IS 709/809: Computational Methods for IS Research — Fall 2017

Times: Tuesday 7:10pm – 9:40pm
Location: Sherman Hall 150
Instructor: Nirmalya Roy
Instructor’s Office Location and Hours: ITE 421 Monday 1:30 – 3:00pm, or by appointment
Instructor’s Email: nroy at umbc dot edu

Course Descriptions: 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 robust and reliable 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 (graph theory, computational complexity, information and communication technology, operational managements, system performance evaluation 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 details.

Course Topics:
• Algorithmic Complexity
• System Modeling and Performance Measurements
• Information and Communication Technology
• Smart Service Systems Applications

Course Overview:
• Each student will be required to present 1 paper in the entire semester.
• There will be one mid-term exam, and no final exam.
• There will be a Cyber-Physical Systems or Smart Service Systems testbed development project. The main idea is to use smart devices or Internet-of-Things (IoT) to assemble real systems for collecting real data traces. We will be using off-the-shelf commercially available devices, smart phones, wireless sensors, smart plugs or any other devices for this purpose. The main intuition behind this semester long development project is getting real data for your research project. All equipment will be provided by the instructor.

• The next most important component of the course will be the final research project. You are expected to do these projects in groups of 2 (preferably). Think about the research project problem early, discuss your ideas with me, read papers in the area, formulate your solution, and finally collect data from your testbed and implement, and present it to the class.

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

Recommended Textbooks (Optional):

Course Requirements and Grading:
Course Participation + Research Reflection + Individual Paper Presentation 15%
Homework 20%
1 Midterm Exam 30%
Testbed Development + Group Research Project + Final Project Report 35%

Tentative Course Schedule:
(Subject to change as the semester progresses)

Week Date Topic Handout/Assignment Due Notes
 1  9/5 Course overview, logistics, etc.

Introduction to Algorithm Analysis and System Modeling

Course Syllabus Introduction
 2  9/12 Math Review for Computational Methods and Algorithm Analysis

Research Reflection (Smart Service Systems, CPS discussion)

Homework 1 Math Review 1

Math Review 2

 3  9/19 Computational Complexity

Research Reflection (Smart Service Systems + CPS)

Homework 1 Computational Complexity

SSS Project Review 1

SSS Project Review 2

 4  9/26 Computational Complexity

Research Reflection (Smart Service Systems + CPS)

Homework 2 Reflection Schedule:

TIMELI project: Wenbin

Smart Stormwater: Bipen

Big Data Time Series: Charles

 5  10/3 Sorting Algorithm Analysis [Insertion sort, Selection sort, Shellsort, Mergesort, Quicksort, Decision trees, Counting Sort, External sorting (Multiway Merge) etc.] Homework 2 Reflection Schedule:

Deep Learning: Zaher Faridee

Fraud Detection: Xiangru Chen


 6  10/10 Sorting Algorithm Analysis



Collision Warning System: Pei Guo

Always-On Health Monitoring:  Sreeni

RouteMe2: Jack Shan

iSee Project: Peichang Shi

Hazards SEES:  Neha Singh

 7  10/17 Introduction to Graph Algorithms, Topological Sort, Shortest Paths Intro Graph Algorithms

Shortest Path

 8  10/24 Research Paper Presentation Sreenivasan Ramasamy
Jack Shan
Peichang Shi
Neha Singh
Wenbin Zhang
 9  10/31 Research Paper Presentation Bipendra Basnyat
Xiangru Chen
Abu Zaher Faridee
Pei Guo
Charles Karpati
10 11/7 Network Flow; Minimum Spanning Tree Applications (Prim’s and Kruskal’s Algorithms)

Smart Service or Cyber-Physical Systems Performance Evaluation: Queueing Theory

Homework 3 Network Flow
MST (Textbook DSAA: Chapter 9, Section 9.3, 9.4, 9.5)
12 11/14
Research project progress pitch
[5-minutes oral presentation]
Erlang Concept, Basic Model & Notation, Little’s Theorem
Poisson process & Exponential distribution, Markovian Property, Memorylessness, Stochastic Process, Markov Process
Homework 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)
13 11/21 Birth & Death process, Markovian Systems

