It’s Alive! AI with Julieta Martinez

We are delighted to welcome our next curator, Julieta Martinez (@yoknapathawa). Julieta is a PhD candidate in the department of computer science at the avatarUniversity of British Columbia, where she previously received a Master’s degree in computer science as well. Julieta was born and raised in Mexico City.

Julieta conducts research in computer vision. The main focus is on algorithms that look at humans in images and video and infer actions and body configurations. Julieta is also interested in systems for large-scale similarity search, with a focus on fast visual search in large databases. So if you have questions about algorithms, this is your chance to ask a scientist! Here’s her story.

Why/How did you end up in science? Thanks to a little bit of chance and a whole lot of luck. I grew up in Mexico city, and originally wanted to study math. However, I was lucky enough to be offered a full-ride scholarship at an engineering college that, alas, did not offer math degrees. I ended up picking computer science because I figured it would be closely related to math. Being a giant nerd, grad school felt like a natural step, and that’s how I ended up pursuing grad studies at UBC.


I remember hearing about IBM’s Deep Blue defeating world chess champion Garry Kasparov, and the hype about artificial intelligence as the panacea for all of humanity’s problems that ensued. However, somehow that promised future never seemed to materialize! After finishing my undergrad, I was (and still am!) keen on pushing our current technological limitations towards understanding and replicating human intelligence.


I work on computer vision; a subfield of artificial intelligence where the goal is to make computers gather semantic information from images similarly to how humans do. Common tasks include recognizing objects in images, finding human bodies and predicting intentions in video, or estimating physical properties such as depth and speed of objects from video footage. Currently, a lot of work in computer vision relies heavily on (and is often at the forefront of) applied machine learning; i.e., making machines learn new tasks as automatically as possible.


Many people are probably wondering not if, but rather *when*, a machine is going to take over their job; and recent media coverage of my field has done little to help assuage these fears. I would like to use my opportunity at @realscientists to put the recent progress of machine learning and artificial intelligence in context, and help people outside my field to think about these changes more critically.

I am an avid speed chess player (it’s a thing!). My online account is over here. I’m always open to friendly challenges


How would you describe your ideal day off?  In the morning go for a hike or a bike ride. Then go and read for a while in a coffee shop in East Vancouver. Play some dodgeball or beach volleyball in the afternoon, and of course some speed chess before going to bed!


Please welcome Julieta to Real Scientists!

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