The single most important factor in any educational program is not the curriculum, the campus, or the facilities. It is the people in the room.
At CyberMath Academy’s Summer 2026 program at Harvard Faculty Club, Boston, MA, the people in the room are not retired professors or enthusiastic recent graduates. They are active researchers, current practitioners, and decorated competitors — people who use advanced mathematics and artificial intelligence in their daily professional lives, and who bring that work directly into the classroom.
Here is who your child will learn from this summer.
Igor Ganichev — Google Brain · TensorFlow · UC Berkeley PhD · MIT
Igor Ganichev is a member of the Google Brain team, where he works on TensorFlow — the open-source machine learning framework that underlies a significant proportion of the world’s AI applications. When you use an application that runs a machine learning model — whether for image recognition, natural language processing, or recommendation — there is a reasonable chance it is built on infrastructure that Igor has contributed to.
His academic background is among the most decorated in computer science. He holds a PhD in Computer Science from UC Berkeley and a Bachelor’s degree in Mathematics and Computer Science from MIT — two of the three or four most competitive institutions in the world for these fields.
What makes Igor exceptional as an instructor is not just his credentials. It is that he teaches from a position of active engagement with the frontier of the field. When he introduces students to neural networks, he is not explaining something he learned decades ago. He is explaining something he is working on now.
At CyberMath Academy, Igor teaches the mathematical foundations of machine learning — linear algebra, optimization, neural network architecture — and connects each concept to real systems used in industry. Students leave his sessions understanding not just how to use AI tools, but how they are built.
“The instructors from MIT were incredible — they made complex topics feel exciting. My daughter is now seriously considering a STEM career.”
— Parent · New York, USA
Ibrahim Suat Evren — IMO 2019 Gold Medalist · MIT Mathematics
In July 2019, Ibrahim Suat Evren traveled to Bath, United Kingdom, to compete at the International Mathematical Olympiad — the most prestigious mathematics competition for students in the world. Approximately 600 students from over 100 countries compete each year. Ibrahim won a Gold Medal.
He also won a Bronze Medal at the 2019 Balkan Mathematical Olympiad — a regional competition that draws some of the strongest young mathematicians in Europe and Asia.
He is currently studying Mathematics at MIT.
The International Mathematical Olympiad tests a kind of mathematical ability that is genuinely rare: the capacity to approach problems that no one has solved before, with only fundamental tools, and produce a complete, rigorous, elegant proof. This is not the mathematics of textbooks and examinations. It is mathematics as creative work.
At CyberMath Academy, Ibrahim teaches advanced proof-writing, olympiad problem-solving strategies, and the kind of mathematical reasoning that separates students who perform well on standardized tests from those who think like mathematicians. His sessions are some of the most demanding — and most transformative — in our program.
Students who have no prior competition experience find that even a few sessions with Ibrahim recalibrate their sense of what mathematics is and what it is capable of. Students who are already competing find that his insights accelerate their preparation more than months of solo study.
Dr. Umut Eser — Head of Machine Learning, Cellarity · Harvard Medical School
Dr. Umut Eser is the Head of Machine Learning at Cellarity, a biotechnology company applying AI to understand how individual cells behave — with the goal of developing better treatments for disease. Before joining Cellarity, he conducted research at Harvard Medical School.
His work sits at the intersection of machine learning, biology, and medicine — three fields that are increasingly inseparable. The models his team builds do not recommend movies or filter spam. They analyze biological data at a resolution and scale that was impossible without AI, and they do so in the service of understanding and treating diseases that affect millions of people.
Dr. Eser’s guest lectures at CyberMath Academy bring this work into the classroom in a way that is accessible to students aged 9–16 without being simplified to the point of distortion. He explains what his team actually does, what mathematical tools they use, and why the problems they are working on matter. Students who have heard him speak consistently report that these sessions changed their understanding of what a career in mathematics or science can look like.
One student wrote after the program: “I had no idea that the math I was learning could be used to fight cancer. Now I want to study computational biology.” That response — from a 14-year-old who arrived at camp thinking they were interested in competition math — captures what Dr. Eser’s presence in our program makes possible.
Darrel Deo — Stanford Bio-X · BioRobotics Research
Darrel Deo is a researcher at Stanford Bio-X, the interdisciplinary biosciences institute at Stanford University. His research focuses on BioRobotics — engineering systems that interact with living biological systems, guided by neural signals.
