UMBC ACM STUDENT CHAPTER

On behalf of the UMBC Association for Computing Machinery (ACM) Chapter you are invited to Hi-Tea for this Spring 2020 semester.
Date: Friday, February 21st, 2020
Time : 12:00 p.m. to 12:30 p.m.
Venue: ITE Building, 3rd Floor hallway outside of Suite 325
Refreshments, Meet with students, faculty and staff.

This is an opportunity to find out about interesting cutting/bleeding-edge research,mingle, network, explore ideas, collaborate and treat yourself to a wonderful lunch while you’re at it! Faculty, staff and especially students of the CSEE department are encouraged to participate.

Location : outside ITE 325 (Hi-Lunch)
Date: December 6th 2019, Friday
Time: 1pm to 1:30 pm (Hi-Lunch)

“Improving Optimization by Combining Quantum and Conventional Computing“

Speaker: Ajinkya Borle
PhD Candidate, Computer Science

Time : 12PM – 1PM, Friday December 6th 2019

Location: ITE 325b, UMBC

“My Experience as an Intern at Facebook..!!. A personal account on what Facebook looks for in its hires”

Speaker: Itay Tamary Undergraduate Student, Computer Science UMBC

Time : 11:30 PM – 12:30, Friday November 1st 2019

Location : ITE 346, UMBC

On behalf of the UMBC Association for Computing Machinery (ACM) Chapter you are invited to Hi-Tea for this Fall 2019 semester.
Date: Friday, November 1st, 2019
Time : 12:30 p.m. to 1:00 p.m.
Venue: ITE Building, 3rd Floor hallway outside of Suite 325
Refreshments, Meet with students, faculty and staff.

The ACM and IEEE student chapters of UMBC are back together for an event to mark and celebrate the mid-point of this fall semester.

Take this opportunity to mingle, network, explore ideas, collaborate and treat yourself to a wonderful feast while you’re at it! Faculty, staff and especially students of the CSEE and IS departments are encouraged to participate.

Location : Engineering Atrium

Date: October 21st 2019, Monday

Time: 12pm to 2pm

Hosted by: ACM and IEEE

Hope to see you all there..!!!!!!

Presented by :  

Dr. David Chapman Assistant Professor, Computer Science 11:30 PM – 12:30PM , Friday October 11th 2019 , ITE 346, UMBC

Abstract:
We propose to develop a novel quantitative algorithm to estimate the probability of malignancy of pulmonary nodules from a time series of successive LDCT screenings in patients with a high risk of developing lung cancer.  Lung cancer kills approximately 200,000 Americans annually and is responsible for 25% of all cancer-related deaths. Imaging with Low Dose Computed Tomography (LDCT) has been proven to reduce Non-Small Cell Lung Cancer (NSCLC) mortality by 20% and has become standard guidelines (NLST 2011a,b).  These new clinical guidelines have led to hospitals, including Mercy Medical Center in Baltimore, to collect an abundance of LDCT images of high risk individuals since 2014. These LDCT images along with additional CT/biopsy and PET/CT images collected by Mercy hospital in Baltimore have now been organized into an IRB exempt clinical research dataset to use anonymous radiology imagery for the purpose of training and evaluation of improved Computer Aided Diagnosis (CAD) algorithms.  Imaging biomarkers including cross-sectional diameter, calcification patterns, irregular margins, wall thickness all of which are known to have discriminating power to differentiate benign and malignant pulmonary nodules.  Furthermore, temporal changes in the size and biomarker characteristics of pulmonary nodules over multiple images are also highly informative and yield greater ability to differentiate malignancy.  The proposed CAD algorithm will be capable of detecting and quantifying temporal changes of imaging biomakers in order to estimate malignancy probability.  The algorithm will make use of convolutional neural networks for feature extraction as well as recurrent neural networks to analyze the temporal changes in extracted features.  The Mercy hospital dataset contains approximately 30,000 chest CT images. Training of the algorithm will incorporate semi-supervised learning using chest CT images from Mercy as well as the public portion of the NLST dataset.  A fraction of the Mercy images will be designated for evaluation of the sensitivity and specificity of the proposed algorithm for determining nodule malignancy.  Pulmonary nodules remain a challenging area for clinical management decision-making, and improved analysis of malignancy including temporal changes of imaging biomarkers have the potential to reduce patient morbidity and mortality through earlier and more accurate diagnosis.  

On behalf of the UMBC Association for Computing Machinery (ACM) Chapter you are invited to Second Hi-Tea for this Fall 2019 semester.
Date: Friday, October 11th, 2019
Time : 12:30 p.m. to 1:00 p.m.
Venue: ITE Building, 3rd Floor hallway outside of Suite 325
Refreshments, Meet with students, faculty and staff.

On behalf of the UMBC Association for Computing Machinery (ACM) Chapter you are invited to first Hi-Tea for this Fall 2019 semester.
Date: Friday, September 27, 2019
Time : 12:30 p.m. to 1:00 p.m.
Venue: ITE Building, 3rd Floor hallway outside of Suite 325
Refreshments, Meet with students, faculty and staff.