Sagar Kamarthi

Professor,  Mechanical and Industrial Engineering
Director Data Analytics Engineering Program,  MS in Data Analytics Engineering

Contact

Social Media

Office

  • 305 SN
  • 617.373.3070

Research Focus

Machine learning applications in smart and sustainable manufacturing; predictive analytics for smart and connected health; data driven approaches to mass customized instruction

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, visualization, and manufacturing. 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. His recent awards include the 2020 University Excellence in Teaching Award, 2019 College of Engineering Martin W. Essigmann Outstanding Teaching Award, and the 2016 College of Engineering Outstanding Faculty Service Award.

Kamarthi Receives Excellence in Teaching Award

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

  • 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

  • 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)
  • Director of IE Graduate Program (Sep. 2007 – Aug. 2015)
  • Director of CSYE Graduate Program (May 2012 – Sep. 2013)

Research Overview

Machine learning applications in smart and sustainable manufacturing; predictive analytics for smart and connected health; data driven approaches to mass customized instruction

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

Selected Publications

  • M. Xu, S. Radhakrishnan, S. Kamarthi, and X. Jin, (2019). “Resiliency of mutualistic supplier manufacturer networks,” Scientific Reports, Vol. 9, Issue 1:13559, doi: 10.1038/s41598-019-49932-1.
  • S. Radhakrishnan, Y.-T. T. Lee, S. Rachuri, S. Kamarthi, (2019). “Complexity and Entropy Representation for Machine Component Diagnostics,” PLOS ONE, https://doi.org/10.1371/journal.pone.0217919.
  • M. Xu, X. Jin, S. Kamarthi, N. Alam, (2018). “A failure-dependency modeling and state discretization approach for condition-based maintenance optimization of multi-component systems,” Journal of Manufacturing Systems, Vol. 47, pp. 141-152.
  • S. Radhakrishnan, S. Erbis, J.A. Isaacs, S. Kamarthi, (2017). Novel Keyword Co-Occurrence Networks Based Methods to Foster Systematic Reviews of Scientific Literature, PLOS ONE, https://doi.org/10.1371/journal.pone.0172778.
  • M.G. Uddin, K.S. Ziemer, A. Zeid, Y.-T.T. Lee, S. Kamarthi, (2017). Process Control Model for Growth Rate of Molecular Beam Epitaxy of MgO (111) Nanoscale Thin Films on 6H-SiC (0001) Substrates, International Journal of Advanced Manufacturing Technology, 91(1-4), 2017, 907–916.
  • S. Radhakrishnan, A. Duvvuru, S. Sultornsanee, S. Kamarthi, (2016). Phase Synchronization Based Minimum Spanning Trees for Analysis of Financial Time Series with Nonlinear Correlations, Physica A: Statistical Mechanics and its Applications, 444, 2016, 259-270.
  • S. Kamarthi, S. Sultornsanee, A. Zeid, Recurrence Quantification Analysis to Estimating Surface Roughness in Finish Turning Processes, International Journal of Advanced Manufacturing Technology, 87(1-4), 2016, 451–460
  • S. Erbis, Z. Ok, J.A. Isaacs, J.C. Benneyan, S. Kamarthi, Review of Research Trends and Methods in Nano Environmental, Health and Safety Risk Analysis, Risk Analysis: An International Journal, 2016, 1-18
  • S. Erbis, S. Kamarthi, A. Abdollahi-Namin, A. Hakimian, J.A. Isaacs, Stochastic Goal Programming Model for Sustainable CNT-Enabled Lithium-Ion Battery Manufacturing, Environmental Science: Nano, 3, 2016, 1447-1459

Faculty

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.

Faculty

Apr 23, 2020

Kamarthi Receives Excellence in Teaching Award

MIE Professor Sagar Kamarthi received the Northeastern University Excellence in Teaching Award.

Faculty

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”.

Faculty

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 Fellow Matthew Eckelman, CEE Yongmin Liu, MIE Outstanding Teacher of First Year Engineering Students Joseph Depasquale, Chemistry Brian O’Connell, FYE Sumi Seo, Mathematics Matthew Webber, […]

Faculty

Sep 07, 2018

$1.2M NSF Grant to Develop Objective Pain Assessment Sensing System

Yingzi Lin, MIE associate professor and director of the Intelligent Human-Machine Systems Lab, to lead $1.2M NSF grant to develop a Continuous Objective Multimodal Pain Assessment Sensing System (COMPASS) that improves pain assessment and management, reduces opioid dependency and advances the field of pain management research and patient safety.

Aug 03, 2016

COE to Represent NU at NAE

Sagar Kamarthi (MIE) has been selected as a panel speaker and Andrew Myers (CEE), Yunsi Fei (ECE), & Edgar Goluch (ChE) have been invited to participate at the National Academy of Engineering's eighth Frontiers of Engineering Education (FOEE) symposium.

May 02, 2016

Faculty and Staff Awards 2016

2016 Faculty and Staff Awards Congratulations to all the winners of the faculty and staff awards, and to everyone for their hard work and dedication during the 2015-2016 academic school year. Faculty Fellow Kaushik Chowdhury, ECE Carol Livermore, MIE Marilyn Minus, MIE Rising Star Staff Award Gabrielle Fiorenza, Co-op Nicole Nightingale, Dean’s Office Outstanding Teachers […]

Aug 12, 2014

Battling Unemployment

MIE professor Abe Zeid, associate professor Sagar Kamarthi, & STEM Director of Programs & Partnerships Claire Duggan were awarded a $700K NSF grant to TRANSFORM liberal arts curriculum towards manufacturing.

Mar 20, 2014

Congratulations to recipients of the FY15 TIER 1 Interdisciplinary Research Seed Grants

19 COE faculty were recipients of FY15 TIER 1 Interdisciplinary Research Seed Grants for 11 different projects representing over $500K dollars of investment in research.

Jul 09, 2012

Sparking Engineering Ideas

As part of an ITEST grant, led by MIE professor Abe Zeid & associate professor Sagar Kamarthi, two videos that encourage students to pursue engineering have won a CINE Golden Eagle Award and a Silver & Bronze Telly Award. View the videos here. The ITEST program through research and model-building activities seeks to build understandings of best practice […]

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