“These amazing additions to our faculty roster bolster Cornell Tech’s unwavering commitment to fostering innovation, groundbreaking research, and collaborative learning in the AI era,” said Cornell Tech Dean and Vice Provost Greg Morrisett. “With a passion for pushing the boundaries of knowledge, our new faculty will make a tremendous impact on our campus, in the tech industry and academia, as well as the global community.”

The new faculty are joining a distinguished cohort of professors who are dedicated to shaping the next generation of tech leaders and innovators across key areas including artificial intelligence and machine learning and health. Their work will contribute to the advancement of knowledge and the development of innovative solutions that make a positive impact on society.

Effective July 1, 2023:

Frank Pasquale is a Professor of Law at Cornell Tech and Cornell Law School. He is an expert on the law of artificial intelligence (AI), algorithms, and machine learning. Before coming to Cornell, Pasquale held chaired professorships at the University of Maryland, Seton Hall University, and Brooklyn Law School. His books include The Black Box Society (Harvard University Press, 2015) and New Laws of Robotics (Harvard University Press, 2020). He has published more than 70 journal articles and book chapters and co-edited The Oxford Handbook on the Ethics of Artificial Intelligence (Oxford University Press, 2020) and Transparent Data Mining for Big and Small Data (Springer-Verlag, 2017).

Jae-sun Seo joins Cornell Tech as an Associate Professor in the Electrical and Computer Engineering department. Dr. Seo comes from Arizona State University, where he was an Associate Professor in the School of Electrical, Computer and Energy Engineering. His research interests include efficient hardware design of machine learning / neuromorphic algorithms and integrated power management. Dr. Seo was a visiting researcher at Intel and Meta, and he has been recognized with awards from IBM, NSF, Intel, and IEEE.

Kyra Gan is an Assistant Professor of Operations Research and Information Engineering at Cornell Tech. Prior to joining Cornell, Dr. Gan was a postdoctoral fellow in the Department of Harvard Statistics and earned her Ph.D. degree in Operations Research from the Tepper School of Business at Carnegie Mellon University. Her research interests include adaptive/online algorithm design in personalized treatment under constrained settings, computerized/automated inference methods, robust causal discovery in medical data, and fairness in organ transplants.

Udit Gupta joins the Department of Electrical and Computer Engineering as a visiting assistant professor for the Jacobs Technion-Cornell Institute. His research lies at the intersection of computer architecture, systems for machine learning, and sustainable computing. During his PhD in computer science at Harvard University he was also a Visiting Research Scientist at Meta AI.

Effective August 16, 2023:

Alex Conway is joining Cornell Tech as an Assistant Professor in Computer Science. Prior to Cornell Tech, Conway served as a researcher at VMware Research Group where he primarily focused on randomized data structures and their applications to memory and storage systems. He earned his PhD in computer science from Rutgers University.

Effective January 1, 2024:

Raaz Dwivedi comes to Cornell Tech as an Assistant Professor of Operations Research and Information Engineering. He earned his Ph.D at EECS, UC Berkeley. His research focuses on building effective strategies for personalized decision-making with theory and methods across causal inference, reinforcement learning, and distribution compression, and applications to healthcare. Prior to Cornell, he was a postdoc jointly between Harvard and MIT, and spent time at Microsoft Research. He has received the President of India Gold Medal at IIT Bombay, the Berkeley Fellowship, teaching awards at UC Berkeley and Harvard, and a best student paper award for his work on optimal compression.

About Cornell Tech

Cornell Tech is Cornell University’s groundbreaking campus for technology research and education on Roosevelt Island in New York City. Our faculty, students and industry partners work together in an ultra-collaborative environment, pushing inquiry further and developing meaningful technologies for a digital society. Founded in partnership with the Technion-Israel Institute of Technology and the City of New York, Cornell Tech achieves global reach and local impact, extending Cornell University’s long history of leading innovation in computer science and engineering.

The post Cornell Tech Welcomes Six New Faculty Members in 2023-24 Academic Year appeared first on Cornell Tech.

