Sagar Kamarthi
Associate Dean of Graduate Education,
Office of the Dean
Professor,
Mechanical and Industrial Engineering
Director of Data Analytics Engineering Program,
MS in Data Analytics Engineering
Founder & Advisor of Advanced and Intelligent Manufacturing,
MS in Adv. & Int. Mfg.
Office
- 305 SN
- 617.373.3070
Research Focus
Sensing, diagnostics and prognostics for manufacturing machines and equipment, AI for smart and sustainable manufacturing and manufacturing systems integration, Machine learning models for personalized medicine and healthcare, Computational methods for pain assessment physiological sensing systems, Engineering education, personalized learning, and mass-customized instruction model
About
Dr. Sagar Kamarthi is a Professor of Mechanical and Industrial Engineering and Director of Data Analytics Engineering Program at the Northeastern University, Boston, MA. He received his MS and PhD degrees from the Pennsylvania State University. He teaches courses in data analytics and visualization. His research interests are in machine learning applications in smart manufacturing and personalized healthcare. His published over 200 peer-reviewed research papers. He received multiple best paper awards. He secured over $11 Million worth of research funding from various funding agencies. He received the 2021 Data Analytics and Information Systems Teaching Award from IISE, the 2020 University Excellence in Teaching Award from Northeastern University, 2019 College of Engineering Martin W. Essigmann Outstanding Teaching Award and the 2016 College of Engineering Outstanding Faculty Service Award. Founding Director of MS in Data Analytics Engineering Program, and Founder and Advisor of MS in Advanced and Intelligent Manufacturing.
https://news.northeastern.edu/2020/04/29/the-future-of-manufacturing-will-be-data-driven-are-we-up-to-the-task/
Education
- PhD in Industrial Engineering, Pennsylvania State University, 1994
- MS in Industrial Engineering, Pennsylvania State University, 1990
- BS in Chemical Engineering, Sri Venkateswara University, India, 1983
Honors & Awards
- 2024 Faculty Research Team Award
- 2022 Institute of Industrial and Systems Engineers (IISE) Fellow Award
- 2022 Excellence in Mentoring Award
- 2021 Data Analytics & Information Systems (DAIS) Data Analytics Teaching Award from the DAIS division of the Institute of Industrial and Systems Engineers, which is a primer national society for industrial engineering.
- Northeastern University 2020 Excellence in Teaching Award, given in recognition of the faculty members’ depth of knowledge in the subject, their ability to provide effective links among course content, research, and experiential learning, and the rigor of the course content.
- Distinguished paper award for “Machine component fault classification using permutation entropy and complexity representation of vibration signals,” 1st International Conference on Industry 4.0 and Advanced Manufacturing, National Science Seminar Complex, Indian Institute of Science, Bangaluru, India, Jun. 28-29, 2019.
- The 2019 Martin Essigman Outstanding Teaching Award from the College of Engineering, Northeastern University, Boson.
- Invited panel speaker on “Data Driven Instruction” at the National Academy of Engineering‘s eighth Frontiers of Engineering Education (FOEE) Symposium, September 27, 2016.
- The 2016 Outstanding Faculty Service Award from the College of Engineering, Northeastern University, Boson.
- The 1998 Dell K. Allen Outstanding Young Manufacturing Engineer Award. This award is conferred by the Society of Manufacturing Engineers (SME) and ranks in stature with the SME International Honor Awards and the SME Award of Merit.
- The 1996 Pritsker Doctoral Dissertation Award for “On-Line Tool Wear Estimation in Turning Through Sensor Data Fusion and Neural Networks.”. This award is given by Institute of Industrial Engineers to the outstanding doctoral dissertation research in the areas related to industrial and manufacturing engineering.
- The 1995 Theoretical Development Award (2nd runner-up) for “Convergence Behavior of an Iterative Process: Application to Neural Networks.” at the International Conference on Artificial Neural Networks in Engineering (ANNIE’95), St. Louis, Missouri, USA.
- The First Prize in the Fourth Annual Graduate Research Exhibition (1989) for “Optical Neural Networks and Their Applications in Manufacturing Systems,” at The Pennsylvania State University, University Park, PA, USA.
