People and Topics

2019 Fall Schedule

All meetings are held in EE 013.








Software Engineering

Image Database


Human Behavior




Embedded 2


Embedded 1



VIP lecture (EE 129)

Due to the large number of team mebers, it is not possible changing the regular meeting time. If your schedule does not fit into a particular team, you have to move to another team.


Forest Inventory Analysis

Use computer vision to calculate the sizes of trees (called diameter at breath height, or DBH), recognize the species of trees, and their locations. For Fall 2019, the team has two major goals: (1) handle multiple trees in a single frame and (2) handle trees in a nautral forest.

The following images show the result from a distance sensor and the tree image (before and after denoising).

forest03 forest04 forest05

The following images show how the team measures the diameter at breath height.

forest00 forest01 forest02

Readings for new members:

Analysis of Drone Video

This project creates computer vision solutions recognizing objects captured by cameras mounted on drones. In Fall 2019, the team will create a set of video clips for the following purposes:

  • Construct three-dimensional geometries of objects: The video clips will capture cardboard boxes of different sizes, together with a wide range of objects and several with known sizes.

  • Detect and track multiple moving objects: The clips include moving objects. The drone itself is also moving. The purpose is to correctly identify these objects and track their movements.

  • Segmentation: Create pixel-wise labels of different objects.

  • Re-identify people: Determine whether the same person has been before.

This project is supported by NSF CNS-1925713

Readings for new members:

Embedded Vision 1

Recent progress in computer vision has focused primarily in general-purpose object detection using datasets with many (hundreds) categories of objects (such as humans, dogs, vehicles, furniture, buildings, etc.). For many applications, however, the number of possible objects can be limited. For example, inside an airport terminal, elephants or eagles are not expected. This project will use computer graphics to synthesize images and videos of these scenarios. The synthesized data is used to train computer vision running on embedded systems (also called edge devices). Doing so can reduce network traffic and make the system more scalable. Moreover, sensitive information (such as human faces) may be detected and protected before the data leaves the cameras.

Readings for new members:

Analyze Human Behavior in Video

The purpose of this team is to use real-time video analytics to detect dangerous behavior or safety violation in workplace (such as factories), raise alerts to prevent injury, or provide post-event analysis to prevent future occurrences. In Fall 2019, the team will focus on solving these problems in an indoor environment with multiple cameras:

  • Where are the people (including re-identifying the same person in different cameras)?

  • Where does each person face?


Software Engineering for Machine Learning

This project creates a process for developing reproducible software used in machine learning. In Fall 2019, the team’s focus is to create tools that faciliate code review. The tools analyze the histories of version control repositories and automatically identify possible defects within a pull request. The tools will also collect metrics relating to the code review.



Computer vision is still not perfect and humans outperform computers in many situations. This team builds computer tools (human interfaces) for humans to identify unexpected properties (called “bias”) in data used to train computer programs. These tools are computer games and the players (crowds) describe the characteristics in the data.

Reading for new members:

crowdsource03 crowdsource02

crowdsource05 crowdsource04

Image Database

This system integrates computer vision and database. After the objects in images are detected, the information is stored in a database so that it is searchable. The team has built a prototype of the system processing multiple video streams simultaneously. The team will focus on improving the performance (scalability) for lower latency as well as investigating new storage systems.

Reading for new members:

Embedded Vision 2

This project investigates computer vision solutions that can perform the following tasks in an embedded computer (small enough to be inside a typical camera)

  • Obtain aggregate information (such as the number of people and their genders)

  • Detect faces

  • Encrypt the faces before sending the data to storage

The sensitive data (faces) never leaves the camera. Only authorized people with the decryption key can see the faces. The concept is illustrated below.


Readings for new members:


David Michael Barbarash

Landscape Architecture, Purdue

Dave Cappelleri

Mechanical Engineering, Purdue

Shuo-Han Chen

Institute of Information Science, Academia Sinica

Yung-Hsiang Lu

Electrical and Computer Engineering, Purdue

Vinayak Rao

Statistics, Purdue

Guofan Shao

Professor, Forestry and Natural Resources, Purdue

George K. Thiruvathukal

Computer Science, Loyola University Chicago.

Mark Daniel Ward

Statistics, Purdue

Keith E. Woeste

Forestry and Natural Resources, Purdue

Ming Yin

Computer Science, Purdue


Graduate Students

Abhinav Goel: Doctoral Student, Improve Neural Networks’ Energy Efficiency

Sara Aghajanzadeh: Master Student, Detect Faces and Protect Privacy

Ryan Dailey: Master Student, Discover Network Cameras

Caleb Tung: Doctoral Student, Using Contextual Information from Network Cameras to Improve and Evaluate Computer Vision Solutions

Undergraduate Students and 2019 Fall Teams

Image Database

Manage images and video so that they can be searched.

