In a recent discussion on GIAI Square, a student raised concerns about networking opportunities in the SIAI 2.0 AI MBA program, particularly about the strength of the alumni network and its impact on career opportunities post-graduation. As a professor and industry professional, I provided my perspective based on both academic experience and real-world industry exposure.

With the AI MBA program now divided into technical and business tracks, it is essential to address both career paths. This article focuses on networking for technical students—those who aim to work in AI/DS engineering roles or quantitative finance, where expertise matters more than connections alone.

Networking in STEM: Beyond Job Fairs and School Prestige

During my own education, job fairs were frequent, with Fortune 500 companies sending HR teams to present their hiring strategies. However, let me be blunt—these events were not about hiring top talent. They were primarily about company branding and industry presence. 99.9% of attendees did not walk away with job offers.

For technical professionals, the challenge is clear: your value must be demonstrated through skill, not just credentials. Schools provide a foundation, but true expertise comes from independent work. The best networking strategy for technical students involves:

• Building a strong GitHub presence: Employers often search candidates online. A well-documented portfolio of math-heavy AI/DS projects will do more for your career than a LinkedIn profile alone.
• Engaging in open-source AI projects: Actively contributing to repositories increases visibility among technical hiring managers.
• Participating in AI/DS research communities: Whether through Kaggle competitions, research publications, or AI forums, showcasing expertise is critical.
• Technical blog writing and discussions: Sharing insights on AI/DS applications and mathematical concepts demonstrates thought leadership.

SIAI’s Approach to Technical Networking

Unlike traditional MBA programs, where networking often means connecting with HR teams, SIAI’s technical track focuses on peer and mentor-driven networking. Our approach emphasizes:

• Direct engagement with senior AI researchers: Instead of prioritizing HR-led job fairs, SIAI hosts expert-led discussions on hiring preferences.
• Research-driven networking: SIAI students gain access to real-world AI/DS projects, allowing them to work alongside experienced professionals.
• GIAI’s Future Investment Arm: As part of its long-term vision, GIAI—the mother institution of SIAI—plans to launch its own investment vehicles, including a hedge fund focused on computational finance. This initiative will provide high-performing technical students with opportunities to transition into quantitative finance roles and AI-driven investment research.

What Really Matters for AI/DS Professionals?

For technical track students, networking is not about the quantity of connections but the quality of expertise. AI hiring decisions are often made by technical leaders, not HR representatives. Companies look for candidates who demonstrate:

• A deep mathematical and computational foundation
• Strong problem-solving abilities in AI and Data Science
• Independent research or engineering contributions

If you focus on becoming an expert in your domain, networking will follow naturally—senior researchers and industry professionals will recognize your work and recommend you for opportunities.

More information available in the discussion thread.

Unlike typical AI bootcamps, SIAI offers in-depth AI education with a strong foundation in mathematics, statistics, and real-world business applications.
The MSc AI/Data Science program at SIAI emphasizes rigorous scientific studies, ensuring students master the theoretical and practical aspects of AI.
SIAI’s MBA AI programs incorporate extensive business case studies, with a new MBA AI/Finance track focusing on corporate finance and financial investments.

Beyond AI Bootcamp

AI bootcamps have become a popular way to enter the field, promising job-ready skills in a matter of months. However, these programs often emphasize coding without the necessary depth in mathematical reasoning, algorithmic theory, or real-world application complexities. While they may provide an entry point, they fall short in developing expertise necessary for advanced AI research, business strategy, and financial decision-making.

At the Swiss Institute of Artificial Intelligence (SIAI), we go beyond the standard bootcamp approach. Our programs are built on a foundation of rigorous academic principles, blending mathematical and statistical rigor with AI-driven business applications. We train professionals to understand AI at its core, rather than just using pre-built libraries and models.

Recognizing the need for structured AI education, SIAI’s mother institution, the Global Institute of Artificial Intelligence (GIAI), offers a 'free' AI bootcamp course on GIAI LMS(https://lms.giai.org). This courseware serves as an introductory learning platform, providing accessible AI and data science fundamentals for beginners.

