Sagar Kamarthi

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

• 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)

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

College Research Initiatives

• Advancing Nanotechnology through Innovation in Materials Engineering
• Engineered Cyber-Social-Physical Systems

Department Research Areas

• Healthcare Systems
• Resilient Systems
• Smart and Sustainable Manufacturing