CSEE Professor Marie desJardins has been named as UMBC’s Presidential Teaching Professor for 2014-17. Dr. desJardins joined the UMBC faculty in 2001 after earning her Ph.D. in computer science from UC Berkeley and spending ten years at SRI International as a research scientist. She has made significant contributions to the fields of artificial intelligence and machine learning, published over 100 peer-reviewed papers, brought over six million dollars to UMBC as PI or co-PI in external grant funding, and held leadership positions in the top professional organizations in her field.

Dr. desJardins is an outstanding teacher, earning praise from students in courses from freshman-level courses for non-majors to specialized graduate-level seminars. She was named one of UMBC’s “Professors Not to Miss” in 2011 and is one of the first cohort of Hrabowski Academic Innovation Fellows. She is also well known for mentoring students at all levels, having graduated ten Ph.D. and 22 M.S. students, mentored over 50 undergraduates in research and served on the dissertation and thesis committees of more than 30 other students. She was recently recognized for mentoring by the National Center for Women & Information Technology, who selected her as one of four awardees of the 2014 NCWIT Undergraduate Research Mentoring Award.

Within the department and university, Dr. desJardins’s commitment to teaching and student success goes far beyond the classroom. She has served as the computer science undergraduate program director and led an effort to redesign the introductory computing course to better serve new students. In addition to mentoring her own graduate students, she is the Faculty Advisor for the Women in Science and Engineering Graduate Association and a member of the Center for Women in Technology Advisory Board. She is regularly invited to participate on panels, give presentations in the Honors Forum and other campus events, and to run workshops for graduate students and junior faculty.

Outside of UMBC, Dr. desJardins has built an international reputation as an advocate for high-quality education, mentoring, and diversity at all levels of the profession. She has been chair, mentor, reviewer, and/or panelist of the AAAI/SIGART Doctoral Consortium for the last 14 years; this event has provided valuable feedback and mentoring to hundreds of computing graduate students during that time. She co-founded the AAAI Educational Advances in Artificial Intelligence annual symposium. She regularly publishes articles on her research and innovations in computing education, including tools and techniques for classroom teaching, new courses, and analyses of the state of computer science education at the high school level.

Dr. desJardins is also a nationally recognized leader in computer science education and has received multiple NSF awards to support her work in this area. She gives frequent presentations around the state and the country on high school computer science education and preparing a diverse population of students to succeed in computing careers. She is a founding member of the Maryland chapter of the Computer Science Teachers Association, and has organized several professional development workshops for high school teachers. Her NSF-funded CE21-Maryland (Computing Education for the 21st Century) grant explored the landscape of high school CS education in Maryland, culminating in a statewide summit for educators, administrators, and community members that was held at UMBC in May 2013. A recent NSF CE-21 grant will result in curriculum creation and professional development for 100 Maryland high school teachers, focused on the new CS Principles course that is scheduled to become a new AP offering in 2016. Other funded grants in the educational arena include her Hrabowski Innovation Fund award to create the ACTIVE Center, an NSF TUES award that is developing a new freshman-level computing course, an NSF T-SITE grant to build a community of transfer scholars in IT/engineering, as well as multiple smaller awards to run workshops and support graduate student development.

We congratulate Professor desJardins for her selection as Presidential Teaching Professor and look forward to the Presidential Faculty and Staff Awards ceremony on Wednesday, April 2 in the University Center Ballroom. Not only is she an outstanding and dedicated classroom teacher, her contributions to research, teaching, mentoring, and educational innovations have been broad and sustained.

CSEE Professor Marie desJardins has been named as UMBC’s Presidential Teaching Professor for 2014-17. Dr. desJardins joined the UMBC faculty in 2001 after earning her Ph.D. in computer science from UC Berkeley and spending ten years at SRI International as a research scientist. She has made significant contributions to the fields of artificial intelligence and machine learning, published over 100 peer-reviewed papers, brought over six million dollars to UMBC as PI or co-PI in external grant funding, and held leadership positions in the top professional organizations in her field.

Dr. desJardins is an outstanding teacher, earning praise from students in courses from freshman-level courses for non-majors to specialized graduate-level seminars. She was named one of UMBC’s “Professors Not to Miss” in 2011 and is one of the first cohort of Hrabowski Academic Innovation Fellows. She is also well known for mentoring students at all levels, having graduated ten Ph.D. and 22 M.S. students, mentored over 50 undergraduates in research and served on the dissertation and thesis committees of more than 30 other students. She was recently recognized for mentoring by the National Center for Women & Information Technology, who selected her as one of four awardees of the 2014 NCWIT Undergraduate Research Mentoring Award.

Within the department and university, Dr. desJardins’s commitment to teaching and student success goes far beyond the classroom. She has served as the computer science undergraduate program director and led an effort to redesign the introductory computing course to better serve new students. In addition to mentoring her own graduate students, she is the Faculty Advisor for the Women in Science and Engineering Graduate Association and a member of the Center for Women in Technology Advisory Board. She is regularly invited to participate on panels, give presentations in the Honors Forum and other campus events, and to run workshops for graduate students and junior faculty.

