Katie McConky Headshot

Katie McConky

Assistant Professor
Department of Industrial and Systems Engineering
Kate Gleason College of Engineering

Office Location

Katie McConky

Assistant Professor
Department of Industrial and Systems Engineering
Kate Gleason College of Engineering


BS, MS, Rochester Institute of Technology; Ph.D., State University of New York at Buffalo


Dr. Katie McConky received her B.S and M.S in Industrial Engineering from the Rochester Institute of Technology, and her Ph.D. in Operations Research from the State University of New York at Buffalo. Prior to joining the RIT faculty, Dr. McConky worked as a research scientist for CUBRC Inc. for seven years. While at CUBRC she gained a broad range of experiences as a research scientist on military situation awareness applications and intelligence community data mining projects, as well as the opportunity to be the lead data scientist for energy data analytics start-up TROVE.

Dr. McConky is using her experiences working with 6 different utilities on load forecasting, demand response optimization, energy efficiency program optimization, and energy theft detection to develop a research program focused on sustainability in the energy space. Of particular interest is the use of smart meter data to develop optimal residential and industrial demand side management strategies.

To see more about Professor McConky visit her website.


Currently Teaching

0 Credits
This class introduces students to state of the art research and research methods in industrial, systems, and sustainable engineering. Presentations include off campus speakers and students/faculty presentations on current research under way in the department.
3 Credits
Topics and subject areas that are not regularly offered are provided under this course. Such courses are offered in a normal format; that is, regularly scheduled class sessions with an instructor.
4 Credits
An introduction to optimization through mathematical programming and stochastic modeling techniques. Course topics include linear programming, transportation and assignment algorithms, Markov Chain queuing and their application on problems in manufacturing, health care, financial systems, supply chain, and other engineering disciplines. Special attention is placed on sensitivity analysis and the need of optimization in decision-making. The course is delivered through lectures and a weekly laboratory where students learn to use state-of-the-art software packages for modeling large discrete optimization problems.
0 Credits
The second in a two course sequence that introduces students to research methods in industrial engineering and presents the state of the art in industrial engineering research. The two-course sequence is designed to promote discussion and interaction on IE research topics and to present research methods such as conducting critical reviews of research literature, initiating background research on a thesis topic, and preparing a formal thesis proposal.
0 - 4 Credits
A supervised investigation within an industrial engineering area of student interest. Professional elective.
1 - 3 Credits
This course is used by students who plan to study a topic on an independent study basis. The student must obtain the permission of the appropriate faculty member before registering for the course. Students registering for more than four credit hours must obtain the approval of both the department head and the adviser.
1 Credits
The first course in a series of courses for engineering honors students focused on how innovative products are developed, designed and manufactured to effectively meet the expanding needs of a global economy. This one-credit hour seminar course focuses on the key elements associated with the process of concept creation; namely, how individuals identify promising ideas for new products and how these ideas are shaped and refined in ways that will optimize the product's success in the marketplace, from the perspective of customer demand.
3 Credits
An introductory course in operations research focusing on modeling and optimization techniques used in solving problems encountered in industrial and service systems. Topics include deterministic and stochastic modeling methodologies (e.g., linear and integer programming, Markov chains, and queuing models) in addition to decision analysis and optimization tools. These techniques will be applied to application areas such as production systems, supply chains, logistics, scheduling, healthcare, and service systems.

