Rainbow Chocolate Cake

My husband's birthday was two months ago and we really didn't get a chance to celebrate it at the time. As we were walking through the grocery store today, I asked him if he would like to make a birthday cake / dessert this weekend to properly celebrate it! I left him in the baking aisle to ponder all the possibilities. A few minutes later, he rounded the corner with a box of Rainbow "Tie-Dye" cake mix and chocolate funfetti icing!

When we got home, I started reading and following the instructions. The beginning was pretty straight forward. Mix cake mix, water, oil and, interestingly, only egg whites (no yolks). I can only assume that is to not tint the colors? Not sure though. Then, I split the cake batter into six bowls with approximately 2/3 cup cake batter in each bowl.

The box came with three packets of dye (yellow, red, and blue) and instructions on how many drops of each color to fill the six bowls of cake mix. I really liked the brightness of the red ad blue colors.

I don't have two 8" round cake pans, but the instructions said a greased 9x13 pan would work. For this pan, I poured all the cake batter from each individual bowl into the 9x13 pan in the following order: red, orange, yellow, green, blue, and finally purple. The trick is to pour the next color directly in the middle of where the last color was poured. Each layer will slowly spread out as you add an additional layer. The last step is to lightly jiggle the pan to even out any cake batter that may have been uneven. This was the cake after I jiggled it a bit.

I baked the cake in a 350F oven for 32 minutes and tested with a knife to see if it was done. Looked good to me! Then I let it cool an hour before trying to decorate it with icing.

After letting it cool, I removed it from the pan and covered it in chocolate funfetti icing! We sang Happy (belated) Birthday to him and enjoyed it after our dinner tonight. Hopefully, this post will inspire a little color creativity in your next baking adventure!

Sampling Points Proportional to Intensity in an Image

A few months ago Aaron Lun shared some code with me to sample points based on intensity of pixels in an image. With his permission, I thought it would be fun to share it here!

The idea is you start with some image (either in a JPEG or PNG format). Here I'm using Rafa's twitter profile picture in a JPEG format.

I'm using the readJPEG() function from the R jpeg package to read in the JPEG image. Alternatively, you can use the readPNG() function from the R png package to read in a PNG image. If you read the help file of readJPEG(), it says it "reads an image from a JPEG file/content into a raster array". OK, first off what do we mean by raster? You can think of a raster image as a rectangular grid of pixels. In the help file for the readJPEG() function, it states "most common files decompress into RGB channels (3 channels)" where RGB stands for red, green and blue.

So, this means for each pixel in the original image, we should expect 3 values (one for red, green and blue). These values range between 0 and 1 and can be thought of the intensity of each color in the image. Below, I'm showing the intensity values from the three channels colored on the same scale (top row) and colored in the red, green and blue scales (bottom row).

To get the intensity of how light a particular pixel is, we can just average the values across the three channels. Next, we flip the intensity scale by subtracting the intensity scale from 1 to see how dark/black a particular pixel is. This makes the previously dark areas now light and vice versa (top left figure below). To control the contrast of intensity, you can just take powers of the intensity values. The lighter the background, the better the contrast will be between the individual and background on the original scale. Depending on the background in the original image, the skin tone, etc you may need to tinker with the contrast. I've shown the original scale and powers of 2, 4, and 8 below.

Next, we sample points proportional to the (powered) intensity level, add a bit of random noise and then plot the sample points along the x and y axis. In the picture below, you can really see how the power is very important to control the contrast levels.

Happy sampling!

Women in Statistics and Data Science Conference 2016

Happy New Year everyone! After a wonderful holiday break, I was excited to find my copy of AMSTAT News from the American Statistical Association in my mailbox! Someone pointed out to me that if you look close enough, I can be found standing in the background on the front cover of the Dec 2016 issue. So, apparently I can check off 'being on the cover a magazine' from my bucket list. ;)

The picture was a snapshot of an audience at talk from the Women in Statistics and Data Science Conference (WSDS) 2016 which was held in Charlotte, North Carolina Oct 20-22, 2016. The cover story can be found here. In 2014, I attended the inaugural Women in Statistics Conference and was fortunate enough to attend the conference again this year! Looking back through blogposts this fall, I realized I did not write a blog entry after I returned from the conference, but I did manage keep a few notes in a Github repo of a few of the talks. Here, I want to summarize some of my thoughts and experiences of WSDS 2016 and at the end I describe a few suggestions for future WSDS conferences. I was not able to attend all the talks mostly because there were many concurrent sessions happening at the same time, but I hope this highlights at least a portion of the conference!