While the free bootcamp offers valuable foundational training, it is designed only as a stepping stone to more advanced studies. For those seeking deeper expertise, SIAI’s MSc and MBA AI programs provide the next level of education.

MSc AI/Data Science: The Scientific Approach

SIAI’s MSc AI/Data Science program is built on the pillars of mathematics, statistics, and scientific computing. Unlike bootcamps that focus mainly on coding skills, our MSc program ensures students develop a strong understanding of:

• Advanced mathematical modeling for AI
• Statistical inference and probability theory
• Computational optimization and algorithmic design
• Theoretical and applied machine learning
• AI research methodologies and scientific experimentation

Graduates of this program are equipped not only to implement AI models but to develop new AI techniques and contribute to scientific advancements in artificial intelligence and data science.

MBA AI/Big Data: Business-Driven AI Case Studies

While the MSc program takes a research-oriented approach, SIAI’s MBA AI/Big Data program focuses on real-world business applications. This program is structured around in-depth AI and data science case studies, helping executives and business professionals understand:

• How AI is applied in marketing, operations, and strategy
• The role of data-driven decision-making in business transformation
• Ethical and regulatory challenges of AI deployment in enterprises
• Case studies of AI implementation across diverse industries

Unlike AI bootcamps that offer surface-level exposure to business analytics, SIAI’s MBA AI/Big Data program ensures professionals gain practical insights into AI’s role in corporate decision-making.

Introducing MBA AI/Finance: AI in Corporate Finance and Investment

Building upon the success of MBA AI/Big Data, SIAI is launching MBA AI/Finance, a specialized track integrating AI with corporate finance and financial investment strategies. This program provides:

• AI-driven corporate financial analysis: Understanding how AI can optimize budgeting, forecasting, and risk management in enterprises.
• AI applications in investment strategies: Learning how hedge funds, asset managers, and financial institutions leverage AI to enhance portfolio management, algorithmic trading, and risk assessment.
• Case studies on AI in financial decision-making: Reviewing how major firms have successfully integrated AI into financial operations and strategic investments.

This program is designed for finance professionals, investment analysts, and corporate executives looking to harness AI in financial decision-making. Unlike bootcamp courses that barely scratch the surface, MBA AI/Finance provides deep, case-based learning tailored for real-world applications.

Why SIAI? A Path Beyond the Bootcamp Mentality

SIAI stands apart from standard AI bootcamps by emphasizing:

• Scientific Depth: Mathematical and statistical foundations critical for true AI expertise.
• Real-World Case Studies: Business-oriented applications that translate AI into tangible business results.
• Specialized Tracks: Focused programs in AI/Data Science, AI/Big Data, and AI/Finance to meet diverse career needs.

For those looking to develop a genuine AI expertise beyond a crash course, SIAI offers a structured, rigorous, and research-driven educational experience. Whether through the MSc AI/Data Science track for scientific mastery or the MBA AI programs for business and finance applications, SIAI ensures that students receive an education that truly sets them apart in the AI industry.

GIAI's primary research objective with the coming cycle's of MSc AI/Data Science is to build a graph-based Shapley Value for HR contribution analysis. In case you are not familiar with Shapley Value, it is a game-theory concept for properly allocating group project's gains/costs, which was first introduced in 1951 and awarded Nobel Prize in 2012.

The idea for this model originally came from one of the business case study classes(BUS501) in the MBA AI/Big Data program. In the class, students were given the task of testing a model to measure each student's contribution to group projects. Some students wanted to extend the model by incorporating participation in forum discussions as an additional metric.

This idea gained traction and has since been integrated into all course evaluations at the Swiss Institute of Artificial Intelligence (SIAI). Now, we aim to take this model beyond the classroom and make it more general and business-friendly. The goal is to refine it into a structured, scalable framework that can address a key challenge in corporate HR analytics: how to accurately measure multi-stage and indirect contributions in large organizations.

