The following testimonials were collected from AUC students who majored in Mathematics, either as their primary major or alongside another major.
The following testimonials were collected from AUC students who majored in Mathematics, either as their primary major or alongside another major.
Gehad Salem (2024). For people who don’t know me, I’m a graduating senior double majoring Computer Science with Mathematics. I have encountered many things where having a math background was really helpful for me.
When I was interning at Dell Technologies, they were once surprised that I have some math background as there were a team about data analytics where they were searching for people good with coding and math skills (one of the managers there asked me about the double major programs at AUC and was really happy that they found something similar to the target audience they were searching for, I don’t know if they still are looking for people or not currently).
When I was doing research (in computer science or quantum computing) a lot of the published papers especially in quantum had some mathematical notation and things related to group theory, so it is beneficial a lot if someone is interested in research. Also, in theoretical computer science, algorithms need to have proof of correctness and optimization techniques where most of this is just math. Currently, I’m taking Algorithms and Complexity theory, and the course has a lot of proof and I feel to have an added edge over the grad students in the course just because of the basic understanding of how proofs work.
I tried to mention something in the industry area and in research to make sure that math is integrated in both of them, not just research as most people believe but it for sure depends on your main motivation and goals.
Omar Hatem (2024). I would say a double major in mathematics has benefits both as soft skills and tangible skills.
Tangible skills:
Any advanced area of computer science requires some specific area of study in pure math. For example: Cryptography (Number Theory), Signal Processing (Analysis), Data Science (Statistics), Networks (Graph Theory and Stochastics), Graphics (Topology and Differential Geometry), Complexity Theory and Algorithms (Logic), Simulation (Numerical Analysis).
When applying to masters programs or even just regular job positions, the math major indicates a knowledge that makes you a more preferable applicant. The soft skills in my opinion are immeasurably more valuable.
Mathematics involves proof writing and that skill has helped me become much better at articulating my thought process (which is a massive plus in any interview where the recruiter wants to see how you think).
Following the proof writing skill, it forces you to really question any assumption and be very clear in your thinking which makes you almost always spot blind spots and fallacies that other people might fall into.
The math major also forces to never be satisfied with assuming that something "just works" and that is a key differentiator between good and bad software engineers
For a real life example, in one of my internships, we were tasked with writing a predictor for racial bias in some dataset. A lot of my colleagues ran right away into using some advanced ML model to immediately show that there was a bias. I decided instead to first rigorously define the problem in mathematical terms, then I used some theorems to show that a certain simple technique would be sufficient to find bias. The trick was there was actually no bias and my team and I were the only ones to figure this out because we did not complicate our solution and actually understood the problem.
In my opinion, a math major is an added plus to anyone working in the computer science field. To be perfectly honest, it is at its best in more research oriented contexts and would be a clear boost on the CV, but its investment here in AUC is not that expensive. I guess you can still do an AI or cybersecurity concentration with it because the electives would overlap. So, in conclusion, I think it is helpful to most people but it is an investment that will require some time and effort.
Anas Ibrahim (2024). Hi all! I graduated the past Spring with a BSc in Mathematics and a BSc in Computer Science.
I’m currently at University of Waterloo in an MMath (Master’s) in Computer Science program. My work is specifically related to Theoretical Cryptography, where everything is basically math. To be completely objective it’s a school that advocates for Theory (and Theoretical Computer Science is basically just math) so much. The Computer Science School is even part of the Math faculty, and the LIBRARIANS know LaTeX. However, the school is reputable even (maybe specially) for industrial companies (like Google). Guess why? They look for rare thinkers and theorists to be the source of innovation of their technology. (I’m not sure of the past statement to be honest, but I highly think it is the case. Someone please correct me if I’m being biased. I want to be completely objective here.)
I got admitted to my program because my supervisor noted my experience with math, specifically Algebra and Number Theory, along with strong theoretical foundations of Algorithms and how to implement them fast (mathematical abstract ones, with proofs).
I’m currently taking two Computer Science courses:
Quantum Information Processing: the whole world is talking about Quantum computing and it seems to be the future of technology, so it seems to be great, rare, and much required in the industry for people who really know Quantum computing, can provide research insights on it, and can implement and understand its algorithms well. Guess what! It’s all theoretical, proof-based, math. All assignments are full of proofs, and students are expected without a doubt to be able to know how to write proofs well.