Sripath Mishra


Akshay Pawar


Aditya Chakraborty

Hojoung Jang

Ethan Lee

Vina Nguyen

Drone Video

Create datasets of drone video, recognize objects, estimate the sizes.

Karthik Maiya


Jacob Harmon


Avanish Subbiah

Rufat Imanov

Katherine Sandys

Justin Qualley

Victor Oduduabasi

Embedded Vision 1

Detect and encrypt faces to protect privacy using embedded computer.

Nobelle Tay


Alex Xu


Raghu Selvaraj

Siddharth Srinivasan

Uday Thapar

Andrew Liu

Peter Huang

Youngsik Yoon

Vaastav Arora

Abhinav Goel

Doctoral Student

Embedded Vision 2

Identify the specific features (called distinctiveness) of different visual dataset. Use one dataset with many labels to help train machine models for another datasets with few labels.

Ashley Kim


CAM² Leader

Li Yon Tan


Isha Ghodgaonkar

Fischer Bordwell

Kirthi Shankar Sivamani

Shuhao Xing

Ziyad Alajmi

Jackson Moffet

Arnav Ballani

Chiche Tsai

Caleb Tung

Doctoral Student

Ryan Dailey

Masters Student

Teaching Assistant

Crowdsourcing for Data Bias

Use crowdsourcing to identify unintended bias in visual data and label data.

Xiao Hu


Haobo Wang


Somesh Dube

Kaiwen Yu

Gore Kao

Anirudh Vegesana

Forest Inventory

Use computer vision to recognize tree species and estimate their sizes.

Yezhi Shen


Minh Luong Nguyen


Nick Eliopoulos

CAM² Leader

Ya Ling Tsai

Carolyn Tustin

Zoe Yang

David Jarufe

Human Behavior in Video

Understand human behavior and obtain aggregate information from video.

Moiz Rasheed


Ethan Glaser


Wenxi Zhang

Taher Dohadwala

Xiangyu Zhang

Amogh Shanbhag

Seungjoon Rhie

Sara Aghajanzadeh

Masters Student

Software Engineering

Create procedure for developing high-quality and reproducible software.

Noah Curran


Weiqing Huang

David Wood

Connor Chadwick

Ryan Firestone

Akhil Chinnakotla

Esteban Gorostiaga Zubizarreta

ZhiFei Chen

Midori Kisanuki


Achinthya Soordelu

James Lee

He Li

Anthony Fennell

Jenil Patel

Ehren Marschall

Fengjian Pan

Nirmal Asokan

Sanghyun Cho

Shengli Sui

Woojin Kim

Ajay Gopakumar

Jiancheng Wang

Sitian Lu

Juncheng Tang

Milos Malesevic

Mina Guo

Hanyang Liu

Zhenming Zhang

Zaiwei Zhang

Jiaju Yue

Huanyi Guo

Jeanne Deng

Zhenming Zhao

Anthony Kang

Qingshuang Chen

Yuhao Chen

Borui Chen

Sriram Rangaramanujan

James Tay

Kyle Mcnulty

Seth Bontrager

Pranjit Kalita

Subhav Ramachandran

Everett Berry

Erik Rozolis

Bolun Zhang

Andrew Green

Yukun An

Daniel Dilger

Yexin Wang

Zhifan Zeng

Joseph Sweeney

Ryan Schlueter

John Laiman

Jay Patel

Yutong Huang

Yuxiang Zi

Zhanxiang Hua

Weizhi Li

Yash Pundlik

Ramyak Singh

Nanxin Jin

Kyle Martin

Hao Zou

Sam Yellin

Wenzhong Duan

Aparna Pidaparthi

Changyu Li

Deepika Aggrawal

Hanwen Huang

Hussni Mohd Zakir

Sihao Yin

Weiqing Huang

Christopher Jovanovic

Ahmed Kaseb

Wengyan Chan

Meera Haridasa

Deeptanshu Malik

Vadim Nikiforov

Matthew Fitzgerald

Youngsol Koh

Mehmet Alp Aysan

Cailey Farrell

Yifan Li

Lucas Neumann

Robert Gitau

Zhi Kai Tan

Spencer Huston

Mohamad Alani

Lucas Wiles

Yuxin Zhang

Chau Minh Nguyen

Shunqiao Huang

Minh Nguyen

Dhruv Swarup

Video by Current and Former Members