Meet Your Professor: Dr. Frank Ferraro, 12-1 Mon 3/25, ITE 239

Prof. Ferraro’s teaching and research areas include AI, language understanding and machine learning

Maryland Data Science Conference, 1/14 CANCELED DUE TO WEATHER

MD Data Science Conference
Monday, 14 January, UMBC
Canceled due to Weather

Miner & Kasch has decided to cancel the conference tomorrow due to the snow storm and reschedule it.

“While around UMBC the snow seems to be letting up, we have several speakers and attendees from other areas that have raised concerns about being able to attend. We want to be able to have the event at a time when we can have everyone that wanted to participate be able to attend. We will start working on a backup date immediately and send a notice to all of you as soon as we hear more. For now, we will refund all the tickets and have you re-register for the new date once we have the new details.”

Miner & Kasch

, a AI and data science consulting firm founded by two UMBC alumni, will hold a one-day Data Science Conference at UMBC on Monday, January 14 in the Performing Arts & Humanities Building. Tickets are free for current UMBC students.

The event brings together local companies and professionals to share what new and exciting things they are doing with their data. It will be attended by business managers, startup founders, software engineers, data scientists, students, and other curious people that want to learn more about the cutting edge of data science, machine learning, and AI.

See the conference website for topics and speakers and to register.

Alumni startup at bwtech@UMBC earns unique award for AI work with UMBC research team

Alumni startup RedShred earns unique award for AI work with UMBC research team

The artificial intelligence startup RedShred—cofounded by two UMBC alumni and housed in the bwtech@UMBC incubator—has received a rare Phase II Small Business Innovation Research Award from the National Science Foundation to expand in a new direction, in collaboration with UMBC faculty and graduate students.

Jeehye Yun ‘97, computer science, and Jim Kukla ‘97, M.S ‘00, computer science, launched RedShred in 2014, with the support of a Phase I Small Business Technology Transfer Award from NSF. For the past four years, RedShred has created software to help universities and other institutions sort through complex government listings in search of opportunities (requests for proposals, or RFPs) that meet their needs and expertise. The new Phase II award will support RedShred as they make their products available to companies in the commercial sector.

“At RedShred our mission is to help people read less and win more,” says Yun. “We’re excited about this Phase II grant, which allows us to commercialize our Phase I research and development, and develop new mechanisms to help people understand increasingly complicated documents.”

UMBC faculty and students have collaborated with RedShred to advance the technologies behind their products. Tim Finin, professor of computer science and electrical engineering, and several graduate students have worked with RedShred to better understand how large documents, such as RFPs, tend to be structured, even when each one is formatted differently and doesn’t follow a template. They describe this process as identifying the document’s semantic DNA.

By defining and identifying the core elements of each RFP, UMBC student researchers have been able to create “at-a-glance” summaries of these highly complex documents that provide all the necessary information and save the client the time of wading through levels of detail.

“Our collaboration with RedShred has given UMBC students great opportunities to participate in both basic and applied research focused on developing an innovative commercial product,” explains Finin. “This has involved both undergraduate and graduate students majoring in computing as well as the arts and humanities. For example, computer science graduate student Muhammad Rahman Ph.D. ‘18, computer science, developed a problem he encountered when working with RedShed into his Ph.D. dissertation, which he completed his summer.”

Adapted from a UMBC News article written by Megan Hanks

MD-AI Meetup holds 1st event at UMBC 6-8pm Wed 10/3, 7th floor library

MD-AI Meetup holds 1st event at UMBC
6-8pm Wed 10/3, 7th floor library

A new Maryland-based meetup interest group has been established for Artificial Intelligence (MD-AI Meetup) and will have its first meeting at UMBC this coming Wednesday (Oct 3) from 6:00-8:00pm in the 7th floor of the library. The first meeting will feature a talk by UMCP Professor Phil Resnik on the state of NLP and an AI research agenda. Refreshments will be provided. The meetup is organized by Seth Grimes and supported by TEDCO, local AI startup RedShred, and the Maryland Tech Council.

If you are interested in attending this and possibly future meetings (which will probably be monthly), go to the Meetup site and join (it’s free) and RSVP to attend this meeting (if there’s still room). If you join the meetup and RSVP, you can see who’s registered to attend.

These meetups are good opportunities to meet and network with people in the area who share interests. It’s a great opportunity for students who are will be looking for internships or jobs in the coming year.