By Adam Conner-Simons, Cornell Tech

Earlier this month, Cornell Tech Dean Greg Morrisett announced that a gift from Howard Morgan PhD ’68 and his wife Eleanor, will be used to endow a new faculty chair awarded to professor Huseyin Topaloglu, an expert in operations research whose work focuses on dynamic programming and applications in supply chain logistics.

“Eleanor and I are thrilled that endowing this chair allows us to support the scholarship of brilliant faculty like Huseyin who have had such an important impact in the field of operations research,” Morgan said. “It’s very meaningful  to help make this kind of work possible and invest in the future of Cornell Tech during this critically important campaign for the campus and the university.”

Morgan has had strong ties with Cornell for many decades, going back to his time as a PhD student and, briefly, as a professor. He was a member of Cornell’s College of Engineering Advisory Council from 2012 to 2019, and has been a member of the Cornell University Board of Trustees since 2019. Later this month he will also be joining the Cornell Tech Council, formerly the Board of Overseers. (He was even in the room in 2011 when New York City Mayor Mike Bloomberg announced that Cornell and Technion had won the competition to build what’s now Cornell Tech.)

His background makes him especially well-suited to endow a chair to Topaglu, as his PhD in the 1960s was also in operations research. Morgan later taught at the Wharton School of the University of Pennsylvania, where his research on networks and user interfaces led to his bringing the ARPAnet to Philadelphia in 1974.

“Howard has been a long-time supporter of Cornell Tech, from mentoring postdocs in the Runway Program to serving as a member of the Cornell Tech Visiting Committee,” said Cornell Tech Dean Greg Morrisett. “We’re so very grateful for the support of individuals like Howard and Eleanor in being able to grow our faculty and expand our research footprint.”

Cornell Tech is raising $500 million as part of a larger university-wide campaign that aims to raise $5 billion by 2026. The funds Cornell Tech is raising will support important needs across campus, ranging from financial aid and academic programs to research and endowed professorships. Learn more on our Giving page.

The post New Gift Endows Morgan Chair to Operations Research Professor appeared first on Cornell Tech.

However, it was human (and not artificial) intelligence that led Isha Gandhi to join the ML team at Splunk — a data-monitoring software company that has partnered with 92 of the Fortune 100 companies on their ML projects and other technology challenges.

Combining a Passion for Entrepreneurship and Product Management with Technology

As a teenager, Gandhi wanted to be a professional tennis player — but developed an interest in mechanical engineering in the hopes of becoming a product designer.

I found mechanical engineering to be a very tangible application of math and science.”

Working in the automotive industry exposed her to the world of AI. She was a project manager on the digital transformation team, where she worked on proof of concepts for technologies to improve production and supply chain efficiency. For example, computer vision and deep learning can be used to detect defects in car components on the assembly line.

Over time, she was drawn to the idea that technology can be used to solve human challenges.

When Gandhi entered Cornell Tech, she realized that she needed to complement her interest in product management with a deeper understanding of the technology industry and the specific skills needed within that realm.

She was drawn to product management because it entails thinking critically to understand consumer needs and then translating those needs into action. She loved the fact that product management entails engineering, marketing, design, and even law. While studying, she took technical courses like Applied ML and participated in Startup Studio, and the skills she gained there not only helped her round out her product management expertise but also gave her the confidence to “conceptualize ML features at Splunk.” Her strengthened insights and belief in her own abilities enabled her to collaborate with data scientists and ML engineers post-graduation.

Currently working as a product manager for Splunk’s ML team, Gandhi helps drive strategy and builds ML-based services for Splunk’s product portfolio. Working very closely with product teams, she collaborates with her colleagues to define solutions and deliver features and functionality. Driving the product life-cycle requires conducting market research, analyzing telemetry and metrics, defining success criteria, consolidating specifications, and writing product requirements documents (PRDs).

Though my day-to-day role consists of a lot of writing, the cool part is actually having the opportunity to craft and paint a product vision behind it all.”

Relatively new to the job, Gandhi credits her internship at Splunk, along with her academic training, in shaping her career. During the internship, she was responsible for a series of competitive and market assessments and had an opportunity to recommend ML use cases for Splunk’s products. Some of her ideas from this internship eventually found their way into product roadmaps as key differentiators.

