Varun Garg summarizes his academic and industrial experience in data science and signal processing. He completed his PhD at UMass Lowell focusing on data science and signal processing. In industry, he has worked as an algorithm engineer at Veoneer developing object detection pipelines and unsupervised machine learning models for processing GPS trajectories. His academic research focused on utilizing sensor data from participatory devices to analyze static and dynamic spatiotemporal phenomena. This included identifying static road conditions using vehicle sensor data collected over time as well as collaboratively localizing and tracking vehicles in real-time using RF signals. He also developed methods to understand complex situations by sequencing sensor observations using deep learning models. His work has been published in peer-reviewed conferences

1. Overview of My Industrial and Academic
Experience
Varun Garg
Department of Electrical and Computer Engineering
University of Massachusetts Lowell

2. About Myself
• Currently working as an Algorithm Engineer at Veoneer
• Completed my Ph.D. in computer engineering at University of
Massachusetts Lowell
• Research Interest: Data science and Signal Processing.
• Radar Signal Processing: Single Vehicle Tracking, Multiple Vehicle
tracking using Radar Measurements, Familiar with Detection List,
Measurement Clustering
• Specialized domains: Machine learning, Tracking, State estimation,
probabilistic modeling, Trajectory data mining
• Programming languages: Python, Matlab, C++, R, Java
• Deep learning Packages: Pytorch, Keras, Tensorflow, and Scikit-learn
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3. Summary
• My academic and industrial experience focuses on utilizing sensor
data to analyze Spatio-Temporal (ST) phenomena
• ST phenomena: a phenomenon in a certain location and time such
as road conditions (static), trajectory of a vehicle (dynamic), etc
• The presented work focuses on utilizing sensors owned by common
public for development inexpensive monitoring applications.
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4. Outline of Presentation
1 Industrial Experience: Data Science Internships
2 Introduction to My Academic Experience
Motivation
Problem Formulation
3 PART 1: Using Participatory Sensing for Identification of Static ST
Phenomena
4 PART 2: Collaborative Identification & Tracking of Dynamic ST
Phenomena
5 PART 3: Understanding a Situation Happening in an Operational
Environment
6 Publications
4

5. Industrial Experience: Data
Science Internships

6. Data Science Internship: Veoneer, 2019:
Developing Object Detection Pipeline With Yolo
• Developed data-processing modules of ML pipeline and
tested Yolo Object detector on GPU Servers
Figure 1: Pedestrian Detection. Figure Credits [1]
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7. Machine Learning Internship: Veoneer, 2020:
Multi-level Unsupervised Machine Learning
• Developed a framework of Unsupervised ML for processing
GPS trajectories at different granular levels
• Implemented methods for performing multi-sensor data
fusion on the processed data.
Figure 2: Trajectory Data Processing [2]
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8. Introduction to My Academic
Experience

9. Objectives of My Academic Work
• My work focuses on analyzing sensor data to study different types of
Spatio temporal Phenomena
• Static ST phenomena does not change much in a short period of time,
e.g., road conditions, maps, roads, facilities, etc
• Dynamic ST phenomena can change within a short period of time, e.g.,
vehicle trajectory, etc.
• Participatory Sensing: individuals participating in sensing tasks when they
are available (often using their own sensing devices, such as phones,
vehicles, etc.)
• My work focuses on answering the following research questions:
• [Q1-Q2]: How can we identify static Dynamic ST phenomena respectively
using participatory sensing?
• [Q3]: How can we make sense of or understand a situation happening in
an operational environment?
• My research findings on [Q1] – [Q3] are organized under three parts.
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10. MOTIVATION: Why Should We Care?:
Improve Maintenance/Recovery Efforts
Figure 3: Participatory sensing utilized for effective monitoring and
surveillance. Figure Credits (Ushahidi)
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11. Why Should We Care?: Crime Prediction
• Mohler, G. O., M. B. Short, Sean Malinowski, Mark Johnson, G. E. Tita, Andrea L.
Bertozzi, and P. J. Brantingham. “Randomized Controlled Field Trials of Predictive
Policing.” Journal of the American Statistical Association 2015:
Figure 4: UCLA study shows predictive policing algorithms are successful in
the prediction of crime. Figure Credits (Predpol).
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12. ST Phenomena: Examples & Types
1 Static ST Phenomena may not change in a short period of time, such as road
conditions, cartographic maps, roads, facilities, and utilities
2 Dynamic ST Phenomena changes in a short period of time, such as the
trajectory of a vehicle
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13. General Research Problem
How can we utilize participatory sensing to analyze
ST phenomena?
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14. Relation Between Parts of Thesis
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15. PART 1: Using Participatory
Sensing for Identification of
Static ST Phenomena

