USF CS Night 2017: Poster abstracts

posted Dec 7, 2017, 8:39 AM by Rosa Maria Garay   [ updated Dec 7, 2017, 9:28 AM ]

·         Validating and Restructuring Voter Data for Effective Marketing

Presenters: Chaitanya Mattey, Melanie Baybay, Neha Bandal

Sponsors:  Jude Barry and Christopher Weiss, VoterPros

Faculty Advisor:   CS 690 Master's Project, Prof. Olga Karpenko

VoterPros is a web-based service that provides candidates with direct-mail marketing tools for various levels of government election anywhere from local school board to statewide elections. Unfortunately, voter data is messy and dynamic as district boundaries often change. Further, the process for retrieving such data from the data provider is incredibly slow. We introduce two solutions: dynamic outlier detection of voter data and a cache to store previous user requests which is achieved by data restructuring. With these additions, the VoterPros web-service is more robust and user-friendly.

 ·         Activity Analyzer By Using Sensor Data

Presenters: Anjani Bajaj, Bhargavi Kommineni, Surada Lerkpatomsak

Sponsor: Jim Huang

Faculty Advisor: CS 690 Master's Project, Prof. Olga Karpenko

In this project, we have built an end-to-end system that collected raw accelerometer sensor data from any Android based smartphone for applying the best predictive classifier model that infers if a person is walking or running. The data collection system can scale to automate data aggregation and processing to support any number of data contributors towards the data model pipeline workflow. The native Android client allows the user to generate labeled (walking/running) accelerometer time-series data that gets uploaded directly to our data collection backend system. We visualized the collected data and generated features for the same, which were used to construct and evaluate several classifiers. This full end-to-end system can allow us to further extend future data analysis with more ease.


·         Design and Development of a New Modality for Unified Scalable Processing in SnapLogic

Presenters: Chengcheng Wang, Yiding Liu, Dingyi Chen

Sponsor: Greg Benson, SnapLogic

Faculty Advisors: CS 690 Master's Project. Prof. Greg Benson, Prof. Olga Karpenko

To date, SnapLogic approaches to data intensive processing have introduced modality in the user-visible programming model. The goal of this project is to develop a proof-of-concept implementation to show how we can use the core of Apache Flink combined with the SnapLogic Standard mode data model and expressions to efficiently support application integration, data intensive processing and streaming computation in a unified manner. We implemented SnapLogic pipeline in both native Flink and integration programs of Flink and SnapLogic code, and compared their performance. Our results demonstrate that it is feasible to use Apache Flink and also support the Standard Mode data model and expressions of SnapLogic. In this way, we can benefit from Flink’s novel de/serialization, memory management, scalable batch computation, and scalable streaming computation.


·         PagerDuty Incident Dashboard

Presenters: Alec Hsu, Liang Wang, Ethan Wilcox

Sponsor: Richard Just, Twitter

Faculty Advisor: CS 690 Master's Project, Prof. Olga Karpenko

The PagerDuty Incident Dashboard is a web-based visualization tool integrated with PagerDuty, an incident aggregation and dispatch service.  PagerDuty aggregates logs from monitoring tools into actionable incidents, and alerts on-call engineers based on specified management policies.  The dashboard serves as a tool to aggregate and filter PagerDuty incident data and display temporal trends.  It focuses on long-term trends based on both organization categories and temporal groupings.  A user can choose various teams, services, and escalation policies to group together in a custom organization, and contrast and compare incident counts and averages for those chosen metrics.


·         Jenkins Cluster Management

Presenters: Gauri Joshi, Priyam J. Patel,  Rushabh Shah, Siwadon Saosoong

Sponsor: Bernhard Gass, Twitter

Faculty Advisor: CS 690 Master's Project, Prof. Olga Karpenko

Jenkins Cluster Management deals with different tools such as Rundeck, Jenkins, Docker, Tomcat and RESTful web services. It is considered as a tool used to automate various tasks in a company environment, which requires less man power. You can automate various tasks such as running jobs on Jenkins via Rundeck, moving of jobs, slaves from one Jenkins master to another.


·         Better Cave Surveying

Presenters: Mathieu Clément, Rohith Madhavan

Sponsor: John Billings

Faculty Advisor:  CS 690 Master's Project, Prof. Olga Karpenko

In this project we built a relatively inexpensive 3D imaging system, using components such as a LiDAR (Light Detection and Ranging) sensor, servomotors, a rotary encoder and a microcontroller. We then assembled the data gathered on the field (e.g. from caves) into a point cloud and used that to create a mesh that can be visualized in a viewer application that we developed.


·         Lunisolar

Presenters: Aishwarya Chandrashekhar, Bindu Balasubramanian

Sponsor: Jon Rahoi

Faculty Advisor: CS 690 Master's Project, Prof. Olga Karpenko

The lunar and solar calendars are widely available, but there is no provision to view them in a unified way. Hence it becomes difficult to understand how the dates and holidays from each calendar relate to each other. Also, In today’s world, it is crucial to have a global mindset. To ensure team collaboration and organizational effectiveness globally, it is important for employees to enhance their awareness of global workplace cultures. The website we developed for this project provides a unified way to visualize all the lunar and solar holidays on a circular Gregorian calendar and allows users to go forward and backwards through the years.


·         Investigating Full-Body Embodiment in Virtual Reality with Physiological Signals

Presenters: Yi Yang, Bingkun Yang

Sponsor: Prof. Beste Yuksel

Faculty Advisors:     CS 690 Master's Project, Prof. Olga Karpenko

We investigate the effects of full-body enabled tracking in virtual reality on users by analyzing their physiological signals using electrodermal activity and electrocardiography. Our preliminary results suggest that users may be responding differently physiologically to different conditions in virtual reality.


