About DMO

Decision making is a cognitive process that involves selecting a course of action or choice from among multiple alternatives. It's a fundamental aspect of human life and is present in various contexts, ranging from everyday situations to complex professional, personal, and strategic scenarios, such as resource allocations, risk management, strategic planning, cybersecurity, supply chain management, Web information systems (e.g., information retrieval and recommender systems), and so forth. Factors influencing decision making may include cognitive biases (mental shortcuts that can lead to errors in judgment), emotions, cultural and social influences, personal experiences, time constraints, and the availability of information.

Optimization, on the other hand, is a systematic process that aims to find the best possible solution based on specific criteria. It involves mathematical and computational techniques to minimize or maximize an objective function while adhering to a set of constraints. Optimization seeks to identify the globally optimal solution, which is the best solution achievable according to the defined criteria. Multiple optimization techniques have been proposed and applied in machine learning and AI, such as convex optimization, non-linear optimization, evolutionary algorithms, multi-objective optimization, game theroies, etc.

Welcome to the Center for Decision Making and Optimization (DMO) directed by Dr. Yong Zheng, where the future of informed decision-making meets the power of cutting-edge optimization techniques. In an increasingly complex and interconnected world, the ability to make informed choices and optimize outcomes is paramount. DMO is your gateway to a world of innovative research, advanced methodologies, and practical applications that empower individuals, organizations, and communities to navigate the complexities of decision-making with confidence and precision.

At DMO, we are dedicated to advancing the science and practice of decision-making and optimization across a wide range of disciplines and industries. Our multidisciplinary team of experts combines the latest advancements in mathematics, data science and AI, as well as domain-specific knowledge to tackle real-world challenges and create tangible solutions. Whether you're a business leader seeking to enhance operational efficiency, a researcher exploring new frontiers in optimization, or a policymaker aiming to make data-driven choices, DMO is your partner in unlocking the full potential of your decisions.

Research Interests

• General Areas: Data Science, Machine Learning, Artificial Intelligence
• Decision Making (DM): Multi-Criteria DM, Group DM, Multi-Objective DM, Human-Centric DM, Personalization, Recommender Systems, etc.
• Optimization: Linear/Non-Linear Optimization, Multi-Objective Optimization, Game Theories, etc.
• Application Areas: FinTech, Education, E-Commerce, Online Streaming, Social Media, etc.

Publications (Selected)

• Yong Zheng, Kumar Neelotpal Shukla, Jasmine Xu, David (Xuejun) Wang, Michael O'Leary. "MOPO-LSI: An Open-Source Multi-Objective Portfolio Optimization Library for Sustainable Investments", Software Impacts, Vol. 16, 100499, Elsevier, 2023 (impact factor = 2.1)
• Yong Zheng. "Tutorial: Educational Recommender Systems", The 24th International Conference on Artificial Intelligence in Education (AIED), Japan, July 2023 (Tutorial Website)
• Yong Zheng, David (Xuejun) Wang. "Multi-Criteria Decision Making and Recommender Systems", The 28th ACM Conference on Intelligent User Interfaces (ACM IUI), Sydney, Australia, March, 2023
• Yong Zheng, Kumar Neelotpal Shukla, Jasmine Xu, David (Xuejun) Wang, Michael O'Leary. "Multi-Objective Portfolio Optimization Towards Sustainable Investments", The 6th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (ACM COMPASS), Aug, 2023
• Yong Zheng, David (Xuejun) Wang. "Hybrid Multi-Criteria Preference Ranking by Subsorting". The 14th Multidisciplinary Workshop on Advances in Preference Handling (M-PREF) at IJCAI 2023, Aug, 2023
• Yong Zheng, David (Xuejun) Wang. "Multi-Criteria Ranking by Using Relaxed Pareto Ranking Methods", Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization (ACM UMAP), June, 2023
• Yong Zheng, David (Xuejun) Wang. "A Comparative Study of Preference Ordering Methods for Multi-Criteria Ranking", Proceedings of the 10th IEEE Swiss Conference on Data Science, June, 2023
• Yong Zheng, David (Xuejun) Wang. "Multi-Objective Optimization and Recommendations", at the IEEE International Conference on Data Mining (ICDM), Dec, 2022 (Tutorial Website)
• Yong Zheng, David (Xuejun) Wang. "Multi-Criteria Ranking: Next Generation of Multi-Criteria Recommendation Framework", IEEE Access, Vol. 10, p. 90715-90725, IEEE, 2022 (impact factor = 3.9)
• Yong Zheng, David (Xuejun) Wang. "A Survey of Recommender Systems with Multi-Objective Optimization", Neurocomputing, Vol. 474, p. 141-153, Elsevier, 2022 (impact factor = 6.0)
• Yong Zheng, Juan Ruiz Toribio. "The Role of Transparency in Multi-Stakeholder Educational Recommendations". User Modeling and User-Adapted Interaction, Vol. 31 (3):5, p. 513–540, Springer, 2021 (impact factor = 3.6)
• Yong Zheng, David (Xuejun) Wang. "Multi-Objective Recommendations", Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Aug, 2021 (Tutorial Website)
• Yong Zheng. "Preference Corrections: Capturing Student and Instructor Perceptions in Educational Recommendations", Smart Learning Environments, Vol. 6 (1):29, Springer, 2019 (impact factor = 4.8)
• Muthusamy Chelliah, Yong Zheng, Sudeshna Sarkar, Vishal Kakkar. "Recommendation for Multi-Stakeholders and through Neural Review Mining", Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM), Beijing, China, Nov, 2019 (Tutorial Website)
• Yong Zheng. "Multi-Stakeholder Recommendations: Case Studies, Methods and Challenges", Proceedings of the 13th ACM Conference on Recommender Systems (RecSys), Copenhagen, Denmark, September 19th, 2019 (Slide and Demo)
• Yong Zheng. "Multi-Stakeholder Personalized Learning with Preference Corrections", Proceeding of the 19th IEEE International Conference on Advanced Learning Technologies (IEEE ICALT), Maceió - Alagoas, Brazil, July 15-18, 2019 (Acceptance rate: 15.1%)
• Yong Zheng, Nastaran Ghane, Milad Sabouri. "Personalized Educational Learning with Multi-Stakeholder Optimizations", Adjunct Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization (ACM UMAP), Cyprus, June, 2019
• Yong Zheng, Shephalika Shekhar, Alisha Anna Jose and Sunil Kumar Rai. "Integrating Context-Awareness and Multi-Criteria Decision Making in Educational Learning", Proceedings of the 34th ACM SIGAPP Symposium on Applied Computing (ACM SAC), Limassol, Cyprus, April, 2019 (Acceptance rate: 24%)
• Yong Zheng and Alisha Anna Jose. "Context-Aware Recommendations via Sequential Predictions", Proceedings of the 34th ACM SIGAPP Symposium on Applied Computing (ACM SAC), Limassol, Cyprus, April, 2019 (Acceptance rate: 24%)
• Yong Zheng. "Utility-Based Multi-Criteria Recommender Systems", Proceedings of the 34th ACM SIGAPP Symposium on Applied Computing (ACM SAC), Limassol, Cyprus, April, 2019 (Acceptance rate: 24%)
• Yong Zheng. "Exploring User Roles In Group Recommendations: A Learning Approach", Adjunct Proceedings of the 26th ACM Conference on User Modeling, Adaptation and Personalization (ACM UMAP), Singapore, July, 2018
• Yong Zheng, Tanaya Dave, Neha Mishra and Harshit Kumar. "Fairness In Reciprocal Recommendations: A Speed-Dating Study", Adjunct Proceedings of the 26th ACM Conference on User Modeling, Adaptation and Personalization (ACM UMAP), Singapore, July, 2018
• Yong Zheng. "Identifying Dominators and Followers In Group Decision Making Based on The Personality Traits",  Proceedings of HUMANIZE Workshop at ACM Conference on Intelligent User Interfaces, Tokyo, Japan, 2018
• Yong Zheng. "Criteria Chains: A Novel Multi-Criteria Recommendation Approach", The 22nd ACM Conference on Intelligent User Interfaces (ACM IUI), Limassol, Cyprus, March 13-16, 2017 (Acceptance rate: 17%)