16. Overview
• Road conditions were considered as the static ST phenomena
• Since road conditions do not change for multiple weeks, we can utilize data from
multiple participants collected at different times
Figure 5: Figure Credits [3] 13

17. My Approach
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18. Conclusion: Identification of ST Phenomena
• We presented the potential of ML based methods with a specific application
example to identify ST phenomena by using data from multiple participants
• The outcomes of this research are published in the following peer-reviewed
publications:
• Varun Garg, Brooks Saunders and Thanuka Wickramarathne, “Situational
Awareness with Ubiquitous Sensing: The Case of Robust Detection and
Classification of Targets in Close Proximity,” in Proc. Int. Conf. on
Information Fusion (FUSION), pp. 1-8, 2019.
• Thanuka Wickramarathne, Varun Garg and Peter Bauer, ”On the Use of
3-D Accelerometers for Road Quality Assessment,” in Proc. IEEE 87th
Vehicular Technology Conference (VTC Spring), pp. 1-5, 2018.
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19. PART 2: Collaborative
Identification & Tracking of
Dynamic ST Phenomena

20. Overview: Collaborative Identification & Tracking
Dynamic ST Phenomena
• Trajectory of a target vehicle is considered as Dynamic ST Phenomenon
• Participatory vehicle only sniffs the RF emission from the target vehicle
• Target vehicle is not communicating with the participatory vehicle
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21. Approach
• The Block Range from RSSI is beyond the scope of this thesis
• Assuming that the ranges are provided to the sensing system
• We focus on blocks Range Filter & Target Position Localization
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22. Experiment Scenarios
• Simulated in SUMO
• Average Target vehicle speed ≈ 7-30 miles/hour
• Sampling rate Fs = 10Hz
• Lane changes are allowed
• Scenarios:
• Different Urban scenario with traffic lights, and turn were simulated
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23. Conclusion: Collaborative Identification & Tracking of
Dynamic ST Phenomena
• We presented a novel method for range-based target vehicle localization using
RF emissions collected by participatory vehicles.
• Participatory sensing based methods can be utilized for developing new driver
assistance systems
• Results of this research were presented following publications:
• Varun Garg and Thanuka Wickramarathne, “A Unknown Vehicle Discovery,
Localization and Tracking via Signals of Opportunity with Encoder-Decoder Networks
for Trajectory Prediction Under Signal Decay/Loss,” in Proc. IEEE Systems, Man,
and Cybernetics Society (SMC), March 2022, Accepted
• A. Wyglinksi, T. Wickramarathne, D. Chen, N. Kirsch, K. Gill, T. Jain, Varun Garg,
T. Li, S. Paul, and X. Zhang, “Phantom Car Attack Detection Via Passive
Opportunistic RF Localization,” IEEE Access, 2023
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24. PART 3: Understanding a
Situation Happening in an
Operational Environment

25. Overview
Figure 6: Figure Inspired from [4, 5]
• Basic Terminology
• Observation: sensor output having time, event signature & location
• Signature: an indicator of an event, e.g., loud sound
• Event: a singular activity related to an underlying situation, e.g., shouting
• Situation: described by a sequence of events, e.g., a protest 20

