DeepAir: Predicting Radio Spectrum Usage at Scale with Deep Temporal Convolutional Networks. - Amir Ghasemi; Janaki Parekh (ICC Workshops, 2021)

Deep Learning based Localization of LTE eNodeBs from Large Crowdsourced Smartphone Datasets. - Amir Ghasemi; Janaki Parekh (VTC Spring, 2021)

Enabling Universal Connectivity via Data-Driven Policymaking: A North American Case Study. - Janaki Parekh; Chaitanya Parekh; Amir Ghasemi (IEEE Commun. Mag., 2021)

Data-Driven Modelling of Mobile Network Demand for Efficient Spectrum Management. - Janaki Parekh; Amir Ghasemi; Halim Yanikomeroglu (PIMRC, 2023)

DeepAir: Enabling Data-Driven Dynamic Spectrum Sharing via Scalable Forecasting. - Amir Ghasemi; Janaki Parekh (IEEE Trans. Cogn. Commun. Netw., 2023)

Towards Efficient Spectrum Management: A Data-driven Assessment Framework for Local Licensing. - Kareem E. Baddour; Mohamad Alkadamani; Mathieu Châteauvert; Janaki Parekh; Colin Brown; Adrian Florea (DySPAN, 2024)

Informing Wireless Spectrum Policy-Making via Crowdsourced Data-Driven Insights: A Case Study. - Amir Ghasemi; Janaki Parekh (DySPAN, 2024)

DeepSpectrum: A Localized Demand Estimation Model for Mobile Spectrum using Deep Learning. - Janaki Parekh; Amir Ghasemi; Halim Yanikomeroglu (VTC Fall, 2024)

Spectrum efficiency through data: A methodology for evaluating local licensing strategies. - Mohamad Alkadamani; Colin Brown; Kareem E. Baddour; Mathieu Châteauvert; Janaki Parekh; Adrian Florea (Comput. Networks, 2025)

Modeling Local Demand for Mobile Spectrum: An Interpretable Machine Learning Approach. - Janaki Parekh; Elizabeth Yackoboski; Amir Ghasemi; Halim Yanikomeroglu (IEEE Open J. Commun. Soc., 2025)