Machine learning and AI for cybersecurity: a technical chat with DISA

The UMBC Cyber Defense Lab

Machine Learning and Artificial Intelligence: A Technical Chat with the Defense Information Systems Agency

James Curry
Lead Engineer–DoD Cyber Security Range
Defense Information Systems Agency (DISA)

12:00–1:00pm Friday, 28 September 2018, ITE 227, UMBC

A broad reaching brief on the scope and scale of the DISA Mission, followed by a dive into DISA’s efforts to develop Machine Learning and Artificial Intelligence to help defend the nation’s cyber infrastructure. Attendees are highly encouraged to ask questions.

James Curry is the Lead Engineer of the DoD Cyber Security Range (CSR). The CSR’s mission is to replicate the DoD Information Network (DODIN) environment at lab scale, while maintaining high-fidelity realism. As Lead Engineer, Mr. Curry led the design, acquisition, and implementation of two first-of-its-kind technologies: a Virtual Internet Access Point (vIAP) and a Virtual Joint Regional Security Stack (vJRSS). These technologies enable the DoD Workforce to train in an IaaS-on-demand environment that realistically matches DISA’s core infrastructure. Mr. Curry is a Scholarship for Service (SFS) recipient (2008-2009) and received his masters and bachelors of science in computer science from New Mexico Tech. Email:

Host: Alan T. Sherman,

The UMBC Cyber Defense Lab meets biweekly Fridays. All meetings are open to the public. Upcoming meetings for Fall 2018 include the following.

Oct 12 Enis Golaszewski, The 2018 UMBC SFS study
Oct 26 Enis Golaszewski, Using tools in the formal analysis of cryptographic protocols
Nov 9 Razvan Mintesu, Legal aspects privacy
Dec 7 Tim Finin, A knowledge graph for cyber threat intelligence

Prof. Cynthia Matuszek named one of AI’s 10 to Watch

Cynthia Matuszek named one of AI’s 10 to Watch

UMBC CSEE Professor Cynthia Matuszek was named as one AI’s 10 to Watch by IEEE Intelligent Systems. The designation is given every two years to a group of “10 young stars who have demonstrated outstanding AI achievements”. IEEE Intelligent Systems accepts nominations from around the world, which are then evaluated by the the publication’s editorial and advisory boards based on reputation, impact, expert endorsement, and diversity. Dr. Matuszek was recognized for her research that “combined robotics, natural language processing, and machine learning to build systems that nonspecialists can instruct, control, and interact with intuitively and naturally”.

Professor Matuszek joined UMBC in 2014 after receiving her Ph.D. in Computer Science from the University of Washington. At UMBC, she established and leads the Interactive Robotics and Language Lab that integrates research on robotics and natural language processing with the goal of “bringing the fields together: developing robots that everyday people can talk to, telling them to do tasks or about the world around them”.

Here is how she describes her research in the IEEE Intelligent Systems article.

Robot Learning from Language and Context

As robots become more powerful, capable, and autonomous, they are moving from controlled industrial settings to human-centric spaces such as medical environments, workplaces, and homes. As physical agents, they will soon be able help with entirely new categories of tasks that require intelligence. Before that can happen, though, robots must be able to interact gracefully with people and the noisy, unpredictable world they occupy.

This undertaking requires insight from multiple areas of AI. Useful robots will need to be flexible in dynamic environments with evolving tasks, meaning they must learn and must also be able to communicate effectively with people. Building advanced intelligent agents that interact robustly with nonspecialists in various domains requires insights from robotics, machine learning, and natural language processing.

My research focuses on developing statistical learning approaches that let robots gain knowledge about the world from multimodal interactions with users, while simultaneously learning to understand the language surrounding novel objects and tasks. Rather than considering these problems separately, we can efficiently handle them concurrently by employing joint learning models that treat language, perception, and task understanding as strongly associated training inputs. This lets each of these channels provide mutually reinforcing inductive bias, constraining an otherwise unmanageable search space and allowing robots to learn from a reasonable number of ongoing interactions.

Combining natural language processing and robotic understanding of environments improves the efficiency and efficacy of both approaches. Intuitively, learning language is easier in the physical context of the world it describes. And robots are more useful and helpful if people can talk naturally to them and teach them about the world. We’ve used this insight to demonstrate that robots can learn unanticipated language that describes completely novel objects. They can also learn to follow instructions for performing tasks and interpret unscripted human gestures, all from interactions with nonspecialist users.

Bringing together these disparate research areas enables the creation of learning methods that let robots use language to learn, adapt, and follow instructions. Understanding humans’ needs and communications is a long-standing AI problem, which fits within the larger context of understanding how to interact gracefully in primarily human environments. Incorporating these capabilities will let us develop flexible, inexpensive robots that can integrate into real-world settings such as the workplace and home.

