2. Background And Accomplishments
Grew up on small farm in South Dakota.
Developed strong work ethic.
4-H member for 10 years where I performed community service projects (e.g. clean road
ditches, cemetery clean-up) and I exhibited livestock, horticulture, baking goods, etc.
Physics B.S degree from South Dakota’s premier engineering and science university.
Graduated with honors (GPA: 3.5/4.0).
Received a minor in mathematics.
Prepared and instructed freshman physics recitation courses.
Physics M.S. and PhD degree from the University of Tennessee.
Performed research at the Center for Laser Applications, University of Tennessee Space
Institute.
Graduated with honors (GPA: 3.7/4.0).
Research and stipend funded with NASA space grant fellowship.
Jordan G. Ennis fellowship award that was based on scholarly merit.
Outstanding Graduate Research Assistant Finalist
Vice President of Finance and Records and Senator in the Student Government Association
Hobbies and interests: spending time with family, fishing, camping, hiking

3. •Performed independent and collaborative research towards the development of a medical device
for non-invasive glucose monitoring using optical coherence tomography (OCT) methods.
•Created customized signal and image processing algorithms to analyze and quantify glucose
induced scattering changes by utilizing MATLAB toolboxes (e.g. Statistics, Curve Fitting, Signal
Processing, Image Processing).
•Responsible for analyzing human clinical trial results and afterwards preparing technical reports,
summaries, and quantitative analyses for other GlucoLight team members.
•Theoretically modeled the medical device imaging performance using ZEMAX software by
evaluating the geometric image formation, optical aberrations, stray light analysis, and the optical
transfer function.
•Experimentally evaluated the medical device imaging performance by constructing an external
camera detector system utilizing LabVIEW software control and the Vision Development Module.
•Experience working closely with outside testing laboratories and research institutions.
•Commanded an extensive knowledge of the relevant scientific literature: glucose monitoring
devices, optical and biomechanical skin properties, and OCT principles and applications.

4. Optical Coherence Tomography
Interferometric technique with a
broadband optical source that
avoids detection of multiple
scattered photons.
OCT system’s signal can only
form when the optical path length
in the sample arm matches that
in the reference arm within the
coherence length of the source.
Key advantage is the capability
of detecting photons
backscattered from different
layers in the sample with high
Larin, K., Motamedi, M., Ashitkov, T., and Esenaliev, R., “Specificity of
resolution (~10–20 μm). noninvasive blood glucose sensing using optical coherence
tomography technique: a pilot study,” Phys Med Biol., 48(10), 1371-
1390 (2003).

5. Glucose
Measurements with
OCT
Increasing glucose increases the refractive
index of the base medium, and thus
decreases the refractive index mismatch
between the base medium and the scattering
centers.
Decreases the scattering of the sample and
subsequently lowers the intensity of
backscattered photons detected by the OCT
instrument. I = I 0 exp( − μt z )
Slope of the Beer-Lambert exponential law is
proportional to the total attenuation coefficient
of ballistic photons, µt = µa + µs.
Since µa << µs in the near-infrared spectral
range, the change in the slope is proportional
to the change in the scattering coefficient
Larin, K., Motamedi, M., Ashitkov, T., and Esenaliev, R., “Specificity of
noninvasive blood glucose sensing using optical coherence
tomography technique: a pilot study,” Phys Med Biol., 48(10), 1371-
1390 (2003).

6. Signal and
Image
Processing
•Developed advanced
algorithms to preprocess
OCT image.
•Filter out noise from
100 200 300 400 500 600 700 800 900 1000
input signals (frequency Axial depth ( μm)
75
filter design, Fourier Stratum corneum
analysis) 70
Prickle cell layer
•Correlation 65
•Thresholding
R fle d p w r (d )
e cte o e B
Epidermis Dermis
60
•Aggregate 3-D OCT 55
image into a 1-D 50
exponential signal.
45
40
35
0 200 400 600 800 1000
Axial depth ( μm)

7. Algorithm Development and
Improvement
Analyze slope and morphological p p
signal changes associated with
glucose changes.
Characterize the data by
aggregating results from multiple
test subjects using MATLAB
statistics and curve fitting toolbox.
Redesigning algorithms
Performed optimization with
several dependent variables to 0 50 100
Paramter #
150 200 250
achieve the best overall system
performance.