In practice, this means working on prosthetic limbs that respond to signals from the nervous system, exoskeletons that allow people with paralysis to move, and surgical robots that operate with precision beyond human capability. These are technologies that directly restore function to people who have lost it — and the mathematics underlying them is more sophisticated than almost anything in the standard pre-university curriculum.
Control theory, differential equations, signal processing, machine learning — Darrel’s work draws on all of them, woven together in the service of a problem that is both technically demanding and deeply human.
His sessions at CyberMath Academy show students that mathematics is not a set of abstract techniques with occasional “real-world applications” tacked on as afterthoughts. It is the language in which the most consequential engineering of our time is conducted. Students who previously struggled to see why mathematics mattered leave his sessions with a different perspective entirely.
Nicholas Pascucci — NASA · MIT · AI Verification
Nicholas Pascucci studied spacecraft AI verification at MIT and has deployed real AI systems for NASA. His work is concerned with one of the most demanding problems in applied mathematics: proving, with mathematical certainty, that an AI system will behave correctly before it is trusted to operate in an environment where errors are catastrophic.
When a spacecraft is launched, its onboard AI systems must work. There is no opportunity to patch a bug after launch, no chance to roll back to a previous version if something goes wrong. The mathematical methods used to verify these systems — formal verification, logic systems, constraint programming, proof theory — are among the most rigorous in all of applied mathematics.
Nicholas brings this rigor into the classroom. His sessions are not about space as a romantic destination. They are about the mathematics of certainty — about what it means to prove that a system is correct, not merely to test it and hope for the best. For students who are drawn to both mathematics and engineering, these sessions open a window into a world of applied mathematics that most students never encounter before university.
What These Instructors Have in Common
Looking across our faculty, several things stand out.
First, every one of them is currently doing the work they teach. Igor is at Google Brain. Dr. Eser is at Cellarity. Darrel Deo is at Stanford Bio-X. Nicholas Pascucci has worked with NASA. Ibrahim Suat Evren won his IMO Gold Medal and went directly to MIT. None of them are teaching material they learned a decade ago and have been explaining ever since. They are teaching from active engagement with the frontier of their fields.
Second, every one of them teaches from genuine enthusiasm — not performed enthusiasm, but the kind that comes from working on problems you find genuinely interesting and important. Students are extraordinarily good at detecting the difference. They respond to the real thing in a way they do not respond to the performance.
Third, they represent different faces of what advanced mathematics and AI look like in practice. A Google Brain engineer and a BioRobotics researcher and an IMO medalist and a NASA AI verifier — these are not interchangeable examples of the same thing. They are genuinely different kinds of mathematical work, pursued for different reasons, with different tools. Students who encounter all of them leave with a much richer sense of what the field contains than students who see only one corner of it.
The Environment They Create
Faculty quality is necessary but not sufficient. What matters equally is the environment that faculty create — the norms, expectations, and culture that determine how students engage.
At CyberMath Academy, our faculty create an environment where difficulty is expected and respected. Where getting something wrong in front of peers is treated as the beginning of learning rather than evidence of inadequacy. Where questions are welcomed even when — especially when — they challenge the direction of the lesson.
This is harder to engineer than it sounds. It requires instructors who are confident enough in their own expertise to be genuinely comfortable not knowing the answer to a student’s question. It requires a culture where intellectual risk-taking is visibly valued. And it requires the right students — which is why the peer environment at CyberMath Academy, drawing students from 50+ countries who have self-selected into an intensive academic program, is itself a key component of the educational experience.
Learn From Them This Summer — Harvard Faculty Club, Boston, MA
Our Summer 2026 program runs July 20–31 at Harvard Faculty Club, Boston, MA. Students aged 9–16, from all mathematical backgrounds, are welcome. No prior coding experience is required for our AI and Machine Learning track.
Formal letter grades, Certificates of Completion and Mastery, and letters of recommendation for eligible students make this a credential that carries real weight — not just a summer activity.
The people described above will be in the room. The question is whether your child will be.
“My son attended CyberMath at Harvard and came home a completely different student. His confidence in math skyrocketed and he made friends from six different countries. Best investment we’ve ever made in his education.”
— Jennifer M., Parent · California, USA
Apply for Harvard · Boston — July 20–31, 2026
Meet all faculty → cybermath.org/faculty · [email protected] · cybermath.org