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MODELS Conference'22, Montreal
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ICSE Conference'19 (source-ICSE)


ABOUT ME

I am Rijul Saini

Photo of Me
I am currently working as a Data & Applied Scientist at Bombardier Aerospace. I obtained my Ph.D. degree from McGill University, where I first joined as a Master's student in 2017 and then fast-tracked into the Ph.D. program in 2019.
During my doctoral studies, I was mentored by Professor Gunter Mussbacher, and my research focused on developing a recommendation system that assists practitioners such as requirements engineers and software engineers in quickly creating domain models from informal requirements expressed in natural language. This system uses Natural Language Processing and Machine Learning techniques to extract and process domain information, and it builds queryable trace models in the form of knowledge graphs to explain decisions and facilitate system-user interactions.
In addition to my academic pursuits, I gained valuable industry experience while interning at the National Research Council Canada and Bombardier Aerospace. During these internships, I applied my skills in Machine Learning, Data Science, and Software Engineering to develop tools for predictive analytics. Before joining McGill University, I worked as a software analyst at Accenture from 2013 to 2017. I earned a Bachelor of Technology degree in Computer Science and Engineering in 2013.


RESEARCH INTERESTS

  • Machine Learning
  • Natural Language Processing
  • Data Science
  • Recommendation Systems
  • Model-Driven Requirements Engineering


Latest News

  • Invited Reviewer for the Science of Computer Programming Journal, February 2023
  • Received the Silver medal 🥈 in the ACM Student Research Competition, October 2022
  • Our research paper has been selected for presentation and publication at MODELS'2022, Montreal, Canada


Community Service

  • Invited Reviewer for the Science of Computer Programming Journal, February 2023
  • General Co-Chair for the 12th International Model-Driven Requirements Engineering (MoDRE) Workshop at RE'22
  • Program Committee Member for Tools and Demonstrations track in 25th International Conference on Model Driven Engineering Languages and Systems (MODELS'22)
  • Program Committee Member of MDE Intelligence Workshop at MODELS'21
  • Reviewer of SoSyM Journal Papers
  • Judge in Summer Undergraduate Research in Engineering (SURE) poster presentation at McGill University
  • Student volunteer at RE'2021 and MODELS'21


