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Businesses and organizations of all shapes and sizes use models to understand and improve the performance of their operations. For example, online retailers use models to quantify the impact of different order fulfillment policies. On-demand transportation systems use models to make better decisions on how to assign passengers to drivers. Fintech companies use models to make automated trading decisions on millisecond time scales.

The data & modeling group at Cornell Tech includes quantitative researchers with backgrounds in computer science, electrical engineering, business, and operations research, developing models for decision-making problems in a variety of areas including logistics, retail, marketing, biotech, finance, and healthcare. Their work involves building models to automate decision-making processes, designing algorithms to efficiently solve these models, and using statistics and machine learning techniques to make predictions.

Our research group works with a number of large tech companies and startups. Through such interactions, we are able to play a leading role in implementing the next generation of sophisticated models in practice, while having the opportunity to find out about research problems that are of critical interest to the industry.

 

 

 

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Huseyin Topaloglu

Professor Topaloglu’s group works on assortment planning problems in online retail operations. Online retailers have the opportunity to customize the product assortment that is offered to each individual customer. The challenge is to use data from past purchases to understand how each customer would make a choice among the products offered to them, and use this information to decide which assortment of products to offer to a customer landing on a website. The work involves integrating ideas from econometrics and psychology to build models of the customer choice process and developing combinatorial optimization techniques to choose the right product assortment to offer.

 

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Vikram Krishnamurthy

Professor Krishnamurthy’s group works on statistical signal processing and controlled sensing problems. The fundamental ideas revolve around Bayesian inference, stochastic optimization and game theory. The applications of the research are in three areas. The first application area is in smart adaptive radar tracking systems where the radar system can adapt its behavior in real time using feedback control, and where natural language processing models are used to determine anomalies in target trajectories. The second application area is in understanding how social sensors (human decision makers) interact and influence each other over a social network. This involves related ideas in behavioral economics and revealed preferences, information fusion and is backed up by real-world data from YouTube and psychometric experiments. The final application area is in modeling and controlling the dynamics of artificial cell membranes and nano-scale molecular machines/sensors built out of such membranes.