·         Property Management Website

Presenters: Fu Tan, Omer Akin

Sponsor: Jose Alvarado

Faculty Advisor: CS 690 Master's Project, Prof. Olga Karpenko

Property Management Website is a all-in-one platform that serves both property managers/owners and renters. On our website, property managers are able to create and list their properties for rent, receive and review renter applications, charge renters different kinds of fees, receive maintenance requests from renters, as well as visualize revenues and expenses. Renters can search and apply for properties, send maintenance requests to property managers, check a list of payment requests and pay them through the website.


·         Influential: An Influencer Marketing Platform

Presenters: Jiali Ding,  Zhenchao Zhang,  Tuo He, Jinjian Guo

Sponsor: Jose Alvarado

Faculty Advisors: CS 690 Master's Project, Prof. Olga Karpenko

Influential is an online platform for brand marketers to manage campaigns, cooperate with regional directors, build connection with social media influencers as well as consumers, and automate payments. It supports multiple roles and provides a full set of functionalities for marketers to implement influencer marketing, including a real-time messaging service. It also offers consumers a channel to voice their opinions by allowing them to comment on reviews written by influencers. Influential currently focuses on restaurants, but can be easily extended to other domains.


·         Trip Sharing App

Presenters: Xue Kang, LingHsin Hsu, Ruiling Yuan

Sponsor: Scott Zhu

Faculty Advisors: CS 690 Master's Project, Prof. Olga Karpenko

Many people who commute through the city and travel around the world have similar itineraries. There are many benefits of sharing travel plans: it allows users to find companions, make use of carpooling, split travel costs,  or share space in the car trunk. We built a Trip Sharing iOS application which helps users make the most of the shared itinerary information. With our trip-sharing app, users are able to create trips, search for trips, ask to join a trip or get feedback from a trip owner.


·         Appointment Scheduling Website

Presenters: Brent Rucker, Sherry Feng, Karen Diaz Paucar, Marbo Cheng Ye, Catherine Yu

Sponsor: Jose Alvarado

Faculty Advisor:  Prof. Beste Yuksel, CS490

Independent contractors find it difficult to promote their services (such as photography, modeling, plumbing, etc.) and availability to the public. It is also challenging for the general public to find short-term services. Independent contractors and the general public turn either to the different social media channels, job search engines, or Craigslist, to either find short-term services or promote their own. Subsequently, matching the availability, finding the service needed, and tracking the payment can be quite a challenge.  Our appointment scheduling website allows independent contractors to make their services and availability accessible and viewable to the public, and enables people to book an independent contractor.


·         Rotation Scheduler

Presenters: Jeremy Kerby, Arseniy Novitskiy, Code Cole, Amanda Fimbres

Sponsors: Richard Just and Remy DeCausemaker, Twitter

Faculty Advisor:  Prof. Beste Yuksel, CS490

Rotation Scheduler is a scheduling tool that will intelligently determine who will be on duty for a task. To help with this, we are using a third party service called PagerDuty and their API. Rotation Scheduler is a Python application that implements a MySQL database, user interface, and communicates with a third party API. Furthermore, we have laid the ground work to implement a genetic algorithm in the future. This application has a variety of features and allows for customizable user settings.


·         Food Waste Reporter

Presenters: Mohamed Elafifi, Mitchell McPartland, Max Sciarra

Sponsor:  Mick Washo and Richard Hsu

Faculty Advisors:  Prof. Beste Yuksel, CS490

Food Waste Reporter is an online web application that was created for the USF branch of the Food Recovery Network.  The Food Recovery Network at USF recovers leftover food from the USF campus and donates this food to various shelters in the Bay Area.  Written in Javascript and utilizing frameworks such as React, Node, and Express, this application will be used to collect, store, and process "food waste" data that is reported by the users.  The administrators of the USF branch of the Food Recovery Network will use our application to view and edit all of the "food waste" data that is stored in our database.  The purpose of this project is to provide a platform for the USF Food Recovery Network in which users can report food waste to the FRN directly and seamlessly.


·         Pants Rebuild Refactor

Presenters: David Katz, Denali Marsh, Edward Ra, Michael Tran

Sponsor: Yi Cheng, Twitter

Faculty Advisors: Prof. Beste Yuksel, CS490

Our team is contributing to an open source project called Pants with Twitter being the main contributor. Pants, a build system designed to set up large codebases for engineers, impacts not only Twitter, but also Medium, Foursquare, Square, etc. With the help of our sponsors and mentors, Yi Cheng, Nick Howard, Ity Kaul, and Daniel Wagner-Hall, we are adding new features to the tool. This happens through public code reviews on Github, where our sponsors (and anyone) have the opportunity to suggest improvements. If our work looks good, then a mentor merges our code, and we become official contributors to Twitter! As for the features themselves, interaction with Pants happens entirely through the command line. Therefore, we have gradually added new command line options to interact with Pants. So far, we have worked mostly in Python and Go, but also in other languages like Java and Scala since Pants supports a multitude of formats. Overall, learning about the Pants codebase and the open source process has proved highly rewarding and invigorating!


 ·         Open Source Metrics

Presenters: Casey Haber, Gordon Li, Nyssa Chennubhotla, Miguel Arreguin

Sponsor: Remy DeCausemaker

Faculty Advisor: Prof. Beste Yuksel, CS490

The Twitter Open Source Team is focused on the creation of a simple yet powerful tool for assessing the health of open source projects. We are creating a better system for driving community involvement and increasing overall code health. Metrics dashboards exist but can be heavy, cluttered and rarely provide the quick facts and data that are needed to drive towards healthier software projects. We are building a lightweight and cost-free visualization based report system that leverages the GitHub API and can be deployed on GitHubPages as a static Javascript bundle. Our report is broken up into a four part narrative: Discovery, Usage, Retention and Activity. Based on these metric categories and the real time comparison of software projects, our report will inform and help drive a better understanding of your Open Source project.


