About

Jacqueline Isaacs is a Professor and Vice Provost for Faculty Affairs at Northeastern University College of Engineering. She holds a PhD in Materials Science and Engineering from the Massachusetts Institute of Technology, earned in 1991. Her research focuses on the economic-environmental assessment of alternative manufacturing and nanomanufacturing routes towards sustainable design and manufacturing, as well as the societal implications of nanomanufacturing. She collaborates across disciplines including political science, philosophy, industrial hygiene, and industrial engineering. Isaacs is involved in the development and assessment of educational games for engineering students and K-12 outreach activities. She is also affiliated with the School of Public Policy and Urban Affairs and serves as Associate Director of the Center for High-rate Nanomanufacturing. Her work emphasizes sustainable manufacturing practices, societal impacts of nanotechnology, and educational innovation in engineering.

Research topics

• Computer Science
• Knowledge management
• Business
• Data Mining
• Engineering
• Multimedia
• Data science
• Economics

Selected publications

• Prediction of Clinical Complication Onset using Neural Point Processes

  arXiv (Cornell University) · 2025-02-18

  preprintOpen accessSenior author

  Predicting medical events in advance within critical care settings is paramount for patient outcomes and resource management. Utilizing predictive models, healthcare providers can anticipate issues such as cardiac arrest, sepsis, or respiratory failure before they manifest. Recently, there has been a surge in research focusing on forecasting adverse medical event onsets prior to clinical manifestation using machine learning. However, while these models provide temporal prognostic predictions for the occurrence of a specific adverse event of interest within defined time intervals, their interpretability often remains a challenge. In this work, we explore the applicability of neural temporal point processes in the context of adverse event onset prediction, with the aim of explaining clinical pathways and providing interpretable insights. Our experiments span six state-of-the-art neural point processes and six critical care datasets, each focusing on the onset of distinct adverse events. This work represents a novel application class of neural temporal point processes in event prediction.

  PublisherOA PDFDOI

• Improvements to Disassembly Lot Sizing With Task Control Through Reinforcement Learning

  Journal of Advanced Manufacturing and Processing · 2025-08-18

  articleOpen access

  ABSTRACT This research presents a novel methodology to control disassembly tasks for cost‐efficient component recovery from end‐of‐life products, fostering remanufacturing. Inventory management is an integral part of systems that assemble or disassemble products. Unlike assembly systems, disassembly operations pose a unique challenge, as they can lead to inventory accumulation and risk uncontrolled growth without careful management. Disassembly system inventory management is complex due to various factors, including non‐uniform demand for disassembled components, uncertainty in demands for salvage components, the arrival of different end‐of‐life product variants, end‐of‐life product condition variation, and processing time variation. These complexities often lead to unexpected inventory fluctuations, resulting in high inventory costs, inventory shortages, and customer dissatisfaction due to uncertainty in component availability. These inventory fluctuations can be mitigated if a real‐time decision‐making system supports disassembly processes. This study explores an innovative approach to addressing these complexities and controlling disassembly tasks using Deep Reinforcement Learning (DRL). This approach offers a more effective alternative to traditional methods. Experiments on Quantum‐dot LED (QLED), Organic LED (OLED), and Quantum Dot OLED (QD‐OLED) TV disassembly systems demonstrate the effectiveness of the DRL approach. Compared to the Multiple Elman Neural Networks (MENN) method, the DRL model offers a 21% reduction in inventory accumulation and a 12% improvement in demand satisfaction for the disassembly setup in the study.

  PublisherOA PDFDOI

• stEm Peer Academy: Building a Community of Practice

  2024-02-06

  articleOpen access

  Northeastern in 1995 and has focused her research pursuits on assessment of the regulatory, economic, environmental and ethical issues facing the development of nanomanufacturing and other emerging technologies.Her 1998 NSF Career Award is one of the first that focused on

  PublisherOA PDFDOI

• Reinforcement Learning for Disassembly Task Control

  Computers & Industrial Engineering · 2024-03-06 · 4 citations

  articleOpen access

  Inventory management is an integral part of systems that assemble or disassemble products. However, unlike assembly operations, disassembly operations can accumulate soaring work-in-process inventory if not carefully managed. Inventory management in disassembly systems is particularly challenging due to unexpected fluctuations in the inventory levels of subassemblies and salvage components caused by factors such as multiple demand arrivals, multiple core (used product or part) arrivals, uncertainty in core and demand arrivals, uncertainty in core condition, and varying processing times. Complexity and complications in disassembly systems lead to high operating costs and customer dissatisfaction, which may ultimately discourage remanufacturing. This article proposes an innovative approach employing Deep Reinforcement Learning (DRL) to control tasks in disassembly systems with the aforementioned complexities and complications. This approach presents a viable alternative to conventional disassembly lot sizing methods. The effectiveness of the approach is explored through experiments conducted on disassembly systems for Quantum-dot LED (QLED), Organic LED (OLED), and Quantum Dot OLED (QD-OLED) TVs. We analyze the potential impacts of the RL-driven policies on inventory accumulation and unfulfilled demand in the disassembly systems and compare them with the Multiple Elman Neural Networks (MENN) mechanism of controlling disassembly tasks. Further, we illustrate the generalizability of the proposed approach across three Markov Decision Process (MDP) configurations under Poisson arrivals, demands, product obsolesces, and processing rates. Simulation results indicate a 21% reduction in inventory accumulation, a 12% reduction in unfulfilled demand for less complex setups, and substantial performance improvements in intricate setups compared to the performance of MENN. This work bolsters the concept of remanufacturing by controlling disassembly tasks to make component recovery from EOL products cost-efficient.

