Ramandeep S. Randhawa
  • Research
  • Teaching
  • Games
Machine Learning / Deep Learning
  • S. Krishnamurthy, P. Jain, D. Tripathy, R. Basset, R. Randhawa, H. Muhammad, W. Huang, H. Yang, S. Kummar, G. Wilding, R. Roy. Predicting Response of Triple-Negative Breast Cancer to Neoadjuvant Chemotherapy Using a Deep Convolutional Neural Network–Based Artificial Intelligence Tool. JCO Clinical Cancer Informatics, vol. 7, 2023.
  • W. Huang, R. Randhawa, P. Jain, S. Hubbard, J. Eickhoff, S. Kummar, G. Wilding, H. Basu, R. Roy. A Novel Artificial Intelligence-Powered Method for Prediction of Early Recurrence of Prostate Cancer After Prostatectomy and Cancer Drivers. JCO Clinical Cancer Informatics, Feb:6, 2022.
  • W. Huang, R. Randhawa, P. Jain, K. A. Iczkowski, R. Hu, S. Hubbard, J. Eickhoff, H. Basu, R. Roy, Development and Validation of an Artificial Intelligence-Powered Platform for Prostate Cancer Grading and Quantification. JAMA Network Open. Nov 1;4(11) 2021.
  • R. S. Randhawa, A. Modi, P. Jain, and P. Warier, Improving Boundary Classification for Brain Tumor Segmentation and Longitudinal Disease Progression. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2016. deep learning cnn ai healthcare
  • R. S. Randhawa, P. Jain, and G. Madan, Topic Modeling Using Distributed Word Embeddings. Feb 2016. deep learning machine vec2topic
Operations Management
  • N. Bakshi, J. Kim, R. S. Randhawa, Service Operations for Justice-On-Time: A Data-Driven Queueing Approach. Manufacturing and Service Operations Management, vol. 27, no. 1, 305-321, 2024.judiciary legal queues
  •  A. Bassamboo, R. Randhawa, and C. Wu, Optimally Scheduling Heterogeneous Impatient Customers. Manufacturing and Service Operations Management, vol. 25, no. 3, 1066-1080, 2023.queueing scheduling
  •  K. Drakopoulos and R. S. Randhawa, Why Perfect Tests May Not be Worth Waiting For: Information as a Commodity. Management Science (Fast Track), vol. 67, no. 11, 6678-6693, 2021.COVID-19 information operations epidemics testing
    • INFORMS podcast link
  •  K. Drakopoulos, S. Jain and R. S. Randhawa, Persuading Customers to Buy Early: The Value of Personalized Information Provisioning. Management Science, vol. 67, no. 2, 828-853, 2020.rm revenue management strategic
    • Interview link
  • N. Golrezaei, H. Nazerzadeh and R. S. Randhawa, Dynamic Pricing for Heterogeneous Time-Sensitive Customers. Manufacturing and Service Operations Management, vol. 22, no. 3, 429-643, 2020.rm revenue management strategic
  • H. Nazerzadeh and R. S. Randhawa, Near-Optimality of Coarse Service Grades for Customer Differentiation in Queueing Systems. Production and Operations Management, vol. 27, no. 3, 578-595, 2018. pricing mechanism design incentive
  • J. Kim, R. S. Randhawa, and A. R. Ward, Dynamic Scheduling in a Many-Server Multi-Class System: The Role of Customer Impatience in Large Systems. Manufacturing and Service Operations Management, vol 20, no. 2, 285-301, 2018. hazard rate diffusion control stochastic
  • J. Kim and R. S. Randhawa, The Value of Dynamic Pricing in Large Queueing Systems. Operations Research, vol. 66, no. 2, 409-425, 2017. dcp diffusion
  • C. Corona and R. S. Randhawa, The Value of Confession: Admitting Mistakes to Build Reputation. The Accounting Review, vol. 93, no. 3, 133-161, 2017.
  • R. S. Randhawa, Optimality Gap of Asymptotically-derived Prescriptions with Applications to Queueing Systems. Queueing Systems, vol. 83, 131-155, 2016.accuracy order
  • A. Bassamboo and R. S. Randhawa, Scheduling Homogeneous Impatient Customers, Management Science, vol. 62, no. 7, 2129-2147, 2016.call centers
  • M. Haviv and R. S. Randhawa, Pricing in Queues without Demand Information, Manufacturing and Service Operations Management, vol. 16, no. 3, 401-411, 2014.
  • S. Gilbert, R. S. Randhawa, H. Sun, Optimal Per-Use Rentals and Sales of Durable Products and Their Distinct Roles in Price Discrimination, Production and Operations Management, vol. 23, no. 3, 393–404, 2014.
  • A. Bassamboo, L. Y. Chu, and R. S. Randhawa, Designing Flexible Systems using a New Notion of Supermodularity, Operations Research Letters, vol. 41, no. 1, 107-111, 2013.
  • R. S. Randhawa, Accuracy of Fluid Approximations for Queueing Systems with Congestion-Sensitive Demand and Implications for Capacity Sizing, Operations Research Letters, vol. 41, no. 1, 27-31, 2013.
  • A. Bassamboo, R. S. Randhawa, and J. Van Mieghem, A Little Flexibility is All You Need: On the Value of Flexible Resources in Queueing Systems, Operations Research, vol. 60, no. 6, 1423-1435, 2012.
  • A. Bassamboo, R. S. Randhawa, and A. Zeevi, Capacity Sizing under Parameter Uncertainty: Safety Staffing Principles Revisited, Management Science, vol. 56, no. 10, 1668-1686, 2010. call centers
  • A. Bassamboo and R. S. Randhawa, On the Accuracy of Fluid Models for Capacity Sizing in Queueing Systems with Impatient Customers, Operations Research, vol. 58, no. 5, 1398-1413, 2010. Third place, 2010 INFORMS JFIG Competition.call centers
  • A. Bassamboo, R. S. Randhawa, and J. Van Mieghem, Optimal Flexibility Configurations in Newsvendor Networks: Going beyond Chaining and Pairing, Management Science, vol. 56, no. 8, 1285-1303, 2010.
  • C. Corona and R. S. Randhawa, The Auditor’s Slippery Slope: An Analysis of Reputational Incentives, Management Science, vol. 56, no. 6, 924-937, 2010.
  • S. Kumar and R. S. Randhawa, Exploiting Market Size in Service Systems, Manufacturing and Service Operations Management, vol. 12, no. 3, 511-526, 2010.pricing
  • A. Bassamboo, S. Kumar, and R. S. Randhawa, Dynamics of New Product Introduction in Closed Rental Systems, Operations Research, vol. 57, no. 6, 1347–1359, 2009.netflix
  • R. S. Randhawa and S. Kumar, Multi-Server Loss Systems with Subscribers, Mathematics of Operations Research, vol. 34, no. 1, 142-179, 2009.
  • R. S. Randhawa and S. Kumar, Usage Restriction and Subscription Services: Operational Benefits with Rational Users, Manufacturing and Service Operations Management, vol. 10, no. 3, 429–447, 2008.netflix
  • R. S. Randhawa and S. Juneja, Combining Importance Sampling and Temporal Difference Control Variates to Simulate Markov Chains, ACM: Transactions on Modeling and Computer Simulation, 14(1), 1-30, 2004.