[4eyes] Practice Talk for Thesis Defense

[Dianna Han]

Hi everyone,

Thank you for replying to my previous emails despite the unfortunate issue
with my email account!

We have reserved 2-3:30pm on Sep. 22nd (Friday) at 2003 Elings (the regular
lab meeting time & location). Everyone is welcome, and light refreshments
will be provided. I look forward to your feedback.

Regards,
Dianna


On Fri, Sep 15, 2017 at 12:57 PM, Dianna Han <dianna.han at gmail.com> wrote:

> Hi everyone,
>
> I'm so sorry for the inconvenience - something is wrong with my cs email
> account.
>
> Please reply to my gmail account here if you are interested in my practice
> talk. Thanks!
>
> Best,
> Dianna
>
> ---------- Forwarded message ---------
> From: Dianna Han <dianna at cs.ucsb.edu>
> Date: Fri, Sep 15, 2017 at 11:45 AM
> Subject: Practice Talk for Thesis Defense
> To: <ilab-users at lists.cs.ucsb.edu>
>
>
> Hi everyone,
>
> I have my defense scheduled on Tuesday, Oct. 3rd. I'm hoping to have a
> practice talk for the lab members next week before the formal presentation.
> I've attached the abstract of my thesis below; if you are interested,
> please let me know what time you prefer - I'm thinking about 9/20
> (Thursday) or 9/21 (Friday), most likely a time in the early afternoon. I
> really appreciate your help and look forward to your feedback on the talk.
>
> Thank you!
>
> Regards,
> Dianna
>
> ------------
> Abstract:
>
> Recent technology has provided many tools and methods that allow
> researchers to look further into the blackbox of human minds. These include
> electroencephalogram (EEG), magnetoencephalography (MEG), positron emission
> tomography (PET), and magnetic resonance imaging (MRI), among which EEG and
> functional MRI (fMRI), two non-invasive techniques, dominate the field.
> These two major instruments have enabled researchers to identify latent
> neural processes and decode certain important cognitive states. Following
> the recent advances in neuroimaging technology that enable the concurrent
> recording of EEG and fMRI, much effort has been made to integrate these two
> modalities in order to leverage their complementary powers, or identify and
> characterize the underlying mechanism of neurovascular coupling. Numerous
> controlled experiments have been carefully designed and carried out, and
> large volumes of data have been recorded. Multiple statistical methods,
> including machine learning, have been applied to interpret these data, or
> to mine the corpus of information they create.
>
>
> In this dissertation, we continue in this line of study, and propose
> methods built upon advanced statistical tools: HMM- and RNN-based methods
> for modeling and classifying cognitive states, and a temporal extension of
> canonical correlation analysis (CCA) for identifying temporal correlations
> between different modalities. We chose to tackle the complicated problem of
> decoding neural processes through two very different experimental data
> sets. In one study, fMRI data were collected when subjects were exposed to
> video/audio cues to induce craving of cigarettes; in the other study, both
> EEG and fMRI data were collected simultaneously when subjects were
> presented with visual stimuli in a spatial working memory task. Dynamic
> models that capture the temporal patterns in the data and exploratory
> methods that reveal the underlying relationship between modalities were
> designed and tested over the data sets. The findings and reflections of
> these studies are described here. Our work is one step closer to the goal
> of unlocking the secrets of human minds.
>
>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <https://lists.cs.ucsb.edu/pipermail/ilab-users/attachments/20170919/ccde5e3a/attachment.html>