Alex Masgai, BrickSimple

BrickSimple is a software company that provides software solutions to other companies. Throughout the summer and during my senior year, I worked on quality assurance, develops, AW, and research and development in blockchain technologies.

I debugged both the hardware and the software of a new fitness app for a client, rewrote the backend for a Django website, wrote a proof-of-concept decentralized application using Node JS and Solidity, deployed a mining node in Ethereum-based network, and wrote a Python script that installs, updates and removes developmental tools. At the end of the summer portion of my internship, I presented my findings on blockchain research in a company-wide meeting.

Justin Branco, Delaware Valley Community Health

My internship at Delaware Valley Community Health (DVCH) proved to be one of the most beneficial factors of my learning experience at St. Joseph’s University. The projects I was tasked with included: managing the setup and distribution of iPads to doctors, initializing with healthcare software necessary for doctor-patient interaction, developing new plans and alternatives for patients to connect with their doctors via technology, researching new healthcare technologies that would be beneficial for DVCH to implement, and working with IS/IT professionals to deal with any ongoing software/hardware problems that DVCH might face.

My time at Delaware Valley Community Health taught me that the professional world is dynamic and fast-paced. I found myself tasked with many projects at a single time. Many of the projects had to do with our Patient Portal, which is an online hub where patients can access their health records and talk with their healthcare provider without stepping in the office. I made changes to some of the technology that DVCH has to offer their patients, including the Patient Portal and their website. A key project was updating their email scripts using HTML. Another main project was implementing iPads into a healthcare provider’s everyday routine. The use of this technology would promote a closer relationship between patient and doctor and also allow doctors to access vital healthcare software on the go.

Although not all of the projects I was tasked with were completed before my departure from DVCH, I found that my presence at the company was not only beneficial for me but to them as well. During this internship, I learned how to implement the skills I acquired in the classroom to everyday life.

Yifan Chen and Wenhao Ruan, Databricks

Thursday, April 18, 2019 (11:00 am)

Databricks is a unified analytics engine that allows rapid development of data science applications using machine learning techniques, such as classification, linear and nonlinear regression, clustering, etc. Existence of myriad sophisticated computation options, however, can become overwhelming for designers as it may not always be clear what choices can produce the best predictive model given a specific data set. Further, the mere high dimensionality of big data sets is a challenge for data scientist to gain a deep understanding of the results obtained by a utilized model.

Our research provides general guidelines for utilizing a variety of machine learning algorithms on the cloud computing platform, Databricks. Visualization is an important means for users to understand the significance of the underlying data. Therefore, it is also demonstrated how graphical tools, such as Tableau, can be used to efficiently examine results of classification or clustering. The dimensionality reduction techniques, such as Principal Component Analysis (PCA), which help reduce the number of features in a learning experiment, are also discussed.

To demonstrate the utility of Databricks tools, tow big data sets are used for performing clustering and classification. A variety of machine learning algorithms are applied to both data sets and it is shown how to obtain the most accurate learning models employing appropriate evaluation methods. During the presentation, we will introduce the workflow of conducting an ML model training and describe the method to choose the proper classification and regression algorithms.

One of the data sets will be chosen to demonstrate how we implemented unsupervised learning (K-means) on an unlabeled data set for classification (Kernel S V M) We will also briefly discuss model evaluation and time efficiency. Finally, we will present the visualization of classification after applying PCA.

Supervisory Special Agent Ryan Landers, Department of Homeland Security

Thursday, November 1, 2018 (11:00 – 12:00)

Homeland Security Investigations 

Biography: Supervisory Special Agent (SSA) Ryan Landers supervises the Cyber Crime Investigations Task Force (C2iTF) at the  U.S. Department of Homeland Security (DHS), Homeland Security Investigations (HSI) in the Philadelphia, Pennsylvania, office.  SSA Landers previously served as the Cyber Crime Advisor to the Assistant Secretary for Cyber Policy at DHS Headquarters in Washington, D.C.

