IS 709/809: Computational Methods for IS Research — Fall 2020

Times: Tuesday 4:30pm – 7:00pm

Location: Online

Instructor: Nirmalya Roy

Instructor’s Office Location and Hours: Online, Thursday 10:00 am – 11:00 am, or by appointment

Instructor’s Email: nroy at umbc dot edu

Course Description and Rationale: Computational methods are inevitable tools for many facets of information systems research. These methodologies are used as fundamental tools and techniques in research and advanced practice in information systems, with particular focus on networking hardware and software technologies that deal with data and systems. Data becomes useful when it provides meaningful information through data analysis and mining, pattern recognition and learning, information extraction and visualization. System becomes useful when it meets the required end performance metrics through the governing policies and procedures and underlying models and simulations. Sophisticated data analysis and system performance measurements require a mixture of skills ranging from algorithmic foundation, data mining, machine learning, computational modeling, and information systems performance evaluation. This course covers the mixture of these skills with the goal of providing information science graduate and masters students with the ability to employ them in future research. The course is project- based, allowing students to understand the use of computational methods to pursue research objectives and interests.

Course Objectives: The purpose of this course is to provide a comprehensive foundation to apply computational research methods in solving problems in Information Systems. This course should enhance students’ reasoning, problem-solving and modeling abilities, particularly in dealing with algorithmic problems. More specifically, the course has the following objectives:

  • Familiarize students with the concepts and applications of computational techniques (machine learning, data science, graph theory, computational complexity, information and communication technology, operational managements etc) to solve computational problems.

  • Teach students how to think and formalize problems algorithmically and experimentally.

  • We will not assume any background beyond high school level mathematics and familiarity with programming concepts. However, students are expected to spend time in learning the concepts in this course, many of which will be covered in detail.

Course Topics:

    • Algorithmic Complexity

    • System Modeling and Performance Measurement

    • Computational Techniques for Cyber-Physical Systems

    • Computational Techniques for Smart Service Systems

    • Information and Communication Technology

    • Applications

Prerequisites: IS 698 (Smart Home Health Analytics) or IS 733 (Data Mining) or consent of the instructor.

Instructional Methods: Classroom Lectures

Recommended Textbooks (Optional):

  • Data Structures and Algorithm Analysis in C++ (4th Edition) by Mark Allen Weiss, Addison- Wesley, 2013 (

  • Fundamentals of Queueing Theory, 4th Ed., by Donald Gross & John F. Shortle & James M. Thompson & Carl M. Harris. John Wiley & Sons, Inc, 2008 (




Handout/ Assignment





  • Course overview, logistics, etc.

  • Introduction to Algorithm Analysis and System Modeling



  • Math Review for Computational Methods and Algorithm Analysis

  • Research Reflection (Smart Service Systems, CPS, FW discussions)



  • Computational Complexity

  • Research Reflection (Smart Service Systems + CPS + FW)



  • Computational Complexity

  • Research Reflection (Smart Service Systems + CPS + FW)



  • Computational Complexity (Contd.)

  • Research Reflection Schedule Arjun, Josh, Sahara, Masud



HW 2



  • Research Reflection (Contd.) Xiangyang, Pretam, Debvrat

  • Sorting Algorithm Analysis [Insertion sort, Selection sort, Shellsort, Mergesort, Quicksort, Decision trees, Counting Sort, External sorting (Multiway Merge) etc.]

  • Research Paper Presentations Zahid Hasan



  • Sorting Algorithm Analysis (Contd.)

  • Research Paper Presentations Jeanette Huang, Tobi Odunsi, Pretom Roy Ovi



  • Research Paper Presentations Josh Bolton, Sahara Ali, Masud Ahmed, Xiangyang Meng, Tashnim Chowdhury, Ali Alsarhan

  • Research Proposal

Research Proposal



  • Introduction to Graph Algorithms, Topological Sort, Shortest Paths, Network Flow; Minimum Spanning Tree Applications (Prim’s and Kruskal’s Algorithms)

  • Research Paper Presentations Debvrat Varshney, Arjun Pandya



  • Introduction to Graph Algorithms (Contd.)

  • Smart Service or Cyber-Physical Systems Performance Evaluation: Queueing Theory, Erlang Concept, Basic Model & Notation, Little’s Theorem

Network Flow

MST (Textbook DSAA: Chapter 9, Section 9.3, 9.4, 9.5)



  • Poisson process & Exponential distribution, Markovian Property, Memorylessness, Stochastic Process, Markov Process, Birth & Death process, Markovian Systems

  • Research project progress pitch [5-minutes oral presentation]

HW 3

Intro Queueing Theory (Textbook FQT: Chapter 1, Sections 1.1, 1.2, 1.3, 1.4, 1.5, 1.7, 1.8, and 1.9)



  • Single Server system: M/M/1-Queue; steady state probabilities, M/M/1 performance measures

  • Exam Review

Simple Queueing Model (Textbook FQT: Chapter 2, Sections 2.1 and 2.2)

Exam Review



  • Exam

HW 4

Research Paper Presentation Schedule:

(See above)

Research Papers:

In this course we will be discussing various papers recently published in ACM/IEEE UBICOMP (IMWUT) / ISWC 2020. Please check the following link.