Single Server system: M/M/1-Queue; steady state probabilities, M/M/1 performance measures
Exam Review
Homework 4 Simple Queueing Model  (Textbook FQT: Chapter 2, Sections  2.1 and  2.2)

Exam Review

14 11/28 Exam Preparation
15 12/5 Exam Homework 4
16 12/12 Final Research & Development Project Presentation Final Project Report Template Sample

IEEE Style File

Research Paper Presentation Schedule:
(To be determined)

Group No. Team Members Project Title Devices/Datasets
1 Pei Guo Driving behavior detection using smartphone and smart wristband
2 Abu Zaher Md Faridee, Sreenivasan Ramamurthy Analyzing and mitigating sensor heterogeneity using Deep Learning
3 Neha Singh Deep Learning Techniques for Sentiment Analysis
4 Charles Karpati Audio Visulization
5 Peichang Shi Image classification based on Kaggle data  Data: Kaggle data
6 Xiangru Chen, Jack Shan Effect of Social Trust on the Lending Performance in An Online Financial Credit Market
7 Wenbin Zhang Self-organizing photo map
8 Bipen Basnyat Implementation of Unsupervised Feature Learning in Scene Recognition Devices: Raspberry Pi, UltraSonic Sensor, Camera
Data: Self-curated


Research Papers:

Reducing Inefficiencies in Taxi Systems
Chenguang Zhu (Stanford University), Balaji Prabhakar (Standford University) [Pei Guo]

MobileDeepPill: A Small-Footprint Mobile Deep Learning System for Recognizing Unconstrained Pill Images, ACM MobiSys 2017 [Peichang Shi]

Dynamically pre-trained deep recurrent neural networks using environmental monitoring data for predicting PM2.5. Neural Computing and Applications, 2016 [Sreenivasan Ramasamy]

Beat Induction, Peerapong Dhangwatnotai, Rajendra Shinde, Pawin Vongmasa, 2006 [Charles Karpati]

DeepEye: Resource Efficient Local Execution of Multiple Deep Vision Models using Wearable Commodity Hardware,
Akhil Mathur (Nokia Bell Labs), Nicholas D. Lane (Nokia Bell Labs, University College London), Sourav Bhattacharya, Aidan Boran, Claudio Forlivesi, Fahim Kawsar (Nokia Bell Labs), MobiSys 2017

DeepMon: Building Mobile GPU Deep Learning Models for Continuous Vision Applications
Huynh Nguyen Loc, Youngki Lee, Rajesh Krishna Balan (Singapore Management University), MobiSys 2017

BigRoad: Scaling Road Data Acquisition for Dependable Self-Driving
Luyang Liu, Hongyu Li (WINLAB, Rutgers University), Jian Liu (Stevens Institute of Technology), Cagdas Karatas (WINLAB, Rutgers University), Yan Wang (Binghamton University), Marco Gruteser (WINLAB, Rutgers University), Yingying Chen (Stevens Institute of Technology), Richard Martin (WINLAB, Rutgers University), MobiSys 2017

Characterizing Smartwatch Usage in The Wild
Xing Liu, Tianyu Chen, Feng Qian (Indiana University), Zhixiu Guo (Beijing Jiaotong University), Felix Xiaozhu Lin (Purdue University), Xiaofeng Wang (Indiana University), Kai Chen (Chinese Academy of Sciences), MobiSys 2017

Predicting and deterring default with social media information in peer-to-peer lending, Ge, Ruyi, et al. Journal of Management Information Systems 2017 [Xiangru Chen]

Contextual Spatial Outlier Detection with Metric Learning
Guanjie Zheng; Susan L. Brantley; Zhenhui Li (Pennsylvania State University), KDD 2017 [Wenbin Zhang]

Learning to Count Mosquitoes for the Sterile Insect Technique
Yaniv Ovadia (Google); Yoni Halpern (Google); Dilip Krishnan (Google); Josh Livni (Verily); Daniel Newburger (Verily); Ryan Poplin (Google, Inc.);Tiantian Zha (Verily (Google Life Sciences));D. Sculley (Google, Inc.)