Optimization for Data Science: the TA for this course is a PhD student in Math. The course uses concepts that I studied under Wafik Lotfallah’s guidance (thank you so much) from Analysis (and point-set topology), along with lots and lots of Linear Algebra. If you open the course notes, you will never know it’s a computer science course. Everything is just theorems, lemmas, and proofs (precise ones). The professor skips lots of steps in the lecture that I would not in any world would’ve known if I wasn’t a Math major. Then, when I saw right in front of my eyes theorems in Topology get used in Data Science and Machine Learning directly, I started to feel I have been oblivious from the fact that these concepts are just Math and I should be attracted to them as well rather than think they’re just plain old engineering.
In short, if you want to work on changing the world with technology and innovations, even in an industrial company, I think you may want to have a math major. To be precise with a basic example: Hey Analysis students, you will take a theorem in Topology called “The Extreme Value Theorem.” This theorem is emphasized for its importance in Optimization, since basically you want to select a vector in R^n that gives the minimum/maximum value of a function subject to constraints that may describe a compact set.
Ahmed Tokhy (2024). Hey guys! I'm Tokhy, a graduating senior double majoring in Mathematics & Computer Engineering. I came from a background that had nothing to do with Math at all (med school) and at some random time while taking Discrete I realized that I basically never learned actual Mathematics. The way new questions kept stimulating my mind throughout the course & whole major understanding the foundation of basically all modern sciences was enough motivation to get in deeper. Studying Math as a major didn't only fulfill my curiosity for different science basis but also had a direct impact on my work in computer science throughout the last two years (I've been working in R&D).
In my R&D role, I've been applying formal logic to optimize our software architecture. Using techniques from propositional and predicate logic to create more robust formally documented systems, I've been able to rigorously verify the correctness of our work, catching subtle bugs that traditional testing missed. I've also leveraged set theory and graph theory to model complex data relationships, leading to more efficient database designs especially in working with billions of detailed data (once we needed to label 5B unlabelled pharmacy data, some graph theory techniques helped reduce the running time by MARGINS).
Also, getting deep into the foundations rabbit hole I discovered reverse mathematics which instantly clicked with me and it is the foundation of my computer science thesis (applying it to a customized architecture of the transformers, trying my best to recognize its lost potential).
Basically, the major for me was not only about learning Math, but it allowed me to actually have a deep understanding of things that we always use & take for granted. This alone is enough to promote new ideas that may someday reshape critical parts of modern sciences.
Amer Esmail (2024). Hi everyone. This is my last semester, and I am double majoring in Mathematics and Computer Engineering. I agree with all the points stated above, especially the soft skills part mentioned by Omar. Math really gives you a different perspective wherever you go. But I would like to emphasize that effect with some real life examples:
I did an internship at Google where I worked on static analysis i.e. finding problems in a code without running it. I was trying to find redundant conditions in the code and make them more simple in an automatic manner. I ended up thinking a lot about logical implications, making sure that some other condition guarantees the conditions that need removing to ensure programs are equivalent. That needed some logic and I could only find a generic organized solution by thinking abstractly on the problem just like what we do in math courses.
I also did a research internship at the Lab of Security and Cryptography (LASEC) which is under the School of Computer and Communication Sciences in EPFL. The internship was mainly math. I developed a symmetric cryptographic system using Elliptic Curves, and I used a lot of modern algebra and number theory. It wouldn't have been possible without taking both courses at AUC.
All in all, I found math to be helpful in giving me a better and more abstract perspective on many problems.
Bassel Akrum (2024). Hello everyone. My name is Bassel. I graduated spring 2023 with a double major in Computer Science and Mathematics. I work as a data scientist and currently a masters student at imperial college london. For me personally, I chose to pursue the second major which in my case was mathematics because I was interested in data science and the theory behind it. I specifically chose courses related to data science or adjacent to it. The maths electives were also very flexible and allowed me to gain a variety of knowledge regarding important data science topics like linear algebra, statistics, probability, time series, regression and much more. Not only that but the mathematics major gave me the proper skill set to allow me to look at problems with various approaches and not look at things face value. It really opened my mind in the way I logically approach things even in my real life problems, not just work related. Also having a mathematics background is a huge plus in almost any field you go into and gives you an edge when applying to jobs or when pursuing higher education.