- The First Prize in the National Student’s Design Competition (1982) for “Manufacture of Formaldehyde,” conducted by Indian Council for Science Museums, Bangalore, India.
Teaching Interests
- Manufacturing systems
- Neural networks and deep learning
- Data Mining in engineering
- Data visualization for visualization
- Capstone design projects in manufacturing, healthcare, and data analytics
Leadership Positions
- Founding Director of Data Analytics Engineering Program (Jan. 2016 – to date)
- Founder and Advisor of MS in Advanced and Intelligent Manufacturing (Sep. 2020 – to date)
- Director of IE Graduate Program (Sep. 2007 – Aug. 2015)
- Director of CSYE Graduate Program (May 2012 – Sep. 2013)
Professional Affiliations
- Senior Member of the Institute of Industrial Systems Engineers (IISE)
- Senior Member of the Society of Manufacturing Engineers (SME)
- Member of Institute for the Operations Research and the Management Sciences (INFORMS)
- Member of American Society for Engineering Education (ASEE)
Research Overview
Sensing, diagnostics and prognostics for manufacturing machines and equipment, AI for smart and sustainable manufacturing and manufacturing systems integration, Machine learning models for personalized medicine and healthcare, Computational methods for pain assessment physiological sensing systems, Engineering education, personalized learning, and mass-customized instruction model
AI-Assisted Manufactring (AIM) and Tele-Augmented Manufacturing (TEAM)
The main goal of AI-assisted manufacturing (AIM) is to enhance human capabilities and performance with AI assistance: human + AI = Success. In this approach, factory workers and AI assistants collaboratively perform manufacturing operations that otherwise cannot be effectively and efficiently performed by either one of them on their own individually. The expected outcomes of AI-assisted manufacturing are reduced operating costs, improved production efficiency, quality assurance, and prevention of unplanned downtime. Tele-augmented manufacturing (TEAM) is a subset of AI-assisted manufacturing, wherein the workers are located at a remote location as opposed being physically present on the factory floor. Broader impacts of tele-augmented manufacturing are:
- Eliminate geographical barriers to the engagement of skilled manufacturing workforce during COVID-19-like situations as well as normal circumstances.
- It addresses challenges posed by pandemics like COVID-19 and natural disasters like hurricanes to human mobility and human density. Permits factories to be located in less expensive and less dense areas (e.g., rural areas), while they can be operated by skilled workforce located in economically prosperous and socially vibrant communitie.
- It could reverse the emigration of manufacturing from the US to low-wage countries; it could utilizes manufacturing workforce located anywhere in the US and beyond, while onshoring factories.
- With onshore manufacturing enabled by tele-augmented manufacturing, supply chains could become geographically more compact, cost- and time-efficient, reliable, and resilient.
- It allows workers to operate in a clean, safe and healthy environment.
- It could promote eco-friendly commute and logistics, manufacturing productivity, flexible work practices, and quality time for families while facilitating social and physical distancing when needed.
Selected Research Projects
Developing Integrative Manufacturing and Production Engineering Curricula That Leverage Data Science (IMPEL), NSF ID: DUE-1935646, $2M, Sagar Kamarthi is PI, Oct, 2019-Sep. 2022.
Overview: The main goal of this project is to address the data science skills gap in the current production engineering workforce and to ensure the future workforce is prepared to succeed in the Industry 4.0 environment. The project will design, develop, and deploy online data science curricula targeting professionals, undergraduate students, and community college students interested in advancing their skills and knowledge for smart and advanced manufacturing. To allow learners tailor the curricula to their individual needs, the project will build a course-module recommendation system that prescribes the right set of courses/modules taking into consideration the learner’s aptitude, competency, and workplace needs. Using a design-based research approach to iteratively design, test, and revise the learning courses and modules based on active and experiential learning principles supported by the learning sciences literature, the project team will study the effectiveness of the online courses in serving multiple audiences and rigorously evaluate the program objectives and outcomes.
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1935646
SCH: INT: Collaborative Research: Novel Computational Methods for Continuous Objective Multimodal Pain Assessment Sensing System (COMPASS), NSF ID: SCH-1838621, $614k, Sagar Kamarthi is Co-PI, Sep. 2018-Aug. 2022.