Understanding Team Contribution in Multi-Staged Work Environments

Traditional regression-based models for performance evaluation assign proportional credit based on direct contributions. While useful, they assume that all contributions are immediate and directly observable within a single stage of work. However, in real-world workplaces:

• Projects are multi-staged and often take months or years to complete.
• Some contributions emerge over time, rather than being immediately visible.
• Key individuals may act as connectors or enablers, rather than direct output producers.

To address these challenges, I am developing a new model that leverages graph-based Shapley value calculations. Unlike conventional models, this approach:

• Captures contributions that unfold over multiple project cycles.
• Identifies knowledge-sharing roles that support long-term success.
• Quantifies the impact of ‘helpers’ who enable others to succeed without always producing measurable outputs themselves.

Leveraging Communication Data to Measure Contribution

To make this model applicable in business settings, I plan to incorporate email and chat data as key sources of information. These internal communication networks serve as vital indicators of:

• How knowledge flows within an organization.
• Who provides critical insights, guidance, and solutions.
• Which employees are silent contributors who strengthen a team’s efficiency over time.

This naturally raises concerns about privacy, and I want to emphasize that ethical implementation is a key priority. While companies may find it reasonable to analyze work-related communication, employees must also have the right to:

• Opt out if they do not wish to be evaluated using this model.
• Maintain separate communication channels—one strictly for business, another for personal interactions.

Building on Traditional Contribution Models

This model does not aim to replace existing HR analytics but rather to complement them. Traditional evaluation methods already track:

✅ Task completion and project logs (Jira, Trello, Asana) ✅ Document collaboration (Google Docs, Notion, Confluence) ✅ Meeting participation and scheduling (Google Calendar, Outlook) ✅ Code commits and technical contributions (GitHub, GitLab)

However, these approaches primarily measure direct, immediate contributions. By integrating a graph-based structure, this model adds an extra rung on the ladder, allowing us to:

• Identify individuals whose contributions emerge across multiple projects.
• Detect key connectors and enablers within an organization.
• Assign Shapley value-based credit to those who facilitate success beyond direct outputs.

Why Does This Matter? The Role of 'Helpers' in Teams

Many workplaces unintentionally overlook contributors who are not direct project leaders. These individuals—whom I call 'helpers'—are vital in ensuring long-term efficiency, knowledge-sharing, and problem-solving.

• Traditional performance metrics reward project leaders, often missing those who facilitate success behind the scenes.
• A graph-based evaluation helps reveal these hidden contributors, ensuring fair recognition.
• Large-scale organizations rely on cross-team knowledge flow, which is difficult to quantify with traditional models.

By refining this methodology, we aim to provide a more balanced and fair assessment of who truly drives organizational success.

A Practical Application: Fairer Bonus Allocation

A major application of this research is in corporate HR, where annual bonus allocation is often based on direct deliverables. However:

❌ Employees who create long-term strategic advantages often go unnoticed. ❌ Those who enable cross-team collaboration are rarely rewarded. ❌ Many companies struggle to identify silent contributors who significantly impact multiple projects.

Our model seeks to address this by providing data-driven, fairer evaluations that recognize both direct and indirect contributions. This could help businesses:

• Improve bonus distribution fairness.
• Identify emerging leaders within the company.
• Strengthen team efficiency and collaboration.

Next steps after computational multi-stage cooperative game ― Auto driving and squadron drones

Game theory models are often hard to solve, but it is much harder to design a set-up for closed form solutions as well as desired equilibrium paths. After all, this is why not 'mathematical' but 'computational' approach is expected to be much more industry-friendly and we also expect to solve it within a reasonable amount of time and effort, if we can be free from theoretically robust mathematical model.

One other reason SIAI is focused on this topic is to extend the model for coordinated group behaviors in response to counterparties. Current self-driving mechanism only passively updates information from surrounding cars on the road, to the best of my knowledge. But when other cars move around with erratic behavior, for example if the driver is drunk, then evasive driving will perform far better off if the algorithm can confirm that the erratic driving is not a mistake by a sober driver but a failure of correction by a drunk driver. The same intuition becomes more pronounced if it is a drone war, especially when not a single but more than dozens of drones move together.

