Developing the student of tomorrow

Scott Friedman (BSBA ’01) has always had a passion for education. As Partner and Co-Founder of Soroban Capital Partners, Friedman is actively involved in the ever-changing world of AI. By funding the first AI Professorship at UF Warrington, he’s ensuring the next generation has access to AI education. 

Q: How did your time at the University of Florida and Warrington set you up for success in your career, and why did you want to support the first AI Professorship?

Friedman: I believe strongly in Science, Education, and the American Dream. My family’s journey is a testament to the unbreakable human spirit. Some of my family members endured unimaginable hardships in Eastern Europe where despite being highly regarded in their fields of Science, Mathematics, and Engineering, they were persecuted for their religious beliefs and identity.  My family members made the gut-wrenching, and courageous decision to abandon all they had known to create a new beginning in the United States.  

Arriving with nothing but their education and a steadfast belief that with the power of time, freedom, and resilience to reshape their own destiny, they could create a brighter future. A future not just for themselves but also for their children and subsequent generations. I am a living testament to their legacy, and it’s my mission to honor my grandparents and ancestors by advancing these principles and making a lasting, positive imprint on the lives of others. I am personally committed to the foundational principles of Science, Education, and the American Dream, and I am a firm believer in the principle of paying it forward.

The reason that I am so passionate about the American Dream values is likely due to my childhood. I spent my early years in Florida, where my parents relocated when I was just an infant. We lived in a modest, lower middle-class community, but what we lacked in material wealth, we more than made up for in educational richness. The cars on our block might have been pre-owned, but I never felt deprived. I was fortunate to have loving parents, intellectually stimulating friends, and a treasure trove of books and resources that would be the envy of any young learner.

My grandparents, who were deeply rooted in science and mathematics, were a constant presence in my life. We would engage in card games and board games that revolved around elements of risk, chance, and probability. For dinners, holidays, and special occasions, my family circle also included my aunt, an educator in history and economics, and my uncle, an MIT PhD who collaborated with other brilliant PhDs from Stanford and NASA on projects like the Apollo radiation-blocking aluminum shield for the Mission to the Moon. The tales shared during these board games and family gatherings profoundly influenced my outlook on the potential opportunities that the future could hold.

My father transitioned from learning to be a chemical engineer to becoming a distressed restaurant investor when we moved to Florida. He believed in logic, scientific rigor, and a strong work ethic. His business model was straightforward but effective: he would buy distressed or struggling restaurants for the value of the equipment was worth inside, and he would either turn them around for a profit or liquidate the assets to recoup his original investment. I spent a lot of time working in his restaurants. I wanted to run the cash register, but he started me out washing dishes and busing tables. He often told me that his favorite investments were investing in his people and his customers. Regardless of the outcome, he maintained a positive outlook, referring to any setbacks as his “tuition”, often then pointing to his head, and being thankful for the lessons they provided. These lessons were the seeds of learning and would create better opportunities. My father taught me the art of entrepreneurship, perseverance, and adaptation. My mother, a professor in French and Linguistics, instilled in me a love for language, creativity, curiosity, and learning. I am forever grateful.

The collective emphasis on hard work, lifelong learning, and meaningful contribution from all these incredible role models profoundly influenced me during my early years.

I also benefited immensely from a robust public school system. I attended public school from elementary school through high school, and then one day, my father opened the mail and read a letter that said I was gifted a full academic scholarship to the University of Florida. That single sheet of paper encapsulated years of hard work and the sacrifices my family had made on my behalf.

Upon my graduation from the University of Florida, I started my career at Goldman Sachs in New York.  Remarkably, my education cost me nothing, and it was a perfect example of the 80/20 rule – I had learned enough of the foundational principles to succeed, and I was primed for the specialized mastery that would come later. When the time for such mastery arrived, Goldman Sachs provided an unparalleled training ground. I owe a huge debt of gratitude to Goldman Sachs for my financial education and preparing me for the rigors of the professional world.

I am not a genius. I am a simple straightforward guy with a public-school education. My role within the unique arbitrage and principal strategies group at Goldman Sachs, and the incredible global opportunities in my career that followed, led to invitations to speak at prestigious institutions like Yale and Harvard. I left each institution with tremendous respect for the intellectual horsepower of the students, and the profound domain mastery of the PhD’s and professors. What truly amazed me, however, was the unparalleled quality and scope of resources available to both students and faculty. It was unlike anything I had ever seen. 

As the year came to an end, I took a portion of my annual income and year end bonus money, and I decided to give back by donating the first Bloomberg Terminal to the business school at the University of Florida.  Given that I enjoyed a cost-free education and an invaluable student experience at the University of Florida, I felt compelled to “pay it forward” and set the stage for the success of future students.