·         Designing A Difference

Presenters: Malachy Lin-Nugent, Jay Ng, Sonyu Liu, Hongjiang Qiang

Sponsor: Calvin Liang,  Designing a Difference

Faculty Advisor: Prof. Beste Yuksel, CS490

The project will help automate Designing a Difference's supply chain service that links local retailers and fashion designers with apparel manufacturers. Currently communication and order requests between the two parties are done manually (pen and paper). With the e-commerce website that we built the process will be much faster, and Designing a Difference will be able to scale its services to be able to handle a greater number and magnitude of orders.


·         Investigating Affect in Learning Through Posture and Gesture Detection

Presenter: Shengcai Cheng

Faculty Advisor: Prof. Beste Yuksel, Directed Study

We investigate the detection of engagement, boredom, and frustration during learning through posture and gesture detection using computer vision techniques. We use a Microsoft Kinect to measure if the learner is leaning to the left or the right, near or far, or moving. We also detect whether they are raising their hand to their face. We compare our predictions to learners’ subjective self-reports of their affective states.


·         UX Design Website Prototype "Bakeology"

Presenters: Lorina Dzhamankulova, Prescott Carlson, Eve Jonas

Faculty Advisor: Prof. Jeff Johnson, CS486

Our team is working on the website “Bakeology”, which is a social network for baking enthusiasts that allows them to discover new ideas and recipes with fellow bakers. The goal is to connect people of all ages with the same interests and passion for baking. Initially, we oriented towards an older female audience, but later tests proved that the range of our target audience is much bigger and includes more people of different backgrounds who should be interested in using it. As the result, we now know that we need to build a website that would be engaging and user friendly for everyone. In order to achieve this, we have created our first prototype of our product that will be improved based on the test results and feedback from the users tests that we will perform with participants of different age and backgrounds.


·         Files with Friends

Presenters: Aishwarya Chandrashekhar, Mathieu Clément

Faculty Advisor: Prof. Jeff Johnson, CS686

In this day and age of the digital era, file storage and file sharing are a major use case for most cloud services. Our app aims to allow friends to share files (documents, music, images) between each other and retrieve them later. The “friends” use instant messaging to let others know about new files. Alternately, users will also be notified when a file is shared with them.


·         DMV Appointments Redesign           

Presenters: Neha Bandal, Rohith Madhavan

Faculty Advisors: Prof. Jeff Johnson, CS686

The existing process for scheduling appointments on DMV App is cumbersome and the idea is to streamline the process and make it more user friendly. In the new prototype, we are proposing step by step process to schedule appointment which is easy to follow and more interactive.


·         Simple Raspberry Pi Clusters

Presenters: Derek Dang, Alec Taggart

Faculty Advisor: Prof. Greg Benson, CS 398 Raspberry Pi Cluster Design

A cluster is a group of computers connected together via a network. Clusters are used to run parallel and distributed programs, provide distributed services, and are the basis for cloud computing. We have developed cluster software to make it extremely easy to configure and run a cluster of Raspberry Pi computers. Our software provides a platform to explore parallel and distributed concepts and implementations, especially in the context of a class. We believe with our software and low-cost Raspberry Pi Zero computers it is feasible for every student to have their own cluster for projects and coursework. Our implementation results in two modified images, which can be generated from the official Raspbian pi-gen tool, the server and client, to easily boot up a fully configured cluster. For more information, visit:


·         The Design and Implementation of Containers for xv6

Presenters: Marcus Chong and David Katz

Faculty Advisor: Greg Benson (CS 326 Operating Systems)

The xv6 operating system is a reimplementation of Unix Version 6 for x86 processors and the PC architecture from MIT that we use in the CS 326 Operating Systems class at USF. Containerization allows operating systems to fully isolate an execution environment that is lighter weight than full virtualization.  Containers can fully isolation the process namespace, the file system, and physical resources such as CPU time and memory. For service deployment, containerization greatly benefits engineering organizations that create multiple products on the same host. On the individual scale, containerization helps protect your files and data from malicious or buggy behavior while also simplifying the packaging of software dependencies. While pioneered primarily at Google, we now see widespread use of containers using Docker. Our xv6c project extends xv6 to support containerization. We have file system and process isolation, as well as fair scheduling and some additional features.


·         Predicting Phenotype from Genotype with Machine Learning

Presenter: Rob Reeves

Faculty Advisor: Prof. Patricia Francis-Lyon, Bioinformatics

Genomic variants such as Single Nucleotide Polymorphisms (SNPs) are known to be a major factor influencing many physical traits, diseases, and other phenotypes. With the rise of economical DNA sequencing/genotyping services such as 23andMe, publicly available genomic data is growing exponentially. This presents an opportunity to use genomic data for health risk assessments and predictive analytics.

This project applies supervised machine learning, without domain knowledge, to publicly available genomic data to predict a phenotype from SNP values alone, and identify SNPs and their interactions that are important to the disease or trait. The code base was structured and engineered according to best practices for ease of use by citizen scientists who can apply it to the prediction of a variety of diseases or traits. As a proof of concept this project predicted eye color with 89% accuracy and succeeded in identifying from ~1 million SNPs those that are most influential to eye color prediction. All genes known to be influential in eye color were detected, along with a known polygenic interaction. The next step is to apply this technique to identify novel SNPs and their interactions in predicting a phenotype such as Schizophrenia.


