talk: Machine Learning: New Methodology for Physical & Social Sciences, 1pm ET 3/24
24 hour LIDAR backscatter profiles and PBLH points generated from image machine learning system
The Infusion of Machine Learning as a New Methodology for the Physical and Social Sciences
Dr. Jennifer Sleeman
CSEE, UMBC
1:00-2:00 pm ET, Wednesday, March 24
Online via WebEx
Machine learning has made improvements in many areas of computing. Recently attention has been given to infusing social science methodology with machine learning. In addition, the physical sciences have begun to embrace machine learning to augment their physical parameterization and to discover new features in their computations. I will describe my work that relates to these new emerging areas of research. I will first describe our machine learning research efforts related to understanding the changing role of climate and its effects on society. I will describe how this methodology was also applied to understanding cyber-related exploits. As part of this work, I developed an expertise in generative modeling, which led to a patent in generative and translation-based methods applied to imagery. These ideas were fundamental to a contribution in machine learning using quantum annealing. Quantum computing holds promise for deep learning to reach model convergence faster than classical computers. I will describe work related to developing a new hybrid method that overcame qubit limitations for image generation.
In addition, I will describe my current work related to machine learning for the Physical Sciences. As part of a multi-disciplinary team from UMBC and other universities, my current work explores ways to augment and replace existing physical parameterizations with neural network based models. I have led a research effort to calculate the planetary boundary layer’s height (PBLH) used for ceilometer-based backscatter profiles and satellite-borne lidar instruments. This work addresses the largest uncertainty in climate change, namely the role of aerosols (dust, carbon, sulfates, sea salt, etc.). We employ a novel method that includes a deep segmentation neural network that uses near-time continuous profiles forming an image to determine boundary layer heights. This method overcomes limitations in wavelet approaches which are unable to identify the PBLH under certain conditions. I will also give a preview of two efforts related to Long Short Term Memory (LSTM) neural networks related to learning PBLH changes over time. These research efforts result from collaborations with two students in the UMBC CSEE department and are being published and presented at the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physics Sciences.
Dr. Jennifer Sleeman is a Research Assistant Professor in Computer Science at the University of Maryland, Baltimore County (UMBC). Her research interests include generative models, natural language processing, semantic representation, image generation, and deep learning. Dr. Sleeman received the prestigious recognition of being a 2019 EECS Rising Star. She was also recognized in 2017 as one of the best Data Scientists in the Washington, DC region by DCFemTech. She defended her Ph.D. thesis, Dynamic Data Assimilation for Topic Modeling (DDATM) in 2017 under Tim Finin and Milton Halem. Her thesis-related work was awarded a Microsoft “AI for Earth” resource grant in 2017 and 2018 and also won the best paper award in the Semantic Web for Social Good Workshop presented at International Semantic Web Conference in 2018. She was an invited guest panelist at the AI for Social Good AAAI Fall Symposium in 2019 and was also an invited keynote speaker at the Sixth IEEE International Conference on Data Science and Engineering (ICDSE 2020), where she presented her ideas related to AI for Social Good and Science. She is an active research scientist in generative deep learning methods for which she holds a patent. She has over 12 years of machine learning experience and over 22 years of software engineering experience, in both academic and government/industry settings. She is currently funded by NASA and NOAA (PI). She also teaches Introduction to Artificial Intelligence at the University of Maryland, Baltimore County (UMBC) and currently mentors two Master’s students
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24 hour LIDAR backscatter profiles and PBLH points generated from image machine learning system The Infusion of Machine Learning as a New Methodology for the Physical and Social Sciences Dr....
https://www.csee.umbc.edu/2021/03/talk-machine-learning-as-a-new-methodology-for-the-physical-and-social-sciences-1pm-et-3-24/
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Sun, 21 Mar 2021 10:28:02 -0400
Sun, 21 Mar 2021 10:28:02 -0400
talk: (Don’t) Mind the Gap: Bridging the Worlds of People and IoT Devices, 1-2 ET 3/23
TIPPERS is an IoT data management middleware system developed at UCI that manages IoT smart spaces by collecting sensor data, inferring semantically meaningful information, and providing developers with data for intelligent applications.
