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| 00:00 | the director of the pattern analysis laboratory the idea of this laboratory is to |
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| 00:06 | artificial intelligence and machine learning techniques in applications. We cover a lot of |
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| 00:14 | areas but we mainly focus on applications physics and astronomy. Now what we |
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| 00:22 | is always the result of uh collaborative . Um I want to acknowledge that |
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| 00:28 | have very bright and hardworking students that crucial to to attain good results in |
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| 00:35 | laboratory. Okay. Right. So to position our research lab in in |
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| 00:42 | in a specific area of study, do artificial intelligence and sometimes we we |
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| 00:50 | use of certain areas within artificial intelligence outside machine learning such as search techniques |
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| 00:57 | knowledge representation. Uh but we mainly on on what is known as machine |
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| 01:04 | . And with machine learning we do and regression and clustering mainly some reinforcement |
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| 01:13 | and genetic algorithms now are areas of within machine learning fall mainly in what's |
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| 01:21 | transfer learning. The problem of building model in certain tasks. For example |
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| 01:28 | astronomy you want you may want to how to classify certain types of stars |
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| 01:34 | when a new survey, astronomical survey , then you realize that your previous |
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| 01:39 | is no longer applicable because of the data because of the new instrumentation and |
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| 01:45 | on. And the question is how you adapt the previous model to the |
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| 01:49 | task uh to leverage that knowledge and you gain before avoiding having to start |
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| 01:57 | scratch. So this area of transport is also known as the main |
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| 02:03 | Uh Similar area called metal learning is how to create learning systems that that |
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| 02:11 | and adapt over time and learn how correct a based learning system. So |
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| 02:17 | learning to learn the learning system now how to correct itself automatically. |
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| 02:25 | we've also been exploring to new One is called symbolic learning and the |
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| 02:29 | one is called Discovery Systems and I'll more about this. As I |
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| 02:34 | we have many applications of machine We we do use deep learning and |
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| 02:41 | I mentioned, transfer learning domain imitation metal learning to areas in astronomy, |
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| 02:49 | science. Lately we've been working on main research collaborations with astronomers uh here |
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| 02:58 | the U. S. And in . One of the projects is about |
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| 03:02 | super knobby caesar is a massive explosions are crucial because they help us by |
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| 03:10 | these explosions and and the light it to measure the distance to different |
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| 03:16 | And also we were working on a identifying Boyd's in the large structure of |
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| 03:25 | universe using machine learning techniques. I that we have new areas here of |
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| 03:32 | . 11 of them is called symbolic within the whole area discovering systems, |
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| 03:39 | equations. There's been a hype there uh on how to use machine learning |
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| 03:43 | discover new equations because we focus in and astronomy. But this applies to |
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| 03:50 | areas in in basic science like biology chemistry. And the idea is that |
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| 03:55 | you have certain data um and certain about, you know, how the |
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| 04:00 | was gathered, What are the laws that that that area where the data |
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| 04:07 | was gathered? Data is how can use machine learning to come up with |
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| 04:13 | equations that explain the data? So whole idea is how to discover new |
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| 04:18 | in science. Alright? So if you're interested at any point to join |
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| 04:26 | lab, just send me an I'll be happy to discuss that with |
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| 04:29 | . Thank |
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