·        Simulating a large dataset of ECG readings for MS training site

Presenters: Anjani Bajaj, Rushabh Shah, Max Alfaro

Sponsor: Dr. Robert Horton, Microsoft

Faculty Advisor: Prof. Patricia Francis-Lyon, Bioinformatics

Our project is to simulate electrocardiography (ECG) data for use in health care analytics demonstrations and exercises. We started by writing a Shiny app to scan through an ECG dataset to find good examples of ECG waveforms, then wrote a second Shiny app to fit a parameterized curve to a selected waveform. By fitting the simulated waveform to the peak locations generated by a rhythm simulator developed in an earlier student project, we will be able to generate simulated ECG data that contains statistical signals in both heart rate and heart rate variability. We have also developed functions to compactly encode this data in JSON format for transmission to the cloud.


·         Uncovering Bias in Deep Learning Model for Disease Prediction

Presenter: Melanie Baybay, Chaitanya Mattey, Alec Hsu

Sponsor: Tom Brander, Influence Health Inc.

Faculty Advisor: Prof. Patricia Francis-Lyon, Bioinformatics

We aim to improve Influence Health’s Deep Learning Model for Disease Prediction. The output from this model is analyzed by searching for insights and patterns to inform better adjustments and improvements in future iterations of the Model. These improvements are achieved by analyzing the demographics and disease history of various patients. We observe cases where the model performs extremely well, cases where certain diseases are confused with each other and used this information to improve the next iteration of the model.


·         Investigation of TCGA in search of therapy target for triple negative breast cancer

Presenters:  Luika Timmerman, Rashmi Manjunath

Sponsor: UCSF Helen Diller Family Comprehensive Cancer Center

Faculty Advisor: Prof. Patricia Francis-Lyon, Bioinformatics

There is a subtype of breast cancer that is associated with aggressive progression, high levels of recurrence and the affliction of younger women. These are known as triple-negative/basal-like breast cancers (TNBC), as they test negative for expression of three proteins, two of which are the target of therapeutics in common use in the breast cancer clinic (estrogen, progesterone and HER-2 receptors). A specific therapeutic target has not yet been found to treat TNBC and prognosis remains poor for women with these tumors. In an effort to identify such a target for triple-negative breast cancer (TNBC), the Timmerman lab at the UCSF Helen Diller Cancer Center is working to gain an understanding of the mechanisms and genomic relationships involved in unregulated cellular proliferation in TNBC. Potential targets have been identified for the development of drugs that target tumor metabolism.

As part of that effort, bioinformatics investigations utilizing genomic and proteomic data to gain insights that might be actionable in the treatment of breast cancer were conducted. The TCGA breast cancer dataset was explored for patterns of genomic alterations and expression that are associated with breast cancer subtypes, noting particularly how these differ with TNBC as opposed to other breast cancer subtypes.  A principal components analysis has been conducted of expression levels of PAM50 genes in the cancers of 1208 patients, as well as PCA analysis of the differential expression of these genes in the tumor vs normal breast tissues of patients.  Exploration of the potential targets identified by the Timmerman lab have been conducted. Additionally, clustering techniques have been employed to infer missing subtypes so as to augment the basal-like category in the TCGA breast cancer dataset for future analysis.

Assistant Professor, Tenure-Track Department of Computer Science, University of San Francisco

posted Aug 31, 2017, 8:38 AM by Rosa Maria Garay   [ updated Aug 31, 2017, 8:41 AM ]

The Department of Computer Science at the University of San Francisco is accepting applications for two tenure-track Assistant Professor positions starting in August 2018.  This search seeks candidates from the field of Computer Science or a closely-related field.

The Department of Computer Science is a diverse and growing community with 10 full-time faculty members, over 200 undergraduate CS majors, and over 70 masters students. While we are growing, the CS department maintains small class sizes with 30 or fewer students, project-based learning, and a high degree of student-teacher interaction.

The department is highly collaborative both within and outside the department. In addition to an undergraduate CS major and a masters program in CS, the department also supports a new interdisciplinary undergraduate major in Data Science as well as a masters program in Analytics. Several faculty members are NSF funded and collaborate with industry in Silicon Valley and the San Francisco Bay Area. 

The University of San Francisco is located in the heart of one of the world’s most innovative and diverse cities, and is home to a vibrant academic community of students and faculty who achieve excellence in their fields. Its diverse student body enjoys direct access to faculty, small classes and outstanding opportunities in the city itself. USF is San Francisco’s first university, and its Jesuit mission helps ignite a student’s passion for social justice and a desire to “Change the World From Here.” For more information, visit USF has been recognized as a top 20 entrepreneurial research university (Forbes 2015).

Job Responsibilities:  The successful candidates will be expected to teach both undergraduate and graduate courses, maintain an active research program that involves students, and perform service duties to the CS department and university.  The standard teaching load for this position at USF is two 4-unit courses per semester with an additional third course every fourth semester (2-2-2-3 over two years). 


Minimum Qualifications:  Applicants must have a Ph.D. in Computer Science or a closely-related field  completed prior to August 2018.  Strong applicants from all CS sub-disciplines will be considered. Applicants must demonstrate both exceptional teaching ability  and a strong potential for independent and collaborative research.   In addition, an understanding of and commitment to support the mission of the University of San Francisco are required.

To apply, please submit a CV, teaching statement, diversity statement, research statement, cover letter, and three letters of reference to To receive full consideration application must be complete and submitted by January 2, 2018. Please send questions regarding this position to

The University of San Francisco is an Equal Opportunity and Affirmative Action Employer.  The university does not discriminate in employment, educational services, or academic programs on the basis of an individual’s race, color, religion, religious creed, ancestry, national origin, age (except minors), sex, gender identity, sexual orientation, marital status, medical condition (cancer-related and genetic-related), disability, or other bases prohibited by law, and will provide reasonable accommodations to individuals with disabilities upon request.  We particularly encourage minority and women applicants for all positions.

