Yashar Deldjoo – Computer Science – Best Researcher Award

Dr. Yashar Deldjoo - Computer Science - Best Researcher Award

Polytechnic University of Bari - Italy 

Professional Profiles

Early Academic Pursuits

Yashar Deldjoo's academic journey reflects a commitment to excellence and a focus on cutting-edge research in the field of Computer Science and Electrical Engineering.

Master's Degree at Chalmers University of Technology, Sweden

Yashar began his graduate studies at Chalmers University of Technology in Sweden, where he earned a Master's degree in Electrical Engineering. His exceptional academic performance, graduating with the highest rank and a straight A+ GPA, set the stage for his future success.

Ph.D. in Computer Science at Polytechnic University of Milan, Italy

Continuing his pursuit of knowledge, Yashar earned his Ph.D. in Computer Science, specializing in Recommender Systems, from the Polytechnic University of Milan in 2018. His distinction in completing his Ph.D. marked the beginning of his impactful contributions to the field.

Professional Endeavors

Tenure-Track Assistant Professor at Polytechnic University of Bari, Italy

Currently serving as a Tenure-Track Assistant Professor at the Polytechnic University of Bari, Italy, Yashar has found a platform to not only continue his research but also to impart knowledge to the next generation of computer scientists.

Active Participation in the RecSys Community

Yashar is a prominent figure in the Recommender Systems (RecSys) community, actively participating in major conferences such as SIGIR, RecSys, CIKM, and more. His engagement underscores his commitment to staying at the forefront of research and innovation in his field.

Focus on Trust in Recommender Systems

Yashar's research focus centers on integrating elements of trust within recommender systems. Emphasizing adversarial robustness, privacy, explainability, and fairness, he aims to enhance the reliability and ethical considerations of recommendation algorithms.

Exploration of Generative AI and Large Language Models

Yashar's recent research delves into the realm of "Generative AI and Large Language Models (LLMs)." His work aims to understand and mitigate the potential risks and challenges posed by these models in the context of Machine Learning (ML) and Recommender Systems (RS).

Accolades and Recognition

Recognized Among the Top 2% of Research Scientists

In 2022, Yashar's contributions to the research community were acknowledged when he was recognized among the top 2% of research scientists by the Stanford University Ioannidis database and Elsevier. This recognition is a testament to the impact of his work.

Editorial Roles and Contributions to Leading Journals

Yashar serves as an Associate Editor at ACM Computing Surveys, showcasing his expertise and commitment to advancing the field. His guest-editorial roles for leading journals, including the Journal of Information Processing and Management and ACM Transaction on Recommender Systems, underscore his influence in shaping academic discourse.

Impact and Influence

Yashar's influence extends beyond his academic roles. Serving as a PC for major conferences, including the benchmark track at the NeuroIPS conference, and as a senior PC for conferences like SIGIR, CIKM, and the Web Conference, he actively contributes to shaping the academic landscape.

Legacy and Future Contributions On Computer Science

Yashar's legacy is characterized by his impactful contributions to literature reviews, collaborations with renowned institutions and companies, and his involvement in organizing ML competitions. As he continues his journey, his future contributions are anticipated to further push the boundaries of knowledge in Recommender Systems and AI ethics. Yashar Deldjoo's early academic pursuits, professional endeavors, accolades, and influence in the Recommender Systems community collectively showcase a researcher and educator dedicated to advancing the field of Computer Science. His legacy and future contributions hold the promise of continued excellence and innovation.

Notable Publications

A unifying and general account of fairness measurement in recommender systems 2021

Fairness in recommender systems: research landscape and future directions 2021

Auditing Consumer- and Producer-Fairness in Graph Collaborative Filtering 2023