Gurobi is a company that provides a mathematical optimization solver, the Gurobi Optimizer, which helps businesses make better decisions by finding optimal solutions to complex problems. Pulse 2.0 interviewed Gurobi Sr. Data Science Strategist Jerry Yurchisin to gain a deeper understanding of the company.
Jerry Yurchisin’s Background
Could you tell me about your background and how it shaped your journey into data science, particularly in mathematical optimization? Yurchisin said:
“I’ve been involved with math in some form or another for most of my adult life. After earning a Bachelor of Education degree in Integrated Mathematics, I went on to complete master’s degrees in both Applied Mathematics and Operations Research and Statistics. I also spent a fair amount of time teaching these subjects as an adjunct professor at a local community college.”
“While in grad school, I took a course in mathematical optimization and linear programming – from that point on, I was hooked. I worked on optimization problems throughout my early career as a consultant, as well as in my later role as a federal contractor. I began to pivot towards more data-science-oriented work as that field rose in popularity, and was able to develop that skill set by supporting various analytics projects over the years. The rise of machine learning in particular was a great opportunity for me to learn about data science, and it helped me to recognize the importance data would hold in the future.”
Company’s Core Mission
What drew you to this company, and how does your work as a Data Scientist contribute to its core mission and product development? Yurchisin explained:
“I’ve always been interested in both math and the opportunity to teach it to others. When I heard about Gurobi in grad school, I was immediately interested in their potential to drive more widespread adoption of the optimization projects I’d been working on.”
“With hands-on experience in both fields, I felt well-suited to help share the benefits of mathematical optimization with data science audiences who might not be as familiar with it. My role is ultimately to reach the data science and artificial intelligence communities and demonstrate how incorporating mathematical optimization into their skillset offers complimentary technology to what they’re already using every day. It’s not unwieldy or unobtainable, but rather approachable and almost immediately beneficial to their decision-making capacity.”
Rewarding Project
What has been your most rewarding or exciting project here so far, and what made it stand out to you? Yurchisin noted:
“As a former teacher, I take pride in being a part of Gurobi’s efforts to help educate data scientists on the ins and outs of mathematical optimization. I’ve had the opportunity to create lessons in our Optimization for Data Scientists training series – Opti 101, 201, and 202 – that provide a fresh perspective on mathematical optimization for those who may be curious about its application to their data-centric work.”
“I worked with both great partners and a number of our technical support Gurobi Experts to develop these courses, creating engaging and approachable content that’s geared towards data scientists who are interested in a road to adoption.”
Unique Challenges
What are some unique challenges you’ve encountered as a Data Scientist working in mathematical optimization, and how did you tackle them? Yurchisin acknowledged:
“As someone who has worked professionally on both sides of the coin, I know how challenging the marriage of data science and optimization can appear at first. Many data scientists share a common misconception that optimization requires an in-depth knowledge of mathematical concepts and is therefore a challenge to apply to their work. They choose to focus instead on the purely predictive power of data science, leveraging analytics tools like machine learning and deep learning to produce data-driven insights that they can then use to make decisions themselves.”
“What I think they’re missing out on with this approach is the next logical step in the decision-making process: prescription. AI and ML can take you so far by parsing out data into actionable predictions, but they don’t assess these predictions and prescribe the best possible decision – that’s the role of optimization. By adding a prescriptive element to data insights, optimization helps take the guesswork out of decisions in a way that data science cannot do on its own. That’s why I joined Gurobi – to help companies discover how the marriage of data science and optimization can make their decisions as informed and future-proof as possible.”
Core Products
What are Gurobi’s core products and features? Yurchisin pointed out:
“Our core product is the Gurobi Optimizer, the world’s fastest mathematical optimization solver. A solver is a set of algorithms that take a model — a mathematical representation of a system or problem built using mathematical equations and inequalities representing your objectives and constraints — and finds the optimal solution. Essentially, the model defines the problem, while the solver determines the answer.” “Our core product is the Gurobi Optimizer, the world’s fastest solver. A solver is a set of algorithms that take a mathematical model — a system or problem built using mathematical equations — and calculates the optimal solution using the given constraints. Essentially, the model helps to define the problem, while the solver determines the answer.”
“The user’s job is to take their problem and represent their decisions, constraints, and objectives mathematically. They then use their favorite programming language to code the problem and call the solver, which takes it from there and provides an action plan which yields the optimal outcome. In a lot of cases the solver you use is extremely important, particularly when you need to solve large problems fast. This enables the world’s leading organizations to make unbiased decisions at the speed of business, regardless of whatever challenges or disruptions may arise.”
“The Gurobi Optimizer allows you to model your problem the way you need to, be it simple linear expressions or complex nonlinear relationships, while solving your problem to global optimality. We also offer support from our team of PhD-level experts, who are ready and willing to help users with comprehensive guidance and technical assistance to help you get from proof of concept to production.”
“The Gurobi Compute Server helps teams take the intense computations needed to solve optimization problems and offload them onto dedicated and clustered servers. This helps support large-scale optimization projects across various platforms without making things too overcomplicated or complex. Similarly, the Gurobi Instant Cloud enables users to run the Optimizer on their choice of Azure or AWS cloud environments at scale. Ultimately, we want to make optimization as easy and accessible as possible across diverse compute environments.”
Customer Success Stories
Can you share any specific customer success stories? Yurchisin concluded:
“As someone who loves sports and their relationship to math, data, and statistics, one of my favorite customer success stories has to be our work with the National Football League (NFL). Every year, the NFL faces a daunting challenge: how can we create the most exciting and fair schedule possible? This question needs to account for a plethora of challenges, from broadcast rights, to free agents, to stadium availability and travel schedules. And until the last decade, this entire exercise was completed by hand on a six-foot square wooden board in an NFL office.”
“Since adopting Gurobi in 2013, the NFL has been able to massively simplify this process while simultaneously providing more assessable scheduling options than ever before. Using mathematical optimization, planners can generate and analyze over 50,000 schedules in the time it used to take them to generate only five options. This allows the planners to worry less about formulating the options and instead dedicate more time to determining the most fair and exciting schedule possible for the teams, players, and fans.”