As the years passed, I increasingly engaged with the PhDs, professors, and students at the University of Florida. As my own career and experience advanced, watching the University positively evolve and take on new challenges was incredibly rewarding. 

Then something incredible happened: The University of Florida received the fastest supercomputer on any academic campus across the entirety of the United States. Chris Malachowsky, who is both brilliant and generous, along with his partners at NVIDIA, donated an unparalleled supercomputer, known as HiPerGator AI, to the University of Florida.

On the heels of this tremendous gift, faculty members swiftly initiated dialogues with Wall Street professionals and financial experts. The aim was to ensure students had comprehensive access to the tools, resources, and curriculum they needed to excel.

I was having a lot of conversations with the leaders across the university. Specifically, I was having a conversation with “Saby” Mitra, the dean of Warrington College of Business, who also happened to have experience conducting research and teaching PhD students in areas of computational design science including optimization and AI methods for IT infrastructure design and cloud computing. 

Saby Mitra’s research and teaching have always been at the intersection of business and information technology. Whether it’s a technology company transforming the business world or a regular business transforming itself through technology, that’s where all the excitement is today and where the opportunities will be tomorrow. Andy Naranjo, a Ph.D. and Chairman of the Finance, Insurance, and Real Estate departments, also joined our discussions. He was deeply committed to enhancing the student experience in the classroom and was insistent on exposing students to diverse schools of thought to foster a genuine learning environment.

Jim Hoover, a Clinical Professor at Warrington with a broad expertise in analytics, machine learning, and AI, brought a unique perspective to the table. Having previously worked at one of the world’s largest consulting firms, he has advised on integrating AI into business operations. Now, he’s translating those real-world consulting case studies into educational experiences within the classroom. Over a series of conversations, we discussed that the student of tomorrow would need to master four key domains: 1) Business Fundamentals 2) Engineering 3) Mathematics/Statistics 4) Computer Science/Programming/AI and ML. 

A traditional business education covering Finance, Economics, and Accounting would just be the starting point. As we transition from an analog world to a digital landscape, students would also need a grasp of engineering concepts, systems, and understanding interconnected systems. Given the probabilistic nature of our world, a strong foundation in statistical methods used in AI would be essential, especially since humans tend to be most comfortable with simple linear pattern recognition, and machines tend to excel at non-linear pattern recognition. Finally, to fully leverage the NVIDIA supercomputer’s capabilities, students would need to understand the principles of computer science and programming. 

The last challenge that remained was securing a rising talent in the Finance department who could inspire this multidisciplinary approach and finding the funding for it. Both Saby and Andy were optimistic about identifying the right individual, given more financial certainty. Their eagerness to seize this opportunity for their students and faculty was palpable.

I was so inspired by the generosity of Chris Malachowsky and his co-founders at Nvidia, and the passion from Saby and Andy, that I accepted the opportunity to join forces, and I decided to gift and endow the first Professorship in AI/ML at Univ of Florida to accelerate faculty work in Machine Learning and applications of Artificial Intelligence in Finance.  The collective ambition is to provide University of Florida students with a cutting edge and all-encompassing educational toolkit, so that they can craft extraordinary futures for themselves.

Jon Cannon serves as the Executive Director of Development and Alumni Affairs at the Warrington College of Business, where he oversees the institution’s fundraising and alumni engagement initiatives. Jon’s steadfast guidance, kindness, and support along the way were instrumental in bringing this AI Professorship gift to fruition.

It is neat to see that The Professorship is already contributing significant benefits for everyone involved. For example, Alejandro Lopez-Lira, a PhD, and a fast-rising star in the field, is already accelerating faculty work in Machine Learning and applications of Artificial Intelligence in Finance.  Early on, Alejandro taught ChatGPT how to quantitatively trade using a modern approach to Sentiment Analysis, and his academic paper virtually overnight, became one of the top most read academic papers across Wall Street and the United States.

The goal is clear: to supercharge the students at the University of Florida, and ideally students across the leading public universities in America, with the resources to not only achieve success, but to also shape extraordinary futures. This professorship is already proving to be a positive transformative force for all involved.

Q: Where does your passion for AI come from?

Friedman: AI is at the intersection of technology and liberal arts. It’s where science meets creativity. It reminds me of growing up with my scientifically oriented father and my creative artistic mother. Just as the PC made computing personal, and the iPhone made the internet mobile, AI has the potential to make technology intuitive, even human-like. 

I have always been fascinated by games of chance and the art of connecting individual puzzle pieces into a cohesive whole. Think of time and progress as a dynamic river, flowing and ever-changing. I like to visualize a bunch of tiny individual streams or individual puzzle pieces that are all starting to converge.  Sometimes, the stream may speed up, hinting at the rapids ahead. If you could see all these vectors or streams pointing in the same direction, it would be like recognizing an approaching wave while surfing. You would know something monumental was coming. 