USF CS Night 2016: Student Projects

posted Dec 8, 2016, 10:34 AM by Rosa Maria Garay   [ updated Dec 13, 2016, 12:05 PM ]

Following are some of the student projects presented at USF CS Night 2016. Projects were presented from
the Masters and Senior Project courses, the Bioinformatics course, and from some faculty-sponsored projects

Using Animation to Alleviate Overdraw in Multi-class Scatterplot Matrices

Presenter:                 Helen Chen (

Faculty Advisors:      Profs.Sophie Engle and Alark Joshi

Scatterplots are a widely-used technique for visualizing multivariate datasets. Even though scatterplots play an important role in data visualization,
they have known issues with overdraw. Overdraw occurs when points or glyphs are drawn on top of each other and obscure the underlying data.
Overdraw affects the ability of viewers to correctly understand the data distribution and discern relationships among subgroups of the data.
There are a variety of techniques for alleviating overdraw, none of which involve animation. Our research aims to use animation to visualize
multidimensional data for multi class scatterplot matrices and compare its efficacy in alleviating overdraw against that of other techniques.   

Student Record Verification App - A Decentralized Application

Presenters:                Mayank Thirani 
                                     Ryan Zhu
                                     Jakob Tarnow

Sponsor:                     Jim Huang

Faculty Advisors:     CS 690 Master's Project, Prof. Olga Karpenko

The Student Record Verification App will utilize the characteristics of Blockchain to solve the trust problem between recruiters from human resource
departments around the world needing to verify the applicants' claim of authenticity of their education degree without relying on intermediary third party
making that verification. Each transaction in the Blockchain is verified by consensus of a majority of the participants in the network. The Blockchain contains
all verifiable student records and past transactions. Allowing a recruiter to validate that a student’s educational background matches that of it’s respective
registrar via Blockchain, removes the need for a central entity and leads to faster attestation of student records.

Ten-X Hackday Tool

Presenters:               Jeremiad Raymond
                                    Teng Hu
                                    Yi Xiao

Sponsor:                   Jon Rahoi
Faculty Advisor:        CS490 Senior Project, Prof. Jeffrey Johnson

Ten-X Hackday Tool is an online web application designed as a platform to our sponsor company (Ten-X) for an annual programming competition
called “Hackday”. This platform will be used to collect, store, and process data entered by participants and graders. The purpose of this project is to
provide a better platform for Hackday organizers to control the flow of Hackday by handling the most repetitive tasks.

Presenter:              Dominic Mortlock
Sponsor:                The client would be students interested in learning more about unix and scripting concepts. Also, the game aims to be a fun
                                 challenge that people can practice both their unix knowledge and their problem solving skills.

Faculty Advisor:    Prof. David Wolber

Sudokil is a hacking/scripting themed puzzle game about using Unix-like commands on a terminal to control computers, robots, and various other devices.
Progress through levels and get access to different puzzle elements while collecting more scripts, permissions and tools.
Customer Ticket Classification Engine - Applying Machine Learning Algorithms to SnapLogic Metadata

Presenters:              Min Chen
                                   Shiyi Tan

Sponsor:                  Prof. Gregory Benson

Faculty Advisor:     CS690 Master's Project, Prof. Karpenko

SnapLogic customer service team needs to prioritize customer tickets and measure customer satisfaction, which was previously done manually and
was very time consuming. To automate this process, we built two engines, one for prioritizing tickets and one for the sentiment analysis of customer
comments. We first analyzed the ticket data and fit the two models, then used the models to predict the ticket priority and the sentiment of the comment
(“neutral” vs “negative”). If the ticket is labeled as “high priority” or contains a negative comment, our system sends an alert to the customer support team.
That allows the team to handle interactions with customers more wisely and saves their time.

Snap Recommendation Engine

Presenter:               Thanawut Ananpiriyakul

Sponsor:                 Prof. Gregory Benson

Faculty Advisor:     CS690 Master's Project, Prof. Karpenko

SnapLogic has been providing data integration services for years. A snap is a pre-built component that performs an operation on data. A pipeline is a
graph (DAG) of snaps which executes a specific task. In order to successfully build a pipeline, the user needs to select the right snap and connect it correctly
to the previous snap. For this project, we built the engine that recommends the most likely next snaps to users. We achieved 88% hit rate in the final prototype
implemented in Python. It means that 88% of time "deciding on the type of snap + searching for it among 100 types of snaps + dragging and dropping it to
canvas" will be reduced to "1 click.”

My Smart Financial Advisor - A Mobile Application for Mutual Fund Investment Management

Presenters:                Richard Wang
                                     Chen-Ning Chi
                                     Kaynat Quayyum

Sponsor:                    Stephen Y. Pak, The Core Group

Faculty Advisor:         CS690 Master's Project, Prof. Olga Karpenko

Mutual fund investment currently makes up a vast proportion of the retirement assets for Americans. At the same time, as mobile devices attain increasing
capabilities and popularity, more people switch from PC to mobile devices such as tablet computers and smartphones. We provide a platform to buy and sell
mutual fund shares on both iOS and Android devices. This enables users to manage mutual fund investment anywhere and anytime. Our application is
implemented in C# using Xamarin that allows us to build iOS and Android apps from a single shared codebase. Our app provides a good user experience
with high level of security.

