I've known I wanted to go to Stanford since I was 11 years old when I read a book that said something like "having a degree from Stanford University is a big deal." From then on I had an almost problematic obsession with doing well in everything so that I would be admitted.Because I knew I wanted to go there, I worked really hard in high school to stand out from my classmates. I went to a moderately wealthy high school, and competing academically and socially with people that have access to generational wealth takes strategy. I chose to take on 2-3 extracurriculars each year. Freshman year I was the president of the freshman class and captain of the freshman basketball team and played volleyball, Sophomore year I was a chair in the same student government, on varsity basketball and JV volleyball, and participated in the competitive mathematics club. Junior year I cranked up the AP courses, taking essentially everything AP, still playing on Varsity Basketball and Volleyball. Senior year I was captain of the Basketball team along with my other extracurriculars and APs. There probably were other things, as this was over a decade ago. In terms of academic performance, I was ranked #8 in my class upon graduation with above a 4.8 GPA.Even though I had a strong record, I was still nervous to apply, and so I decided to apply to the Restrictive Early Action round, which means you cannot apply early to any other school, although you are not forced to attend upon acceptance. Luckily I was accepted, and decided to not apply to any other school While I put myself in a good position to be accepted, nobody else at my school was, including those that had better academic records than me. This could be because of my application essay, in which I told the story of why I have 9 siblings in my family and how that has helped me grow as a person. I think the academic performance and the uniqueness of my story were helpful in standing out from the crowd of perfect transcripts.

I think that the answer below on drones for agriculture and food security addresses many of the main advantages we can take. Using drones can reduce the manpower necessary for high throughput and can help monitor and optimize resources, which can be very beneficial for the public. However, we must look ethically at every stakeholder that would be affected by these integrations before we can truly understand the benefits.

At its core, computer vision involves taking in images and processing them for real-world significance. In terms of living spaces, people have used computer vision for things like home construction planning, decoration arrangement planning, and trying out furniture in your house. This is cool and all, but I really wish we would explore computer vision for accessibility in our living spaces. If we had robust visual understanding systems that had seamless integration with natural language synthesizers, we could provide more enhanced accessibility tools for the blind living alone. If we had visual assistance robots that could do household tasks like cooking and cleaning, those with mobility issues could really benefit. There will always be an unexplored importance in areas that don't make tech companies money, and those areas often have the greatest impact.

My biggest advice to women starting out their careers in tech is to be confident. Often times, I feel that women are discouraged from pursuing highly technical careers because of male dominance or the lack of confidence in their ability to make it happen. One thing that I've realized is that men are not better at women inherently at programming. Removing humility for a moment, I'm probably better than the majority of men in my program at software development. This is because of the confidence that my family and mentors have instilled in me along the way that has allowed me to push through the difficult situations, including failing classes, internship/scholarship/fellowship rejections, and the general abuse that is almost unavoidable as a woman in this field, while developing my skills to a professional level. My other big piece of advice is to understand that tech is difficult for the majority of people, and to find a community of supporters in your early days. When I first began programming academically 10 years ago, it was nothing like I had ever done before - I had a little experience with web programming and computer architecture, but let me tell you, that introductory C programming course made me cry on several occasions. I actually vowed at one point to never program again. However, I had a community of women around me in computer science and other engineering fields, and they really built me up, helping me learn the concepts in ways that made sense to me, reviewing my problem sets for bugs that I wouldn't have noticed, and giving me confidence that I probably had no business having with my skill level at the time. I wouldn't have had the balls to take some of the risks that I did without the confidence they instilled in me.;It is so important to find a community, and they do exist outside of academia. For example, baddies in tech is a community I'm part of that is provides to support, recruitment and mentorship for black women in their early tech careers.

I think that often times people assume that one's identity as a black women is by default interwoven into one's academic experiences, and while that may true, I never believed that the marginalization I face based on my identity meant that there was something I could not achieve. Quite frankly, someones racism or sexism towards me is their own problem, and over time I've become immune to it. Additionally I believe the glass ceiling is a metaphor people put on us that inadvertently gives some people imposter syndrome. To me, there are no glass ceilings. I know that if I'm able to build technology solve problems for many people, and advocate for myself while doing it, I'll be able to have whatever impact I want on the world and gain recognition for doing it. 