All of these individual streams were starting to flow faster, and converge:

Exponential Growth in Computing Power: Moore’s Law originally posited that the number of transistors on a microchip would double approximately every 18 to 24 months, leading to a corresponding increase in computing power. In simpler terms, it’s like saying that the “brainpower” of computers would essentially double in that time frame, all while costs remained relatively stable or even decreased.

Like some of the inexorable laws of physics, Moore’s Law has largely held true, forecasting the doubling of transistors in a dense integrated circuit roughly every two years. Even when this law began showing signs of slowing, advances in parallel computing, specialized hardware like GPUs and TPUs, and cloud infrastructure picked up the slack. This created an environment where training highly complex machine learning models became increasingly feasible. In a way, Moore’s Law is like the principle of geometric progressions in nature—each step forward is not merely additive; it’s exponential.

Big Data: We’ve been generating data at an unprecedented rate. In a data-driven world, the “datafication” of everything—texts, images, even emotions—provided fertile ground for machine learning algorithms to grow. Similar to looking up at the vastness of the stars and the cosmos, the data universe has been expanding. This gave researchers ample resources to train more complex models. Each piece of data is like a byte in a software program; the more you have, the more you can potentially do. In AI, this data becomes the raw material for training increasingly sophisticated models. 

Advancements in NLP and Deep Learning: Prior milestones in Natural Language Processing (NLP) and deep learning signaled the impending leap. From the initial works on Recurrent Neural Networks (RNNs) to the advancement of architectures like Transformers, AI was growing not just “bigger,” but also “smarter.” These trends coalesced into the development of models that could perform tasks previously unimaginable, from sophisticated language translation to almost human-like conversational abilities.

Interdisciplinary Synergy: Computer science began borrowing more from neuroscience, linguistics, and mathematics, fostering a rich, cross-disciplinary environment where groundbreaking ideas could emerge.

Open Source & Collaboration: Remember how correspondence and debates amongst Albert Einstein, Niels Bohr, Max Planck, and Erwin Schrodinger revolutionized our grasp of physics? Thanks to modern platforms like GitHub and preprint servers like arXiv, scientific and computational knowledge was shared more freely and quickly. This melding of minds and fields created a fertile breeding ground for innovation. 

I should also add one more force, that in my opinion is important for America, and for humanity.

Economic Imperatives and Increased Investment: When society sees value, it invests. This investment becomes the force that accelerates innovation. As the practical applications of AI became clearer, from business uses to healthcare, there was an influx of funding into Research & Development, creating a fertile ground for high-risk, high-potential projects.

In essence, AI’s growth was accelerated by this multi-disciplinary approach, much like how a cross-functional team in a startup might catalyze product development. This rapid circulation of wisdom, capital, and innovation perpetuates a self-reinforcing cycle of progress.

Overall, AI is the kind of seismic shift that comes once in a lifetime, challenging us to rethink what’s possible.  The rise of ChatGPT was not an isolated event but a link in a long chain of technological evolution, a chain that is far from reaching its final form. This is where my passion for AI stems from. 

I should also say that I get the privilege to work with some of the most truly talented people in the industry. They are always learning and improving. In particular, my business partner has been a tremendous friend, a teacher, and is a superb investor.  I tell everyone all the time that I have awesome partners and teammates. On top of their dedication, I joke that sometimes their commitment is so intense, and their intellect so sharp, that they might as well be human supercomputers.

Q: How have you seen AI impact your field?

Friedman: Wow, yes there are a lot of changes.

First off, I should say that I get the privilege to work with some of the most truly talented people in the industry. They are always learning and improving. In particular, my business partner has been a tremendous friend, a teacher, and is a superb investor.  I tell everyone all the time that I have awesome Partners and teammates. On top of their dedication, I joke that sometimes their commitment is so intense, and their intellect so sharp, that they might as well be human supercomputers.  

Let me give a few simple examples of what I have seen as AI’s impact in algorithmic trading, risk management, portfolio management, and sentiment analysis.

Algorithmic Trading: It’s not just about buying and selling; it’s about analyzing data at a scale and speed that were previously unimaginable. Imagine AI as the quantum mechanics of the financial world. Just as quantum mechanics revolutionized our understanding of matter and energy in Physics, AI algorithms have fundamentally altered the landscape of trading. They can process and analyze data at speeds that are, for all practical purposes, orders of magnitude faster than the “blink of an eye”; furthermore, we are heading towards a near instantaneous future, much like probabilistic quantum particles that exist in multiple states at once. 