An Exploration of Single Nucleotide Polymorphisms on Type 2 Diabetes Outcome

Presenters:                Michael Totagrande
                                     Irina Popova

Sponsor:                    Sean Kimbro, North Carolina Central University and La Creis Kidd from University of Louisville

Faculty Advisor:         CS640 Bioinformatics, Professor Patricia Francis-Lyon

Type 2 Diabetes (T2D) affects millions and is characterized by the inability to produce enough insulin, resulting in improper glucose regulation. With
numerous direct risk factors, including increased body mass index (BMI), race, high blood pressure, and the presence or absence of certain single
nucleotide polymorphisms (SNPs), T2D is a complicated disease. Herein, we explore over 600,000 SNP frequencies for more than 2000 individuals
in order to determine their impact on T2D outcome.
Using Face Tracking for Computationally-Efficient Visualization of Large Vector Data

Presenter:                Thanawut Ananpiriyakul

Faculty Advisor:       Prof. Alark Joshi

Visualizing large vector data is computationally expensive. Given that human beings can only visualize a certain region of a screen at a time, we have developed
a novel face tracking-based technique for visualization of large vector data. This focus+context visualization of vector fields reduces visual clutter and helps the
user visualize features of interest. We chose to use streamline and glyph-based methods to represent the vector data. Users can interact with the data in real time,
choosing regions of interest through a mouse, a touch interface, or their face. The presented visualization technique results in frame rate that is almost 5 times
higher than the full detail visualization of vector data.

Exploring Leap Motion for Intuitive Interaction of Scientific Data

Presenter:                Shiyi Tan

Faculty Advisor:     Directed Study, Prof. Alark Joshi

We explore use of the Leap Motion with intuitive interaction of medical data, trying to help practitioners interact with large, high-resolution datasets.
We use VTK for the visualization pipeline that includes data processing, surface extraction/volume rendering, and basic user interaction. We facilitate
freeform interaction without the use of a mouse and keyboard using the Leap Motion. With the Leap Motion controller, users can explore the 3D data
and perform basic interaction such as rotation, translation, and zooming in. 

Computational Enzymology

Presenters:                Stephanie Martin
                                     Meriam Vejiga
                                     Adrian Ramirez

Sponsor:                    Distributed Bio is a an antibody discovery, engineering, informatics and services company focused on
                                     producing next generation antibody libraries and revolutionary vaccines.

Faculty Advisor:         CS640 Bioinformatics, Professor Patricia Francis-Lyon

Enzymes are the original organic chemists (OCC), capable of catalyzing a wide variety of reactions that have great therapeutic potential. Many enzymes
have been cataloged and annotated using the Gene Ontology, a gene annotator's reference, and categorized by the Enzyme Commission, a database that
classifies enzymes based on the nature of their enzymatic activity. We took advantage of these two databases to mine for homology groups with similar
enzymatic activity, but different substrates. We characterized these enzyme groups by sequence variability and enzymatic variability. This work provides a
foundation for the creation of a new class of enzyme replacement therapy and for the creation of a new generalized synthesis technology.

Mechanistic Indicators of Childhood Asthma

Presenter:                Stephanie Styx

Sponsor:                   Dr. ClarLynda Williams-DeVane from North Carolina Central University sponsored the mechanistic indicators of childhood asthma project.
                                    Her objective for this project is to identify key environmental exposure contributors to asthma subtypes of varying severity.

Faculty Advisor:        CS640 Bioinformatics, Professor Patricia Francis-Lyon

Understanding the relationship between environmental factors and their affect on asthmatic children. Through principal component analysis, we looked at the
correlation of how much variance there is when asthmatic children are exposed to similar or different environmental factors. The cohort of patients analyzed in
this project were asthmatic African American children from Detroit, Michigan.

Computational Enzymology

Presenters:                Stephanie Martin
                                     Meriam Vejiga
                                     Adrian Ramirez

Sponsor:                   Dr. Jacob Glanville, former Principal Scientist at Pfizer, PhD in Computational and Systems Immunology at Stanford University School of Medicine
                                    and current Chief Science Officer of Distributed Bio.

Faculty Advisor:         CS640 Bioinformatics, Professor Patricia Francis-Lyon

Knowing the three dimensional structure of proteins is essential to understanding how the protein functions. Currently protein structures are determined
through x-ray crystallography, which can be difficult and laborious for some projects. Dr. Jake Glanville, CSO of Distributed Bio, created a coding package to
predict the structure of B cell and T cell receptors. This code draws on probabilistic alignment and hidden markov modeling and uses Hmmr3.0 and the
NCBI BLAST toolkit to identify potential templates for homology modeling then generates a model using UCSF’s Modeller. We tested the potential of the script to accurately produce models by using an input, self-models=0, to remove any template with more than 95% identity to the query sequence,
ensuring the program didn’t fetch the known crystal structure of the query for homology modeling. After generating hundreds of models, we used another script,, to calculate the root mean squared deviation (RMSD) of the generated model superimposed on the published structure to validate whether
this package has the potential to accurately predict the variable regions of antibodies.
Ozone exposure causes differential expression of genes involved in cell growth and DNA binding

Presenter:                 Chelsea Yee,
                                    Amrita Rishi

