2. Why?
• We are constantly bombarded with political rhetoric that
shape our political views.
• How are we ACTUALLY represented by our elected
officials?
• How does and will our congress vote on topics we care
about?
• Do senator voting records exhibit polarized behavior?
• How can we find misbehaving and polarized senators?
• What action can we take?
15. Democrat RepublicanInd
Clustering Senator Voting with Jaccard distance
Elizabeth Warren (MA)
Dianne Feinstein (CA)
Bernie Sanders (VT)
d
Mitch McConnell (KY)
John McCain (AZ)
16. Democrat RepublicanInd
Clustering Senator Voting with Jaccard distance
Elizabeth Warren (MA)
Dianne Feinstein (CA)
Bernie Sanders (VT)
d
Mitch McConnell (KY)
John McCain (AZ)
Rand Paul (KY)
Marco Rubio (FL)
Ted Cruz (TX)
17. Democrat RepublicanInd
Clustering Senator Voting with Jaccard distance
Elizabeth Warren (MA)
Dianne Feinstein (CA)
Bernie Sanders (VT)
d
Mitch McConnell (KY)
John McCain (AZ)
Rand Paul (KY)
Marco Rubio (FL)
Ted Cruz (TX)
18. Do votes align with bill sponsors?
Republican Sponsor
• The bill sponsor is the member of congress that
introduces the document for consideration.
Democrat Sponsor
19. Infer directionality of biochemical reactions using Langevin
dynamics
Robert Vogel
Developed new parameterization of therapeutic drugs
using insight from nonlinear dynamical systems
20. Voting Distributions and the Simulated Senate
• Sample 5000 experimental senates using parameters from data
• Data exhibit a more diverse distribution then simulation
• Potential next step, use the Ising model to model pairwise interactions
Republican SponsorDemocrat Sponsor
22. Jaccard Index for Measuring Polarity
• Jaccard Index measures the number identical votes
between Senator i and Senator j normalized to total votes
• Polarity index is the average Jaccard index between
Senator i and all Senators in party R.
Jij =
|vi vj|
|vi [ vj|
JiR =
1
NR
X
j2R
Jij
23. Distribution of polarity index
• If party politics were not a factor, these distributions
would overlap
24. Jaccard Distance for Senator Clustering
• Jij 1 the more similar Senator i votes to Senator j.
• Hierarchical clustering utilizes a dissimilarity measure.
Standard solution 1 - Jij
dJ (i, j) = 1
|vi vj|
|vi [ vj|
| {z }
Jij
25. Votes are strictly partisan
• Fraction of votes along party line, most votes are partisan
27. Topic modeling of legislative summaries
Wordspaceperbill
TopicSpace
Bills
Topics
T S = S’
Y N0
Congress person Topic Probabilities
P
T’ = P’
New bill in topic space
Probability of vote
P
Y
N
0
Prediction
Clustering
28. Can we make predictions of senator votes from
legislative documents?
Topic 1
Topic 2
Topic 3
Senator 1 Vote
Document 1
Document 2
Document N
Topic M
29. Can we make predictions of senator votes from
legislative documents?
Topic 1
Topic 2
Topic 3
Senator 1 Vote
Document 1
Document 2
Document N
Topic M
VoteSenator 2
30. Can we make predictions of senator votes from
legislative documents?
Topic 1
Topic 2
Topic 3
Senator 1 Vote
Document 1
Document 2
Document N
Topic M Senator L Vote
VoteSenator 2
31. Legislative document reduction to topics
• 2559 legislative summaries
• Constructed 6403 word basis from text by:
• removing stop words (e.g and, that,
this, a)
• removing non-english words
• stemming (e.g. rested equal to rest)
• TF-IDF
• Cosine similarity to group topics
32. Legislative document reduction to topics
• 2559 legislative summaries
• Constructed 6403 word basis from text by:
• removing stop words (e.g and, that,
this, a)
• removing non-english words
• stemming (e.g. rested equal to rest)
• TF-IDF
• Cosine similarity to group topics
• Result: No structure in bill data,
more data needed!
Documents
Documents
33. Document dimensionality reduction not sufficient
with PCA
95% of the variablity
corresponds to > 1000
dimensions
A small topic space, represents
a small portion
of the variability
34. tSNE dimensionality reduction suggests
no structure in bill data
• Each point is a document in the reduced space defined by tSNE
• t-distributed Stochastic Neighborhood Embedding maps points
from a high to a low dimensional space by minimizing the
Kullback-Leibler Divergence (minimize information loss).
36. Why only choose Bills and Amendments?
• In general, these documents can become law
• Other votes are for approving nominations for office and
resolutions.
• Resolutions can be very diverse as shown below.