(Don’t) Mind the Gap: Bridging the Worlds of People and IoT Devices
Dr. Roberto Yus
University of California, Irvine
1:00-2:00 pm ET, Tuesday, 23 March 2021
online via WebEx
The Internet of Things (IoT) has the potential to improve our lives through different services given the diversity of smart devices and their capabilities. For example, the IoT can empower services to make the re-opening of business during the current pandemic safer by monitoring adherence to regulations. But the large amounts of highly heterogeneous data captured by IoT devices typically require further processing to become useful information. The challenge is thus for IoT systems to determine which sensor data has to be captured/stored/processed/shared to, for instance, determine the occupancy of a specific office building or the spaces in which a potential exposure took place. This becomes even more challenging when IoT systems have to take into account the privacy preferences of individuals, such as the need to prevent sharing data about their daily patterns or habits.
In this talk, I will discuss my efforts into helping IoT systems bridge the gap between the world of IoT devices and the world where people act. First, I will introduce a model to represent knowledge about sensors/actuators, people, spaces, events, and their relationships. Based on the model, I will explain an algorithmic solution to translate user requests and privacy preferences defined in a high-level, more semantically meaningful way into operations on IoT devices and their captured data. Second, I will talk about the enforcement of privacy preferences in the context of the IoT. Finally, I will overview my experience building and deploying an IoT data management system, TIPPERS, which has been deployed at UC Irvine and two US Navy vessels and is soon to be deployed on other campuses. I will conclude the talk by discussing the exciting future work opportunities towards supporting the next generation of ubiquitous IoT data management systems and technologies that autonomously, transparently, and at scale, balance the trade-off between providing users with high utility and respecting people’s privacy requirements.
Roberto Yus is a Postdoctoral Researcher in the Computer Science department at the University of California, Irvine working with Prof. Sharad Mehrotra. Before that, he spent a year as a visiting researcher at the University of Maryland, Baltimore County working with Prof. Anupam Joshi and Prof. Tim Finin. He obtained his Ph.D. in Computer Science from the University of Zaragoza, Spain, funded through a 4-year fellowship from the Spanish Ministry of Science and Innovation. His research interests are in the fields of data management, knowledge representation, privacy, and the Internet of Things (IoT). His research focuses on the design of semantic data management solutions to empower IoT systems to understand user information requirements and user privacy preferences and adapt their operations taking those into account. Roberto’s research has been published in top-tier conferences and journals such as VLDB and the Journal of Web Semantics. He is part of the editorial board of the “Sensors” and “Frontiers in Big Data” journals and has served as part of the organizing and program committee of several conferences and workshops in addition to serving as an external reviewer for multiple conferences and journals.
The post talk: (Don’t) Mind the Gap: Bridging the Worlds of People and IoT Devices, 1-2 ET 3/23 appeared first on Department of Computer Science and Electrical Engineering.
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TIPPERS is an IoT data management middleware system developed at UCI that manages IoT smart spaces by collecting sensor data, inferring semantically meaningful information, and providing...
https://www.csee.umbc.edu/2021/03/talk-dont-mind-the-gap-bridging-the-worlds-of-people-and-iot-devices-1-2-et-3-23/
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Thu, 18 Mar 2021 11:22:54 -0400
talk: Towards Contextual Security of AI-enabled platforms, 1-2 pm ET 3/22
Towards Contextual Security of AI-enabled platforms
Dr. Nidhi Rastogi
Rensselaer Polytechnic Institute
1-2:00pm ET, Monday, 22 March 2021
The explosive growth of Internet-connected and AI-enabled devices and data produced by them has introduced significant threats. For example, malware intrusions (SolarWinds) have become perilous and extremely hard to discover, while data breaches continue to compromise user privacy (Zoom credentials exposed) and endanger personally identifiable information. My research takes a holistic approach towards systems and platforms to address security-related concerns using contextual and explainable models.