8. Multivariate Statistics/Analysis
Principal component 3.5
Best diff: 0.0035; Base diff: 0.23; PC diff: 3.6e-005
Best RT data reduction: all
Best RT data reduction: subset
analysis
Baseline RT data reduction: all
3 Baseline RT data reduction: subset
PC data reduction: all
PC data reduction: subset
2.5
2
Linear regression analysis 1.5
1
Understand trends within 0.5
the data and factors that 0
-1 -0.8 -0.6 -0.4 -0.2 0
Data reduction pearson
0.2 0.4 0.6 0.8 1
change from person to p
0.7
10
person and between 13
0.6
different systems/sensors. 16
0.5
Assisted engineering
0.4
17
y
department to identify 4
0.3
system outliers. 8
0.2
0.1
9
10 13 16 17 4 8 9

9. 1
0.9
Mitigate
0.8 Sources of
0.7
0.6
Error
0.5
•Quantify and correct
0.4
motion artifacts.
0.3
0.2
•Identify sensor/skin
0.1
interface and general
0
30 35 40 45 50 55 60 65 70 75 loss of power
0.1 problems.
0.09
0.08 •Recognize system
0.07 failures.
0.06
0.05
•Temporal system
0.04
performance
degradation
0.03
•Improper sensor
0.02
0.01
placement.
0
30 35 40 45 50 55 60 65 70 75

10. Feature Selection and Data Mining
Technique to select a subset of relevant features for
building robust learning models.
Random Forest
Support Vector Machines
2
Gtrain13
Btrain13
1.5 Btrain9
Gtrain4
Btrain4
1 Gvalid9
Bvalid9
Gvalid4
Bvalid4
0.5
Train line
0
-0.5
-1
-1.5
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3

11. Calibration Analysis
Number of independent blood
stick measurements to use for
calibrating OCT signal into
actual glucose concentration.
Choosing an appropriate initial
calibration gain.
Subsequent calibration points
Frequency
Quality and weight
Developing appropriate
performance metrics

12. Design, Analyze and Present Clinical
Trial Results
Assisted in designing clinical tests based on results
Vary time when meal tolerance glucose test is applied
Analyzed hypoglycemic, euglycemic, and hyperglycemic
conditions.
Physiological lag out of hypoglycemia.
Generated an overall and a subject specific report of the
clinical trial results for GlucoLight co‐workers using
customized MATLAB algorithms and MATLAB Report
Generator.

13. 1
0.9
0.8
Doppler
D g e o p rfu n (a .)
0.7
e re f e sio .u
0.6
OCT: Blood 0.5
0.4
Flow 0.3
0.2
0.1
•Analyzed high and low 0
200 300 400 500 600 700 800 900 1000
frequency components of Axial depth ( μm)
70
OCT interferogram to
measure localized perfusion 65
within the dermis due to the
Doppler shift caused by the 60
R fle d p w r (d )
e cte o e B
moving scatterers (i.e. red 55
blood cells).
50
•Glucose induced scattering
45
changes were measured over
perfused and unperfused 40
tissue layers.
35
200 300 400 500 600 700 800 900 1000
Axial depth ( μm)

14. 1
OCT Signal Increased 175
B o g co co ce tra n (m /d )
g L
Glucose 0.75 levels of
O T slo e sig a (a .)
n l .u
perfusion
Correlation
lo d lu se n n tio
150
0.5
p
125
C
•Observable layers of 0.25
perfusion have high 100
Pearson linear correlation
0
coefficients, 0.94 (420 µm) 0 50 100 150 200 250 300 350
Test time (min.)
and 0.91 (680 µm). 1 Immeasurable
levels of
175
B o g co co ce tra n (m /d )
perfusion
g L
0.75
O T slo e sig a (a .)
n l .u
lo d lu se n n tio
150
0.5
•Layers with unobservable
p
perfusion, 0.46 (270 µm) 125
C
and 0.61 (490 µm). 0.25
100
0
0 50 100 150 200 250 300 350
Test time (min.)

