Erisa Terolli
Teaching Assistant Professor
Charles V. Schaefer, Jr. School of Engineering and Science
Department of Computer Science
Education
- PhD (2018) Sapienza University of Rome (Computer Science)
Research
Data/Graph Mining
Social Computing
Social Computing
Experience
Postdoc Researcher - Max Planck Institute of Informatics (2018 - 2021)
Visiting Research Scholar - Brown University - 2017
Visiting Research Scholar - Brown University - 2017
Institutional Service
- ADAPT S-STEM program Member
- DEI Committee Chair
- First Generation Low Income Mentorship Program Member
- SWICS Student Club Advisor Chair
- Developing Curriculum Committee Member
- ADAPT S-STEM program Member
- Google Developer Student Club Advisor Chair
- SWICS Student Club Advisor Chair
- NSF BPC Plan Workshop Member
- First Generation Low Income Mentorship Program Member
- Academic Ambassador Member
- CS NTT Search Committee Member
- DEI Committee Member
- Academic Ambassador Member
- CS Chair Search Committee Member
Appointments
- Teaching Assistant Professor - Stevens Institute of Technology Sep 2021 - present.
Honors and Awards
- Google Anita Borg Fellowship 2015
- ICT Awards - Female in ICT 2016
- ICT Awards - Female in ICT 2016
Selected Publications
Conference Proceeding
- Guimareas, A.; Terolli, E.; Weikum, G. (2021). Comparing Health Forums: User Engagement, Salient Entities, Medical Detail. 2021 Conference on Computer Supported Cooperative Work and Social Computing. 2021 Conference on Computer Supported Cooperative Work and Social Computing.
https://dl.acm.org/doi/fullHtml/10.1145/3462204.3481748.
Journal Article
- De Stefani, L.; Terolli, E.; Upfal, E. (2021). Tiered Sampling: An Efficient Method for Counting Sparse Motifs in Massive Graph Streams. ACM Transactions on Knowledge Discovery from Data (TKDD) (5 ed., vol. 15, pp. 1-52). ACM.
https://dl.acm.org/doi/abs/10.1145/3441299.
Courses
- Introduction to CS - Fall 2022, Spring 2022, Fall 2021
- Mathematical Foundations of Machine Learning - Fall 2022, Spring 2022, Fall 2021
- Fundamentals of Computing Spring 2022
- Mathematical Foundations of Machine Learning - Fall 2022, Spring 2022, Fall 2021
- Fundamentals of Computing Spring 2022