First I will say that I don't have an extensive background on agriculture. However in the short tern and from a resource consumption perspective, the monitoring capabilities that unmanned vehicles possess can allow for the collection of consumption data that can be later optimized to reduce resource wastage of water, fertilizer, electricity, and other important resources. Building capabilities that analyze the environment could additionally help prevent damage to that environment, such as groundwater pollution and other problematic situations. From the food insecurity perspective, Indoor, vertical farming could also potentially optimized and improved through the incorporation of UAVs to provide fresh produce to areas with food insecurity. Such a system, however, would need to be evaluated by agriculture scientists in terms of the environmental impact of the system itself, what resources would be needed to maintain it, and the potential for contamination of food through in close proximity to the system.

I think that this application could have a huge impact, but needs to be thought about carefully from not just the financial benefit and food safety perspective, but also from an ethical perspective that includes all affected parties and environments.

I think that success is subjective. While it may seem that I've had a straightforward, uncomplicated trajectory to where I am now and that I've already made it, the journey has not been that simple, and I honestly haven't accomplished the majority of my goals in life. Up to this point I've often gone with the flow in choosing my experiences and prioritized doing things that I feel will be beneficial to others, particularly those in my community. This remains one of my guiding principles today.

I think this is a bit dangerous. Attempting to ascertain sentimental correlations and apply them to huge financial decisions may work in certain contexts and you could definitely train a model with 99% training accuracy on this task, but future situations that are dependent on complex human action can never be adequately represented by a numerical parameterization and a finite state machine. If the model is not large enough, we will not learn all the possible combinations of interactions. If it is too large, then we only learn the context of our training dataset. That being said, you could do both and get good results during training. Personally, I do not have extensive NLP experience or Bayesian experience in production, but their fundamentals suggest that they would learn this type of model well independently or in conjunction. Naïve Bayes is good for state estimation-based decision making, and NLP can be used to model language and extract sentiment. However, these models depend completely on the input dataset that one utilizes, and the chosen labels (if using a supervised method) that are often subjective. Using data from the internet is also dangerous because it is next to impossible to have humans annotate every piece of training data without spending a large amount of money, and learning from problematic input data can lead to problematic situations.

To your first question, I believe they somewhat already have. If you look purely at the numbers, I believe they already lead the world in newly published AI research or are quite close to that. In my opinion, its because the K-12 education system is much more tech focused there, and until the US competes at that level of education as well (which seems somewhat unlikely, considering the state of public education in the US government), we will surely be not just outpaced, but completely dominated in the next few decades. Also at the doctoral level at many Ivy+ schools, almost half of PhDs in tech come from international students, so you can draw whatever conclusions you'd like from that.

Computer Science is within the School of Engineering and is widely considered an engineering major. It is also quite related to Electrical Engineering in regards to subject matter, and because my concentrations in both degrees were related it was a natural transition, and many of my undergraduate courses counted for credit towards my master's degree. Specifically, my undergraduate degree was concentrated in artificial intelligence (focused on visual and audio processing), and my master's degree was concentrated in signal processing (also focused on visual and audio processing).

Oftentimes modern construction suffers because  there are still many required processes that are paper processes (think change orders, work tickets, etc) and furthermore, compliance is a non-negotiable requirement. With even simple innovations such as digital management applications, wecan reduce the amount of time and loss due to managing the work site. Additionally, AI interventions can provide first-pass (pre-human) evaluation for things that obviously need fixing, again improving construction throughput without sacrificing  quality.

I’ve worked on many projects, including clustering error detection for hotels and travel, robotic based touch device testing, visual tracking apps for virtual reality,  firmware for automatic glucose testing, mobile vision research, and a variety of deep learning tasks. Personally, the projects that I enjoy the most are those that actually provide value beyond financial benefit for companies, so I didn’t particularly enjoy any of my internships, which was a large motivation in pursuing a PhD.

Personally I don’t have much knowledge on European and German robotics aside from their publications, which seem to be on par with every nation. In my opinion everyone is at a very similar level, and leaders can be pushed aside quickly given luck in research findings. I’ll also say that I’m not a Candidate yet, as I have one more requirement for qualifications to complete. I also don’t have an educated perspective on German education to answer that question well, but I always urge students to choose a graduate program that will support them personally and financially towards their ultimate career goal.

The best advice I’ve received about writing essays was “facts tell, stories sell.” I believe that Professors simply get exhausted reading identical looking applications, so the most important thing you can do is tell a story. Allow people to understand who you are, empathize with you, and want to give you a chance. The implicit bias many people have to support people of their own race is because they add their own personal stories to applications of students with similar backgrounds as them, and to compete with this, it is important to tell your story as truthfully and profoundly as possible. If they understand where you come from and want to give you a chance, bias can more easily be set aside.