Select Scholarship

Journal Paper
Saxena, Harshit, Omar Aponte, and Katie McConky. "A Hybrid Machine Learning Model for Forecasting a Billing Period's Peak Electric Load Days." International Journal of Forecasting 35. (2019): 1288-1303. Web.
McConky, Katie and Vaibhav Rungta. "Don't Pass the Automated Vehicles! System Level Impacts of Multi-vehicle CAV Control Strategies." Transportation Research Part C 100. (2019): 289-305. Web.
Abikarram, Jose Batista, Katie McConky, and Ruben Proano. "Energy Cost Minimization for Unrelated Parallel Machine Scheduling under Real Time and Demand Charge Pricing." Journal of Cleaner Production 208. (2019): 232-242. Web.
Okutan, Ahmet, et al. "Forecasting Cyberattacks with Incomplete, Imbalanced, and Insignificant Data." Cybersecurity 1. 15 (2018): 1-16. Web.
Amoroso, Briana, Eric Hittinger, and Katie McConky. "Keeping Your Cool - A Multi-Stakeholder Look at AC Sizing." Building and Environment 131. (2018): 306-329. Print.
McConky, Katie, Roger B. Chen, and Glenn R. Gavi. "A Comparison of Motivational and Informational Contexts for Improving Eco-Driving Performance." Transportation Research Part F: Traffic Psychology and Behaviour 52. (2018): 62-74. Print.
Abikarram, Jose Batista and Katie McConky. "Real Time Machine Coordination for Instantaneous Load Smoothing and Photovoltaic Intermittency Mitigation." Journal of Cleaner Production. (2017): 1406-1416. Web.
Published Conference Proceedings
Aponte, Omar and Katie McConky. "Renewable Electricity Generation Impact on Peak Demand and Response Strategies." Proceedings of the IISE Annual Conference 2019. Ed. Unknown. Orlando, Florida: n.p., 2019. Web.
Okutan, Ahmet, et al. "CAPTURE: Cyberattack Forecasting using Non-Stationary Features with Time Lags." Proceedings of the IEEE Conference on Communications and Network Security. Ed. Ryan Gerdes, Seth Andrews, and Russ Walker. Washington DC, DC: IEEE, 2019. Web.
Werner, Gordon, Shanchieh Jay Yang, and Katie McConky. "Leveraging Intra-Day Variations to Predict Daily Cyberattack Activity." Proceedings of the 2018 IEEE International Conference on Intelligence and Security Informatics (ISI). Ed. Nitesh Saxena and Ponnurangam Kumaraguru. Miami, Florida: IEEE, 2018. Web.
Werner, Gordon, et al. "Forecasting Cyberattacks as Time Series with Different Aggregation Granulatiry." Proceedings of the 2018 IEEE International Symposium on Technologies for Homeland Security (HST). Ed. Unknown. Woburn, Massachusetts: IEEE, 2018. Web.
Stolze, David, Katie McConky, and Shanchieh Jay Yang. "Automated Feature Engineering of Time-Series Data to Improve Classifier Performance." Proceedings of the 2018 Industrial and Systems Engineering Conference. Ed. K Barker, D Berry, and Chase Rainwater. Orlando, Florida: IISE, 2018. Web.
Saxena, Harshit and Katie McConky. "Forecasting Models for Short Term Peak Load Prediction." Proceedings of the Institute of Industrial and Systems Engineers Annual Conference. Ed. Katie Coperich, Elizabeth Cudney, and Harriet Nembhard. Pittsburgh, Pennsylvania: IISE, 2017. Web.
Okutan, Ahmet, Shanchieh Jay Yang, and Katie McConky. "Predicting Cyber Attacks With Bayesian Networks Using Unconventional Signals." Proceedings of the CISRC '17 12th Annual Conference on Cyber and Information Security Research. Ed. John Goodall. Oak Ridge, Tennessee: ACM, 2017. Web.
Werner, Gordon, Shanchieh Jay Yang, and Katie McConky. "Time Series Forecasting of Cyber Attack Intensity." Proceedings of the CISRC '17 - 12th Annual Conference on Cyber and Information Security Research. Ed. John Goodall. Oak Ridge, Tennessee: ACM, 2017. Web.
McConky, Katie, et al. "Evaluating the Integration of Operations Tasks While Optimizing ISR Activities." Proceedings of the SPIE Defense + Security. Ed. Tien Pham and Michael A Kolodny. Anaheim, California: SPIE, 2017. Web.
Abikarram, Jose Batista and Katie McConky. "Machine Coordination for Minimizing Instantaneous Peak Electricity Demand." Proceedings of the 2016 Industrial and Systems Engineering Research Conference. Ed. H. Yang and MD Sarder. Anaheim, CA: n.p., 2016. Web.
Gnanasundaram, Sanjairaj and Katie McConky. "2016 Industrial and Systems Engineering Research Conference." Proceedings of the Electricity Demand Modeling at an Occupant and Appliance Level. Ed. H. Yang and MD Sarder. Anaheim, CA: n.p., 2016. Web.
Amoroso, Briana, et al. "Optimizing Air Conditioner Size Considering Thermostat Usage Patterns." Proceedings of the 2016 Industrial and Systems Engineering Research Conference. Ed. H. Yang and MD Sarder. Anaheim, CA: n.p., 2016. Web.
Invited Keynote/Presentation
Chen, Roger, et al. "A Queuing Analysis of Operational Inefficiency in Public PEV Charing Stations." Transportation Research Board 97th Annual Meeting. Transportation Research Board. Washington DC, DC. 7 Jan. 2018. Conference Presentation.
Bliss, Erika, Katie McConky, and Jay Yang. "Analyzing Temporal Patterns in Topics for Phishing Emails." IISE Annual Conference. IISE. Orlando, FL. 15 May 2018. Conference Presentation.
McConky, Katie, et al. "Automated Tracking and Assessment of Measures of Performances and Effectiveness For HADR Efforts." 2016 INFORMS International Meeting. INFORMS. Wikoloa Village, HA. 14 Jun. 2016. Conference Presentation.
McConky, Katie, Hector Ortiz-Pena, and Moises Sudit. "A Framework Supporting the Separation of Cognition Performance from Execution Environment." 2015 INFORMS Annual Meeting. INFORMS. Philadelphia, PA. 1 Nov. 2015. Conference Presentation.
Peer Reviewed/Juried Poster Presentation
Okutan, Ahmet, et al. "Cyber Attack Prediction of Threats from Unconventional Sensors (CAPTURE)." Proceedings of the ACM Conference on Computer and Communications Security 2017. Ed. Kevin Hamlen and Heng Yin. Dallas, TX: ACM.
Chen, Roger B., Katie McConky, and Glenr R. Gavi. "Comparison of Motivational and Informational Contexts for Improving Eco-Driving Performance." Proceedings of the Transportation Research Board Annual Meeting. Ed. Transportation Research Board. Washington, DC: Transportation Research Board.
Book Chapter
Grasman, Scott, Abhijit Gosavi, and Katie McConky. "9 Operations Research." The Engineering Management Handbook. Ed. John V. Farr, S. Jimmy Gandhi, and Donald N. Merino. Huntsville, AL: American Society for Engineering Management, 2016. 53-79. Print.
Full Patent
McConky, Katie, et al. "System and Method for Remote Activity Detection." U.S. Patent 9098553. 4 Aug. 2015.