Outside of UMBC, Dr. desJardins has built an international reputation as an advocate for high-quality education, mentoring, and diversity at all levels of the profession. She has been chair, mentor, reviewer, and/or panelist of the AAAI/SIGART Doctoral Consortium for the last 14 years; this event has provided valuable feedback and mentoring to hundreds of computing graduate students during that time. She co-founded the AAAI Educational Advances in Artificial Intelligence annual symposium. She regularly publishes articles on her research and innovations in computing education, including tools and techniques for classroom teaching, new courses, and analyses of the state of computer science education at the high school level.

Dr. desJardins is also a nationally recognized leader in computer science education and has received multiple NSF awards to support her work in this area. She gives frequent presentations around the state and the country on high school computer science education and preparing a diverse population of students to succeed in computing careers. She is a founding member of the Maryland chapter of the Computer Science Teachers Association, and has organized several professional development workshops for high school teachers. Her NSF-funded CE21-Maryland (Computing Education for the 21st Century) grant explored the landscape of high school CS education in Maryland, culminating in a statewide summit for educators, administrators, and community members that was held at UMBC in May 2013. A recent NSF CE-21 grant will result in curriculum creation and professional development for 100 Maryland high school teachers, focused on the new CS Principles course that is scheduled to become a new AP offering in 2016. Other funded grants in the educational arena include her Hrabowski Innovation Fund award to create the ACTIVE Center, an NSF TUES award that is developing a new freshman-level computing course, an NSF T-SITE grant to build a community of transfer scholars in IT/engineering, as well as multiple smaller awards to run workshops and support graduate student development.

We congratulate Professor desJardins for her selection as Presidential Teaching Professor and look forward to the Presidential Faculty and Staff Awards ceremony on Wednesday, April 2 in the University Center Ballroom. Not only is she an outstanding and dedicated classroom teacher, her contributions to research, teaching, mentoring, and educational innovations have been broad and sustained.

Underwater Acoustic Communication and
Networking for Ocean Sampling

Dr. Aijun Song

Assistant Research Professor
College of Earth, Ocean, and Environment
University of Delaware, Newark, DE 19716

11:45am – 12:45pm Thursday, 27 March 2014, ITE325b, UMBC

On our planet Earth, the marine ecosystem is undergoing significant changes due to human activities and natural processes. These changes call for enhanced capabilities to sample and communicate in the oceans. With this background, underwater acoustic communication has attracted much attention across multiple disciplines, as a means to access oceanographic data in real-time and to support navigation of underwater vehicles. This talk will focus on my recent efforts in 1) characterization and modeling of the ocean environment as a communication medium, 2) development of high data rate acoustic modems, both software and hardware, and 3) application of underwater acoustic communication networks in ocean sampling.

Dr. Aijun Song received his Ph.D. degree in the Department of Electrical Engineering, University of Delaware, Newark, Delaware in 2005. From 2005 to 2008, he was a postdoctoral research associate at the College of Earth, Ocean, and Environment (CEOE), University of Delaware. During this period, he was also an Office of Naval Research (ONR) postdoctoral fellow, supported by the special research award in the Ocean Acoustics program. Since 2008, he has been an Assistant Research Professor of the CEOE, University of Delaware. His research interests include advanced signal processing and communication techniques for mobile radio frequencies as well as for underwater acoustic environments, underwater acoustic signal propagation, and the general area of ocean sampling.

The Baltimore Code Craftsmanship meetup group will hold its monthly meeting at 7:00pm on Thursday, March 27 in room 107 of the Physics on the UMBC Campus. The meetup is for students, faculty and software developers in the Baltimore area that care about the quality of their work and want to practice and improve their programming skills, share what they know and learn new things from others.

This is a hands on coding user group with no presentations. Each meeting will be a dojo where we will go through a challenging software craftsmanship exercise that focuses on clean code, test-driven development, design patterns, and refactoring. We will pair up and practice on a kata in order to learn and apply the values, principles, and disciplines of software craftsmanship. Come with your laptop equipped with your favorite programming and automated unit testing environment. If you don’t have a laptop, come anyway, we will need only one laptop for every two people. Be prepared to pair up, learn, share and have fun!

Join the meetup and register to attend at the meetup event page.

The Baltimore Code Craftsmanship meetup group will hold its monthly meeting at 7:00pm on Thursday, March 27 in room 107 of the Physics on the UMBC Campus. The meetup is for students, faculty and software developers in the Baltimore area that care about the quality of their work and want to practice and improve their programming skills, share what they know and learn new things from others.

This is a hands on coding user group with no presentations. Each meeting will be a dojo where we will go through a challenging software craftsmanship exercise that focuses on clean code, test-driven development, design patterns, and refactoring. We will pair up and practice on a kata in order to learn and apply the values, principles, and disciplines of software craftsmanship. Come with your laptop equipped with your favorite programming and automated unit testing environment. If you don’t have a laptop, come anyway, we will need only one laptop for every two people. Be prepared to pair up, learn, share and have fun!

Join the meetup and register to attend at the meetup event page.