IMWUT 2019-2020

A Multisensor Person-Centered Approach to Understand the Role of Daily Activities in Job Performance with Organizational Personas, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous TechnologiesDecember 2019

Cross-Dataset Activity Recognition via Adaptive Spatial-Temporal Transfer Learning, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies December 2019 Yue (Jeanette) Huang

"He Is Just Like me”: A Study of the Long-Term Use of Smart Speakers by Parents and Children, ACM Interact. Mob. Wearable Ubiquitous Technol., Vol. 4, No. 1, 2020 Ali Alsarhan

Investigating Users' Preferences and Expectations for Always-Listening Voice Assistants, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, December 2019

A wearable magnetic field based proximity sensing system for monitoring COVID-19 social distancing, ISWC '20: Proceedings of the 2020 International Symposium on Wearable Computers, September 2020 Tashnim Chowdhury

AuraRing: Precise Electromagnetic Finger Tracking, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, December 2019

Towards the Design of a Ring Sensor-based mHealth System to Achieve Optimal Motor Function in Stroke Survivors, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, December 2019 Debvrat Varshney

Machine Learning for Phone-Based Relationship Estimation: The Need to Consider Population Heterogeneity, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, December 2019 Xiangyang Meng

Intermittent Learning: On-Device Machine Learning on Intermittently Powered System, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, December 2019 Tobi Odunsi

Conversational Technologies for In-home Learning: Using Co-Design to Understand Children’s and Parents’ Perspectives, ACM CHI 2020

Privacy Adversarial Network: Representation Learning for Mobile Data Privacy, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, December 2019 Zahid Hasan

Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, March 2020 Josh Bolton

Classifying, Segmenting, and Tracking Object Instances in Video with Mask Propagation, CVPR 2020 Masud Ahmed

Graph Neural Networks for Social Recommendation, World Wide Web Conference, 2019 Pretom Roy Ovi

Oct 13 Presentations:

Oct 20 Presentations:

Oct 27 Presentations:

Nov 3 Presentations:

Sample Research Project Reports:

David Welsh and Nirmalya Roy. Smartphone-based Mobile Gunshot Detection, In Proceedings of the 13th Workshop on Context and Activity Modeling and Recognition (CoMoRea’17), co-located with PerCom, March 2017. [pdf]

H M Sajjad Hossain, Md Abdullah Al Hafiz Khan, and Nirmalya Roy. SoccerMate: A Personal Soccer Attribute Profiler using Wearables, in Proceedings of the 1st IEEE PerCom International Workshop on Behavioral Implications of Contextual Analytics (BICA), co-located with PerCom, March 2017. [pdf]

Abu Zaher Md Faridee, Sreenivasan Ramasamy Ramamurthy, H M Sajjad Hossain, and Nirmalya Roy. HappyFeet: Recognizing and Assessing Dance on the Floor, in Proceedings of the ACM 19th International Workshop on Mobile Computing Systems and Applications (HotMobile), Feb. 2018 [pdf]

Varun Mandalapu, Lavanya Elluri and Nirmalya Roy. Developing Machine Learning based Predictive Models for Smart Policing, in Proceedings of the 1st IEEE International Students Workshop on Smart Computing (SmartStudents), co-located with SmartComp, pp. 1-6, Washington D.C., June 2019

Research Projects:

Team No

Team Members


Devices/ Datasets


Debvrat Varshney & Tashnim Chowdhury

Semantic segmentation on medical images to detect COVID-19


Zahid Hasan

Contactless Heart Rate Sensing using Camera on Edge

MERL rPPG, UBFC-rPPG, & in-house rPPG


Yue Huang & Xiangyang Meng

Anomaly Detection for Spatio-temporal Taxi Trip

NYC Yellow Cab Trip Record 2016


Arjun Pandya & Tobi Odunsi

Energy consumption forecasts on multivariant time series

Engie Wind Farm & U.S. Energy Information Administration


Sahara Ali & Pretom Roy Ovi

Tracking Wildfires based on Satellite Data and Aerial Imagery

Fire-smoke dataset (DeepQuestAI), FIRM’s Active Fire Data (MODIS C6)


Ali Alsarhan & Josh Bolton

Students' Performance Prediction

Student Performance Dataset, University of Minho, Portugal


Masud Ahmed

Debris tracking and classification

In-house datasets collected by Drone