37. Graduate Research: An overview
• Langevin Dynamics to:
• figure out direction in biochemical reactions, and
• testing isolation of a network motif.
• Bifurcation analysis to identify:
• nodes in a network sensitive to therapeutic inhibition
38. Biochemical Noise
• Flow cytometry measures the
relative quantity of <= 12
biochemical species per cell at
a rate of 20,000 cells per
second.
• Fluorescent molecules are
coupled to antibodies that
specifically bind to a
biochemical species.
• Quantity of molecules is
proportional to fluorescent
signal
−2 −1 0 1 2
−4
−3
−2
−1
0
1
2
3
4
PMA 1
PMA 2
PMA 3
Log2 Normalized pMEK
Log2NormalizedppERK
PMA 1
PMA 2
PMA 3
PMA 1
PMA 2
PMA 3
−2 −1 0 1 2
−4
−3
−2
−1
0
1
2
3
4
PMA : 1
Log2 pMEK
Log2ppERK
PMA : 1
−2 −1 0 1 2
−4
−3
−2
−1
0
1
2
3
4
PMA : 2
Log2 pMEK
Log2ppERK
PMA : 2
−2 −1 0 1 2
−4
−3
−2
−1
0
1
2
3
4
PMA : 3
Log2 pMEK
Log2ppERK
PMA : 3
39. Fluctuations break symmetry
of average measurements
Variance of Y > X
Y
X𝜉x
𝜉y
O
O
• Fluctuations from source node propagates to target
X
Y𝜉y
𝜉x
O
O
Variance of X > Y
True Model False Model
40. Fluctuations break symmetry
of average measurements
Variance of Y > X
0.2 0.3 0.4 0.5 0.6
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Cov(pMEK, ppERK)
Variance
True Model
pMEK
ppERK
0.2 0.3 0.4 0.5 0.6
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Cov(pMEK, ppERK)
Variance False Model
pMEK
ppERK
0.2 0.3 0.4 0.5 0.6
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Cov(pMEK, ppERK)
Variance
True Model
pMEK
ppERK
0.2 0.3 0.4 0.5 0.6
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Cov(pMEK, ppERK)
Variance
False Model
pMEK
ppERK
Y
X𝜉x
𝜉y
O
O
• Fluctuations from source node propagates to target
X
Y𝜉y
𝜉x
O
O
Variance of X > Y
True Model False Model
42. Inhibition of biochemical signaling in cells,
a new parameter 𝛼
L c SRC
pMEK MEKi
ppERK
In preparation for publicationChemical SpeciesChemical Complex Enzymatic reaction Enzymatic Inhibition
43. Inhibition of biochemical signaling in cells,
a new parameter 𝛼
L c SRC
pMEK MEKi
ppERK
L c SRC
SRCi
pMEK
ppERK
In preparation for publicationChemical SpeciesChemical Complex Enzymatic reaction Enzymatic Inhibition
44. Inhibition of biochemical signaling in cells,
a new parameter 𝛼
L c SRC
pMEK MEKi
ppERK
L c SRC
SRCi
pMEK
ppERK
In preparation for publicationChemical SpeciesChemical Complex Enzymatic reaction Enzymatic Inhibition
45. Inhibition of biochemical signaling in cells,
a new parameter 𝛼
L c SRC
pMEK MEKi
ppERK
L c SRC
SRCi
pMEK
ppERK
In preparation for publicationChemical SpeciesChemical Complex Enzymatic reaction Enzymatic Inhibition
46. Nonlinear dynamics
of biochemical inhibition in cells
Chemical Species
Chemical Complex
Enzymatic reaction
Enzymatic Inhibition
In preparation
for publication
47. Nonlinear dynamics
of biochemical inhibition in cells
Chemical Species
Chemical Complex
Enzymatic reaction
Enzymatic Inhibition
L c SRC
pMEK MEKi
ppERK
[MEKi] [MEKi]
In preparation
for publication
48. Nonlinear dynamics
of biochemical inhibition in cells
Chemical Species
Chemical Complex
Enzymatic reaction
Enzymatic Inhibition
L c SRC
SRCi
pMEK
ppERK
[SRCi] [SRCi]
L c SRC
pMEK MEKi
ppERK
[MEKi] [MEKi]
In preparation
for publication
50. Single cell measurements find abnormalities in
tumor patient profiles
• Kullback-Leibler divergence measures the dissimilarity of the single cell
distribution of biochemical signaling features between patient and healthy
donor samples.
Sjk =
X
i2HD
DKL (Pj(xk)||Pi(xk))
=
X
i2HD
Pj(xk) log
✓
Pj(xk)
Pi(xk)
◆
• k = Biochemical species
• j = patient id
• i = Healthy donor