Feature Engineering for Large Scale Predictive
Modeling with Electronic Health Records

Dr. Fei Wang
Healthcare Analytics Research group
IBM T. J. Watson Research Center

1:00pm Wednesday, 26 March 2014, ITE325b, UMBC

Predictive modeling lies in the heart of many medical informatics problems, such as early detection of some chronic diseases and patient hospitalization/readmission prediction. Typically those predictive models are built upon patient Electronic Health Records (EHR), which are systematic collection of patient information including demographics, diagnosis, medication, lab tests, etc. We refer those information as patient features. High quality features are of vital importance to building successful predictive models. In this talk, I will present two feature engineering technologies to improve the quality of the raw features extracted from original patient EHRs: (1) feature augmentation, which constructs more effective derived features from existing raw features by exploring the event sequentiality; (2) feature densification, which imputes the missing feature values via knowledge transfer across similar patients. Along with each technique we also developed a visual interface to facilitate the user exploring the derived features. Finally I will conclude the whole talk with some future research directions.

Dr. Fei Wang is currently a research staff member in Healthcare Analytics Research group, IBM T. J. Watson Research Center. Before his current position he was a postdoc in Department of Statistical Science, Cornell University. He received his Ph.D. from Department of Automation, Tsinghua University in 2008. Dr. Wang’s major research interests include data mining, machine learning as well as their applications in social and health informatics. He actively publishes papers on the top venues of the relevant fields including AMIA, KDD, ICML and InfoVis, and he has filed over 20 patents (four issued). Dr. Wang has given seven tutorials on different topics at ICDM/SDM/ICDM, organized seven workshops on KDD/ICDM/SDM/WSDM, and edited three special issues on Journal of Data Mining and Knowledge Discovery. His Ph.D. thesis was awarded the National Excellent Doctoral Thesis in China. His research paper was selected as the recipient of the Honorable mention of the best research paper award in ICDM 2010, and best research paper finalist in SDM 2011. More information can be found on his homepage.

Host: Prof. Kostas Kalpakis, Sorry, you need javascript to view this email address.

Feature Engineering for Large Scale Predictive
Modeling with Electronic Health Records

Dr. Fei Wang
Healthcare Analytics Research group
IBM T. J. Watson Research Center

1:00pm Wednesday, 26 March 2014, ITE325b, UMBC

Predictive modeling lies in the heart of many medical informatics problems, such as early detection of some chronic diseases and patient hospitalization/readmission prediction. Typically those predictive models are built upon patient Electronic Health Records (EHR), which are systematic collection of patient information including demographics, diagnosis, medication, lab tests, etc. We refer those information as patient features. High quality features are of vital importance to building successful predictive models. In this talk, I will present two feature engineering technologies to improve the quality of the raw features extracted from original patient EHRs: (1) feature augmentation, which constructs more effective derived features from existing raw features by exploring the event sequentiality; (2) feature densification, which imputes the missing feature values via knowledge transfer across similar patients. Along with each technique we also developed a visual interface to facilitate the user exploring the derived features. Finally I will conclude the whole talk with some future research directions.

Dr. Fei Wang is currently a research staff member in Healthcare Analytics Research group, IBM T. J. Watson Research Center. Before his current position he was a postdoc in Department of Statistical Science, Cornell University. He received his Ph.D. from Department of Automation, Tsinghua University in 2008. Dr. Wang’s major research interests include data mining, machine learning as well as their applications in social and health informatics. He actively publishes papers on the top venues of the relevant fields including AMIA, KDD, ICML and InfoVis, and he has filed over 20 patents (four issued). Dr. Wang has given seven tutorials on different topics at ICDM/SDM/ICDM, organized seven workshops on KDD/ICDM/SDM/WSDM, and edited three special issues on Journal of Data Mining and Knowledge Discovery. His Ph.D. thesis was awarded the National Excellent Doctoral Thesis in China. His research paper was selected as the recipient of the Honorable mention of the best research paper award in ICDM 2010, and best research paper finalist in SDM 2011. More information can be found on his homepage.

Host: Prof. Kostas Kalpakis, Sorry, you need javascript to view this email address.

If you have good software skills and are still looking for a summer internship, check out the 2014 Google Summer of Code program. You can earn $5500 by coding for an open source software project this summer. You will probably work remotely, but in close collaboration with a mentor at one of over 100 participating organizations. To maximize your chances, explore the organizations and find one that needs your skills. Details here; apply by Friday, March 21.

If you have good software skills and are still looking for a summer internship, check out the 2014 Google Summer of Code program. You can earn $5500 by coding for an open source software project this summer. You will probably work remotely, but in close collaboration with a mentor at one of over 100 participating organizations. To maximize your chances, explore the organizations and find one that needs your skills. Details here; apply by Friday, March 21.

If you have good software skills and are still looking for a summer internship, check out the 2014 Google Summer of Code program. You can earn $5500 by coding for an open source software project this summer. You will probably work remotely, but in close collaboration with a mentor at one of over 100 participating organizations. To maximize your chances, explore the organizations and find one that needs your skills. Details here; apply by Friday, March 21.