PUBLICATIONS

×
  1. Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J. (2022) Machine Learning-based Incremental Learning in Interactive Domain Modelling. Technical Track, ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems (MODELS '22), October 23--28, 2022, Montreal, QC, Canada, ACM. DOI: 10.1145/3550355.3552421. [Acceptance rate: 27%]
  2. Saini, R. (2022) Automated, Traceable, and Interactive Domain Modelling. ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems (MODELS '22 Companion), October 23--28, 2022, Montreal, QC, Canada, ACM. DOI: 10.1145/3550356.3552372. ACM Silver Medalist 🥈
  3. Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J. (2021) Automated, interactive, and traceable domain modelling empowered by artificial intelligence. Software & Systems Modeling (SoSyM), Springer. DOI: 10.1007/s10270-021-00942-6.
  4. Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J. (2021) DoMoBOT: An AI-Empowered Bot for Automated and Interactive Domain Modelling. Tool Demo, ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS 2021), Demo and Poster Sessions, Fukuoka, Japan, October 2021, ACM. DOI: 10.1109/MODELS-C53483.2021.00090.
  5. Saini, R. and Mussbacher, G. (2021) Towards Conflict-Free Collaborative Modelling using VS Code Extensions. 1st International Hands-on Workshop on Collaborative Modeling (HoWCoM 2021), Fukuoka, Japan, October 2021. ACM DOI: 10.1109/MODELS-C53483.2021.00013.
  6. Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J. (2021) Automated Traceability for Domain Modelling Decisions Empowered by Artificial Intelligence. 29th IEEE International Requirements Engineering Conference (RE 2021), Notre Dame, South Bend, Indiana, USA, September 2021. IEEE CS, 173-184. DOI: 10.1109/RE51729.2021.00023. [Acceptance Rate - 29%].
  7. Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J. (2021) DoMoBOT: A Modelling Bot for Automated and Traceable Domain Modelling. Tool Demo, 29th IEEE International Requirements Engineering Conference (RE 2021), Demo and Poster Sessions, Notre Dame, South Bend, Indiana, USA, September 2021, IEEE CS. DOI: 10.1109/RE51729.2021.00054.
  8. Combemale, B., Kienzle, J., Mussbacher, G., Ali, H., Amyot, D., Bagherzadeh, M., Batot, E., Bencomo, N., Benni, B., Bruel, J.-M., Cabot, J., Cheng, B.H.C., Collet, P., Engels, G., Heinrich, R., Jézéquel, J.-M., Koziolek, A., Mosser, S., Reussner, R., Sahraoui, H., Saini, R., Sallou, J., Stinckwich, S., Syriani, E., and Wimmer, M. (2021) A Hitchhiker’s Guide to Model-Driven Engineering for Data-Centric Systems. IEEE Software, IEEE 38(4):71–84. DOI: 10.1109/MS.2020.2995125.
  9. Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J. (2020) A Neural Network Based Approach to Domain Modelling Relationships and Patterns Recognition. 10th International Model-Driven Requirements Engineering Workshop (MoDRE 2020), Zurich, Switzerland, September 2020. IEEE CS, 78-82. DOI: 10.1109/MoDRE51215.2020.00016.
  10. Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J. (2020) Towards Queryable and Traceable Domain Models. 28th IEEE International Requirements Engineering Conference (RE 2020) - RE@NEXT! Track, Zurich, Switzerland, August-September 2020. IEEE CS, 334-339. DOI: 10.1109/RE48521.2020.00044. DOI: 10.1109/RE48521.2020.00044. [Acceptance rate: 31%]
  11. Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J. (2020) DoMoBOT: A Bot for Automated and Interactive Domain Modelling. 2nd Artificial Intelligence and Model-driven Engineering Workshop (MDE Intelligence 2020), Montreal, Canada, October 2020. ACM, article no. 45, 1-10. DOI: 10.1145/3417990.3421385.
  12. Saini, R. (2020) Artificial Intelligence Empowered Domain Modelling Bot. 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS 2020), Montreal, Canada, October 2020. ACM, article no. 26, 1-6. DOI: 10.1145/3417990.3419486.
  13. Saini, R., Mussbacher, G., Guo, J., and Kienzle, J. (2019) Modelling Bot - A Modelling Buddy. Presentation, 11th Workshop on Modelling in Software Engineering (MiSE 2019), Montreal, Canada, May 2019.
  14. Saini, R., Bali, S., and Mussbacher, G. (2019) Towards Web Collaborative Modelling for the User Requirements Notation Using Eclipse Che and Theia IDE. 11th Workshop on Modelling in Software Engineering (MiSE 2019), Montreal, Canada, May 2019. IEEE CS. DOI: 10.1109/MiSE.2019.00010.
  15. Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J. (2019) Teaching Modelling Literacy: An Artificial Intelligence Approach. Educators Symposium at MODELS 2019, Munich, Germany, September 2019. IEEE CS, 714-719. DOI: 10.1109/MODELS-C.2019.00108.
×
  1. Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J. (2022) Machine Learning-based Incremental Learning in Interactive Domain Modelling. Technical Track, ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems (MODELS '22), October 23--28, 2022, Montreal, QC, Canada, ACM. DOI: 10.1145/3550355.3552421. [Acceptance rate: 27%]
  2. Saini, R. (2022) Automated, Traceable, and Interactive Domain Modelling. ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems (MODELS '22 Companion), October 23--28, 2022, Montreal, QC, Canada, ACM. DOI: 10.1145/3550356.3552372. ACM Silver Medalist 🥈
×
  1. Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J. (2021) Automated, interactive, and traceable domain modelling empowered by artificial intelligence. Software & Systems Modeling (SoSyM), Springer. DOI: 10.1007/s10270-021-00942-6.
  2. Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J. (2021) DoMoBOT: An AI-Empowered Bot for Automated and Interactive Domain Modelling. Tool Demo, ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS 2021), Demo and Poster Sessions, Fukuoka, Japan, October 2021, ACM. DOI: 10.1109/MODELS-C53483.2021.00090.
  3. Saini, R. and Mussbacher, G. (2021) Towards Conflict-Free Collaborative Modelling using VS Code Extensions. 1st International Hands-on Workshop on Collaborative Modeling (HoWCoM 2021), Fukuoka, Japan, October 2021. ACM DOI: 10.1109/MODELS-C53483.2021.00013.
  4. Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J. (2021) Automated Traceability for Domain Modelling Decisions Empowered by Artificial Intelligence. 29th IEEE International Requirements Engineering Conference (RE 2021), Notre Dame, South Bend, Indiana, USA, September 2021. IEEE CS, 173-184. DOI: 10.1109/RE51729.2021.00023. [Acceptance Rate - 29%].
  5. Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J. (2021) DoMoBOT: A Modelling Bot for Automated and Traceable Domain Modelling. Tool Demo, 29th IEEE International Requirements Engineering Conference (RE 2021), Demo and Poster Sessions, Notre Dame, South Bend, Indiana, USA, September 2021, IEEE CS. DOI: 10.1109/RE51729.2021.00054.
  6. Combemale, B., Kienzle, J., Mussbacher, G., Ali, H., Amyot, D., Bagherzadeh, M., Batot, E., Bencomo, N., Benni, B., Bruel, J.-M., Cabot, J., Cheng, B.H.C., Collet, P., Engels, G., Heinrich, R., Jézéquel, J.-M., Koziolek, A., Mosser, S., Reussner, R., Sahraoui, H., Saini, R., Sallou, J., Stinckwich, S., Syriani, E., and Wimmer, M. (2021) A Hitchhiker’s Guide to Model-Driven Engineering for Data-Centric Systems. IEEE Software, IEEE 38(4):71–84. DOI: 10.1109/MS.2020.2995125.
×
  1. Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J. (2020) A Neural Network Based Approach to Domain Modelling Relationships and Patterns Recognition. 10th International Model-Driven Requirements Engineering Workshop (MoDRE 2020), Zurich, Switzerland, September 2020. IEEE CS, 78-82. DOI: 10.1109/MoDRE51215.2020.00016.
  2. Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J. (2020) Towards Queryable and Traceable Domain Models. 28th IEEE International Requirements Engineering Conference (RE 2020) - RE@NEXT! Track, Zurich, Switzerland, August-September 2020. IEEE CS, 334-339. DOI: 10.1109/RE48521.2020.00044. DOI: 10.1109/RE48521.2020.00044. [Acceptance rate: 31%]
  3. Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J. (2020) DoMoBOT: A Bot for Automated and Interactive Domain Modelling. 2nd Artificial Intelligence and Model-driven Engineering Workshop (MDE Intelligence 2020), Montreal, Canada, October 2020. ACM, article no. 45, 1-10. DOI: 10.1145/3417990.3421385.
  4. Saini, R. (2020) Artificial Intelligence Empowered Domain Modelling Bot. 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS 2020), Montreal, Canada, October 2020. ACM, article no. 26, 1-6. DOI: 10.1145/3417990.3419486.
×
  1. Saini, R., Mussbacher, G., Guo, J., and Kienzle, J. (2019) Modelling Bot - A Modelling Buddy. Presentation, 11th Workshop on Modelling in Software Engineering (MiSE 2019), Montreal, Canada, May 2019.
  2. Saini, R., Bali, S., and Mussbacher, G. (2019) Towards Web Collaborative Modelling for the User Requirements Notation Using Eclipse Che and Theia IDE. 11th Workshop on Modelling in Software Engineering (MiSE 2019), Montreal, Canada, May 2019. IEEE CS. DOI: 10.1109/MiSE.2019.00010.
  3. Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J. (2019) Teaching Modelling Literacy: An Artificial Intelligence Approach. Educators Symposium at MODELS 2019, Munich, Germany, September 2019. IEEE CS, 714-719. DOI: 10.1109/MODELS-C.2019.00108.