Using Machine Learning to Improve Emergency Medical Dispatch Decisions
Karen Lavi, Ritvik Kharkar et. al., KDD 2017

Collecting and Analyzing Millions of mHealth Data Streams
Thomas Quisel (Evidation Health); Luca Foschini (Evidation Health); Alessio Signorini (Evidation Health); David Kale (USC Information Sciences Institute)

Large scale sentiment learning with limited labels
Vasileios Iosifidis (Leibniz University of Hanover); Eirini Ntoutsi (Leibniz University of Hanover), KDD 2017 [Neha Singh]

An efficient bandit algorithm for realtime multivariate optimization
Daniel Hill (;Houssam Nassif (;Yi Liu (;Anand Iyer (;S. V. N. Vishwanathan ( KDD 2017

“The Leicester City Fairytale?”: Utilizing New Soccer Analytics Tools to Compare Performance in the EPL seasons
Héctor Ruiz (STATS);Paul Power (STATS);Xinyu Wei (STATS);Patrick Lucey (STATS), KDD 2017

Stock Price Prediction via Discovering Multi-Frequency Trading Patterns
Liheng Zhang (University of Central Florida);Charu Aggarwal (IBM T. J. Watson Research Center);Guo-Jun Qi (University of Central Florida), KDD 2017

Matching Restaurant Menus to Crowdsourced Food Data, A Scalable Machine Learning Approach
Hesam Salehian (Under Armour);Chul Lee (Under Armour);Patrick Howell (Under Armour), KDD 2017

Latent Customer Needs Elicitation by Use Case Analogical Reasoning From Sentiment Analysis of Online Product Reviews, Feng Zhou, Roger Jianxin Jiao and Julie S. Linsey, J. Mech. Design 2015 [Jack Shan]

A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Mobile Users
Hao Wang (Institute of Software, Chinese Academy of Sciences);Yanmei Fu (Institute of Software, Chinese Academy of Sciences);Qinyong Wang (Institute of Software, Chinese Academy of Science);Changying Du (Institute of Software, Chinese Academy of Sciences);Hongzhi Yin (University of Queensland);Hui Xiong (Rutgers University)

Point of Interest Demand Modeling with Human Mobility Patterns
Yanchi Liu (Rutgers University);Chuanren Liu (Drexel University);Xinjiang Lu (Northwestern Polytechnical University, China);Mingfei Teng (Rutgers University);Hengshu Zhu (Baidu Talent Intelligence Center);Hui Xiong (Rutgers University)

DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection
Bokai Cao (University of Illinois at Chicago);Lei Zheng et. al.

The Co-Evolution Model for Social Network Evolving and Opinion Migration
Yupeng Gu (University of California, Los Angeles);Yizhou Sun (University of California, Los Angeles);Jianxi Gao (Northeastern University)

CityBES: A Web-based Platform to Support City-Scale Building Energy Efficiency
Tianzhen Hong (Lawrence Berkeley National Laboratory), Yixing Chen (Lawrence Berkeley National Laboratory), Sang Hoon Lee (Lawrence Berkeley National Laboratory), Mary Ann Piette (Lawrence Berkeley National Laboratory)

Personalised Recommendations for Modes of Transport: A Sequence-based Approach
Gunjan Kumar (University College Dublin, Ireland), Houssem Jerbi (University College Dublin, Ireland), Michael P. O’Mahony (University College Dublin, Ireland)

Smartphone Sensing Meets Transport Data: A Collaborative Framework for Transportation Service Analytics
Y Lu, A Misra, W Sun, H Wu; IEEE TMC Journal 2017

Fusing Social Media and Mobile Analytics for Urban Sense Making
A Misra

Discovering anomalous events from urban informatics data
K Jayarajah, V Subbaraju, D Weerakoon, A Misra, LT Tam, N Athaide; SPIE Defense+ Security, 101900F-101900F-14

Collaboration trumps homophily in urban mobile crowd-sourcing
T Kandappu, A Misra, RT DARATAN

Assessing the Impact of Real-time Ridesharing on Urban Traffic using Mobile Phone Data
Lauren P. Alexander (MIT – Mass. Inst. of Tech.), Marta C. Gonzalez (MIT – Mass. Inst. of Tech.)