The Future of ML

“We can already identify and appreciate how drastically data and ML are changing the world,” said Gandhi, referencing the continued impact data will have on society in the future. “It will pave the way for innovations across industries!”

She points out, however, that ML doesn’t come without ethical risks. She believes she has a responsibility to factor in the human impact of ML on making key business decisions, and she is committed to encouraging and backing initiatives with positive societal impact.

Lessons Learned and Advice for Others

Gandhi is discovering that she can “have it all, but not at once.” She now believes that development takes time, and things change — a lot. She counsels other professionals starting out in their careers to set near-term goals for themselves so they can constantly learn.

Outrunning yourself will not only kill the enjoyment of learning but may lead to tunnel vision, preventing you from identifying golden opportunities and paths that suit your skills.”

Some skills that she’s fine-tuned along her own path are interpersonal communication, the ability to balance engineering techniques and business goals, and the ability to think on her feet, set priorities, and multi-task when needed. Machines may change our future, but human and genuine advice like Gandhi’s can shape careers.

The post When Human Meets Machine at Splunk: Isha Gandhi, ORIE ’21 appeared first on Cornell Tech.

Willy Lin, Product Lead at Anheuser-Busch InBev (ABI), is playing a major role in bringing the timeless industry of beer brewing into the next century. 

Lin’s Path to the Beverage World

Lin first connected with ABI at a blitz interview event during his time studying Operations Research and Information Engineering (ORIE) at Cornell Tech. He was one of four students invited to ABI’s New York headquarters, where he was offered a summer internship. As a member of the product team, his performance was so strong that he was offered a full-time position in the Tech Supply department. This group handles the technology around supply, meaning that it works toward improving the company’s beer production in order to increase the bottom line across the board.

“I’m in the business of making our breweries more efficient through data access, transparency, and predictive analytics,” said Lin. He views his success in the role as something that can be clearly measured through cost savings and improvements in how much beer is produced. 

He starts his day at 7 am with stand-up meetings involving team members from across the globe (such as India and Brazil), followed by more meetings to discuss progress on data science models, infrastructure deployment, department alignment, and other production and management topics. During the afternoon, when his coworkers on the other side of the world have stopped work, Lin uses that time to focus on timelines, implementation plans, e-mails, and user feedback.

Lin’s efforts have helped bring plant floor data to the cloud, and his team performs advanced analytics that help deliver streamlined production and profitability. Their current project, known as the “Digital Factory” journey, entails deploying Internet of Things (IoT) Infrastructure to globally aggregate PLC data, or data from machines and connected devices. So far, it has yielded a completed fermentation KPI machine learning model. Two more are in development, while four breweries have adopted this current model.

But Lin’s role is not limited to short-term automation and analytics. Navigating the wants and needs of all regional zones and aligning them around a global goal is one of his long-term missions. So, his objectives and role extend way beyond one of technology. 

He credits Cornell Tech with helping him prepare for this position. 

I was able to appreciate diverse viewpoints from people with different backgrounds all working towards one product goal. This gave me skills and perspectives that I could use to establish integrated best practices in my work.”

How the Beer Industry is Evolving

Lin said that companies are becoming much more reliant on data to drive insights and value. “Getting accurate and valid data at each step of the brewing process, as well as maintenance, packaging, and energy use are all becoming important,” he said. 

He believes that the industry is behind the curve in terms of data science sophistication, but is quickly catching up. “We will see many more companies relying on data science models, driving advanced process controls to automate many portions of the beer-making process,” he said. 

Lin’s Advice for Job-Seekers is Simple and Powerful

Don’t be afraid to take chances on internships.”

“I turned down full-time DS roles to pursue a riskier internship in product,” said Lin. However, it’s not all been easy — he started his job during the pandemic and face-to-face exposure to the business has been tough. He said that he wishes someone had given him a month-long class on brewing, complete with daily tastings and brewery tours. Direct exposure to internal clients’ pain points, he said, can help a Product Manager deliver better data and more practical solutions to challenges.