Few objective pain assessment techniques are currently available for use in clinical settings. Clinicians typically use subjective pain scales for pain assessment and management, which has resulted in suboptimal treatment plans, delayed responses to patient needs, over-prescription of opioids, and drug-seeking behavior among patients. This project will investigate science-based methods to build a robust Continuous Objective Multimodal Pain Assessment Sensing System (COMPASS) and a clinical interface capable of generating objective measurements of pain from multimodal physiological signals and facial expressions. COMPASS will allow objective measurements that can be used to significantly improve pain assessment, pain management strategies, reduce opioid dependency, and advance the field of pain-related research. Using advanced sensing systems, data fusion algorithms and machine learning models, the PIs will develop a robust, reliable, and accurate pain intensity classification system, COMPASS, for estimating pain intensity experienced by patients in real-time on a 0-10 scale, which is the standard scale used by physicians in clinical settings.
https://nsf.gov/awardsearch/showAward?AWD_ID=1838796
Department Research Areas
Selected Publications
- Li, G., Yuan, C. Kamarthi, S., Moghaddam, M., Jin, X. (2021). “Data science skills and domain knowledge requirements in the manufacturing industry: A gap analysis,” Journal of Manufacturing Systems, Vol. 60, pp. 692-706, https://doi.org/10.1016/j.jmsy.2021.07.007.
- Pouromran, F., Radhakrishnan, S.,Kamarthi, S.(2021). “Exploration of physiological sensors, features, and machine learning models for pain intensity estimation,” PLOS ONE, July 9, 2021, https://doi.org/10.1371/journal.pone.0254108.
- Versek, C., Banijamali, M.A., Bex, P., Lashkari, K., Kamarthi, S., Sridhar, S., (2021). “Portable diagnostic system for age-related macular degeneration sreening using visual evoked potentials,” Eye and Brain, Vol 13, pp. 111-127.
- García, E., Núñez, P.J., Chacón, J.M., Caminero, M.A., Kamarthi, S., (2020). “Comparative study of geometric properties of unreinforced PLA and PLA-Graphene composite materials applied to additive manufacturing using FFF technology,” Polymer Testing, Vol. 91, Nov. 2020, 106860, https://doi.org/10.1016/j.polymertesting.2020.106860.
- Kamarthi, S., Li, W. (2020). Technology Enablers for Manufacturing Resilience in the COVID-19 and Post–COVID-19 Era. Smart and Sustainable Manufacturing Systems, Vol. No. 3, pp. 20200064. 10.1520/SSMS20200064.
- Mohammadi, R.*, Atif, M., Centi, A.J., Agboola, S., Jethwani, K., Kvedar, J., Kamarthi, S. (2020). Neural network-based algorithm for adjusting activity targets to sustain exercise engagement among people using activity trackers: retrospective observation and algorithm development study, JMIR Mhealth Uhealth. Vol. 8, No.9:e18142. doi: 10.2196/18142. PubMed PMID: 32897235.
- Mohammadi, R., Jain, S., Namin, A. T, Scholem Heller, M., Palacholla, R. Kamarthi, S., Wallace, B. (2020). “Predicting Unplanned Readmissions Following a Hip or Knee Arthroplasty: Retrospective Observational Study,” JMIR Medical Informatics, 8(11),e19761. 10.2196/19761.
- Ozturk, A., Mohammadi, R.*, Pierce, T.T., Kamarthi, S., Dhyani, M., Grajo, J.R., Corey, K.E., Chung, R.T., Bhan, A.K., Chhatwal, J., Samir, A.E., (2020). “Diagnostic Accuracy of Shear-Wave Elastography as a Non-Invasive Biomarker of High-Risk Non-Alcoholic Steatohepatitis (NASH) in Patients with Non-Alcoholic Fatty Liver Disease (NAFLD),” Ultrasound in Medicine & Biology, Vol. 46, No. 4, 2020, 972‐980, doi: 10.1016/j.ultrasmedbio.2019.12.020.
- Zeid, A., Sundaram, S., Moghaddam, M., Kamarthi, S., Marion, T., (2019). “Interoperability in Smart Manufacturing: Research Challenges,” MDPI Machines, Vol. 7, No. 2, 21, pp. 1-17, https://doi.org/10.3390/machines7020021.
