Monday, 27 of March of 2017

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LTU MCS’s Robofest featured on a top Chinese media network

Tencent, one of China’s largest media networks, featured the large-scale Robofest event. The large-scale robotic competition and exhibition took place at Beijing University of Aeronautics and Astronautics, and attracted considerable attention.

The network also featured Robofest in an article the can be read on-line:



LTU students participate in the 2016 Mathematical Contest in Modeling, one team wins best student presentation award


Nine LTU students in three teams participated in the 2016 international Mathematical Contest in Modeling (MCM). The team of LTU students Mohit Bansil, Aaron Craig, and Nicholas Paul won an award for best student presentation!

Each year the teams have a choice of two open-ended applied problems to solve, using mathematics, computer science, various sciences, and writing skills.

This year, for Problem A, students were asked to develop a model of the temperature of bathtub water in space and time to determine the best strategy to keep the temperature even and as close as possible to the initial temperature without wasting too much water. Problem B involved developing a time-dependent model to determine the best combination of alternatives that a private firm could adopt as a commercial opportunity to address the space debris problem, and whether an economically feasible opportunity exists. Details can be found at


The teams are:

Mohit Bansil, Aaron Craig, Nicholas Paul;

Jose Rodrigo Sanchez-Vicarte, Kristin Jordan, Xinrui Zhou;

Momin Aftab, Nirmit Changani, Ryan Pakledinaz.


In addition, student Nicole Yu entered the MCM Media Contest with a video documenting the experience of our teams during the contest.

The teams were coached by faculty advisors Michael Dabkowski, Na Yu, Ruth Favro, and Guang-Chong Zhu. Results will be available at the end of April.  Teams are ranked as Successful Participant, Honorable Mention, Meritorious, Finalist, and Outstanding.  Last year over 7,500 teams participated, from the USA, China, and 12 other countries.


















Lawrence Tech to take part in the Midwest Big Data hub.

With a grant of $1.25M from the National Science Foundation, the Midwest Big Data will strengthen research and education collaborations in all fields related to Big Data, as was announced by the White House on November 4th 2015. The Hub is led by the University of Illinois, and member institutions include all major research universities in the Midwest, as well as industry and government partners. LTU will also be represented on the hub’s steering committee.







LTU CS student develops a new computational method to improve crowdsourcing data analysis

Working with researchers from LTU and MTSU, Derek Diamond developed a novel algorithm the can estimate data annotators in crowdsourcing projects. Crowdsourcing is a common tool in industry and academia to analyze complex large databases. Projects such as Zooniverse and services such as Amazon Mechanical Turk are based on crowdsouring to provide industry and academia with solutions to problems that require human analysis of large databases.


Some tasks that are simple for humans are still considered very difficult for computing machines. The concept of crowdsouring uses a large number of users, normally through the internet, to annotate and analyze pieces of information that cannot be analyzed by the existing computer methodology.


Derek Diamond and his collaborators used the idea that inconsistent annotations will be revealed by weaker automatic classification if a machine learning algorithm is trained with these data. That can be used to rank data annotators automatically by their consistency, which can be used to allocate the human resources more efficiently among samples.


The study was peer-reviewed and published in IEEE Transactions on Human-Machine Systems – one of the world’s leading and most competitive computer science scientific journals.


paper reference:

Shamir, L., Diamond, D., Wallin, J., Leveraging pattern recognition consistency estimation for crowdsourcing data analysis, IEEE Transactions on Human-Machine Systems, 46(3), pp. 474-480, 2016



Math and Computer Science LTU Students develop a mathematical model of human aging
















LTU computer science and math double-major Erin Lixie and math major Jameson Edgeworth analyzed longitudinal medical and physiological data from over 5000 people and developed a quantitative model of the human aging. The model shows that the human aging does not progress in a continuous linear fashion, but instead exhibits several stages separated by short periods of rapid aging. It also discovered that the fastest aging occurs around the age of 55.

Human aging is explained by two competing paradigm: One is that aging is the process of stochastic accumulation of irreparable environmental damage, and the other is that aging is driven by biological pathways “programmed” in our body. The study done by the two undergraduate students shows strong evidence that aging is controlled by biological mechanisms, making a significant breakthrough in the field.

Their research was peer-reviewed by one of the world’s most renowned aging researchers, and was published in the journal Gerontology.


Paper reference:

Lixie, E., Edgeworth, J., Shamir, L., Comprehensive analysis of large sets of age-related physiological indicators reveals rapid aging around the age of 55, Gerontology, 61(6), 526-533, 2015














LTU computer science receives NSF funding for big data research

LTU computer science received an award of $115,000 for three years from the National Science Foundation to develop novel algorithms to mine big astronomical databases.

Current and future observational astronomy is based on robotic telescopes collecting very large databases of billions of astronomical objects, making them the world’s largest public databases. LTU helps turning these large databases into knowledge and discoveries by developing state-of-the-art algorithms that can mine through very large databases of both image and numerical data.

Big data is one of the fastest growing fields in computer science in both academia and industry. LTU has developed unique expertise in the field of big data in the form of software tools that can analyze some of the most complex existing databases.

LTU Computer Science faculty chairs a special track in China conference

Dr. Yin Wang of LTU Computer Science is organizer and chair of a special track in MobiMedia 2015. The track will focus on Data Mining in Multimedia, and will serve as platform of communication between some of the leading experts in the field. He will also present two papers at the conference. The conference will take place in Chengdu, China.

Jackson Pollock research continues to attract worldwide attention

Computational analysis of American pioneering artist Jackson Pollock performed at Lawrence Technological University has already attracted substantial attention.  Recent discoveries made at LTU identified mathematical features unique to Jackson Pollock artistic style. The study was featured on the world’s premier popular press such as The Smithsonian Magazine, Wired, The Times, Computer Magazine, Fox News, The Atlantic, and more.


LTU Math major develops a novel expansion

Mansour Hammad, a math major undergraduate at LTU developed a novel expansion approach designed to provide a simplified approach to  a common form of equations. Mansour’s expansion addresses equations of a certain common form, and makes a simplified solution than Lagrange and Taylor expansion. The work can be used to improve analysis of medical data, but also has application to engineering and even sports. Mansour published his discovery in the International Journal on Numerical and Analytical Methods in Engineering. The discovery also attracted attention of the popular press in his home country Saudi Arabia.


LTU students take part in Mathematical Contest in Modeling (MCM)

Nine LTU students in three teams of three participated in the 96-hour international Mathematical Contest in Modeling (MCM) from Feb. 5 – 9, 2015.


Each year the teams have a choice of two open-ended applied problems to solve, using mathematics, computer science, various sciences, and writing skills. This year’s problems were very current. Problem A involved modeling a realistic strategy to eradicate Ebola, given a new medication, and including vaccine development and distribution and other factors.  Problem B involved modeling a useful search for an airplane lost in an ocean, assuming no signals generated, and taking into account different types of search planes and sensors.  Details can be found at


The teams are: Eric Beyer, Bob Gandolfo, Tyler Pleasant; David Inwald, Jose Rodrigo Sanchez-Vicarte, Mark Kenney; Mitzi Cruz, Kristin Jordan, Mohit Bansil.


Results will be available at the end of April.  The teams are ranked as Successful Participant, Honorable Mention, Meritorious, Finalist, and Outstanding.  Last year over 6,700 teams participated, from the U. S., China, and 12 other countries.

MCM15_small MCM15-Day1&2-Team3 MCM15-AllTeams-2 MCM15-Day1&2-Team2 MCM15-Day1&2-Team1b