Yeah ~ I will be with The Data Incubator (an awesome data science fellowship program) this summer

Two weeks ago, I found out I was ranked at top 2% of all applicants and was selected to join the Data Science Fellowship Program with The Data Incubator (TDI), I was so thrilled. I applied it once around Aug. last year, and only went through the semi-finalist and did not get a chance to go further. I reapplied it again around April this year and found out I was in their semi-finalist again right before Ben and I flew to South Africa to meet our good friends for a rock climbing trip.

Let me give you a bit info about TDI data science fellowship program first. It is “an intensive eight-week bootcamp that prepares the best science and engineering PhDs and Masters to work as data scientists and quants. It identifies Fellows who already have the 90% difficult-to-learn skills and equips them with the last 10%”.  The applicant went through three ‘selections’. You apply through their website (here), and the qualified semifinalists are identified by TDI. Then all the semifinalists are in computer programming, math & statistics, and modeling skill test. For this stage, TDI further identifies finalists through semifinalists’ programming, problem-solving skills for real-world problems. As a finalist, you will be interviewed for the data science communication skills with other finalists, and TDI team will decide if you get in the program a week after the interview. About 25% of applicants (~2000 applicants) are selected as semifinalists and 3% are selected as fellows and scholars. See the figure I made bellow (this is only according to the best knowledge I have for the program).

Fellowship Program

Back to my story ;-). Since we were actually at Rockland, South Africa to start our exciting bouldering journey. I was pretty disappointed about giving up 2 or 3 days out of 8 days of our vacation for the programming, problem-solving test. In addition to that, I have to propose and build an independent data science project. I thought about just postponing or canceling my semifinalist opportunity, and enjoyed the vacation because our wifi was so spotty at the rural South Africa anyway. But I’m glad I did not just give it up. It literally took me 7 or 8 hours in our guest house there to download a 220M dataset from TDI for the test. I was thinking about using my Amazon cloud computer for my independent project, but the internet wasn’t very helpful.

201607011610324f7c3

I basically only used the wifi and uploaded my files and answers while everyone left the guest house for their rock climbings, and the best spot for wifi was in our bathroom, lol~~~ uploading a 15M file took me about four hours with multiple fails. LOL…

Luckily, things worked out, and I can’t wait to join TDI’s summer fellow cohort. I’m super excited about learning more advanced machine learning, distributed computing (Spark, Hadoop and MapeReduce) with the smart data brains fellows.

Wish me luck!!!

Some pictures of Ben, Pete, me and our other friends’ rock climbing pictures here, and let’s rock through our 2017.

34474051975_eb809fe331_b33631141504_e7edb32d51_b34438773036_e7f356cda5_h34560732195_b45c19f388_b34349560771_ef4c215ecd_h

Photo Credits: Ben ;-).

34427308326_d2defdbe10_k34430489451_ea2b16dc2d_k18194177_10210045188829569_4652567858509764791_n

18268270_10210088323427907_5126716707500558209_n

Pete got me(the tiny green bug on the rock ;-)) climbing up a wall at Cape Town local climb.

This basically our best vacation so far, and I am glad I made it through TDI and was able to enjoy the climbing after the test. Our friends Pete and Corlie arranged the whole trip and we’re glad we made all the way to the amazingly beautiful South Africa.

 

 

Artificial intelligence on urban tree species identification 人工智能在市区树种识别上的应用

It doesn’t matter which part of the world you are living now,  very diverse tree species are planted around the urban area we live.  Trees in the urban areas have many functions, for example, trees provide habitats for wildlife, clean air and water, provide significant health and social benefits, and also improve property value too.  Wake up in a beautiful morning that birds are singing outside your apartment because you have many beautiful trees grow outside of your space. How awesome is that!

However, tree planting, survey, and species identification require an enormous amount of work that literally took generations and years of inputs and care. What if we could identify tree species from satellite imagery, how much faster and how well we could get tree species identified and also tell their geolocations as well.

A city has its own tree selection and planting plan, but homeowners have their own tree preference, which the identification work a bit complicated, though.

chicagoTrees

(Photo from Google Earth Pro June 2010 in Chicago area)

It’s hard to tell now how many tree species are planted in above image. But we could (zoom in and) tell these trees actually have a slightly different shape of tree crown, color, and texture. From here I only need to have a valid dataset basically tell me what tree I am looking at now, which is a tree survey and trees geolocation records from the city. I will be able to teach a computer to select similar features for the species I’m interested in identifying.

GreeAsh

These are Green Ash trees (I marked as green dots here).

LittleleafLiden.png

These are Littleleaf Linden, they are marked as orange dots.

Let me run a Caffe deep learning model (it’s one of the neural networks and also known as artificial intelligence model) for an image classification on these two species, and see if the computer could separate these two species from my training and test datasets.

Great news that the model could actually tell the differences between these two species. I run the model for 300 epochs (runs) from learning rate 0.01 to 0.001 on about 200 images for two species. 75% went to train the model and 25% for testing. The result is not bad that we have around 90% of accuracy (orange line) and less than 0.1 loss on the training dataset.

nvidia_d_modeltest

I threw a random test image to the model (a green ash screenshot in this case) and it tells the result.

test_trees2

I will be working on identifying other 20 trees species and their geolocations next time.

Let’s get some answer what trees are planted in Chicago area and how it related to the property value (an interesting question to ask), and also what ecological benefits and functions these tree are providing (leave this to urban ecologist if my cloud computer could identify the species)? Check my future work ;-).