You can access a pdf version of the full IEEE AI’s 10 to Watch article here.

2018 Mid-Atlantic Student Colloquium on Speech, Language and Learning

The 2018 Mid-Atlantic Student Colloquium on Speech, Language and Learning (MASC-SLL) is a student-run, one-day event on speech, language & machine learning research to be held at the University of Maryland, Baltimore County (UMBC) from 10:00am to 6:00pm on Saturday, May 12. There is no registration charge and lunch and refreshments will be provided. Students, postdocs, faculty and researchers from universities & industry are invited to participate and network with other researchers working in related fields.

Students and postdocs are encouraged to submit abstracts describing ongoing, planned, or completed research projects, including previously published results and negative results. Research in any field applying computational methods to any aspect of human language, including speech and learning, from all areas of computer science, linguistics, engineering, neuroscience, information science, and related fields is welcome. Submissions and presentations must be made by students or postdocs. Accepted submissions will be presented as either posters or talks.

Program

2018 Mid-Atlantic Student Colloquium on Speech, Language and Learning

The 2018 Mid-Atlantic Student Colloquium on Speech, Language and Learning (MASC-SLL) is a student-run, one-day event on speech, language & machine learning research to be held at the University of Maryland, Baltimore County (UMBC) from 10:00am to 6:00pm on Saturday May 12. There is no registration charge and lunch and refreshments will be provided. Students, postdocs, faculty and researchers from universities & industry are invited to participate and network with other researchers working in related fields.

Students and postdocs are encouraged to submit abstracts describing ongoing, planned, or completed research projects, including previously published results and negative results. Research in any field applying computational methods to any aspect of human language, including speech and learning, from all areas of computer science, linguistics, engineering, neuroscience, information science, and related fields is welcome. Submissions and presentations must be made by students or postdocs. Accepted submissions will be presented as either posters or talks.

Important Dates are:

Submission deadline (abstracts): ~~April 16~~ April 20
Decisions announced: ~~April 21~~ April 25
Registration opens: April 10
Registration closes: May 6
Colloquium: May 12

🗣 talk: Classifying Malware using Data Compression, 12-1 Fri 4/20, ITE229

The UMBC Cyber Defense Lab presents

Classifying Malware using Data Compression

Charles Nicholas, UMBC

12:00–1:00pm Friday, 20 April 2018, ITE 229

Comparing large binary objects can be tricky and expensive. We describe a method for comparing such strings, using ideas form data compression, that is both fast and effective. We present results from experiments applying this method, which we refer to as LZJD, to the areas of malware classification and digital forensics.

Charles Nicholas () earned his B.S. in Computer Science from the University of Michigan – Flint in 1979, and the M.S. and Ph.D. degrees in Computer Science from Ohio State University in 1982 and 1988, respectively. He joined the Computer Science Department at UMBC in 1988. His research interests include electronic document processing, intelligent information systems, and software engineering. In recent years he has focused on the problems of storing and retrieving information from large collections of documents. Intelligent software agents are an important aspect of this work. Host: Alan T. Sherman,

The UMBC Cyber Defense Lab meets biweekly Fridays. All meetings are open to the public.

🤖 talk: Where’s my Robot Butler? 1-2pm Friday 4/13, ITE 231

UMBC ACM Student Chapter Talk

Where’s my Robot Butler?
Robotics, NLP and Robots in Human Environments

Professor Cynthia Matuszek, UMBC

1:00-2:00pm Friday, 13 April 2018, ITE 231, UMBC

As robots become more powerful, capable, and autonomous, they are moving from controlled industrial settings to human-centric spaces such as medical environments, workplaces, and homes. As physical agents, they will soon be able help with entirely new categories of tasks that require intelligence. Before that can happen, though, robots must be able to interact gracefully with people and the noisy, unpredictable world they occupy, an undertaking that requires insight from multiple areas of AI. Useful robots will need to be flexible in dynamic environments with evolving tasks, meaning they must learn from and communicate effectively with people. In this talk, I will describe current research in our lab on combining natural language learning and robotics to build robots people can use in the home.

Dr. Cynthia Matuszek

is an assistant professor of computer science and electrical engineering at the University of Maryland, Baltimore County. Her research occurs at in the intersection of robotics, natural language processing, and machine learning, and their application to human-robot interaction. She works on building robotic systems that non-specialists can instruct, control, and interact with intuitively and naturally. She has published on AI, robotics, machine learning, and human-robot interaction. Matuszek received her Ph.D. in computer science and engineering from the University of Washington.

Department of Computer Science and Electrical Engineering

Inspiring Innovation