WEBVTT 1 00:00:00.000 --> 00:00:05.800 Computer Science is logical and mysterious at the same time 2 00:00:05.800 --> 00:00:08.520 Transfer your human language into the language 3 00:00:08.520 --> 00:00:09.640 the computer understands, 4 00:00:09.640 --> 00:00:13.160 gives you the power to provide human beings with the solutions, 5 00:00:13.160 --> 00:00:15.720 Studies the underlying nature of computations. 6 00:00:15.720 --> 00:00:17.880 Arguably over the past 20 years, 7 00:00:17.880 --> 00:00:22.560 No other field has changed the way we live more than computer science. 8 00:00:22.560 --> 00:00:24.560 machine learning, database, computer networks, 9 00:00:24.560 --> 00:00:28.120 computer music, visualization, software engineering, distributive systems, 10 00:00:28.120 --> 00:00:30.840 advanced topics in machine learning and artificial intelligence, 11 00:00:30.840 --> 00:00:32.040 embedded systems. 12 00:00:32.040 --> 00:00:33.280 At NYU Shanghai, 13 00:00:33.280 --> 00:00:37.320 we currently have about 40 computer science majors every year. 14 00:00:37.320 --> 00:00:40.680 Our very renowned faculty, in their research areas, 15 00:00:40.680 --> 00:00:43.040 they are all outstanding instructors. 16 00:00:43.040 --> 00:00:47.040 My first course was machine learning because it's so cool. 17 00:00:47.040 --> 00:00:50.360 Deep learning methods make you think in a very data-driven way. 18 00:00:50.360 --> 00:00:54.320 It can help us solve so many non-convex problems. 19 00:00:54.560 --> 00:00:58.200 This Lab is used by computer science and engineering students. 20 00:00:58.200 --> 00:01:00.400 We have a course called Digital Logic. 21 00:01:00.400 --> 00:01:03.520 They come here, and they have hands-on experience on devices. 22 00:01:03.520 --> 00:01:06.560 To check digital chips such as a CPU, 23 00:01:06.560 --> 00:01:09.480 compute the sum of two numbers or the multiplication of two numbers. 24 00:01:09.480 --> 00:01:12.840 So they have hands-on experience here with the material. 25 00:01:12.840 --> 00:01:16.560 Here the lighting is controlled automatically by a controller 26 00:01:16.560 --> 00:01:18.160 that was designed by our students. 27 00:01:18.160 --> 00:01:21.480 The lights turn on automatically if it detects people in the room. 28 00:01:21.480 --> 00:01:26.360 The lights dim down a little if there is a lot of sunlight from outside. 29 00:01:26.360 --> 00:01:28.400 Now finally we save a lot of energy. 30 00:01:28.400 --> 00:01:32.800 Music can be interpreted from an information perspective. 31 00:01:32.800 --> 00:01:36.080 So we developed an algorithm that can 32 00:01:36.080 --> 00:01:39.800 automatically learn the difference between different acoustic properties 33 00:01:39.800 --> 00:01:41.360 and transfer the performance. 34 00:01:41.360 --> 00:01:43.600 use haptic device or 35 00:01:43.600 --> 00:01:47.680 ‘Magic gloves’ that you can just put those gloves on your fingers, 36 00:01:47.680 --> 00:01:49.400 on your hands, and it moves. 37 00:01:49.400 --> 00:01:51.920 So you learn the music by feeling about it. 38 00:01:51.920 --> 00:01:53.440 After taking this course I learned that 39 00:01:53.440 --> 00:01:55.640 there's like this whole division of machine learning 40 00:01:55.640 --> 00:01:57.960 and computer science in general that's devoted to 41 00:01:57.960 --> 00:01:59.720 earning about computer music and 42 00:01:59.720 --> 00:02:05.160 how we can kind of link psychology and computers and kind of study this. 43 00:02:05.440 --> 00:02:06.440 At NYU Shanghai 44 00:02:06.440 --> 00:02:07.840 We're very lucky to have our own 45 00:02:07.840 --> 00:02:09.880 high-performance computing environments. 46 00:02:09.880 --> 00:02:12.120 Both our faculty members as well as our students 47 00:02:12.120 --> 00:02:14.520 are allowed to work on their projects there, 48 00:02:14.520 --> 00:02:17.400 if they have any research or resource intensive competitions. 