Sachini Weerasekara
I’m an applied machine learning researcher, a Ph.D. candidate at Northeastern University, passionate about unveiling latent collaborative associations between sequential and temporal data using machine learning.
I work with Prof. Jacqueline Isaacs & Prof. Sagar Kamarthi. My work involves harnessing the power of Artificial Intelligence to drive advancements within the healthcare domain for enhanced treatment efficacies.
Email / LinkedIn / GoogleScholar / GitHub / Blog
Recent Awards
- 2024 LEADERS Fellowship Award from Takeda Pharmaceutical
- 2023 Northeastern COE Ph.D. Research Expo Award
- 2022 Ferretti & Yamamura Award for Research Excellence, Northeastern University
Teaching
- 2022 Fall, 2023 Spring, 2023 Summer I – Teaching Assistant for IE7300 Statistical Learning for Engineering
- 2021 Spring, 2022 Spring – Teaching Assistant for IE7275 Data Mining in Engineering
- 2021 Fall – Teaching Assistant for ME5645 Environmental Issues in Manufacturing Product Use
Featured Projects and Publications
An interpretable deep learning pipeline for predicting pneumonia onset in cardiovascular ICU patients: A multicenter retrospective study
A deep learning pipeline that predicts anticipated event occurrences within next t hrs, leading up to pneumonia
Machine learning for pneumonia onset prediction in the ICU
Machine learning predicts pneumonia 72-24 hrs before the onset! Check out in the link below.
Context-aware neural point processes for clinical event chain prediction.
A framework that predicts clinical event chains by fusing coarse-grained event sequences and fine-grained context sequences.
Learning for Disassembly Task Control
Controlling tasks in a disassembly line is challenging due to uncertainties related to end-of-life products. This work proposes a Deep Reinforcement Learning based control strategy for cost-efficient disassembly.
Hand Posture Recognition
Posture recognition is vital in human-computer interaction, surveillance systems, self-driving cars, deaf and dumb communication, etc… This work builds four classical machine learning models to classify the hand postures of an individual.
Dengue Prediction
In recent years, dengue fever has been spreading. Historically, the disease has been most prevalent in Asia and the Pacific islands. These days many of the nearly half billion cases per year are occurring in Latin America. This study utilizes classical machine learning and neural networks to understand the relationship between climate and dengue dynamics. This understanding can improve research initiatives and resource allocation to help fight life-threatening pandemics.
Keyword Co-Occurrence Network (KCN) for Industry 4.0 for Asset Life Cycle Management (ALCM)
ALCM strategies such as predictive maintenance are vital for economically and environmentally sustainable manufacturing. This work uses NLP to analyze the knowledge base on Industry 4.0 applications for ALCM.
Reshoring with Remote Manufacturing
Offshoring manufacturing operations is questionable because of the rising labor costs in offshoring destinations, supply chain resilience concerns, and thus diminishing cost advantages. Nevertheless, returning operations to the U.S. imposes challenges like heavy capital expenditure, labor costs, and raw material shortages. This study formulates a system dynamics model to investigate the capability of a remote manufacturing workforce in supporting bringing manufacturing facilities back to the U.S.