For one side, the algorithm has to solve a cooperative game for two drones and a coordination game for a group of drones on my side. On top of that, in the presence of enemies, now the algorithm has to take into account enemy drones strategies. So, it becomes a double-sided coordination problem. And lastly, the game does not end in a single stage, if evasive movement works.

Exactly the same logic can be applied to AI units in video games like Football. With current AI, unless the algorithm has a pre-mapped options like Alpha-Go, it cannot dynamically update the optimal responses. The game theory augmented by computational science, therefore, is another challenge that will make current AI more close to real AI.

Join the Research: MSc AI/Data Science at SIAI

This project is one of the key research opportunities in the MSc AI/Data Science program for the 2025-2026 cycle. This project demands more than just enthusiasm for AI—it requires the ability to navigate complex, multi-layered problems where business reality meets mathematical precision.

If you are passionate about:

🔹 Applying cutting-edge machine learning techniques to real-world business challenges. 🔹 Exploring AI-driven approaches to performance evaluation. 🔹 Using graph theory, game theory (Shapley value), and NLP for corporate applications.

Then this could be the perfect research opportunity for you.

💡 Exceptional students who demonstrate strong analytical skills and a commitment to AI-driven research may be considered for scholarships and funding opportunities.

However, I want to be clear—this is not a program for those seeking an easy credential. The MSc AI/Data Science at SIAI is for students who:

✔️ Want to work on serious, high-impact AI research. ✔️ Are ready to challenge traditional methods with new AI-driven approaches. ✔️ Aspire to develop solutions that companies can implement in real-world settings.

I welcome smart, ambitious, and research-driven students to join me in pushing the boundaries of AI for business.

Not sure if a year work will be enough to build a fully robust, easily modifiable, and conceptually intuitive model, but application of the work-in-progress model will be periodically shared as a form of case studies.

Necessary knowledge

• Game Theory
• Network Theory
• Machine Learning
• Large Language Model
• (Some level of) Panel data

Key concepts are discussed in PreMSc (or MBA AI), and deeper ones to come in MSc AI/Data Science.

Most AI-driven HR analytics focus on traditional models. We are developing an advanced, multi-stage contribution evaluation framework—something that could redefine how businesses measure and reward employees' true impact. This is not about minor improvements; this is about setting a new industry standard. Likely mind-set is also strongly emphasized.

If interested, feel free to ask questions in comments through GIAI Square.

Mathematical ability differs across cultures, with Western academia emphasizing abstraction over procedural speed
AI is automating routine calculations, making conceptual thinking more valuable than ever
Future professionals must focus on logical reasoning and model formulation to stay relevant

After years of teaching here at SIAI, we have witnessed a varying cultural differences in perception of experts in AI/Data Science in the western hemisphere and in Asia. What was pronounced the most was the concept of mathematics necessary in this particular field. Most Asian students blindly thought that calculation capacity and problem solving skills are emphasized in our curriculum, just by reading the phrases like 'The Most Rigorous MBA in the world'.

We don't.

And we finally understand where the confusion comes from. Here is our scientific analysis of the differences.

Education researchers often distinguish between procedural fluency (being able to execute mathematical procedures quickly and accurately) and conceptual understanding (grasping the underlying principles and structures of mathematics). Many studies indicate that East Asian education systems emphasize procedural fluency, while Western systems, particularly in higher education, prioritize conceptual depth.

• Research Backing This View: Studies comparing math education in China, Japan, South Korea, and Western countries (such as the US and UK) consistently show that Asian students outperform in procedural tasks but may struggle with non-standard, open-ended problems requiring deeper conceptual thinking (Ma, 1999; Stigler & Hiebert, 1999).

So, we have formalized 3 types of 'Math Genius', and please note that only the last type is needed at SIAI.

1. Calculator
2. Problem Solver
3. Thinker

Let's go over the dichotomy from our definition of 'Math Genius'.

1.Calculator: Speed and Accuracy as Genius

Mathematical ability is often perceived differently across educational systems. In many East Asian countries, proficiency in mathematics is equated with speed and accuracy in calculations. A student who can quickly solve a quadratic equation or compute complex arithmetic is often considered a math genius. This perception aligns with research by Stigler and Hiebert (1999), which highlights that Asian students tend to excel in procedural fluency due to structured and rigorous mathematical training at an early stage.