The post Data Science in Brewing: Willy Lin, ORIE ‘20 appeared first on Cornell Tech.

By Tom Fleischman

The post Ranked Choice, Multimember Districts Blunts Gerrymandering appeared first on Cornell Tech.

Today, Li has put her classic Chinese breakfast business on the back burner and is pursuing her Masters in Operations Research and Information Engineering (ORIE) at Cornell Tech. And while her trajectory may seem surprising to some, for Li, it makes perfect directional sense.

The Road To Cornell Tech

Li grew up in Suzhou, just outside of Shanghai, before moving to California to pursue a bachelor’s degree in computational and applied mathematics at USC. But for Li, her classwork felt too theoretical.

“I wanted to study something that is close to reality and that you can use in your daily life, which is why I double majored in business at the Marshall School,” she said.

Her desire to gain practical skills and apply them to solve real-world problems has been a thread throughout Li’s academic and professional career. With graduation approaching in the spring of 2014, Li explained, “I always had the idea to start my own company. As a business student that’s a common dream for everybody. I’m very practical, so when I wanted to do it, I just did it.”

The only question was what kind of business she would start — a tricky question considering that Li was facing a very major constriction. Since she was in America on an F-1 student visa, Li’s business could only be operational for a single year under her visa’s Optional Practical Training (OPT).

“I didn’t want the business to be too large so that I couldn’t break even after one year,” Li said. “I wanted to start a small business that was very integrated.”

After doing some research around the USC community, Li realized that there was a desire among students for traditional Chinese breakfasts. So she decided to open her own food truck called MorningWay. Not only would it be easy to dissolve, she’d also get a whole range of experiences from marketing to manufacturing to dealing with campus and state authorities to even cooking some of the food herself.

“Before I drove a little Corolla,” Li said. “Now I had to drive my Ford Expedition to tow a 7,500 pound trailer to the health department that was 40 miles away, three different times to get one license.”

The business was exhausting, but successful. After checking her dream of being a business owner off her list, Li was ready for the the next step in her educational and professional journey: Being a part of Cornell Tech’s first ORIE class.

The design of Li’s food truck, Morning Way.

Learning Real-Life Skills In The Classroom At Cornell Tech

While running her business, Li found that in spite of being able to collect a lot of her own data, “neither my math or business major allowed me to do very much with this data.”

Data science had become a professional hot topic, and Li wanted to explore the field. But while a lot of universities offered programs that included data science, they seemed too traditional.

After searching through various graduate school course catalogues, Li was immediately drawn to Cornell Tech’s new program in operations research. Not only was it located in New York City — which Li admits was a big perk — but Cornell Tech also offered core technical classes (like optimization modeling and applied learning) alongside practical classes that put students in real-world scenarios.

Li’s favorite experience so far was Product Studio, a course in which all Cornell Tech students have the ability to work in diverse teams to respond to questions posed by real businesses, ranging from nonprofits to large companies like Google or Amazon.

Li’s team — made up of computer science students, an MBA, as well as a student from the Parsons School of Design — responded to a question posed by AOL, asking them how they might deliver news into smart home environments in a seamless and entertaining way. They decided to create a small device that projected images from phones and other smart devices onto any household surface.

Not only did their product go on to compete in and win a NYC Media Lab Verizon Challenge award of $15,000, but it also fostered friendship and inspired the students to learn a wider range of skills.

“We had a great experience working together, we made hardware and software—there was so much for me to explore,” Li said. “Previously as a business and math student, I didn’t touch on coding a lot. But in this program, I actually had to. I also learned how to write an app with X-code. So I learned a lot by working with the other students.”

For example, Li said that the MBA group members’ ability to bring the team together through organization and motivation taught her strategies about effective communication. But Li hasn’t only learned from her Product Studio teammates. The diverse array of students at Cornell Tech become friends and fellow teachers. Although Li was scared to code at first, she learned the skills quickly thanks to her classmates and now feels confident her programming skills.

Now that Li is nearing graduation, she is starting to think about the next step. Rather than re-opening the food truck, Li is interviewing with companies about data science and quantitative analysis positions. But that doesn’t mean she won’t run her own business again. “I’m thinking in the future I’ll maybe have my own company in the data science field.”