Sponsor:                    Dr. Mehrdad Arjomandi

Faculty Advisor:        CS640 Bioinformatics, Professor Patricia Francis-Lyon

Ozone - a gas with high oxidation potential is a major component of air pollution and has been found to damage the respiratory tissues in humans.
To our knowledge, no one has yet published the results of an exacerbation study utilizing ozone as a model for the impact of air pollutants. An ongoing
study by Dr. Mehrdad Arjomandi and associates at UCSF aims to establish the impact of ozone-induced injury and inflammation in asthma and other
lung diseases. Currently, this study aims to determine the differentially expressed genes (DEGs) in subjects, both with and without asthma, that were
exposed to medium (100ppm) and ambient (200ppm) levels of ozone. Gene expression levels for 18 subjects were determined by Affymetrix microarray
in an ozone-exacerbation study performed by Dr. Arjomandi’s team at UCSF. In partnership with Dr. Arjomandi and Prof. Francis-Lyon(USF), our team
performed statistical analysis of the Affymetrix microarray data in R using the limma package to identify DEGs in airway epithelial tissues in response to
ozone exposure. A total of 68 DEGs was determined from the Affymetrix microarray data for all 18 patients. Among the 68 DEGs, 4 were more frequently
differentially expressed (adjusted p-value < 0.1): MAPRE3, HKR1, MOB3B and ZFR. Genes MAPRE3 and HKR1 were up-regulated whereas MOB3B and
ZFR were down-regulated. Further studies providing new knowledge of the function and downstream effects of these genes can lead to the possibility of
new gene therapy and pharmacological targets.

Muse Mobile App

Presenter:                MD Naseem Ashraf

Faculty Advisor:       CS640 Bioinformatics, Professor Patricia Francis-Lyon

An Android app that leverages Muse headbands to record and transmit eegs from mobile devices easily and quickly.

AI for Princes of California

Presenters:                Kyle Baker
                                     Austin Bushree
                                    Cole Howard

Sponsors:                Jon Rahoi and Justin Sher. Noo Games

Faculty Advisor:        CS490 Senior Project, Prof. Jeffrey Johnson

Princes of California is a strategic board game that is similar to a hybrid of Monopoly and Poker. A single turn consists of playing a tile on the board and
buying up to three shares of any companies that have been built from the tiles on the board. The current built-in opponent makes random moves and is
easily defeated by human players. Our project seeks to use multiple techniques to build a competitive AI opponent for this game.
We will be implementing a heuristic algorithm based on the strategies we have developed while playing the game. We will also be fine-tuning a neural
network using TensorFlow, an open source machine learning package. The network will be trained by playing against random bots. The gameplay tactics
change based on the number of players, so our AI will be trained separately for 2, 3, 4, 5, and 6 player games. The ultimate goal of our project is to build an
AI that strategically places tiles and buys shares of companies to create an entertaining opponent for online players.
Fitness App for Vue Smart Glasses

Presenters:                Scott Zhu
                                     Ji Lu
                                     Shengcai Cheng

Sponsor:                    Jason Gui, Vigo Technologies

Faculty Advisor:        CS690 Master's Project, Prof. Olga Karpenko

Vue is a wearable device, a pair of “smart” glasses designed for everyday use. Our team developed the companion app for Vue on iOS and Android.
The app provides fitness features such as step tracking, calorie counting and inactivity alert that help people lead healthier and more active lives. It
also provides some additional features such as finding the device using the app, and delivering notifications.

Visualization of Hierarchical Time-Series Data Using the Sunburst Technique

Presenters:                Joey Estella,
                                      Marissa Masangcay,
                                      Lyndon Ong Yiu,
                                      Mohammad Bazarbay

Sponsors:                  Profs. Sophie Engle and Alark Joshi

Advisor:                      CS490 Senior Project, Prof. Jeffrey Johnson

The Visualizing Time Series Data project addresses the need to visualize new ways to aggregate large time series data. Often times, data becomes
too large when in its raw form. Then the problem becomes how to aggregate that data, i.e., what kind of metrics (mean, median etc.) and levels (days,
hours, etc.) need to be used to summarize and see patterns and trends from this data.
Our visualization tool attempts to address this problem. Our tool features an interactive dashboard where users are able to view organized data in a
sunburst visualization that displays the data in a meaningful way. Included in the interactive dashboard is an interactive sunburst visualization with a
complementary line chart that corresponds to data in the sunburst. This tool gives added context to otherwise ‘normal’ looking data in order for the user
to gain meaningful and significant conclusions about the data at hand. This tool also features a non-interactive dashboard where various static sunburst
visualizations are displayed in a grid for easy comparisons. These static visualizations feature multiple metrics (mean, median, etc.) across different levels
(days, hours, etc.).

Real Estate Recommendation Engine

Presenters:                Rob Reeves,
                                     Simon Kwong,
                                     Zhe Xu

Sponsor:                    Jon Rahoi, Ten-X

Faculty Advisor:        CS690 Master's Project, Prof. Olga Karpenko

Buying or selling a property is not something most of us do every day. But when the time comes, searching for a new home or office is exhausting. It's stressful,
agitating, and searchers often find themselves settling for less. We developed a recommendation engine for a Ten-X real estate marketplace, that will assist in
alleviating some of that stress. The engine recommends available properties based on past user activity. It uses a graph-based recommendation algorithm that
combines collaborative and content-based filtering.The goal is to maximize the likelihood a user will interact with the recommended properties embedded in the ad.

CyberSecurity at Gap Inc.

posted Nov 9, 2016, 10:34 AM by Rosa Maria Garay   [ updated Nov 9, 2016, 10:38 AM ]

Date: Thu., November 10th, 2016
Time: 11:45am-12:45pm
Location: Harney Science Center (Room 235)

We are delighted to announce a special Cybersecurity event tomorrow sponsored by the local ACM chapter. Richard Noguera (CISO) from Gap Inc. will give an 
overview of information security in general and how they protect customers and data at Gap. John Gearhart (Senior Security Engineer) will walk through some hacker techniques (aka “The Mind of the Adversary”). Andrew Yueng (internship coordinator) will be available to discuss technical internships at Gap. 