However, in higher education, particularly in Western academic institutions, mathematical proficiency is defined differently. The emphasis shifts from speed to logical reasoning, abstract thinking, and the ability to construct mathematical models. Research in mathematics education (Ma, 1999; Li & Collins, 2021) shows that while Asian students tend to perform well in structured mathematical settings, they often face challenges when required to engage in open-ended problem-solving and theoretical abstraction.

2.Problem Solver: Procedural fluency as 'Math Genius'

At the high school level, the focus of mathematics education begins to shift from pure calculation to problem-solving. Advanced mathematics curricula require students to derive solutions from first principles, navigate multi-step logical reasoning, and understand abstract mathematical structures. This transition is critical for success in competitive university entrance exams, as seen in South Korea’s CSAT and similar standardized assessments in other countries.

This is why Asian students excel in competitive math Olympiads, which require both procedural skill and non-standard problem-solving.

As students enter university, particularly in STEM fields, the nature of mathematics evolves further. Research in international mathematics education (Li & Shavelson, 2001) suggests that students who rely primarily on procedural problem-solving may struggle when confronted with theoretical coursework that requires constructing formal proofs and engaging with abstract concepts. This distinction between procedural fluency and conceptual understanding is well-documented in the literature on cognitive development in mathematics (Tall, 2004).

Western academia sees calculation speed as "machine-like" rather than as a sign of intelligence is supported by psychological studies on how different cultures define intelligence.

• Expert Perspective: In Western academia, a "math genius" is often equated with someone who can create new mathematical theories, prove complex theorems, or develop novel models—not just someone who is quick at calculations. This is evident in how Western math competitions, graduate exams, and research expectations focus on deep reasoning rather than speed.
• Historical Context: The Western concept of a mathematical genius is shaped by figures like Gauss, Euler, and Gödel, who were not just quick calculators but pioneers in abstract reasoning.

3.Thinker: The Role of Mathematical Thinking in AI and Data Science Education

In applied fields such as AI and Data Science, mathematical proficiency takes on yet another dimension. While theoretical knowledge remains essential for foundational research, most practical applications of AI do not require deep engagement with mathematical proofs. Instead, students must understand the conditions under which mathematical models apply and be able to critically evaluate their limitations.

Given this reality, the MBA AI/Big Data program at SIAI has been strategically designed to align with industry needs while accommodating different mathematical backgrounds. Rather than focusing on formal proofs, the curriculum emphasizes:

1. Understanding Model Assumptions – Students are trained to recognize the conditions under which different AI models (e.g., neural networks, decision trees) are effective and where they may fail.
2. Applying Mathematics to Business Problems – Instead of proving theorems, the focus is on using mathematical reasoning to optimize decision-making in real-world scenarios.
3. Bridging Procedural Fluency with Conceptual Thinking – While problem-solving remains an essential skill, students are guided to transition towards abstract thinking where necessary, particularly in courses on machine learning interpretability and data-driven strategy.

This approach aligns with the findings of mathematics education researchers (Schoenfeld, 2007), who argue that effective mathematical training must be contextualized within the problems students are expected to solve in their professional careers.

Why This Matters for Asian Students in STEM Fields

Many Asian students who transition to Western universities for undergraduate or graduate studies in STEM fields often experience a sudden drop in their perceived mathematical ability. This is not because they lack intelligence, but because their definition of mathematical proficiency has been shaped differently.

Studies on international students in STEM (Li & Collins, 2021) show that Asian students often find proof-based courses, abstract algebra, and mathematical modeling more challenging compared to their Western peers, precisely because their training has emphasized computational efficiency rather than abstraction

Students who have excelled in rapid problem-solving often struggle with abstract mathematical thinking. They may find courses in theoretical physics, real analysis, or mathematical finance unexpectedly difficult because the emphasis shifts from computation to proof-based reasoning and conceptual applications.