Work Experience

×


    Data and Applied Scientist at Bombardier Aerospace (Mar 2023 - Current)

  • Leading data science team to steer ML-based engine on Azure to provide live recommendations to customers for aircraft parts.
  • Developed a high performance computing tool with a graphical user interface (DataProbe) using Python to enable data analysis.
  • DataProbe saves more than 2 man hours in a day - received an award in Recognition and Mobilization Program, 2023.
  • Developing multivariate variance-based ML solution (PMx) for anomaly detection using time-series data of aircraft sensors.
  • PMx aims to increase the operational efficiency of aircraft and minimize return-to-service times through data-driven decisions.


    • Student Intern at National Research Council Canada (Sep 2022 - Dec 2022)

  • Conducted research for applying deep learning techniques to solve the problem of defect localization (authored NRC publication).
  • Developed YOLO and Faster R-CNN models in PyTorch for classifying parts in vehicles (mAP > 0.90).


    • Student Intern at Bombardier Aerospace (Jan 2021 - Sep 2022)

  • Led an initiative to enable predictive analytics where I built a pipeline using Scikit-Learn to process 3D data and predict aero coefficients using classical Machine Learning models (>95% accuracy).
  • Developed optimization algorithms and applied them to optimize conceptual designs of aircraft.
  • Designed microservices-based architecture and built a graphical user interface using Django and React.


    • Teaching assistant at McGill University (Winter 2021)

  • Software Language Engineering (ECSE 439)


    • Teaching assistant at McGill University (Fall 2020)

  • Introduction to Software Engineering (ECSE 321)
  • Software Requirements Engineering (ECSE 326)


    • Student Developer (Summer 2020 and 2019)

  • Worked with the Red Hat team to bring the co-editing support in Eclipse Che 7.
  • Developed a VS Code extension to enable conflict-free collaborative development and modelling.


