publications

2021

1. ICE/ITMC

   A Generalized Framework for Algorithm Based Team Formation

   Ikhlaq Sidhu , Rajarathnam Balakrishnan, and Sudarshan Gopalakrishnan

   In 2021 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) , 2021

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   Teams must be formed for all kinds of projects and purposes. Team formation is a key activity for innovation, entrepreneurship, class projects, and industry initiatives. Our experience with entrepreneurship and innovation has shown that a project’s results are highly dependent on the quality of team formation. In real life situations, team building is also time consuming. In this work we are developing a method and approach to form teams using algorithms. To do this, we develop a taxonomy for how teams are formed. We consider the key factors for team formation, identify measures for team effectiveness, as well as synthesize the framework with natural team formation processes. More importantly, we are proposing a generalized framework for algorithm based team formation. We provide sample algorithms with this framework to demonstrate its use in practical situations. The work of this paper also informs future characterization of the models developed here, which includes trade-offs for factors including optimality of team effectiveness, computation time, and policy considerations.

2. ICE/ITMC

   Effectiveness Factors for Algorithm Based Team Formation with Data Project Case Application

   Ikhlaq Sidhu , Sudarshan Gopalakrishnan , and Rajarathnam Balakrishnan

   In 2021 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) , 2021

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   Teams must be formed for all kinds of projects and purposes. Team formation is a key activity for innovation, entrepreneurship, class projects, and industry initiatives. In parallel work, we have proposed a generalized framework for Algorithm-based Team Formation. We are interested to apply this framework to the specific task of forming teams for a Data Science project course. In this paper, we have focused on the characteristics teams as correlated by the success of the project itself. We find that by examining approximately 30 project teams, there are characteristics which may be used to set feature values for algorithms that can best match student projects together. In particular, teams who work well together with trust and common backgrounds did perform better. Teams with greater domain experience in coding and ML generally also performed better. Surprisingly, diversity in background did not seem to indicate better performance. However, we did observe that individuals who characterize themselves as optimistic and wide comfort zone among others were more strongly present on teams that performed well, speaking to a correlation of individual characteristics/behaviors to team performance.

2020

1. ASILOMAR

   FuSSI-Net: Fusion of Spatio-temporal Skeletons for Intention Prediction Network

   Francesco Piccoli , Rajarathnam Balakrishnan, Maria Jesus Perez , and 13 more authors

   In 2020 54th Asilomar Conference on Signals, Systems, and Computers , 2020

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   Pedestrian intention recognition is very important to develop robust and safe autonomous driving (AD) and advanced driver assistance systems (ADAS) functionalities for urban driving. In this work, we develop an end-to-end pedestrian intention framework that performs well on day- and night- time scenarios. Our framework relies on objection detection bounding boxes combined with skeletal features of human pose. We study early, late, and combined (early and late) fusion mechanisms to exploit the skeletal features and reduce false positives as well to improve the intention prediction performance. The early fusion mechanism results in AP of 0.89 and precision/recall of 0.79/0.89 for pedestrian intention classification. Furthermore, we propose three new metrics to properly evaluate the pedestrian intention systems. Under these new evaluation metrics for the intention prediction, the proposed end-to-end network offers accurate pedestrian intention up to half a second ahead of the actual risky maneuver.