Ujjal Mukherjee

Ujjal Mukherjee

Associate Professor of Business Administration

  • Email

Contact

306 Wohlers Hall

1206 S. Sixth

Champaign, IL 61820

217-265-5565

ukm@illinois.edu

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Listings

Educational Background

  • M.S., Statistics, University of Minnesota, 2015
  • Ph.D., Business Administration, University of Minnesota, 2015
  • MBA, Xavier Institute, 2002
  • BEME, Jadavpur University, 1997

Positions Held

  • Associate Professor of Business Administration, Business Administration, University of Illinois, 2022 to present
  • Assistant Professor of Business Administration, Business Administration, University of Illinois, 2015-2022
  • Teaching Assistant, Carlson School of Management, 2010-2013

Recent Publications

  • Mukherjee, U. (2022). secDrug: a pipeline to discover novel drug combinations to kill drug-resistant multiple myeloma cells using a greedy set cover algorithm and single-cell multi-omics. Blood Cancer Journal, 12 (3).
  • Mukherjee, U. (2022). Hiding in the Herd: The Product Recall Clustering Phenomenon. Manufacturing & Service Operations Management, Institute for Operations Research and the Management Sciences (INFORMS), 24 (1), 392-410.
  • Schecter, A., Wowak, K., Berente, N., Ye, H., & Mukherjee, U. (2021). A Behavioral Perspective on Service Center Routing: The Role of Inertia. Journal of Operations Management.  link >
  • Ye, H., Mukherjee, U., Chhajed, D., Hirsbrunner, J., & Roloff, C. (2021). Recommending Encounters According to the Sociodemographic Characteristics of Patient Strata Can Reduce Risks from Type 2 Diabetes. PLOS ONE.  link >
  • Zhao, T., Ye, H., Mukherjee, U., & Chhajed, D. (2021). Demand Estimation of Mass-Gathering Healthcare (MGH) in Developing Countries: The Case of Kumbh Mela in India. Naval Research Logistics.  link >
  • Mukherjee, U., Ball, G., Wowak, K., Natarajan, K., & Miller, J. (2021). Paper. Manufacturing & Service Operations Management, INFORMS, 24 (1), 1-698.  link >

Other Publications

Articles

  • Mukherjee, U. (2020). Does Organizational Forgetting Affect Quality Knowledge Gained Through Spillover?-Evidence from the Automotive Industry. Production and Operations Management, 29 (4), 907-934.
  • Mukherjee, U. (2020). Robot-assisted surgical care delivery at a hospital: Policies for maximizing clinical outcome benefits and minimizing costs. Journal of Operations Management, 66 (1-2), 227-256.
  • Mukherjee, U. (2019). Explaining Heterogeneity in Environmental Management Practice Adoption across Firms. Production and Operations Management, 28 (11), 2898-2918.
  • Mukherjee, U. (2018). Assessing the cytotoxicity of ambient particulate matter (PM) using. Chinese hamster ovary (CHO) cells and its relationship with the PM chemical composition and oxidative potential. 179 132-141.
  • Mukherjee, U., Sinha, K., & Kohnke, E. (2017). Delivering Long-term Surgical Care in Under-served Communities: The Enabling Role of International NPOs as Partners. Production and Operations Management.
  • Mukherjee, U., & Sinha, K. (2017). Product Recall Decisions in Medical Device Supply Chains: A Big Data Analytic Approach to Evaluating Judgment Bias. Production and Operations Management.
  • Mukherjee, U., Sinha, S., Bosch, S., & Sinha, K. (2014). Critical and Complex Technological Capability Development for Health Care Delivery: Multiyear Field Study of a Surgical (da Vinci) Robot in a Multispecialty Hospital. Journal of Medical Devices (ASME), 8 (3).
  • Mukherjee, U., & Chatterjee, S. (2014). A Fay-Herriot Type Approach to Better Prediction in Multi-indexed Response with Application to Arctic Sea-water Data Analysis. Journal of Indian Society of Agricultural Statistics, 68 (2).