49 00:02:17.400 --> 00:02:18.440 They can use it. 50 00:02:18.440 --> 00:02:20.800 Online data is everywhere in our life 51 00:02:20.800 --> 00:02:24.680 and computer science can help us to analyze them and to get a better 52 00:02:24.680 --> 00:02:26.480 understanding about what's going on. 53 00:02:26.480 --> 00:02:28.400 Data Structures, in this course, 54 00:02:28.400 --> 00:02:31.640 I can learn how to improve the efficiency of my codes, 55 00:02:31.640 --> 00:02:33.840 Which is very important in programming. 56 00:02:33.840 --> 00:02:39.480 Distributive Systems, it is about how to synchronize computing devices, 57 00:02:39.480 --> 00:02:44.040 computers work together to solve heavy and tedious problems. 58 00:02:44.760 --> 00:02:48.640 Computer science and math, they have deep connections, 59 00:02:48.640 --> 00:02:52.160 Algorithms tell you how to solve problems efficiently. 60 00:02:52.160 --> 00:02:55.120 Computer Networking at NYU Shanghai is really fascinating 61 00:02:55.120 --> 00:02:58.880 because it really provides the foundation for how the internet works 62 00:02:58.880 --> 00:03:02.640 and how every kind of communication in the computer world happens. 63 00:03:02.640 --> 00:03:04.880 Operating systems seemed very daunting to me, 64 00:03:04.880 --> 00:03:06.240 and I didn't know that much about it, 65 00:03:06.240 --> 00:03:09.480 but together here with different students and the faculty here, 66 00:03:09.480 --> 00:03:12.520 was able to learn a lot more in depth about operating systems. 67 00:03:12.520 --> 00:03:16.200 Something I want to explain to them, to teach them is how 68 00:03:16.200 --> 00:03:19.000 rigorous computer science is. 69 00:03:19.000 --> 00:03:22.240 Teaching here is a magnificent experience but also 70 00:03:22.240 --> 00:03:25.600 observing, interacting with students. 71 00:03:25.600 --> 00:03:28.360 Not all computer scientists are nerds. 72 00:03:28.360 --> 00:03:32.280 Our students, they communicate with each other quite often. 73 00:03:32.280 --> 00:03:36.600 Our students are passionate. They are creative and work hard. 74 00:03:36.600 --> 00:03:39.760 They come from different cultural backgrounds. 75 00:03:39.760 --> 00:03:41.840 It's always fascinating to see 76 00:03:41.840 --> 00:03:46.320 how those cultural differences collide and spark wisdom. 77 00:03:46.320 --> 00:03:47.880 Not only teaches you how to think, 78 00:03:47.880 --> 00:03:50.840 t teaches you how they solve the problem in a very 79 00:03:50.840 --> 00:03:53.680 scalable, flexible, efficient way. 80 00:03:53.680 --> 00:03:57.480 In our courses, most of the courses are project-based learning. 81 00:03:57.480 --> 00:04:00.320 We have active learning schemes to provide 82 00:04:00.320 --> 00:04:02.400 project-based learning classes, 83 00:04:02.400 --> 00:04:05.240 where the students actually get to work together, 84 00:04:05.240 --> 00:04:10.920 interview potential users, design and develop the system and eventually 85 00:04:10.920 --> 00:04:15.240 deliver and maintain a working product by the end of the semester. 86 00:04:15.240 --> 00:04:19.600 Students get to spend a year abroad in New York or Abu Dhabi, 87 00:04:19.600 --> 00:04:22.880 which also provides a very rich set of elective courses 88 00:04:22.880 --> 00:04:25.120 that students can potentially take. 89 00:04:25.120 --> 00:04:27.800 NYU Shanghai, NYU New York is all together 90 00:04:27.800 --> 00:04:29.480 I think that’s what is so good about this place 91 00:04:29.480 --> 00:04:31.680 It’s because you're getting so many different experiences 92 00:04:31.680 --> 00:04:33.120 by being in one place. 93 00:04:33.120 --> 00:04:37.520 I’m very proud that many of our students have gone on to top 94 00:04:37.520 --> 00:04:39.360 PhD programs in the world. 95 00:05:34.040 --> 00:05:36.480 If you’re kind of a logical thinker, 96 00:05:36.480 --> 00:05:39.480 you will most likely be good at computer science. 97 00:05:39.480 --> 00:05:42.240 But if you're both a logical thinker and a creative thinker, 98 00:05:42.240 --> 00:05:45.200 then you'll be outstanding at computer science.