15. Sensor Optical Modeling
Modeled OCT imaging
sensor with Zemax and
varied the optical
parameters to measure
and optimize physical
and optical
characteristics:
Spot size
Raster size
RMS Wavefront
aberration

16. •Constructed a novel electro-optical imaging medical device to detect and autonomously
quantify ocular disorders.
•Applied real-time adaptive medical device instrument control and data acquisition using
LabVIEW software techniques and USB based measurement and automation devices.
•Performed image processing software analyses to identify several ocular biometrics using
the LabVIEW Vision Development Module and MATLAB Image Processing Toolbox.
•Theoretically analyzed the ocular image used to measure and quantify the ocular
disorders by incorporating eye models and the device's optical parameters into ZEMAX
computer modeling software.
•Published in scientific journals and presented oral and poster presentations during
scientific conferences/meetings.
•Gained experience writing research proposals to government, military, and aerospace
external funding agencies.

17. Lab Bench Prototype
•Independently
constructed ocular
electro-optical device.
•Performed theoretical
ZEMAX optical
modeling prior to
device construction.
•Responsible for
contacting vendors to
purchase optical,
electrical, and detector
components.
•All instrument control
and data acquisition
was developed using
LABVEIW software.

18. Strabismus

19. Strabismus Results
The uncertainty of
experimental results was
found to ~0.3‐1.7 Δ (0.2‐
1.1⁰).
Strabismus detection and
severity was quantified
through the covered eye
movement deviation from
the binocular eye
movement path and by
comparing the uncovered
and covered RMSE.

20. Refractive Errors
1.0
•Estimates the retinal reflex by integrating 0.9
portions of the retinal reflection intensity
0.8
from coaxial and eccentric images.
0.7
•1-D and 2-D Gaussian surface fitting is 0.6
performed to estimate the reflex width 0.5
which is related to larger refractive errors. 0.4
0.3
•Integrated intensity ratio method predicts 0.2
the refractive error for smaller refractive 0.1
errors.
-40.0 -30.0 -20.0 -10.0 0.0 10.0 20.0 30.0 40.0
Photorefraction Eccentricity (mm)

21. Refractive Errors

22. Refractive Error 24
FWHM vs. refraction: 5mm pupil diamter
Thin lens prediction
Results 22 Avg human eye prediction
Max FWHM
Fitted Gaussian FWHM (mm)
Min FWHM
20 Mean FWHM
Experimental and theoretical 18
results show that for larger 16
refractive errors the Gaussian 14
FWHM parameter becomes linear 12
with the refractive error.
10
8
Difference between actual 6
-6 -5 -4 -3 -2 -1 0 1 2 3 4
refractive error and experimental Refractive error (D)
FWHM prediction was calculated to Intensity ratio vs. refraction: 5mm pupil diamter
be less than ~0.7 D. 8
Thin lens prediction
Avg human eye prediction
Fitted integrated intensity ratio
7 Max ratio
Min ratio
Difference between the actual 6
Mean ratio
refractive error and experimental
5
intensity ratio prediction calculated
to be at most ~1 D. 4
3
Cylindrical measurement error was 2
found to be less than 0.6 D.
1
-6 -5 -4 -3 -2 -1 0 1 2 3 4
Refractive error (D)

23. High‐Order Aberrations
Experimental image data was
acquired from human subjects in a
clinical environment with different
types and amounts of high-order
aberrations to determine the
capability to differentiate HOAs.

24. High‐Order Aberration Results
Subject with insignificant
high-order aberrations
(near-sighted subject).
Subject with elevated
high-order aberrations
(keratoconus subject).

25. High Order
Aberration Results
•Complex reflex intensity patterns were
associated with different orders of
Zernike polynomials.
•Image results from refractive error
subjects (N) without HOAs had an
average value of 82% of their reflex
described with the vertical tilt Zernike
term.
•Conversely, mild and moderate KC
subjects had an average vertical tilt
contribution of 31% (A), advanced KC
subjects had 44% (B), very mild KC
subjects had 79% (C), and subjects after
corneal surgery had 34% (D).
•Subjects with HOAs tended to have a
higher percentage of their reflex images
described with higher order Zernike
terms.

26. Grant Proposals
National Institute of Health (R21)
Pediatric vision screening (e.g. strabismus,
refractive errors).
Early keratoconus detection.
NSBRI and TATRC
Autonomous system to detect vision problems
associated with abnormalities and irregularities
of corneal and optical opacities resulting from
cataracts and foreign objects.

27. If you have any questions please don’t hesitate to call or
email me. I can be reached via phone at 610‐217‐
9141(cell) or 931‐841‐3188(home) or via email at
dr.kevin.charles.baker@gmail.com.