The post From Food Truck Owner To Engineering Masters Student appeared first on Cornell Tech.

For Cornell Tech student Noel Alexander, Master in Operations Research and Information Engineering (ORIE) ’17, learning to combine his multiple talents and interests felt like the best option.

As a teenager, Alexander was fascinated by data and mathematical models. His interests led him to pursue a degree in Operations Research at Cornell. During a summer research job, Alexander worked with Cornell assistant professor Dr. Jamol Pender on projects in queuing theory, or the study of human behavior when waiting in lines. More specifically, they looked at the non-stationary arrival processes of customers and how those processes impact queue lengths and customer waiting times.

“Those projects helped me understand what research was all about, and were different than anything I had experienced in my undergraduate coursework,” said Alexander. “I enjoyed the autonomy of the data analysis work I did for him.”

This hands-on experience working with Pender at Cornell eventually led him to pursuing a master’s degree in ORIE at Cornell Tech, where he could gain more experience in applied operations research.

“At Cornell Tech, I’m able to concentrate fully on solving problems with data,” said Alexander. Computer science classes like machine learning and data science have helped him apply his previous studies in operations to new projects.

“Data represents a world of opportunity in so many areas,” said Alexander. “I like being at the intersection of business and technology, and eventually I might come back to Cornell Tech to get my MBA.”

Building off of his work in queuing theory with Pender, Alexander has continued to explore how it might be paired with finance technology, hospital networks, or retail. “By playing with historical data, I can start to build an exploratory analysis of how to solve a business problem,” said Alexander. This could mean modeling how products move off the shelves in a retail setting, or trying to predict which way stock prices will move.

During an internship at a major consulting firm, Alexander worked on forensic data analytics because it spoke to his operations research background.

Though data-related questions are the bulk of Alexander’s studies, he has enjoyed the opportunity to develop entrepreneurial skills in classes like Startup Studio, where he and students from other programs work in teams to develop real products.

“My teammates and I are working on a hardware solution to turn a phone to a computing solution,” said Alexander. “The idea behind our product is to fill the gap between students who don’t have access to computers but might have a smartphone.”

Alexander is hoping the product could be used to expand digital literacy within the school districts the team has visited. “It’s outside of operations research, but it’s fun to think about product narratives and how a product might fit with a customer’s needs.”

Alexander’s variety of experience made him realize that there’s a large demand for professionals who can apply business acumen and data analysis to a number of situations.

“Eventually, I’d like to be able to come at different problems from a consulting perspective,”said Alexander. “Working with such diverse groups of people across several industries has made me see that I can apply data-driven ideas to almost any aspect of a business.”

The post Applied Math: Noel Alexander ‘17 Takes His Operations Research to the Next Level appeared first on Cornell Tech.

The satisfaction of solving the logistical challenges that underpin these expectations motivates Mika Sumida, a PhD candidate at Cornell’s School of Operations Research and Information Engineering (ORIE).

Sumida studied mathematics at Yale before deciding to apply her theoretical skills to real-world situations through ORIE. She describes operations research as “model- and data-driven decision-making in complex environments.” It has applications anywhere you have resources or systems that you want to allocate or run more efficiently, she says.

Dynamic Resource Allocation

Sumida’s current interests lie in city logistics, umbrella term used to describe transportation and related resource allocation challenges in urban environments. She is working with Professor Huseyin Topaloglu on ‘dynamic resource allocation’ problems.

Retailers who operate same day shipping, such as Amazon, have had to make radical changes to how their inventory is processed and shipped. Previously, companies like USPS or UPS or Fedex could take several days to move a product from an inventory store to a customer. That now needs to happen within hours.

This leads to challenges in terms of physically moving units, but also in planning how they are moved. Under old systems, schedule planning might take place overnight and a delivery worker could follow a route over an entire day.

“Now, you have a really dynamic system where orders are coming in throughout the day, and you have to tell workers in real time to go and satisfy that order,” explains Sumida.