  PublisherDOI

• NSF: Integrative Manufacturing and Production Engineering Education Leveraging Data Science Program (IMPEL)

  2021 ASEE Virtual Annual Conference Content Access Proceedings · 2024-02-20

  articleOpen access

  Abstract IMPEL is a transformative workforce education and training program that addresses the current and projected skills gaps and requirements in the area of data science in the U.S. manufacturing sector. The mission of IMPEL is to facilitate lifelong learning for the production engineering STEM workforce through designing sustainable, pedagogically-proven data science curricula via modular courses with interactive online learning labs and experiential project-based learning. The project team has accomplished three main tasks towards the goals of the project in Year 1. (1) Developed a data-driven skills gap and requirement analysis that utilizes the Emsi Labor Market Analytics data to understand the supply-demand tradeoff of critical skills and domain knowledge perceived in the U.S. manufacturing industry. This study also identifies the critical skills and domain knowledge required for data science related jobs that are highly in-demand in today's and future advanced manufacturing industry. (2) Conducted a comprehensive review of emerging research topics and trends in the broader area of manufacturing/production science and engineering that leverage data science. The review also includes a comprehensive analysis of the current state of data science and techniques employed for tackling challenging problems in the Industry 4.0 environment. By performing keyword co-occurrence network analysis, the research team discovers the structure and pattern of knowledge components and their trajectories in the field of manufacturing. (3) Conducted interviews and consultations with industry experts from diverse manufacturing companies to capture, analyze, and incorporate their experiences associated with acquiring and using relevant on-the-job skills in the design of course modules that serve practical needs and learning preferences. The findings from these activities are collectively used to tailor the curricula and courses, and in turn address the skills gaps of individuals in the current and future workforce through optimized modularization and customization of learning materials. The IMPEL team has completed the design and development of the first course, "Data Analytics", and would be completing the second and third courses, "Sensor Analytics" and "Algorithms for Engineering Applications" in next two quarters.

  PublisherOA PDFDOI

• Learning for Disassembly Task Control: An Alternative to Disassembly Lot Sizing

  SSRN Electronic Journal · 2023-01-01

  preprintOpen access
  PublisherDOI

• Technical feasibility of powering U.S. manufacturing with rooftop solar PV

  Environmental Research Infrastructure and Sustainability · 2023-03-01 · 5 citations

  articleOpen accessSenior author

  Abstract The use of renewable electricity is vital for the decarbonization of industry. Industrial firms source renewables through off-site power purchase agreements or on-site installations, though the latter currently supplies <0.1% of industrial electricity demand in the U.S. Manufacturing buildings typically have large, flat rooftops that are ideal for solar photovoltaic (PV) arrays. This study investigates the feasibility of using rooftop solar PV to cover the net annual electricity needs of industry across all U.S. states and manufacturing sectors. Modeled electricity supply intensity for solar PV arrays is compared with the electricity demand per unit of floorspace for average manufacturing buildings derived from the U.S. Department of Energy Manufacturing Energy Consumption Survey. Results show that, depending on geographical location, rooftop solar PV can completely fulfill the electricity requirements of 5%–35% of manufacturing sectors considered on a net annual basis (assuming net metering). Furniture, textiles, and apparel manufacturing can be powered through on-site means in nearly every location, representing 2% of U.S. manufacturing electricity use and 6% of floorspace. Considering seasonal potential during summer months expands the list of feasible sites, particularly in the U.S. Southwest. Compared to off-site sourcing of renewable energy, pursuing on-site PV installations can also enable manufacturers to maintain limited operations during periods of grid disruption, especially when coupled with on-site energy storage. Overall, the results indicate a substantial physical opportunity for industrial firms to expand rooftop solar PV from currently low levels to help meet decarbonization goals.