Alaa Aoun (2024). Hello everyone, I hope you're all doing well. I'm Alaa Aoun, and I graduated last spring with a BS in Mathematics, concentrating in Statistics and Data Analysis. I agree with what my colleagues have already shared, and while I don’t have much to add, I’d like to speak from my simple experience. I chose mathematics as a major because for me it is a fundamental and foundational tool that opens doors to a wide range of fields. I believe that studying mathematics trains the mind to think critically, question assumptions, and view problems from multiple perspectives. Currently, I am pursuing my master’s degree in Computational Science with a focus on Data Analytics at the American University of Beirut (AUB), supported by a full Graduate Assistantship. This semester, I am enrolled in two courses: Machine Learning and Advanced Optimization Techniques. In my Machine Learning class, the professor emphasized the importance of theory, acknowledging that many students find it challenging. However, I found the theoretical aspects less challenging due to my Math background. My professor highlighted that understanding the theory behind the algorithms is essential before moving on to coding, as it helps answer the crucial question of "why?”.
Abdelrahman Ibrahim (2024). Hi all! I graduated from AUC in spring 2022 with a major in Mathematics, and I am currently in my last semester of the maths MSc in the university of Münster with only the master's thesis left to submit. I am also working as a TA in the real analysis course in AUC since I returned to Egypt after finishing the coursework of the MSc and part of the master's thesis. The maths courses in AUC provided me with the prerequisites necessary to follow some of the courses in Münster. For instance, knowledge of modern algebra and graph theory helped me study geometric group theory. Also, doing a guided studies course on model theory with Isabel and Daoud and then writing the thesis with Isabel prepared me for further courses and seminars on the topic, which eventually led to the topic of my master's thesis.
Abdallah Taha (2024). I graduated last spring with a double major in Computer Engineering and Mathematics. The major of mathematics helped me in different ways. Some of which I have listed below:
I am currently a Software Engineer at Siemens working on a tool that does power analysis on digital designs that are eventually manufactured into silicon chips that we rely on heavily in our daily lives. Since my work is not related to the analysis part of the tool I expected to encounter no math in my work, but I was wrong. The circuits in the digital designs were represented as a tree, and the Graph Theory Course was of great help. Not only did I find it helpful, but my manager showed great interest in my knowledge of Graph Theory and Linear Algebra, which he highlighted in a presentation for the upper management towards the end of the release cycle. As one of my colleagues in the team once mentioned, as a software developer, what makes you unique are the things you know other than software development. Someone might disagree with this statement, but mathematics is indeed one of the domains with ubiquitous applications in the software industry, and it's a plus to know math.
In my previous internships at Microsoft, my knowledge of math and statistics helped me understand papers in the field of Natural Language Processing (I had to put in some extra effort, but I didn't start from scratch). As Alaa mentioned math is very crucial in the field of machine learning (and a big reason why a lot of people find this field challenging).
Lastly, I want to stress the importance of the soft skills you gain by studying math (which Omar mentioned). The exercise of solving problems by breaking them down into smaller ones, or proving a statement by proving easier-to-prove statements and putting them together is useful beyond imagination as it improves your problem-solving skills that you will need in your life and your work no matter what you end up doing.
Nabil Boulos (2024). I’m currently a 3rd-year PhD student in Statistics at Cornell University. I pursued a double major in Actuarial Science and Mathematics at the American University in Cairo (AUC). Initially, I declared actuarial science as my major but later added mathematics in my second year. This decision proved to be one of the most pivotal in shaping my academic and professional journey for several reasons.
Mathematics, particularly at higher levels, demands a level of rigor and precision that few other disciplines require. This meticulous training has been instrumental not only in my success within actuarial science but also in my current work in statistics. The discipline pushes you to break down complex issues, identify patterns, and derive solutions systematically. This ability to think in a structured, logical manner has enhanced my approach to statistical models, helping me tackle abstract and complex problems more effectively.
What stands out the most about my mathematics education is how it teaches you to be both precise and thorough. It instills a habit of approaching problems with careful consideration, always keeping an eye on the broader structure of the solution. Whether working on complex statistical analyses or collaborating with research labs in other fields, I’ve found that having this grounding in the underlying framework of problems has given me a clear advantage. It allows me to maintain a comprehensive overview while also attending to the fine details, which is crucial in both theoretical and applied research settings.
In hindsight, choosing to double major in mathematics was not just an academic decision—it was a strategic move that continues to shape my career in profound ways. Universities and research institutions alike recognize the value of a solid mathematics background, knowing that it equips candidates to think critically, adapt to new challenges, and solve problems from multiple angles.