Discovering Congestion Propagation Patterns in Spatio-Temporal Traffic Data
Hoang Nguyen (National ICT Australia), Wei Liu (University of Technology), Fang Chen (National ICT Australia)

On Inferring the Time-Varying Traffic Connectivity Structures of an Urban Environment
Sanjay Chawla (QCRI and University of Sydney), Somwrita Sarkar (University of Sydney), Javier Borge-Holthoefer (Qatar Computing Research Institute), Shameem Ahamed (UTC France and QCRI), Hossam Hammady (Qatar Computing Research Institute), Fethi Filali (Qatar Mobility Innovation Centre), Wassim Znaidi (Qatar Mobility Innovation Centre)

Transfer Learning For Urban Computing: A Case Study For Optimal Retail Store Placement
Ningyu Zhang (Zhejiang University), Huajun Chen (Zhejiang University), Xi Chen (Zhejiang University)

Profiling Pedestrian Activity Patterns in a Dynamic Urban Environment
Minh Tuan Doan (National ICT Australia, The University of Melbourne), Sutharshan Rajasegarar (The University of Melbourne, National ICT Australia), Christopher Leckie (The University of Melbourne, National ICT Australia)

An Interactive Visualization System to Analyze and Predict Urban Construction Dynamics
Tzu-Chi Yen (Sensoro Technology Co., Ltd.), Tzu-Yun Lin (Academia Sinica), Ching-Yuan Yeh (National Taiwan University Hospital), Hsun-Ping Hsieh (National Taiwan University), Cheng-Te Li (Academia Sinica)

Visual Analysis of Route Choice Behaviour based on GPS Trajectories
Min Lu (Peking University), Chufan Lai (Peking University), Tangzhi Ye (Peking University), Jie Liang (Peking University), Xiaoru Yuan (Peking University)

A Scalable Framework for Clustering Vehicle Trajectories in a Dense Road Network
Dheeraj Kumar (Electrical and Electronic Engineering), Sutharshan Rajasegarar (The University of Melbourne, National ICT Australia), Marimuthu Palaniswami (Electrical and Electronic Engineering), Xiaoting Wang (The University of Melbourne), Christopher Leckie (The University of Melbourne, National ICT Australia)

Perspectives on Stability and Mobility of Passenger’s Travel Behavior through Smart Card Data
Zhiyong Cui (Peking University), Ying Long (Beijing Institute of City Planning)
Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore
Shan Jiang (MIT – Mass. Inst. of Tech.), Joseph Ferreira Jr (MIT – Mass. Inst. of Tech.), Marta C. Gonzales (MIT – Mass. Inst. of Tech.)

Characterizing Taxi Flows in New York City
Marjan Momtazpour (Virginia Tech), Naren Ramakrishnan (Virginia Tech)

Predict User In-World Activity via Integration of Map Query and Mobility Trace
Zhengwei Wu (Baidu Research), Haishan Wu (Baidu Research), Tong Zhang (Baidu Research)

On the Spatial Dependencies of Human Mobility and Urban Energy Consumption
Dependencies of Human Mobility and Urban Energy Consumption)
Neda Mohammadi (Virginia Tech), John E. Taylor (Virginia Tech)

Byproducts of Urban Infrastructure Interfaces: Evidence from Parking Compliance
Konstantinos Pelechrinis (University of Pittsburg)

Recognizing Cities from Google Street View – Computer Vision Analysis of Urban Visual Identity
Lezhi Li (Harvard University), Kang Qi Ian Lee (MIT), Jingwen Wei (MIT)

On the Dominant Role of Returners’ Human Mobility Networks on Urban Energy Consumption
Neda Mohammadi (Virginia Tech), John E Taylor (Virginia Tech)

Robustness and Resilience of cities around the world
Sofiane Abbar (Qatar Computing Research Institute, Hamad Bin Khalifa University), Tahar Zanouda (Qatar Computing Research Institute, Hamad Bin Khalifa University), Javier Borge Holthoefer (Universitat Oberta de Catalunya)