John Gearhart, is a Senior Security Engineer with GapTech and leads the Cyber Defense Center – Attack and Penetration Testing team (Red Team). John has over 15 years of hands on information security experience. During that time, he has engineered secure networks, provided malware analysis, incident response, and penetration testing for various industries; including financial, biotech, and retail.

Rich Noguera is Chief Information Security Officer at Gap Inc. His scope of responsibility includes Enterprise Security Architecture, Security Engineering & Operations, Product Security, Strategy, Risk & Compliance, and Cyber Defense. Working across GapTech and the Brands/Business Units, the Information Security team is focused on protecting Gap Customer Data and Sensitive Information from abuse or exposure by unauthorized parties. Prior to Gap Inc., Rich was a leader in Enteprise Risk & Security Strategy at Accenture, Security Governance at Yahoo!, Security Engineering at Symantec, Risk Management at McAfee-Intel, and Enterprise Risk Management at Deloitte & Touche LLP. Rich has spent nearly his entire career within the Information Security industry. Within the community, he is a Founding Board member of the Retail Cyber Information Sharing Center (R-CISC), worked with the Information Security Working Group of the Bay Area Council, worked with the APT group of the Bay Area CISO Council, and supports the Hispanic IT Executive Council. And Rich is an alum of the University of Southern California and holds a Bachelor of Science in Business Administration.

CS Night 2016

posted Nov 7, 2016, 12:18 PM by Rosa Maria Garay   [ updated Nov 7, 2016, 12:19 PM ]


You are cordially invited to attend the 15th Annual USF Computer Science Night!
This year we are pleased to announce a keynote talk by Sarah Clatterbuck: Director of Engineering at LinkedIn and USF alum!

Thursday, December 8, 2016

Lo Schiavo Center for Science and Innovation - room 307 and 3rd floor breakout area 
Harney Science Center - room 232

Parking will be available in the Koret lot at the corner of Turk and Parker

Schedule of Events
  • 6:30 - Posters and demonstrations of student work, food and drink - Lo Schiavo Science 307 and 3rd floor breakout area
  • 7:30 - CS Highlights and Announcement of Top Projects - Harney 232
  • 7:45 - Keynote talk by Sarah Clatterbuck - Harney 232
Please RSVP, or by emailing

Assistant Professor Full-time Renewable

posted Oct 17, 2016, 10:11 AM by Rosa Maria Garay

The Department of Computer Science at the University of San Francisco is accepting applications for a full-time, renewable, non-tenure track Assistant Professor position starting in August 2017. Applicants must have a Ph.D. in Computer Science or a closely-related field. Strong applicants from all CS sub-disciplines will be considered.

Applicants must demonstrate exceptional teaching ability. Applicants will be expected to teach both undergraduate and graduate courses and to perform service duties to the CS department and university.

See for the full job description and application instructions. To receive full consideration applications must be complete and received by January 2, 2017.

Assistant Professor Tenure Track

posted Oct 17, 2016, 10:08 AM by Rosa Maria Garay   [ updated Oct 17, 2016, 10:10 AM ]

The Department of Computer Science at the University of San Francisco is accepting applications for a tenure-track Assistant Professor position starting in August 2017.  Applicants must have a Ph.D. in Computer Science or a closely-related field. Strong applicants from all CS sub-disciplines will be considered.

Applicants must demonstrate both exceptional teaching ability and a strong potential for independent and collaborative research in computer science. Applicants will be expected to teach both undergraduate and graduate courses, maintain an active research program that involves students and perform service duties to the CS department and university.

 See for the full job description and application instructions. To receive full consideration applications must be complete and submitted by January 2, 2017.


posted Sep 13, 2016, 9:20 AM by Rosa Maria Garay

Introduction to Fast Exploratory Data Analysis Experience with R and Visualization

Date: September 15th 
Time: 11:45am-12:45pm
Location: Harney Science Center (Room 235)

R is arguably the best analytics platform in the world that is open-source with more than 9000 packages and is growing rapidly. But it’s hard to start and be productive if you are not programmers. Exploratory ( is a desktop app that provides a simple and easy to use interactive data exploration environment where users can quickly access R’s analytic power and visualization to explore various data ranging from files, databases, to NoSQL, and find valuable insights in a reproducible and collaborative way. 

Kan Nishida (@KanAugust), co-founder/CEO of Exploratory. Prior to Exploratory, Kan has 20 years of experience in building products and solutions in Big Data, Analytics, BI, and Data Visualization fields, at Oracle.

Full-time Faculty Position for 2016-2017

posted May 31, 2016, 1:04 PM by Dave Wolber

The Department of Computer Science at the University of San Francisco invites applications for a one-year full-time faculty position for the 2016-17 academic year (beginning this August!) The position is for a non-tenure-track faculty member whose primary responsibility is teaching, and will include a teaching load of three courses per semester. Though the position is for one year, there is potential for a longer-term.

Applicants must have a Ph.D. or M.S. degree in Computer Science or a related field. For additional information and to apply, please email Department Chair David Wolber at Applications should be submitted electronically and include a cover letter, current CV and teaching statement. You may also submit up to three letters of recommendation 

Note that our review of applications will begin immediately and will continue until the positions are filled. Contact us now!

WICS Award

posted Jan 27, 2016, 2:10 PM by Sami Rollins

The USF Women in Computer Science club is excited to announce that it has received a $3,000 seed award from the National Center for Women in Technology and

The WICS group, led by Janet Chavez, Courtni Wong, India Buckley-Becknell, and Anaelia Ovalle, is excited to use the award to raise visibility of the group and host some exciting events to help broaden participation in computing.

This semester, the club will begin a regular "WICS and Waffles" social hour, and is planning a tech field trip and a hackathon.

Congratulations USF Women in Computer Science!

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