Sumida’s research aim is to find a cost-effective solution that assigns orders to workers using the least amount of resources, while still satisfying customer expectation. In an Amazon warehouse, for example, this means deciding which orders should be propelled at what time, and doing so as they are coming in.

She expects her end solution to be a practical suggestion on how to implement this type of dynamic dispatching, “It will be some sort of algorithm or policy that minimizes cost while still satisfying all the demands,” she says.

Fare-Locking

Sumida has already tackled the issue of fare-locking, an operations research problem facing airlines. Many companies now give customers the option of locking in a fare instead of purchasing it outright. This means that companies have to allocate that seat to that person while the fare is locked. As this could be for one or two weeks it creates a difficulty, explains Sumida.

“You have to keep these seats on reserve, and then they might come back and the customer might not actually buy the ticket, so there’s tension or uncertainty in the status of this ticket.”

Sumida’s solution was to create a fare-setting policy that accounts for the fact that some customers will lock the fare instead of buying it, while others will both lock the fare and buy the ticket. By allowing for the probability of both scenarios, “it allows the airlines to actually plan for that and set the prices accordingly.”

Sumida’s background in theoretical mathematics provides the backbone for the policies, algorithms and techniques she develops to address these logistical conundrums. “Part of operations research is coming up with mathematical tools to better solve those optimization problems,” she says.

Real-World Experience and Applications

Sumida’s work within city logistics and fare-locking has been shaped by interning for companies that face operations research challenges on a day-to-day basis. She helped a faucet manufacturing company in Boston streamline their production, and she worked with a clothing retail company to improve their systems for allocating inventory.

Last summer, while Sumida was based at Cornell Tech, she worked with Homer Logistics — a New York company that handles deliveries for restaurants.

“They pool all of the incoming orders for all of the restaurants that they service, and then they have a fleet of dispatchers who go and satisfy those orders,” she explains. “It saves the restaurant money because they don’t actually have to hire their own delivery.”

Her experience at Homer Logistics gave her a solid view of how dispatching and city logistics work in practice. It also showed her how challenging the problems can be, and this deep understanding inspired her current work on dynamic resource allocation.

For Sumida, establishing industry links and being able to work closely with Professor Topaloglu, are enormous benefits of being part of the School of Operations Research and Information Engineering and Cornell Tech.

“I love the atmosphere, but I also love the excitement of working on real-world problems,” she says. “I can use my background, still do some more theoretical work, but it’s all in view of applications, of how it can be used in the real world.”

The post Mika Sumida: Keeping Customers Happy Through Operations Research appeared first on Cornell Tech.

Ali Sadighian is one of those people. Sadighian has an undergraduate degree in industrial engineering and an MBA from Sharif University of Technology in Tehran as well as a Ph.D. in Operations Research from Columbia University.

Sadighian manages a team of scientists at Amazon’s Inventory Planning and Control (IPC), part of the Supply Chain Optimization Technologies team (SCOT). The group focuses on the automation of Amazon’s supply chain decision making processes. He is currently interested in revenue management, operations management, supply chain management and game theory.

In a Q&A, Sadighian shares his journey to Amazon and what he does on a daily basis.

What types of roles were you considering before graduation?
I was thinking about academic jobs, industry research (i.e. MSR/Yahoo Lab), and consulting.

Why did you select your current role?
I left academia to join a research team at Amazon. It came through a combination of my interest in applied research and network of friends who worked at the Amazon. I wanted to work in an environment where my research had measurable impact on a business.

How do you expect your career path to look like in a few years?
Amazon is reshaping the future of retail and there are many uncharted territories as we expand. I currently manage a research team focused on automating manual decision making processes across Amazon supply chain. We develop algorithms that move billions of dollars of products across a global supply chain, and I expect our domain of influence to grow as the complexity of the business problems grows. Humans simply cannot make decisions at such a large scale and we need automated solutions. Research careers in Amazon grow up to VP level.

What does a typical day look like?