Monitoring Manhattan’s traffic from 5 cameras?
Siheng Chen (Carnegie Mellon University), Yaoqing Yang (Carnegie Mellon University), Jelena Kovacevic (Carnegie Mellon University), Christos Faloutsos (Carnegie Mellon University)

Estimating Evacuation Hotspots using GPS data: What happened after the large earthquakes in Kumamoto, Japan?
Takahiro Yabe (University of Tokyo), Kota Tsubouchi (Yahoo! Japan Research), Akihito Sudo (University of Tokyo), Yoshihide Sekimoto (University of Tokyo)

An Active Learning Framework Incorporating User Input For Mining Urban Data
Nikolaos Zygouras (National and Kapodistrian University of Athens, Greece), Nikolaos E Panagiotou (National and Kapodistrian University of Athens, Greece), Nikos Zacheilas, Ioannis Boutsis (Athens University of Economics and Business, Greece), Vana Kalogeraki (Athens University of Economics and Business, Greece), Dimitrios Gunopulos (National and Kapodistrian University of Athens, Greece) [Bipen Basnyat]

Exploring Foursquare-derived features for crime prediction in New York City
Cristina Kadar (ETH Zurich), José Iria (Mobiliar), Irena Pletikosa Cvijikj (ETH Zurich)

Disaggregating Appliance-Level Energy Consumption: A Probabilistic Framework
Sabina Tomkins (University of California Santa Cruz), Lise Getoor (University of California Santa Cruz)

Make-up Policy: Exams: No make-up exams except through arrangement with the instructor prior to the exam date: and then for reasons deemed valid enough to warrant the making of a new, and potentially harder, test.

Just In Case: Diminished mental health can interfere with optimal academic performance. The source of symptoms might be related to your course work; if so, please speak with me. However, problems with other parts of your life can also contribute to decreased academic performance. UMBC provides cost-free and confidential mental health services through the Counseling Center to help you manage personal challenges that threaten your personal or academic well-being.

Remember, getting help is a smart and courageous thing to do — for yourself and for those who care about you. For more resources get the Just in Case mental health resources Mobile and Web App. This app can be accessed by clicking

The UMBC Counseling Center is in the Student Development & Success Center (between Chesapeake and Susquehanna Halls). Phone: 410-455-2472. Hours: Monday-Friday 8:30am-5:00pm.

Academic Integrity: By enrolling in this course, each student assumes the responsibilities of an active participant in UMBC’s scholarly community in which everyone’s academic work and behavior are held to the highest standards of honesty. Cheating, fabricating, plagiarism, and helping others to commit these acts are all forms of academic dishonesty and they are wrong. Academic misconduct could result in disciplinary action that may include, but is not limited to, suspension or dismissal. Full policies on academic integrity should be available in the UMBC Student Handbook, Faculty Handbook, or the UMBC Directory.

Student Disability Services (SDS): UMBC is committed to eliminating discriminatory obstacles that may disadvantage students based on disability. Services for students with disabilities are provided for all students qualified under the Americans with Disabilities Act of 1990, the ADAA of 2009, and Section 504 of the Rehabilitation Act who request and are eligible for accommodations. The Office of Student Disability Services (SDS) is the UMBC department designated to coordinate accommodations that would allow for students to have equal access and inclusion in their courses. If you have a documented disability and need to request academic accommodations, please refer to the SDS website at for registration information or visit the SDS office in the Math/Psychology Building, Room 212. For questions or concerns, you may contact us at or (410) 455-2459. If you require accommodations for this class, make an appointment to meet with me to discuss your SDS-approved accommodations.

Student Support Services (SSS): UMBC is committed to eliminating discriminatory obstacles that disadvantage students based on disability.  Student Support Services (SSS) is the UMBC department designated to receive and maintain confidential files of disability-related documentation, certify eligibility for services, determine reasonable accommodations, develop with each student plans for the provision of such accommodations, and serve as a liaison between faculty members and students regarding disability-related issues.  If you have a disability and want to request accommodations, contact SSS in the Math/Psych Bldg., room 213 or at 410-455-2459.  SSS will require you to provide appropriate documentation of disability.  If you require accommodations for this class, make an appointment to meet with me to discuss your SSS-approved accommodations.