- Oroojeni Mohammad Javad, M., Agboola, S.O., S., Jethwani, K., Zeid, A., Kamarthi, S. (2019). A Reinforcement Learning–Based Method for Management of Type 1 Diabetes: Exploratory Study,” JMIR Diabetes, Vol. 4. No. 3:e12905, doi: 10.2196/12905.
- Radhakrishnan, S., Lee, Y.-T. T., Rachuri, S., Kamarthi, S., (2019). “Complexity and Entropy Representation for Machine Component Diagnostics,” PLOS ONE, https://doi.org/10.1371/journal.pone.0217919.
- Centi, A. J., Atif, M., Golas, S. B., Mohammadi, R., Kamarthi, S., Agboola, S., Kvedar, J. C., Jethwani, K., (2019). “Factors influencing exercise engagement when using activity trackers,” JMIR mHealth and uHealth, Vol. 7, No. 10, pp. e11603, doi: 10.2196/11603.
- Xu, M., Radhakrishnan, S., Kamarthi, S. and Jin, X., (2019). “Resiliency of mutualistic supplier manufacturer networks,” Scientific Reports, Vol. 9, Issue 1:13559, doi: 10.1038/s41598-019-49932-1.
Jul 24, 2024
PhD Spotlight: Burcu Ozek, PhD’24, Industrial Engineering
Burcu Ozek, PhD’24, industrial engineering, focused research on developing objective pain assessment methods through machine learning, aiming to accurately quantify uncertainty and preexisting pain conditions. She co-authored six peer-reviewed journal articles, with four as lead author.
Feb 01, 2024
Faculty and Staff Awards 2024
The College of Engineering recognized faculty and staff at the annual faculty and staff awards event and thanked everyone for their hard work and dedication in support of our students, college, and university during the 2023-2024 academic year. View award recipients and photo gallery.
Nov 13, 2023
$1.5M ARL Grant To Improve Cybersecurity and Robustness in Additive Manufacturing
MIE Professor Sinan Müftü and Assistant Professor Ozan Özdemir were awarded a $1.5 million research grant by the Army Research Laboratories (ARL) to spearhead innovative initiatives in cybersecurity and enhancement of mechanical robustness in parts and coatings produced through Cold Spray Additive Manufacturing.
May 25, 2022
Kamarthi Receives IISE Fellow Award
MIE Professor Sagar Kamarthi was selected to receive the Institute of Industrial and Systems Engineers (IISE) Fellow Award. The Fellow award is the highest classification of membership in IISE and […]
Apr 15, 2022
Faculty and Staff Awards 2022
Congratulations to all the winners of the faculty and staff awards, and to everyone for their hard work and dedication during the 2021-2022 academic school year.
Oct 01, 2021
PhD Spotlight: Ramin Mohammadi, PhD’20 – Industrial Engineering
Advised by Sagar Kamarthi, Professor of Mechanical and Industrial Engineering Advised by Professor Sagar Kamarthi, mechanical and industrial engineering, the dissertation of Ramin Mohammadi, PhD’20, industrial engineering, was in applied […]
Apr 12, 2021
Kamarthi Receives DAIS Data Analytics Teaching Award
MIE Professor Sagar Kamarthi was selected as the winner of the Data Analytics & Information Systems (DAIS) Data Analytics Teaching Award.
Apr 23, 2020
Kamarthi Receives Excellence in Teaching Award
MIE Professor Sagar Kamarthi received the Northeastern University Excellence in Teaching Award.
Oct 07, 2019
$2M NSF Grant to Develop Data Science Modular Courses for Production Engineering Workforce
MIE Professors Sagar Kamarthi, Interim Dean Jacqueline Isaacs, Assistant Professors Xiaoning Jin, Mohsen Moghaddam, and Assistant Vice Chancellor for Digital Innovation and Enterprise Learning Kemi Jona were awarded a $2M NSF grant for “Developing Integrative Manufacturing and Production Engineering Curricula That Leverage Data Science”.
Apr 29, 2019
Faculty and Staff Awards 2019
Congratulations to all the winners of the faculty and staff awards, and to everyone for their hard work and dedication during the 2018-2019 academic school year. See Photo Gallery Faculty […]