Conference Proceedings

  • Bose, S., Souyris, S., Ivanov, A., Mukherjee, U., Seshadri, S., & , Y. (2021). Control of Epidemic Spreads via Testing and Lock-Down. ( pp. 4272-4279). 60th IEEE Conference on Decision and Control.
  • Mukherjee, U. (2019). A novel computational combination-therapy prediction algorithm (secDrug) identifies the Nampt inhibitor FK866 reverses PI-resistant multiple myeloma. ( 13 ed vol. 79,). Annual Meeting of the American-Association-for-Cancer-Research (AACR.
  • Mukherjee, U., & Chatterjee, S. (2014). Fast Algorithm for Computing Weighted Projection Quantiles, Quantile Regression and Data Depth for High-Dimensional Large Data Clouds. 2014 IEEE International Conference on Big Data.

Presentations

  • Mukherjee, U. (2014). “Critical and Complex Technological Capability Development for Health Care Delivery: Econometric Analysis of the Effect of Technology Innovation on Reducing Process and Outcome Variations Design of Medical Devices Annual Conference.
  • Mukherjee, U. (2014). Critical and Complex Technological Capability Development for Health Care Delivery: Econometric Analysis of the Effect of Technology Innovation on Reducing Process and Outcome Variation Minnesota Healthcare Research Conference.
  • Mukherjee, U. (2014). Critical and Complex Technological Capability Development for Health Care Delivery: Econometric Analysis of the Effect of Technology Innovation on Reducing Process and Outcome Variations Production and Operations Management Meeting.
  • Mukherjee, U. (2014). Critical and Complex Technological Capability Development for Health Care Delivery: Econometric Analysis of the Effect of Technology Innovation on Reducing Process and Outcome Variations Annual MSOM Conference Workshop.
  • Mukherjee, U. (2014). Fast Algorithm for Computing Weighted Projection Quantiles, Quantile Regression and Data Depth for High-Dimensional Large Data Clouds IEEE Conference on Big Data Analytics.
  • Mukherjee, U. (2014). Predicting Failures of High Tech Innovations-in-Use: Application of Predictive Analytics to Big Data on Market Failures of Medical Devices International Symposium of Information Systems.
  • Mukherjee, U. (2014). Predicting Failures of High Tech Innovations-in-Use: Application of Predictive Analytics to Big Data on Market Failures of Medical Devices Medtronic Big Data and Advanced Analytics Symposium.
  • Mukherjee, U. (2014). Predicting Failures of High Tech Innovations-in-Use: Application of Predictive Analytics to Big Data on Market Failures of Medical Devices Production and Operations Management Meeting.
  • Mukherjee, U. (2014). Predicting Failures of High Tech Innovations-in-Use: Application of Predictive Analytics to Big Data on Market Failures of Medical Devices Social Media and Business Analytics Collaborative (SOBACO) Annual Conference.
  • Mukherjee, U. (2014). Predicting Failures of High Tech Innovations-in-Use: Application of Predictive Analytics to Big Data on Market Failures of Medical Devices Wharton Technology and Innovation Conference.
  • Mukherjee, U. (2014). Predicting Failures of High Tech Innovations-in-Use: Application of Predictive Analytics to Big Data on Market Failures of Medical Devices American Society of Quality (ASQ.
  • Mukherjee, U. (2014). Predicting Failures of High Tech Innovations-in-Use: Application of Predictive Analytics to Big Data on Market Failures of Medical Devices Annual Conference of the Institute of Engineering in Medicine.
  • Mukherjee, U. (2014). Predicting Failures of High Tech Innovations-in-Use: Application of Predictive Analytics to Big Data on Market Failures of Medical Devices Annual MSOM Conference Workshop.
  • Mukherjee, U. (2014). Strategic Management of Medical Equipment and Devices: From Maintenance Management to Strategic Asset Management Annual Conference of the North Central Biomedical Association.
  • Mukherjee, U. (2013). Analyzing Sources of Innovation Failures in Medical Devices: An Analytic Framework Production and Operations Management Meeting.
  • Mukherjee, U. (2013). Analyzing Sources of Innovation Failures in Medical Devices: An Empirical Modeling Framework Production and Operations Management Meeting.
  • Mukherjee, U. (2013). Critical and Complex Technological Capability Development for Health Care Delivery: Econometric Analysis of the Effect of Technology Innovation on Reducing Process and Outcome Variation Life Sciences Alley Annual Conference.
  • Mukherjee, U. (2013). Critical and Complex Technological Capability Development for Health Care Delivery: Econometric Analysis of the Effect of Technology Innovation on Reducing Process and Outcome Variations Annual Conference of the Institute of Engineering in Medicine.
  • Mukherjee, U. (2013). Critical and Complex Technological Capability Development for Health Care Delivery: Econometric Analysis of the Effect of Technology Innovation on Reducing Process and Outcome Variations INFORMS Annual Meeting.
  • Mukherjee, U. (2013). Predicting Failures of High Tech Innovations-in-Use: Application of Predictive Analytics to Big Data on Market Failures of Medical Devices INFORMS Annual Meeting.
  • Mukherjee, U. (2012). Sequential Innovation in New Product Development: Product Architecture and Versioning strategy INFORMS Annual Meeting.
  • Mukherjee, U. (2012). Sources of Failures in the Medical Device Industry: An Empirical Analysis Production and Operations Management Meeting.
  • Mukherjee, U. (2011). Strategic Linkages of Modular Product Designs Decision Science Institute Annual Meeting.