This is particularly critical for aspiring data scientists. In real-world applications of data science and AI, the ability to logically build models, understand theoretical underpinnings, and translate abstract mathematical ideas into real-world applications is far more valuable than simply applying pre-existing formulas.

Case 1

Let's just come to an example. A Korean student at SIAI tried his dissertation on a set of data from shipping company's use of tools like containers, boxes, baskets, and folklifts. Unless the data is only for a few clients of the shipping company, it was expected that there will be a number of one-time clients whose use of tools will unlikely be repeated in out of sample data. The student, despite learning that RNN can only be applied to time series without non-stationary movements, was not able to link the learned math concept to RNN and the data. He suffered from gradient's divergence, and tried to control the parameters of RNN instead of 'cleaning' the data itself.

Case 2

Addtionally, many Asian students are too busy jumping on code lines rather than accessing the problem set's background description. In the introductory math and stat courses (STA501, STA502, STA503), we emphasize a lot about how important the data generating process (DGP) can be, like whether the e-commerce company's daily visitor data being from matured incumbents like Amazon or a start-up looking for next round funding. Like case 1, your application of RNN can be challenged depending on how actively the company is engaged in promotions. Little differences in question's setting is thoroughly designed by professors as the change requires an entirely different set of data scientific tools. Many Asian students struggle to understand why an Instrumental Variable (IV) has to be replaced just because the start-up's series-C funding is postponed, for instance. If the company does not need a short-term boost in website visitors, reference data points should remove exploding ups and downs for next month's projection, isn't it?

Cases like this occur a lot among Asian students whose course grade is high enough for us to trust their mastery in skills. And unfortunately, they end up poor performance at the dissertation stage.

Then, is it really a necessary skill? Isn't just an application of previous project's code lines good enough?

AI may soon replace first two types of 'Math Genius'

The rise of AI tools like ChatGPT and other advanced language models is further shifting the definition of mathematical proficiency. While traditional education has emphasized procedural fluency and structured problem-solving, AI can now perform these tasks instantly. Routine calculations, algebraic manipulations, and even structured problem-solving techniques are increasingly automated, reducing the necessity for individuals to master these skills manually.

As AI continues to evolve, it is likely that calculator-type mathematicians and even structured problem-solvers will find themselves increasingly displaced. These AI systems can solve equations, optimize parameters, and generate step-by-step solutions for a wide range of mathematical problems more efficiently than humans. This transformation raises a fundamental question: What kind of mathematical thinking remains irreplaceable?

One of the key limitations of AI in mathematics is its reliance on pattern matching. Despite their computational power, AI tools do not “understand” mathematics in the same way humans do. They recognize patterns in vast datasets and generate responses based on probabilistic relationships rather than true logical reasoning or deep abstraction. Mathematical creativity, proof construction, and conceptual modeling remain beyond the reach of AI, as these require forming genuinely novel insights rather than simply retrieving and recombining existing information.

For this reason, the focus of mathematical education should shift toward logical reasoning, model formulation, and critical evaluation of AI-generated outputs. While AI can provide solutions, human expertise is required to assess their correctness, interpret results, and apply them meaningfully within different contexts. In fields such as AI and Data Science, those who master abstract thinking and theoretical modeling will remain indispensable, while those who rely solely on procedural problem-solving may find their skills increasingly redundant.

Conclusion: Redefining Mathematical Proficiency for AI and Data Science

As mathematics continues to evolve as a discipline, educational institutions must adapt their teaching methodologies to prepare students for both theoretical and applied domains. Traditional views of mathematical ability—whether based on calculation speed or structured problem-solving—must be expanded to include logical reasoning, conceptual understanding, and model applicability.

For students entering AI and Data Science, the ability to think abstractly is crucial for research, but applied roles require a balance between problem-solving skills and an understanding of mathematical conditions. By designing curricula that acknowledge these distinctions, our institution ensures that graduates are equipped to excel in both academic and industry settings.

By aligning mathematical training with practical applications, educators can bridge the gap between traditional perceptions of math proficiency and the skills required for success in the modern AI-driven economy.

In short, SIAI teaches most unlikely replaceable data science tools in AI.