In this talk, I will present ongoing work that analyzes and improves the cybersecurity posture of Internet-connected systems and devices using automated, trustworthy, and contextual AI-models. Specifically, my research in malware threat intelligence gathers diverse information from varied datasets – system and network logs, source code, and text. In [1], an open-source ontology (MALOnt) contextualizes threat intelligence by aggregating malware-related information into classes and relations. TINKER [2, 3] – the first open-source malware knowledge graph, instantiates MALOnt classes and enables information extraction, reasoning, analysis, detection, classification, and cyber threat attribution. At present, the research is addressing the trustworthiness of information sources and extractors.
1. Rastogi, N., Dutta, S., Zaki, M. J., Gittens, A., & Aggarwal, C. (2020). MALOnt: An ontology for malware threat intelligence, In KDD’20 Workshop at International workshop on deployable machine learning for security defense. Springer, Cham.
2. Rastogi, N., Dutta, S., Christian, R., Gridley, J., Zaki, M. J., Gittens, A., and Aggarwal, C. (2021). Knowledge graph generation and completion for contextual malware threat intelligence. In USENIX Security’21, Accepted.
3. Yee, D., Dutta, S., Rastogi, N., Gu, C., and Ma, Q. (2021). TINKER: Knowledge graph for threat intelligence. In ACL- IJCNLP’21, Under Review.
Dr. Nidhi Rastogi is a Research Scientist at Rensselaer Polytechnic Institute. Her research is at the intersection of cybersecurity, artificial intelligence, large-scale networks, graph analytics, and data privacy. She has papers accepted at top venues such as USENIX, TrustCom, ISWC, Wireless Telecommunication Symposium, and Journal of Information Policy. For the past two years, Dr. Rastogi has been the lead PI for three cybersecurity, privacy research projects and a contributor to one healthcare AI project. For her contributions to cybersecurity and encouraging women in STEM, Dr. Rastogi was recognized in 2020 as an International Women in Cybersecurity by the Cyber Risk Research Institute. She was a speaker at the SANS cybersecurity summit and the Grace Hopper Conference. Dr. Rastogi is the co-chair for DYNAMICS workshop (2020-) and has served as a committee member for DYNAMICS’19, IEEE S&P’16 (student PC), invited reviewer for IEEE Transactions on Information Forensics and Cybersecurity (2018,19), FADEx laureate for the 1st French-American Program on Cyber-Physical Systems’16, Board Member (N2Women 2018-20), and Feature Editor for ACM XRDS Magazine (2015-17). Before her Ph.D. from RPI, Dr. Rastogi also worked in the industry on heterogeneous wireless networks (cellular, 802.1x, 802.11) and network security through engineering and research positions at Verizon and GE Global Research Center, and GE Power. She has interned at IBM Zurich, BBN Raytheon, GE GRC, and Yahoo, which provides her a quintessential perspective in applied industrial research and engineering.
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Towards Contextual Security of AI-enabled platforms Dr. Nidhi Rastogi Rensselaer Polytechnic Institute 1-2:00pm ET, Monday, 22 March 2021 via WebEx The explosive growth of Internet-connected...
https://www.csee.umbc.edu/2021/03/talk-towards-contextual-security-of-ai-enabled-platforms-1-2-pm-et-3-22/
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Tue, 16 Mar 2021 21:29:37 -0400
ALERT- THIS is a SCAM
DO NOT send your resume or any personal information
The Career Center works very hard to ensure that job postings in UMBCworks are legitimate positions, however, scammers are becoming more and more creative, especially in these difficult economic times. A recent scam that has been brought to our attention may be reviewed below.
Email Scam
You have been offered a Spring 2021 Internship opportunity at the convenience of your home or, Which serve as a gateway to pay all expenses incurred during this pandemic. This opportunity should be done at leisure taking at most 1hr/day, 2-3 times a week.