Working Papers

  • Hao, S., Xu, Y., Mukherjee, U., Seshadri, S., Ahsen, M., Bose, S., Ivanov, A., Souyris, S., & Sridhar, P. Hotspots for Emerging Epidemics: Multi-Task and Transfer Learning over Mobility Networks.
  • Mukherjee, U., Ye, H., Chhajed, D., Hirsbrunner, J., & Roloff, C. A Framework for Preventive Intervention and Resource Optimization for Population Level Diabetes Care.
  • Mukherjee, U., Ye, H., Chhajed, D., Hirsbrunner, J., & Roloff, C. Socio-Economic and Operational Determinants of Inequity in Healthcare Outcome: From Explanatory to Predictive Modeling.
  • Mukherjee, U. Firm Learning from Failures and Recalls of Products in Supply Chains.
  • Mukherjee, U., & Juneja, B. The Effect of Social Disparity, Access and Affordability on Cardiac Healthcare Delivery: A Population-level Study using National Health and Nutrition Study (NHANES) Multi-year Database.
  • Mukherjee, U., & Sinha, K. Predicting Failures of High Tech Innovations-in-Use: Application of Predictive Analytics to Big Data on Market Failures of Medical Devices.
  • Mukherjee, U., Sinha, S., Bosch, S., & Sinha, K. Enabling Health Care Delivery with High Tech Innovation: A Longitudinal Field Study of Robot-Assisted Surgery.

Grants

  • Dynamic Resource Management in Response to Pandemics, c3.ai Digital Transformation Institute, 2020-2021
  • Social Media and Business Analytic Collaborative (SOBACO), an inter-disciplinary initiative between the Carlson School of Management and the College of Science and Engineering, 2013-2014

Teaching Interests

Supply Chain Management, Business Analytics, Healthcare Operations, and Healthcare Analytics

Research Interests

Predicting failures of innovations-in-use; Technology adoption and mediation in healthcare.

Current Courses

  • Logistics Management (BADM 378) Treats the total flow of materials from their acquisition as basic or unprocessed supplies to delivery of the finished product, as well as the related counter-flows of information that both record and control material movement. Major topics include forecasting material requirements; transportation planning; order processing system; raw material, in-process and finished goods inventory management; packaging; in plant and field warehousing; location theory (space, time, and cost trade- offs); communications; and control.

  • Res Topics in Operations Mgt (BADM 569) Current and classical literature in the area of Operations Management. The topics covered may vary from year to year and may include performance measures, inventory management, planning, scheduling, location, layout, product design, process design, and forecasting.

  • Supply Chain Analytics (BADM 575) The objective of the course is to introduce students to using data analytics for improving decision making in supply chains. With Globalization and digitization of supply chains a large volume of data is getting generated within supply chains. Being able to use the information in the data to improve supply chain functioning is critical to success for many organizations. In this course, students are introduced to data analytic methods such as statistical modeling and machine learning methods for organization, and analysis of large volume of different kind of data that relate to specific aspects of managing and organizing supply chain. This course follows a project based practical learning approach. The course is divided into several modules and students are required to analyze and present data and inferences with respect to these modules. 4 graduate hours. No professional credit. Credit is not given for BADM 575 and BADM 590 (31474) Section SCA.

Contact

306 Wohlers Hall

1206 S. Sixth

Champaign, IL 61820

217-265-5565

ukm@illinois.edu

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