The picture above was taken in a talk by one of my favorite speakers from the 2016 conference, Erin Anika Wiley from Westat, titled "Do you Hear What I Hear?: An Examination of Effective Communication". I managed to slip into the back of jammed packed room and furiously write down notes on the results of a survey she conducted asking about opinions of presentations from statisticians. I love that this was the front cover of the magazine because Erin really had the audience laughing and engaged for her entire talk. Also, it was humbling and enlightening to hear the survey responses on how non-statisticians perceive statisticians based on talks and presentations. Definitely motivation for how we can communicate our results more effectively!

The first keynote address was from Cynthia Clark titled "Consider your legacy". She gave inspirational talk discussing what her contributions have been in her personal and professional life. As a new mother, I sincerely appreciated hearing how she prioritized her family throughout her life.

Another great keynote address was from Stacy Lindborg at Biogen with a talk titled "Know your power". Stacy is a natural at connecting with the audience by sharing personal stories from her life. In this talk, she shared five reflections/tips on having a successful career in the face of many challenges (both personal and professional). One of my favorite quotes from her talk was "We love the things that we are good at", which really resonated with me.

Keeping with theme of careers, a topic I found particularly interesting is how to navigate changes in your career. Michelle Dunn, Donna LaLonde, and Nancy Flournoy shared very honest and personal experiences of following non-traditional career paths, recognizing and moving forward when you have failed, and knowing when to leave a job. A central theme from each of their talks was to always be growing your network and find trusted individuals/mentors to help you navigate these experiences.

Similar to the session in 2014, the 2016 conference included a fantastic panel of the past, current and future ASA Presidents. These women were able to persevere in the face of a highly male-dominated field to become ASA Presidents and become role models for women in the early stages of their career like me. Even with all this progress, I agree with Mary Ellen Bock that there is so much more work still to do with supporting more specific minority groups in this field. For example, she eloquently described her hope of one day seeing a non-white woman on this panel of presidents.

In my opinion, one of the coolest sessions was listening to the amazing Mary W. Gray from American University giving a fascinating discussion on what US laws exist to protect women. If you don't know who she is, you can read a summary on wikipedia, which is pretty incredible! One of my favorite things that I learned about her was that she wears a 3/4 euro lapel pin (noting pay inequality is not limited to the US) to promote "equal pay for equal work" referring to the Equal Pay Act in 1963 and Title VII in 1964.  Sadly I didn't get a picture of the lapel pin, but I love the idea!

Wendy Martinez from the Bureau of Labor Statistics gave a great keynote address on what and how federal statistical agencies are thinking about when it comes to data science. Data science is something I am passionate about, so I really enjoyed this talk. I would have loved to see a larger emphasis on data science education though.

Unfortunately Bin Yu from UC Berkeley was unable to join us in person, but the conference organizers were able to set up a video chat and connect to the big screen!  Her talk was titled "A holistic approach to interdisciplinary research" where she began discussing how she has a people centric view of life and research (people are mysteries to unveil just like research). It was amazing to hear a little bit about her background and how she grew up in China during the cultural revolution (1966-1976) where the universities stopped for 10 years. Something I think that cannot be overstated was she noted that she most appreciates intellectual diversity in collaboration and research. Overall, it was an inspiring talk!

The conference ended with a festive dinner filled with new friends, awards, presentations and delicious desserts!

As the conference took place at a hotel, I managed to find a nice location to write down some notes from the talks on my GitHub page. :) Feel free to check them out for more details on the talks that I attended.



Finally, I want to finish this blogpost with a few suggestions for future WSDS conferences. My intention is for these suggestions to be viewed as constructive to help make this conference stronger and more accessible to women in the future.

1. Fewer talks running concurrently and more posters/speed sessions. I appreciated that the keynote talks did not have other scheduled talks, but at other times I had to choose between 5-6 sessions running concurrently, which led to frustration and disappointment that I missed so many other talks. My arguments for this are similar to the ideas previously described to improve JSM (decreasing the number of contributed sessions and increasing the presence and importance of poster sessions). 
2. More talks from women in data scientist positions in academia, government, and industry. I recognize that the term "data science" is very much being actively debated in many settings, but as this conference now has "data science" in the title, I would love to see a larger discussion of data science and how that relates to statistics, here too. There was such a great representation of perspectives from women who pioneered this field. I think it would be equally beneficial to include the perspective from a new generation of women in data science positions. 
3. Childcare. I was surprised to find out that no childcare was available at this conference. Considering a good portion of this conference was dedicated towards discussing issues related to balancing careers with families, I found a lack of childcare a bit ironic (?). This suggestion is motivated by my own personal experience of recently having a baby. I saw several other WSDS attendees who brought their children and probably could have benefited from a childcare service. There are great childcare examples broken down by cost, size of conference, childcare agency, fees charged, etc.  I hope this will be incorporated in future WSDS conferences. 

Thank you to all the conference organizers for putting together this conference. I look forward to the next one in La Jolla, CA Oct 19-21, 2017!