IS 709/809: Computational Methods for IS Research — Fall 2021

Times: Thursday 7:10pm – 9:40pm

Location: On-campus Meeting Room: Performing Arts & Humanities 107

Online Meeting Room: UMBC Blackboard Collaborate (Virtual)

Instructor: Nirmalya Roy

Instructor’s Office Location and Hours: Online, Thursday 9:00 am – 10:00 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

HW 1



  • Computational Complexity (Contd.)

  • Research Reflection Schedule



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

  • Research Reflection Schedule

HW 2



  • Sorting Algorithm Analysis [Contd.]

  • Research Reflection Schedule



  • Research Paper Presentations



  • Sorting Algorithm Analysis [Contd.]

  • Research Paper Presentations



  • Research Paper Presentations

  • 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



  • Introduction to Graph Algorithms (Contd.)

  • Research Paper Presentations

Network Flow

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



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

  • Exam Review

HW 3

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

Exam Review



Thanksgiving Holiday



  • Exam

Research Paper Presentation Schedule:

(See above)

Research Papers:

In this course we will be discussing various papers recently published in top ACM/IEEE conferences. Please check the following link.

Deep Compressive Offloading: Speeding Up Neural Network Inference by Trading Edge Computation for Network Latency; Shuochao Yao, Jinyang Li, Dongxin Liu, Tianshi Wang, Shengzhong Liu, Huajie Shao, Tarek Abdelzaher, ACM SenSys 2020

Contrastive Self-Supervised Representation Learning for Sensing Signals from the Time-Frequency Perspective; Dongxin Wang, Tianshi Wang, Shengzhong Liu, Ruijie Wang, Shuochao Yao, Tarek Abdelzaher, In Proc. ICCCN, July 2021 Maryam Alomair

CrossRoI: Cross-camera Region of Interest Optimization for Efficient Real Time Video Analytics at Scale; Hongpeng Guo, Shuochao Yao, Zhe Yang, Qian Zhou, Klara Nahrstedt, in 12th ACM Multimedia Systems Conference (MMsys 21')

A Privacy-preserving Data Collection and Processing Framework for Third-party UAV Services, Tianyuan Liu, Hongpeng Guo, Claudiu Danilov, Klara Nahrstedt, in 19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2021. Tobi Odunsi

Real-time Spatio-Temporal Action Localization in 360 Videos, Bo Chen, Ahmed Ali-Eldin, Klara Nahrstedt, Prashant Shenoy, IEEE International Symposium on Multimedia (ISM 2020) Seraj Mostafa

SEAWARE: Semantic-Aware View Prediction System for 360-degree Video Streaming, Jounsup Park, Mingyuan Wu, Eric Lee, Bo Chen, Klara Nahrstedt, Michael Zink, Ramesh Sitaraman, IEEE International Symposium on Multimedia (ISM 2020) Samin Semsar

Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping, A. Rosinol, M. Abate, Y. Chang, L. Carlone IEEE Int. Conf. Robot. Autom. (ICRA), 2020.

Non-Monotone Energy-Aware Information Gathering for Heterogeneous Robot Teams, X. Cai, B. Schlotfeldt, K. Khosoussi, N. Atanasov, G.J. Pappas, J.P. How; IEEE Int. Conf. Robot. Autom. (ICRA), 2021. Kiran Prabhu

Asynchronous and Parallel Distributed Pose Graph Optimization, Yulun Tian, Alec Koppel, Amrit Singh Bedi, and Jonathan P. How; IEEE Robotics and Automation Letters, Oct. 2020.

Enhanced Transfer Learning for Autonomous Driving with Systematic Accident Simulation; Shivam Akhauri; Laura Zheng; Ming Lin; IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020 Leslie Leslie

METEOR: A Massive Dense & Heterogeneous Behavior Dataset for Autonomous Driving; Rohan Chandra1 et. al., 2021 Dataset page: Guy-Alain Aurelien

SS-SFDA: Self-Supervised Source-Free Domain Adaptation for Road Segmentation in Hazardous Environments; Divya Kothandaraman, Rohan Chandra, Dinesh Manocha; 2020 Github page

TERP: Reliable Planning in Uneven Outdoor Environments using Deep Reinforcement Learning Kasun Weerakoon, Adarsh Jagan Sathyamoorthy, Utsav Patel, Dinesh Manocha, 2021 Jumman Hossain

RELLIS-3D Dataset: Data, Benchmarks and Analysis; Peng Jiang, Philip Osteen, Maggie Wigness, Srikanth Saripalli; 2021 Github page Shreepriya Dogra

LiDARNet: A Boundary-Aware Domain Adaptation Model for Lidar Point Cloud Semantic; Peng Jiang and Srikanth Saripalli, 2020

GANav: Group-wise Attention Network for Classifying Navigable Regions in Unstructured Outdoor Environments; Tianrui Guan, Divya Kothandaraman, Rohan Chandra, Adarsh Jagan Sathyamoorthy, Dinesh Manocha; 2021 Github page Xingyan Li

Who2com: Collaborative Perception via Learnable Handshake Communication; YC Liu, J Tian, CY Ma, N Glaser, CW Kuo, Z Kira; ICRA 2020 Dodavah Mowoh

Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation; Yen-Cheng Liu, Yu-Ying Yeh, Tzu-Chien Fu, Sheng-De Wang, Wei-Chen Chiu, Yu-Chiang Frank Wang; CVPR 2018 Sabrina Mamtaz Nourin

A Closer Look at Few-shot Classification; Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, Jia-Bin Huang; ICLR 2019 Uzma Hasan

Towards Scene Understanding: Unsupervised Monocular Depth Estimation with Semantic-aware Representation; Po-Yi Chen, Alexander H. Liu, Yen-Cheng Liu, Yu-Chiang Frank Wang; CVPR 2019 Sancharee Chowdhury

Deep Continuous Fusion for Multi-Sensor 3D Object Detection; Ming Liang, Bin Yang, Shenlong Wang, Raquel Urtasun; ECCV 2018 Maliha Momtaz

A bio-hybrid odor-guided autonomous palm-sized air vehicle; Melanie J Anderson1, Joseph G Sullivan, Timothy K Horiuchi, Sawyer B Fuller1, and Thomas L Daniel; Bioinspiration & Biomimetics 2021, Volume 16, Number 2 Muhammad Hasan Ferdous

Oct 14 Presentations: Maliha Momtaz; Seraj Mostafa

Oct 21 Presentations: Sabrina Mamtaz Nourin; Tobi Odunsi

Oct 28 Presentations: Maryam Alomair; Sancharee Chowdhury; Guy-Alain Aurelien; Shreepriya Dogra

Nov 4 Presentations: Jumman Hossain; Muhammad Hasan Ferdous; Samin Semsar; Uzma Hasan; Dodavah Mowoh

Nov 11 Presentations: Leslie Leslie; Xingyan Li; Kiran Prabhu; Xiaotian Kong

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


Jumman Hossain & Maliha Momtaz

Follow the Soldiers



Sabrina & Sancharee

Crazyflie Tells the Altitude



Xiaotian & Guy

Autonomous Obstacle Avoidance Drone



Leslie & Tobi

Penetration Testing and Security Auditing using Crazyflie



Uzma & Muhammad

Can Crazyflie Detect Human Activity & Flight Anomaly?



Maryam & Shreepriya

Dancing in a Trajectory



Xinyang & Seraj

Tello: Tell me I am on which Floor?



Samin, Dodavah, Kiran

Assistive Drones for Individuals with Visual Impairment