The C2iTF is currently comprised of several federal, state, and local law enforcement agencies in a unified effort to combat the exploitation of the internet for criminal purposes.  The C2iTF’s primary responsibilities include the interdiction of Darknet supplied contraband, including fentanyl and other dangerous drugs from China and other international sources of supply; the disruption and dismantlement of transnational drug trafficking organizations, including cyber-enabled clandestine laboratories responsible for the manufacturing and distribution of drugs via the Darknet and Clearnet; the investigation of the misuse of Bitcoin and other cryptocurrencies to launder illicit drugs and other criminal proceeds; and other traditional cyber crimes, including cyberstalking, business email compromises, and the digital theft of export-controlled data and intellectual property.

SSA Landers has been a criminal investigator with several U.S. law enforcement agencies, including the U.S. Naval Criminal Investigative Service (NCIS), the U.S. Department of Justice, Office of the Inspector General (DOJ/OIG),, the U.S. Department of Homeland Security, and Homeland Security Investigations (DHS/HSI) for the last 17 years.  During SSA Landers’ career, he has conducted a broad scope of criminal investigations, including but not limited to rape, death, larceny,  narcotics, explosives, firearms, public corruption, money laundering, illegal exports, Darknet smuggling, and weapons of mass destruction.

Software Developer Joseph Grayauskie, Accolade, Inc.

Thursday, October 18, 2018 (11:00 – 12:00)

Service Oriented Architecture (SOA)

Biography: Joe graduated St. Joseph’s University in 2007 with a B.S. in Computer Science.  While a student at SJU, he was a member of the Men’s Soccer Team.  Upon graduation, he accepted a position at Accolade, Inc.  Joe is a Software Developer and currently resides with his wife and son in King of Prussia, Pennsylvania.

Marguerite Callahan, Lockheed Martin

Thursday March 30, 2017 (11:00 – 12:00)

The Software Development Process

Biography: With over 25 years in the IT industry, Marguerite has been at Lockheed Martin for 18 years and has been involved in many development efforts with increasing responsibility. Her current role as Lean-Agile Coach helps Lockheed Martin tackle the large software development projects in the age of technical accelerations, where everything is changing so you must allow for flexibility in the software development process but still meet all requirements.

Dr. Jie Wu, Associate Vice Provost, Chair and Laura H. Carnell Professor, IEEE Fellow, Temple University

Thursday February 23, 2017 (11:00 – 12:00)

Algorithmic Crowdsourcing and Applications in Big Data

Abstract: This talk gives a survey of crowdsourcing applications, with a focus on algorithmic solutions. The recent search for Malaysia flight 370 is used first as a motivational example. Fundamental issues in crowdsourcing, in particular, incentive mechanisms for paid crowdsourcing, and algorithms and theory for crowdsourced problem-solving, are then reviewed. Several applications of algorithmic crowdsourcing applications are discussed in detail, with a focus on big data. The talk also discusses several on-going projects on crowdsourcing at Temple University.

Dr. Wei Chang, Assistant Professor of Computer Science, SJU

Thursday November 3, 2016 (11:00 – 12:00)

The Sybil Attack and Its Prevention Techniques

Abstract: We are entering a distributed computing era, where various decisions are individually made at each entity based on the pervasive data from the other entities scattered all over the world. However, most distributed systems are vulnerable to Sybil attacks: by creating a large number of fake identities, an adversary can introduce a considerable amount of false opinions into a distributed system and subvert it. As a result, some entities may make unfair, or even false, decisions. For instance, in some distributed systems, critical resources are assigned based on voting results. If an adversary holds a considerable number of fake identities, he can easily change the overall decision, and unfairly gain more resources. It has been more than a decade since the first appearance of the Sybil attack. In this presentation, Dr. Chang will systematically show the evolution of the Sybil attack and its defense techniques.

Glenn Brunette, Oracle Corporation

Thursday October 20, 2016 (11:00 – 12:00)

The Internet of Things: The Gift of Fire Or a Modern Pandora’s Box?

Abstract: Technology has been woven into nearly every facet of our daily lives. With each passing year, the boundary between the physical world and cyberspace has continued to fade as everyday items have been infused with logic, power, and connectivity. Dubbed the “Internet of Things”, these devices have heralded a new age of innovation and capability, greatly expanding the “art of the possible”. Unfortunately, the capabilities enabled by these technologies are not always positive, expected, or welcome. In this presentation, the benefits and risks of the “Internet of Things” will be discussed. Specific examples will highlight technological challenges, as well as risks to security and privacy. Lastly, recommendations will be offered that illustrate ways in which we can learn from past mistakes.