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utilizing data science to solve issues in computer vision, medical imaging, and neuroscience
I was extremely fortunate to be given this course because it includes some of my favorite topics, notably probability and linear algebra. Additionally, optimization is a major focus of my study, so it is a perfect fit. Since there isn't really a book that contains all of the fundamental math you need for data science, I'm writing my own notes. Despite being simple, this subject can be extremely challenging, and I really enjoy teaching it. In my opinion, it's critical that students get it properly in order to comprehend more complex algorithms.

What about statistics, linear algebra, and optimization piques your interest?

Each one is essential to data science. I feel that probability is a very helpful tool for dealing with uncertainty and incomplete knowledge in a rational manner. We can create linear models with simple geometric meanings using linear algebra. With the aid of statistics, we can draw inferences from data and assess their veracity. In terms of optimization, perhaps ten to twenty years ago, not many people pursuing a master's in statistics would study very much about it, but now it's vital because it enables us to employ more flexible and complicated models. To comprehend the various approaches to data science that they will be exposed to in the program, students must have solid foundations in each of these four areas. MedsDental is a renowned [Dental Billing Company]( https://medsdental.com/) in the united states, equipped of  the revenue cycle experts who are highly proficient in delivering fast and the error-free billing services to dental practices by using the cutting-edge technology.   


How did your prior experience help you in your current position?

My mother was a doctor, and my father was an electrical engineer. I grew up in Madrid. My main interests in high school were in literature, but I ended up majoring in electrical engineering for my degree. Electrical engineering is a popular choice for good students in Spain since it enjoys the same academic prestige that physics does in Germany. Fortunately, I enjoyed my math and programming classes, so everything worked out okay. It also gave me the option to complete the last 18 months at the École des Mines in Paris, which was a fantastic opportunity.

The main focus of École des Mines was management or finance. I simultaneously completed a master's degree at École Normale because I wanted to learn more about computer vision, artificial intelligence, and machine learning. I then realized how much I enjoy these regions. I then completed my master's thesis on magnetic resonance imaging at Philips in Germany, which I really enjoyed doing. Although I enjoyed the application, I came to the realization that I needed to learn more about the theoretical foundations of some of the algorithms, which is why I chose to pursue a Ph.D.

How much does national methodology for math and data science differ?

French students typically have a strong background in mathematics. Due to its lack of traditions, Spain is less theoretical than other countries. Since I was conducting research for a company in Germany when I was there, I can't really comment on it.

I have encountered numerous researchers from American universities who are very applied yet still have an interest in theory. In addition, more theoretical researchers frequently have a strong understanding of applications. This is really appealing to me.

What did you study for your doctorate at Stanford?

I created optimization-based magnetic resonance imaging methods prior to obtaining my Ph.D. I was quite intrigued by these techniques and, as I had already indicated, I was eager to learn more about the underlying theory. My doctoral work at Stanford was supervised by Emmanuel Candès, who has had a major impact on the field of optimization-based approaches to inverse issues. I researched how these algorithms can be used to solve issues like super-resolution in fluorescence microscopy and came up with theoretical assurances that outline the circumstances in which we can anticipate them to function.

What area of study are you currently working on?

On the one hand, I continue to be interested in the theoretical examination of optimization-based data-analysis techniques. On the other hand, I've grown interested in more useful applications, such the neurology field of spike sorting. Processing data from sensors that capture signals from various neurons is required for this issue. To determine which signal originates from each neuron, you must separate these signals. There are numerous intriguing difficulties, such the sheer volume of data. Due to the large number of measurements, it is essential to ensure that the algorithms are only being applied to the data that contains activity; otherwise, computation will become prohibitively expensive. Managing the billing process accurately is not easy as providers might face hurdles in revenue cycle management. Moreover, Net Collection Rate below 95% shows that your practice is facing troubles in the billing process. To eliminate all these hurdles and maintain your NCR up to 96%, MedsIT Nexus [Medical Coding Services]( https://medsitnexus.com/services/medical-billing-and-coding-services/) are around the corner for you so that your practice does not have to face a loss.


Where do you think the next great discovery in data science will come from?

Amazing developments in machine learning have recently been made in the areas of speech recognition and computer vision. Huge datasets with annotated examples are readily available, which has allowed for these advancements. The ability to extend these methods to other domains where we don't have as much curated data, in my opinion, will be a major task in the upcoming years. Some of the concepts should unquestionably translate, but since the data won't be as clearly organized, you'll need to apply it more subtly. For instance, there is a ton of data in the social sciences, economics, and policy fields, but it is typically quite heterogeneous, making it impossible to formulate queries as simple as "Let's identify some person's face."

We currently have innovative big data techniques that are effective for areas like computer vision, but we still need to figure out how to apply them to a variety of other fields where they may have an impact. It's not immediately clear how to compile the data, find the relevant components, and then combine them in complex ways, which is what we can do when we have enormous curated data sets, like in computer vision. I find this to be fascinating.

Do you like working at Courant and CDS while residing in New York?

I was ecstatic to visit New York. Courant, in my opinion, is a fantastic location. I'm happier here than in an engineering department because I'm more math-oriented. However, I believe there is also a direct connection to apps. I'm starting to get interested in neurology, and Courant/CDS is a great place to explore that.
