Katie McConky Headshot

Katie McConky

Department Head

Department of Industrial and Systems Engineering
Kate Gleason College of Engineering
Data Analytics
Operations Research

585-475-6062
Office Location

Katie McConky

Department Head

Department of Industrial and Systems Engineering
Kate Gleason College of Engineering
Data Analytics
Operations Research

Education

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

Bio

Dr. Katie McConky received her BS and MS in Industrial Engineering from the Rochester Institute of Technology, and her Ph.D. in Industrial Engineering 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 Predictive Data Science.

Dr. McConky combines her love of operations research, data science and machine learning to solve complex problems in today’s society. Her work spans developing deep learning models to solve combinatorial optimization problems to energy demand forecasting and emissions aware demand side management. Her work has been applied to uncertainty aware rover mission planning on remote planets, optimization of unmanned aerial surveillance vehicle routing, and optimization of kidney exchange programs. Her work has received funding from a number of agencies including ONR, AFRL, NASA, NYSERDA, and DARPA. 

To see more about Professor McConky visit her website.

585-475-6062

Select Scholarship

Invited Keynote/Presentation
McConky, Katie, Prashant Sankaran, and Moises Sudit. "Combining Machine Learning and Traditional Optimization Approaches to Solve Reconnaissance Missing Planning Problem." Military Operations Research Society Symposium. Rochester Institute of Technology. Rochester, NY. 23 Jun. 2021. Conference Presentation.
Sankaran, Prashant, Katie McConky, and Moises Sudit. "Hybrid Learning-Optimization Solver (HYLOS) Framework to Solve Combinatorial Optimization Problems: A Preliminary Study." IISE Annual Conference. Virtual. Virtual, USA. 22 May 2021. Conference Presentation.
Sudit, Moises, Prashant Sankaran, and Katie McConky. "A Confluence of Machine Learning And Optimization Approaches to Address Combinatorial Optimization Problems." AAAI Spring 2021 Symposium Series. Association for the Advancement of Artificial Intelligence. Virtual, Virtual. 22 Mar. 2021. Conference Presentation.
Aponte, Omar and Katie McConky. "Electricity Demand Threshold Forecasting for Triggering Cost Saving Peak Demand Shaving Actions." 41 International Symposium on Forecasting. International Institute of Forecasters. Virtual, Virtual. 29 Jun. 2021. Conference 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.
Published Conference Proceedings
Werner, Gordon, Shanchieh Jay Yang, and Katie McConky. "Near Real-Time Intrusion Alert Aggregation Using Concept-Based Learning." Proceedings of the International Conference on Computing Frontiers. Ed. Maurizio Palesi and Antonino Tumeo. New York, New York: ACM, 2021. Web.
Aponte, Omar and Katie McConky. "Actionable Peak Electric Load Day Forecasting Methodology for Facilities with Behind the Meter Renewable Electricity Generation." Proceedings of the 40th International Symposium on Forecasting. Ed. George Athanasopoulos and Tao Hong. Virtual, Virtual: n.p., 2020. Web.
Russell, Jeffery and Katie McConky. "Historical and Real-Time GEO Satellite Maneuver Detection Algorithm." Proceedings of the 2020 AAS / AIAA Astrodynamics Specialist Conference. Ed. Kathleen Howell and Felix Hoots. Lake Tahoe, United States: AIAA, 2020. Web.
Collea, Brady and Katie McConky. "Efficient Algorithms for Unrelated Parallel Machine Scheduling Considering Time of Use Pricing and Demand Charges." Proceedings of the 2020 IISE Annual Conference. Ed. L Cromarty, R Shirwaiker, and P Wang. Virtual, Virtual: Institute of Industrial and Systems Engineers, 2020. Web.
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.
Journal Paper
Aponte, Omar and Katie McConky. "Peak Electric Load Days Forecasting for Energy Costs Reduction With and Without Behind the Meter Renewable Electricity Generation." International Journal of Energy Research 45. 13 (2021): 18735-18753. Web.
Olivieri, Zachary T. and Katie McConky. "Optimization of Residential Battery Energy Storage System Scheduling for Cost and Emissions Reductions." Energy and Buildings 210. (2020): 109787-109800. Web.
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.
Peer Reviewed/Juried Poster Presentation or Conference Paper
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.

Currently Teaching

ISEE-301
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.
ISEE-599
0 - 4 Credits
A supervised investigation within an industrial engineering area of student interest. Professional elective.
ISEE-698
0 Credits
One semester of paid part-time work experience in the field of industrial engineering or sustainable engineering. See the graduate program coordinator or RIT’s Office of Cooperative Education for further details.
ISEE-699
0 Credits
One semester of paid full-time work experience in the field of industrial engineering or sustainable engineering. See the graduate program coordinator or RIT’s Office of Cooperative Education for further details.
ISEE-761
3 Credits
Forecasting Methods will provide the engineering student with the skills necessary to perform data driven time series analysis from an engineering applications perspective. A process driven approach will be used covering the entire forecasting process from data preparation and pre-processing techniques to model selection, performance evaluation, and monitoring. A special emphasis will be placed on performance evaluation and improvement of models used to predict RIT energy demand and peak load days. The course will cover topics in data cleansing, data transformation, trend and seasonality analysis, smoothing techniques, regression analysis for forecasting, seasonal and non-seasonal ARIMA models, dynamic regression, neural networks and advanced modeling techniques for multivariate time series analysis. Lectures and assignments will focus on predicting RIT energy demand considering circuits with 2MW solar fields or similar data sets.
ISEE-790
1 - 6 Credits
In conference with a faculty adviser, an independent engineering project or research problem is selected. The work may be of a theoretical and/or computational nature. A state-of-the-art literature search in the area is normally expected. A formal written thesis and an oral defense with a faculty thesis committee are required. Submission of bound copies of the thesis to the library and to the department and preparation of a written paper in a short format suitable for submission for publication in a refereed journal are also required. Approval of department head and faculty adviser needed to enroll.
ISEE-799
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.
MECE-799
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.

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