26. My Approach
Signature
Identification
Location
Data
HDS Data
Streams
Open Source
Data
Observations
for
𝜏
k
Spatial
Clustering
Anchor
Points
Generation Gating
Event-Set
Identification
Model
Event-Set
output for 𝜏k-1
Event-Set
output for 𝜏k
Situation
Prediction
Model
Predicted
Situation ID
Historical Data
Automated
Information Retrieval
Training Set for Event-Set
Identification and Scenario Prediction
Data Pre-Processing Cluster Evolution Event-Set Identification Situation Prediction
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27. Conclusion: Understand a Situation Happening in an
Operational Environment
• We have demonstrated the potential of ML methods in the surveillance domain
• Use of participatory sensing will likely have a major impact on surveillance
applications
• Outcomes of this research are published in the following peer-reviewed
publications:
• Varun Garg and T. L. Wickramarathne, ”ENSURE: A Deep Learning Approach for
Enhancing Situational Awareness in Surveillance Applications With Ubiquitous
High-Dimensional Sensing,” in IEEE Journal of Selected Topics in Signal Processing,
vol. 16, no. 4, pp. 869-878, June 2022
• Varun Garg, Brooks Saunders, and Thanuka Wickramarathne,”Making Sense of It All:
Measurement Cluster Sequencing for Enhanced Situational Awareness with Ubiquitous
Sensing,”in Proc. Int. Conf. on Information Fusion (FUSION), pp. 1-7, 2021.
• Brooks Saunders, Varun Garg and Thanuka Wickramarathne, ”Simulated Evaluation
of Ubiquitous Sensed Situational Awareness Systems, ”in Proc. Int. Conf. on
Information Fusion (FUSION), pp. 1-7, 2019.
22

28. Publications

29. Peer-Reviewed Publications: Conferences
Published:
• Varun Garg, Brooks Saunders, and Thanuka Wickramarathne,”Making Sense of It All:
Measurement Cluster Sequencing for Enhanced Situational Awareness with Ubiquitous
Sensing,”in Proc. Int. Conf. on Information Fusion (FUSION), pp. 1-7, 2021.
• Varun Garg, Brooks Saunders and Thanuka Wickramarathne, “Situational Awareness with
Ubiquitous Sensing: The Case of Robust Detection and Classification of Targets in Close
Proximity,” in Proc. Int. Conf. on Information Fusion (FUSION), pp. 1-8, 2019.
• Brooks Saunders, Varun Garg and Thanuka Wickramarathne, ”Simulated Evaluation of
Ubiquitous Sensed Situational Awareness Systems, ”in Proc. Int. Conf. on Information
Fusion (FUSION), pp. 1-7, 2019.
• Thanuka Wickramarathne, Varun Garg and Peter Bauer, “On the Use of 3-D
Accelerometers for Road Quality Assessment,” in Proc. IEEE 87th Vehicular Technology
Conference (VTC Spring), pp. 1-5, 2018.
23

30. Peer-Reviewed Publications: Journals
Published:
• Varun Garg and T. L. Wickramarathne, ”ENSURE: A Deep Learning Approach
for Enhancing Situational Awareness in Surveillance Applications With
Ubiquitous High-Dimensional Sensing,” in IEEE Journal of Selected Topics in
Signal Processing, vol. 16, no. 4, pp. 869-878, June 2022
• A. Wyglinksi, T. Wickramarathne, D. Chen, N. Kirsch, K. Gill, T. Jain, Varun
Garg, T. Li, S. Paul, and X. Zhang, “Phantom Car Attack Detection Via Passive
Opportunistic RF Localization,” IEEE Access, Jan, 2023
24

31. References i
B. Ghari, A. Tourani, and A. Shahbahrami, “A robust pedestrian
detection approach for autonomous vehicles,” 10 2022.
Y. Zheng, “Methodologies for cross-domain data fusion: An
overview,” IEEE Transactions on Big Data, vol. 1, no. 1, pp. 16–34,
2015.
J. Ahn, Y. Wang, B. Yu, F. Bai, and B. Krishnamachari, “Risa:
Distributed road information sharing architecture,” 2012 Proceedings
IEEE INFOCOM, pp. 1494–1502, 2012.
S. A. Shah, D. Z. Seker, S. Hameed, and D. Draheim, “The rising
role of big data analytics and IoT in disaster management: Recent
advances, taxonomy and prospects,” IEEE Access, vol. 7,
pp. 54595–54614, 2019.
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32. References ii
Mauro Di Pietro, “Clustering geospatial data.”
https://towardsdatascience.com/
clustering-geospatial-data-f0584f0b04ec, 2020.
”Accessed: 2021-01-06”.
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33. Thank You
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