Please send your resume to;
Email: bartonm078@gmail.com for the job description.
Career Services
UMBC.
Here are some things we would like to point in regards to the fraudulent job offer above:
Look at the email domain of the person requesting your resume: (bartonm78@gmail.com). Be CAUTIOUS if the domain does not match the domain of a reputable company.
BE CAUTIOUS of an employer whose posting includes grammatical errors or does not seem to be written professionally. Valid employers will not do this.
The scammer asks the candidate to “email their resume in order to receive a full job description” to proceed. BE CAUTIOUS of an employer who asks for anything out of the ordinary or who is not operating by normal hiring procedures, such as sending personal information or hiring you without interviewing you.
If you have received a scam or fraudulent email:
DoIT Security Team investigates any email scam that we send to them, however, the original emailmust be sent from the recipient of the email scam. In order to assist the DoIT Security Team's investigation, recipients of the email should send security@umbc.edu a notification including the full header set of the message. These headers are not normally displayed but can be easily accessed. For more instruction on how to send the full header set of the message, please refer to: https://wiki.umbc.edu/pages/viewpage.action?pageId=1867970
More information on scams and attacks on the website may be found here: https://itsecurity.umbc.edu
The Career Center is here to support you. If you encounter a position that you fear may be fraudulent, don’t hesitate to reach out to us careers@umbc.edu
Please see the attachment for more information on fraudulent jobs and phishing scams.
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The Career Center works very hard to ensure that job postings in UMBCworks are legitimate positions, however, scammers are becoming more and more creative, especially in these difficult economic...
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Tue, 16 Mar 2021 12:15:19 -0400
Tue, 16 Mar 2021 12:17:03 -0400
UMBC expands live online peer tutoring to include computing courses
Amanda Knapp speaks with a student in the Academic Success Center. Photo by Marlayna Demond ’11 for UMBC.
UMBC expands live online peer tutoring to include computing courses
When Amanda Knapp heard last fall from Anupam Joshi, professor and chair of computer science and electrical engineering (CSEE), that his department wanted to offer online tutoring to students in their courses, she was ready to help make it happen. COVID or no COVID, she says, “It just made sense.”
Knapp is associate vice provost and assistant dean of Undergraduate Academic Affairs, and she manages UMBC’s Academic Success Center (ASC). “We already had an established system in place and could provide the administration, training, staffing, and assessment,” she explains. “CSEE provided the funding and identified potential tutors for a variety of computer science courses, and we did the rest.”
Just a few months after the partnership began, it expanded to include courses in the information systems (IS) department. And this semester, tutors are supporting ten IS computing courses.
In total, the ASC’s new Computing Success Center now offers tutoring for 21 courses from across the College of Engineering and Information Technology (COEIT). And the center continues to grow, supported by COEIT Dean Keith J Bowman and Dean Katharine Cole, Undergraduate Academic Affairs.
Peer tutors help students of any major, from computing fields to the arts and life sciences, learn coding and other computing skills.
“The ethos of UMBC is to share knowledge and collaborate with others instead of being proprietary,” says Helena Mentis, associate professor of information systems. Mentis is COEIT’s associate dean of academic programs and learning, and one of the College leads on the partnership.
“At the end of the day,” she says, “our goal at UMBC is to ensure that there are multiple pathways for students to get the assistance they need to succeed and remove any barriers for access to help.”
You can read more about UMBC’s new Computing Success Center in this UMBC News article.
Adapted from a UMBC News article by Gregory J. Alexander and Dinah Winnick
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Amanda Knapp speaks with a student in the Academic Success Center. Photo by Marlayna Demond ’11 for UMBC. UMBC expands live online peer tutoring to include computing courses When Amanda Knapp...
https://www.csee.umbc.edu/2021/03/umbc-expands-live-online-peer-tutoring-to-include-computing-courses/
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Sun, 14 Mar 2021 13:33:55 -0400
