spatial properties can be learned and utilized to reduce model
complexity and increase accuracy.
- -- Dec 18 -- Last day of Semester
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