    • Teaching assistant at McGill University (Fall 2019)

  • Model-Based Programming (ECSE 223)
  • Introduction to Software Engineering (ECSE 321)
  • Software Requirements Engineering (ECSE 326)


    • Teaching assistant at McGill University (Winter 2019)

  • Introduction to Software Engineering (ECSE 321)
  • Model-Based Programming (ECSE 223)


    • Teaching assistant at McGill University (Fall 2018)

  • Introduction to Software Engineering (ECSE 321)
  • Software Requirements Engineering (ECSE 326)


    • Software Analyst (Dec 2013 - May 2017)

  • Helped Portal and Finance consultants to solve critical issues using using ABAP programming.
  • Developed payroll interfaces that reduced the problem tickets to one-third.
×


    Data and Applied Scientist at Bombardier Aerospace (Mar 2023 - Current)

  • Leading data science team to steer ML-based engine on Azure to provide live recommendations to customers for aircraft parts.
  • Developed a high performance computing tool with a graphical user interface (DataProbe) using Python to enable data analysis.
  • DataProbe saves more than 2 man hours in a day - received an award in Recognition and Mobilization Program, 2023.
  • Developing multivariate variance-based ML solution (PMx) for anomaly detection using time-series data of aircraft sensors.
  • PMx aims to increase the operational efficiency of aircraft and minimize return-to-service times through data-driven decisions.
×


    Data and Applied Scientist at Bombardier Aerospace (Mar 2023 - Current)

  • Leading data science team to steer ML-based engine on Azure to provide live recommendations to customers for aircraft parts.
  • Developed a high performance computing tool with a graphical user interface (DataProbe) using Python to enable data analysis.
  • DataProbe saves more than 2 man hours in a day - received an award in Recognition and Mobilization Program, 2023.
  • Developing multivariate variance-based ML solution (PMx) for anomaly detection using time-series data of aircraft sensors.
  • PMx aims to increase the operational efficiency of aircraft and minimize return-to-service times through data-driven decisions.
×


    Student Intern at National Research Council Canada (Sep 2022 - Dec 2022)

  • Conducted research for applying deep learning techniques to solve the problem of defect localization (authored NRC publication).
  • Developed YOLO and Faster R-CNN models in PyTorch for classifying parts in vehicles (mAP > 0.90).


    • Student Intern at Bombardier Aerospace (Jan 2021 - Sep 2022)

  • Led an initiative to enable predictive analytics where I built a pipeline using Scikit-Learn to process 3D data and predict aero coefficients using classical Machine Learning models (>95% accuracy).
  • Developed optimization algorithms and applied them to optimize conceptual designs of aircraft.
  • Designed microservices-based architecture and built a graphical user interface using Django and React.
×


    Student Intern at Bombardier Aerospace (Jan 2021 - Sep 2022)

  • Led an initiative to enable predictive analytics where I built a pipeline using Scikit-Learn to process 3D data and predict aero coefficients using classical Machine Learning models (>95% accuracy).
  • Developed optimization algorithms and applied them to optimize conceptual designs of aircraft.
  • Designed microservices-based architecture and built a graphical user interface using Django and React.


    • Teaching assistant at McGill University (Winter 2021)

  • Software Language Engineering (ECSE 439)
×


    Teaching assistant at McGill University (Fall 2020)

  • Introduction to Software Engineering (ECSE 321)
  • Software Requirements Engineering (ECSE 326)


    • Student Developer (Summer 2020 and 2019)

  • Worked with the Red Hat team to bring the co-editing support in Eclipse Che 7.
  • Developed a VS Code extension to enable conflict-free collaborative development and modelling.
×


    Student Developer (Summer 2020 and 2019)

  • Worked with the Red Hat team to bring the co-editing support in Eclipse Che 7.
  • Developed a VS Code extension to enable conflict-free collaborative development and modelling.


    • Teaching assistant at McGill University (Fall 2019)

  • Model-Based Programming (ECSE 223)
  • Introduction to Software Engineering (ECSE 321)
  • Software Requirements Engineering (ECSE 326)


    • Teaching assistant at McGill University (Winter 2019)

  • Introduction to Software Engineering (ECSE 321)
  • Model-Based Programming (ECSE 223)
×


    Teaching assistant at McGill University (Fall 2018)

  • Introduction to Software Engineering (ECSE 321)
  • Software Requirements Engineering (ECSE 326)
×


    Software Analyst (Dec 2013 - May 2017)

  • Helped Portal and Finance consultants to solve critical issues using using ABAP programming.
  • Developed payroll interfaces that reduced the problem tickets to one-third.


BLOGS

Demo - Traceable Modelling
Data Science Challenger
Demo - Interactive Modelling
Traceability using Knowledge Graphs
Collaborative Modelling
Demo - Collaborative Modelling
Demo - Traceable Modelling
Demo - Automated and Traceable Modelling
Demo - Interactive Modelling
Demo - Automated and Interactive Modelling
Google Summer of Code 2020
VS Code Extension Atom Teletype (Part-II)
Google Summer of Code 2019
VS Code Extension Atom Teletype (Part-I)
Running Eclipse Che on Minikube
Running Eclipse Che on Minikube

HOW TO FIND ME

Email: rijul.saini@mail.mcgill.ca

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