• Reviewing Business Metrics and identifying how the automated algorithms driving decisions have impacted the metrics
• Deep diving on areas of opportunity to develop new solutions as identified through our metrics (where we do not fare well or “have dropped the ball”)
• Working on the new automation problem inspired by new businesses (Cashier-less store, 1-hour delivery etc)
• Meeting with business partners and development teams
• Working on new business ideas to leverage technology as driver of growth

What’s the most useful thing you learned in school that you still use today?
School taught me the simple building blocks that I use every day to understand the basics of why certain solutions/algorithms work. As a researcher, I am faced with a business environment that continuously evolves and I can’t rely on rigid solutions that solve the problem today but hinder growth in future.

In order to develop the right solution that is flexible enough to allow for such changes, more often than not I need to understand the most basic yet important underlying mechanisms of a situation and develop models that capture these major trade-offs. Even the most complex models I have developed at work start from those fundamentals. School game me the fundamental tools of the trade and imparted on me a deep appreciation for simplifying a problem to capture the major trade-offs.

What’s your advice to a student hoping to get into this field?
Do not underestimate the value the most basic tools and models you learn in school. They might sound simple (and, indeed, they are most of the time) but those fundamentals will help you untangle business problems and remove what I call “ the fog of modelling” that exists in ambiguous situations.

It’s tempting to throw the most advanced tools at a problem,but in long-run, if the use of advanced tools is not coupled with a deep understanding of the simple building blocks underlying those tools — they will hinder growth and the ability to innovate.

The post Where Business and Data Collide appeared first on Cornell Tech.

From healthcare to marketing, everyone wants more of it. Data are collected in a thousand different ways, but just as important as how it’s collected, is how it is interpreted.

That’s where Chao Ding, a quantitative analyst at Google, comes in. He and others in the field of data analysis are responsible for making data valuable for companies.

Ding finished his undergraduate study in Tsinghua University of China and graduated from the PhD program in Operations Research at Cornell in 2012.

In a Q&A, Ding shares what a career as a quantitative analyst looks like and why it’s an exciting field.

What types of roles were you considering before graduation?
I was mostly considering quantitative roles in either tech or financial industry.

Why did you choose your current role?
In one of my research projects with Professors Topaloglu and Rusmevichientong, I had to run some big simulations on Amazon AWS. Later on, I also got interested in AWS’s dynamic pricing strategy for spot instances and attempted to develop a research topic on it. The research topic didn’t pan out but such experience made me interested in cloud computing in general. So when this opportunity within an Operations Research team at Google working on cloud computing problems arose, it sounded like a great fit for me.

How do you expect your career path to look like in a few years?
There are a few possibilities I am exploring. One is to go deeper in my current area of cloud computing resource efficiency. As Moore’s Law is coming to end, this area is becoming more and more important. We need to look at this area more systematically, maybe even define quantitative theories and draw academic research attention in this area. Another one is to grow/expand my machine learning experience, which is on the trajectory to take over the world.

What is a typical day like?
In the morning clear up emails and respond to urgent requests if there is any. Then I meet with project teammates (mostly involving software engineers and quant analysts from multiple teams) to get update on what each other is up to and discuss any issues.

I’ll then bug engineers to learn how things work in current system, write queries or R/Python scripts, stare at the result, and virtualize the data to get a handle of what the data is saying.

I also bounce ideas off engineers about what needs to be done to improve a current system and provide insights based on data analysis. Then I discuss with quantitative colleagues (quant analysts, research scientists) on concrete technical / modeling problems.

If needed, I clear up code and send it to colleagues for review. I also review colleagues’ code, document my ideas and summarize findings.

What’s the most useful thing you learned in school that you still use today?
The most basic things, like probability distribution and optimization concepts, are most useful for me. Not necessarily because I use them directly, but having the basic theories in the back of mind helps me a lot to understand why things are the way they are in an abstract level, and enables me to see what might be missing and could be improved.

What’s one thing you wish you had learned in school/earlier?
I wish I got to know more application problems in which OR and in general analytical methodologies can be used. This would help to expand vision and find what interests you the most.

What’s your advice to a student hoping to get into this field?
Establish a solid analytical foundation, you can’t learn everything and a solid foundation will greatly help you learn new things as needed. Follow your curiosity as it’s the best teacher.

The post What It Means To Be a Data Analyst appeared first on Cornell Tech.