Faculties

Collaborators

Ph.D. Students

Student Supervision

Students marked with * are the ones who had co-authored publications with Dr. Yong Zheng.

• PhD Students & Alumni
• Full-Time PhD Students (Research Projects & Thesis)
• 2024 - Present, TBA


• Part-Time PhD Students (Research Projects & Thesis)
• 2016 - 2020, Diego Sanchez-Moreno* (Part-time PhD Student at University of Salamanca, Spain)
  Dissertation: Improving collaborative filtering music recommender systems: a focus on user characterization from behavioral and contextual factors


• PhD Alumni
• 2020, Diego Sanchez-Moreno; Senior Software Engineer at Syndigo, USA


• MS Students
• Graduate Students in the Joint Program with Universities in Spain (Research Projects & Thesis)
• Maria Delgado Franco, Beatriz Blanco, Project: Grey Sheep Users In RecSys
• Arturo Pavon Estrade, Project: Personality-Aware Recommendations
• Juan Ruiz Toribio*, Project: Transparency and Fairness
• Alejandro Susillo Ridao*, Project: AI for Education
• Raquel Noblejas Sampedro, Project: Mobile Security
• Jorge Torres, Project: Group RecSys
• Gonzalo Florez Arias*; Project: Recommender Systems based on Deep Learning
• Guillermo Canete; Project: Generative Reviews
• Paula Labandeira Campos; Project: Multi-Criteria Recommender Systems


• Other Graduate Students at ITM Department (Research Projects)
• Ruthvik Kilaru, Munawar Ali, Navaneeth Kamath; Project: AI for Education
• Arnold Liu*; Project: Data Science Running Efficiency Over Computing Machines/Clusters
• Shuaiqi Zheng*; Project: Impacts on Educational Learning by the COVID-19 Pandemic
• Mili Singh*, Mayur Agnani*; Project: Identification of Grey Sheep Users In Recommender Systems
• Tanaya Dave*, Neha Mishra*, Harshit Kumar*; Project: Reciprocal Recommendations
• Shephalika Shekhar*; Project: Multi-Criteria Recommender System
• Alisha Anna Jose*; Project: Context-Aware Recommender System
• Nastaran Ghane*, Milad Sabouri*; Project: Multi-Stakeholder Recommendations
• Sridhar Srinivasan*, Kim Taehun*; Project: Malware Detection and Usage Analysis In Mobile Apps
• Archana Subramaniyan*; Project: Personality-Aware Recommendations
• Vih Dogo*, Yash Agrawal*, Shubham Sudhir Madke*; Project: Crime Predictions over the Chicago Transit Authority Services,
  a collaboration with Digby's Detective & Security Agency, Inc.

Contact

Contact:	Dr. Yong Zheng
Office:	Perlstein Hall Room 221, 10 W 33rd St, Chicago, IL (Google Map)
Telephone:	312.567.3575
Fax:	312.567.5248
Email:	yzheng66 [at] iit.edu
Snail Mail:	Dr. Yong Zheng Perlstein Hall RM 221 10 W 33rd Street Chicago, IL, 60616-3792, USA

Sponsors

Industry Collaborators

Academic Collaborators