In even simpler terms, think of the classic Warren Buffett example of investing in a snowball rolling down the hill and accumulating snow. It gains momentum, but it’s limited by human speed and judgment. Algorithmic trading, supercharged by AI, is like a snowball that not only rolls faster but knows exactly which path to take for maximum accumulation. It’s as if this snowball can read the weather, understand the texture of the snow, and calculate the optimal path in a fraction of a second. Just like a snowball gains size and speed as it rolls downhill, algorithmic trading leverages AI to gain insights and make decisions at a pace and scale that were unimaginable before.

Risk Management: My entire career has been one deep dive after the next into the realm of Risk Management, and the field has undergone a remarkable evolution over the years. In this context, AI’s role here offers a lens or a tool to decipher intricate systems and natural phenomena similar to how scientists might think about the “butterfly effect”.

People like to talk about fancy things like Chaos theory, but at its core it really focuses on the behavior of dynamical systems that are highly sensitive to initial conditions. In essence, the butterfly effect tries to explain how small changes can lead to complex and unpredictable outcomes.

Complex adaptive systems, on the other hand, are like the cells of an organism. They consist of a large number of agents that interact with each other, adapt, and evolve over time. These agents can be anything from individual investors in a stock market to cells in a biological organism. The key here is adaptation; the agents in a complex adaptive system learn from their interactions and change their behavior accordingly, leading to the emergence of complex phenomena that are greater than the sum of their parts. AI offers new frontiers in real time risk measurement, monitoring, and risk management.

Portfolio Management: Consider AI as the ecology and genetic algorithms of portfolio optimization. Genetic algorithms are used in computer science to find approximate solutions to optimization and search problems, inspired by the process of natural selection. Similarly, AI can optimize a portfolio by continuously adapting and selecting the best-performing assets for the specified or predicted environment, much like how natural selection favors the fittest organisms.

Sentiment Analysis: AI’s capability to gauge market sentiment is like the machine learning equivalent of social network analysis. Just as social network analysis helps us understand human interactions and relationships, AI can scan through social media, news outlets, and other data sources to gauge market sentiment before it fully manifests. This nuanced understanding leads to invaluable insights and investment opportunities. It’s like having a crystal ball that gives you a sneak peek into the market’s future mood.

All of these domains, along with their associated tools and technologies, are in a state of rapid transformation.

Q: What do you think about the way Warrington has implemented AI education into the Curriculum?

Friedman: In my opinion, the Warrington College of Business is doing for AI education what Chris Malachowsky, Jensen Huang, and NVIDIA have done for the world of graphics and computing—transforming the college into a powerhouse of innovation and practical application. 

The learnings from studying the best practices of world leading organizations like NVIDIA, and the generous resources that the supercomputer provides, are positively impacting everything. I can give a few specific examples of things that I am already seeing within the research halls and classrooms. 

Curriculum: Just as Chris Malachowsky and Jensen Huang revolutionized graphics processing units (GPUs) to handle complex computations in parallel, Warrington is revolutionizing its curriculum to include next generation tools like Python and R. They’re essentially building the “GPUs” of the educational landscape—highly specialized, cross functional, and incredibly powerful clusters of curriculum.

Experiential Learning: Malachowsky and Huang believe in the power of real-world application, as evidenced by NVIDIA’s forays into AI, autonomous vehicles, and more. Similarly, Warrington’s real-world business analytics projects are like NVIDIA’s real-world testing labs, giving students hands-on experience that’s directly applicable in the workforce.

Continuing Education: Malachowsky and Huang are proponents of lifelong learning and adaptation, as seen in NVIDIA’s constant innovation. Warrington’s short courses in AI are like firmware updates for the human brain, keeping everyone up-to-date and competitive.

Faculty: Just as Malachowsky and Huang surround themselves with top engineers and visionaries, Warrington has gathered a faculty of experts in analytics and AI. They’re the “CUDA” cores that make the whole machine run smoothly, each contributing their specialized knowledge.

Partnerships: NVIDIA’s partnerships have always been strategic and impactful, whether it’s with automakers for self-driving tech or with data centers for cloud computing. Warrington’s collaboration with NVIDIA itself is a testament to the power of strategic alliances, creating a synergy that benefits all stakeholders.

Enterprise-wide Initiative: Malachowsky and Huang have always aimed for a holistic approach, integrating NVIDIA’s technologies into a wide array of applications. Warrington’s enterprise-wide focus on AI is similar; they’re not just teaching AI as a subject but integrating it into the very fabric of their educational philosophy.

In the spirit of Chris Malachowsky, Jensen Huang, and NVIDIA, Warrington isn’t merely adjusting to the future; they’re actively sculpting it. They’re transforming the untapped potential of students into the computational powerhouses that will drive the business landscape of tomorrow. I am so grateful to be a part of this journey. The excitement I feel for both the students and the committed faculty is electric. Go USA and Go Gators!