  PublisherOA PDFDOI

• Trends in Adopting Industry 4.0 for Asset Life Cycle Management for Sustainability: A Keyword Co-Occurrence Network Review and Analysis

  Sustainability · 2022 · 19 citations

  • Computer Science
  • Data Mining
  • Computer Science

  With the potential of Industry 4.0 technologies to enable sustainable manufacturing, asset life cycle management (ALCM) has been gaining increasing attention in recent years. This study explores the evolution of Industry 4.0 technology applications to sustainable ALCM from 2002 to 2021. This study is based on keywords collected from 3896 ALCM-related scientific articles published in the Web of Science, IEEE Xplore and Engineering Village between 2002 and 2021. We conducted a review analysis of these keywords using a network science-based methodology, which unlike the tedious traditional literature review methods, gives the capability to analyze a huge number of scientific articles efficiently. We built keyword co-occurrence networks (KCNs) from the keywords and explored the network characteristics to uncover meaningful knowledge patterns, knowledge components, knowledge structure, and research trends in the body of literature at the intersection of ALCM and Industry 4.0. The network modeling and data analysis results identify the emerging Industry 4.0-related keywords in ALCM literature and indicate the recent explosion of connectivity among keywords. We found IoT, predictive maintenance and big data to be the top three most popular Industry 4.0-related keywords in ALCM literature. Furthermore, this study maps relevant ALCM keywords in contemporary literature to the nine pillars of Industry 4.0 to help the responsible manufacturing community identify research trends and emerging technologies for sustainability.

  PublisherOA PDFDOI

• Assessing The Effectiveness Of Using A Computer Game To Bridge A Research Agenda With A Teaching Agenda

  2020-09-03 · 3 citations

  articleOpen accessSenior author

  We assess the impact of an out-of-class computer game designed to develop students' understanding of complex tradeoffs among environmental, economic, and technological issues. By comparing the results across three different courses using survey, essay, and focus groups as instruments, we measure the game's success in a variety of contexts and dimensions. Students increased their self-assessed knowledge about the supply chain and teamwork in the supply chain, they made connections between the environment and business practices as well as external events and the supply chain, but they did not feel that their understanding of sustainability improved. Students in an economics class experienced less increase and knowledge and confidence than did students in either an introductory policy class or a values-oriented course about built systems.

  PublisherOA PDFDOI

• A comparative analysis of economic and environmental tradeoffs of roof-mounted solar plants for manufacturing locations in the U.S.

  2020-01-17

  preprintOpen accessSenior author

  Manufacturing is responsible for approximately one-third of primary energy use and 37% of carbon dioxide emissions globally.As the interest in renewable energy is growing, this study considers the economic feasibility and environmental implications of installing onsite roofmounted solar PV systems on a case study manufacturing facility in five U.S. states (California, Florida, Indiana, New Jersey, and Texas), which have varying levels of solar irradiance, different incentives, solar policies, and manufacturing incentives at both the federal and state level.In these five cases, a combination of high efficiency SunPower solar panels (monocrystalline) with sun tracking technology are considered.The objective of this research is to compare the impact of state incentives and regulatory policies, as well as physical and locational differences, on the economic and environmental performance of high efficiency monocrystalline solar PV panels used for powering manufacturing processes.Using NREL's System Adviser Model (SAM), common financial metrics such as the economic payback period, Net Present Value (NPV) and Levelized Cost of Energy (LCOE) are investigated considering the federal and local incentive policies for the selected states.Energy Payback Time (EPBT) and Greenhouse Gas emissions (GHG) as common environmental performance metrics for life cycle of PVs are compared for different cases.The results indicate, lower LCOE and positive NPV can be achieved under certain conditions with the economic payback time ranging from 3 to 15 years.EPBT is less than two years for the five selected states with the CO2 equivalent abatement cost ranging from $0.5 -$151 per ton.

  PublisherOA PDFDOI

Recent grants

• Assessment of Next Generation Learning Through Computer-Facilitated Networked Play

  NSF · $499k · 2007–2011

• SNM: Designing and Integrating LCA Methods for Nanomanufacturing Scale-up

  NSF · $1.3M · 2011–2017

Frequent coauthors

• Sagar Kamarthi

  Northeastern University

  23 shared

• Matthew J. Eckelman

  20 shared

• Jay Laird

  18 shared

• Christopher J. Bosso

  17 shared

• Carol Barry

  University of Massachusetts Lowell

  15 shared

• James C. Benneyan

  12 shared

• Dhimiter Bello

  University of Massachusetts Lowell

  12 shared

• Serkan Erbis

  Northeastern University

  11 shared

Labs

• Laboratory for Advanced Manufacturing AssessmentPI

Education

• SC.D. and S.M., Materials Science and Engineering

  Massachusetts Institute of Technology

  1991

• BS, Metallurgical Engineering and Materials Science

  Carnegie Mellon University

  1983

Awards & honors

• 2024 Faculty Research Team Award
• 2021 Dean's Award for Meritorious Service, College of Engine…
• 2015 Excellence in Mentoring Award, College of Engineering
• 2012 ELATE Fellow
• 2012-2013 Committee Member, NNI Triennial Review, National R…