- Gradstudents - mailman.cs.vt.edu

[S24 GTAs] New GTAs and GRAD 5004
by Cliff Shaffer 27 Dec '23

27 Dec '23

This is to remind all Spring 2024 GTAs that if you have not completed GRAD 5004 previously, then you are obligated by the Grad School to take it this semester as a condition of employment. You should register for that yourself. It is a 1-credit course, with most of the semester work taking place during a one-day workshop in the week before classes start. -- Dr. Cliff Shaffer Professor and Associate Department Head for Graduate Studies Department of Computer Science Phone: (540) 231-4354 Virginia Tech, Blacksburg, VA 24061 WWW: www.cs.vt.edu/~shaffer

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CS@VT New Grad Student Orientation, Spring 2024
by Cliff Shaffer 19 Dec '23

19 Dec '23

*New Grad Student Orientation *for new CS grad students will be held via Zoom on Wednesday, January 10 at 2:00-4:00. The Zoom link is: https://virginiatech.zoom.us/j/89556859151?pwd=eVJJSXdZQVh5QVI0aCt0bHVlbG56… If you are in the Undergrad/Grad Accelerated Program, and you have not attended a CS new grad orientation session already, then you should come to this one. -- Dr. Cliff Shaffer Professor and Associate Department Head for Graduate Studies Department of Computer Science Phone: (540) 231-4354 Virginia Tech, Blacksburg, VA 24061 WWW: www.cs.vt.edu/~shaffer

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Fw: The Blue Ridge Highlands Regional Science Fair needs your help in finding judges!
by Ribbens, Calvin 18 Dec '23

18 Dec '23

See below if you are interested in being a judge for this event. ________________________________ From: Hermann, Christine <chermann(a)RADFORD.EDU> Sent: Tuesday, December 5, 2023 12:20 PM To: Ribbens, Calvin <ribbens(a)vt.edu> Subject: The Blue Ridge Highlands Regional Science Fair needs your help in finding judges! Dear Dr. Calvin Ribbens, Please forward to colleagues, faculty, graduate students and others that you think would be interested in being a judge. Chris Greetings!, The 33rd Annual Blue Ridge Highlands Regional Science Fair needs your help in finding judges for the Science Fair! The Science Fair will be held at Radford University in the Center for the Sciences (note new location!) on March 2, 2024. Participants in this Regional Science Fair come from sixteen counties, as well as the cities of Big Stone Gap, Bristol, Galax, and Radford in southwest Virginia and include students of two home-school organizations. The 6th through 8th grade students will compete in the Junior Division and the 9th through 12th grade students compete in the Senior Division. Participants enter their projects in one of the following categories: Animal Sciences, Behavioral and Social Sciences, Biochemistry, Biomedical and Health Sciences, Biomedical Engineering, Cellular and Molecular Biology, Chemistry, Computational Biology & Bioinformatics, Earth and Environmental Sciences, Embedded Systems, Energy: Sustainable Materials and Design, Engineering Technology: Statics and Dynamics, Environmental Engineering, Materials Science, Mathematics, Microbiology, Physics and Astronomy, Plant Sciences, Robotics and Intelligent Machines, Systems Software, Technology Enhances the Arts, and Translational Medical Science. The Science Fair committee reserves the right to combine categories once projects are submitted. First, second and third place awards will be given in each category. A number of Special Awards will also be given. Two Grand Prize winners from the Senior Division will be able to participate in the International Science and Engineering Fair (ISEF) to be held in Los Angeles in May 2024, where they will compete against approximately 1600 local, regional, and state science fair winners from 64 countries for 9 million dollars in prizes. At the 2021 ISEF, Shoshana Elgert won a Mu Alpha Theta first place award and a second place award in the Mathematics category. Shohana attended Blacksburg High School. In 2019, Brandon Fan won a $5,000 Oracle Academy Award and a $1500 second place award in Systems Software. He attended Blacksburg High School. On Saturday, March 2, we will begin with a brief organizational breakfast and meeting at 7:30 a.m. The judging will then begin. Award selection will be based not only on the merits of the project, but also on a personal interview with the student contestant. Judging will be concluded by lunchtime. The Blue Ridge Highlands Science Fair website is located at http://sciencefair.asp.radford.edu/. In addition to information about the Science Fair, there is information on contributing to the Science Fair through donations or placing advertisements in the Program Book. The previous Blue Ridge Highlands Regional Science Fairs were an overwhelming success; therefore, we are very excited about being able to continue to offer students in Southwest Virginia this exceptional educational experience. By having a Regional Science Fair in this area, many schools that would not have held local science fairs have now been stimulated to do so, thus enhancing their scientific curriculums. The Blue Ridge Highlands Science Fair website is located at http://sciencefair.asp.radford.edu/. In addition to information about the Science Fair, there is information on contributing to the Science Fair through donations or placing advertisements in the Program Book. Please email me the Judge Response Form<https://1drv.ms/b/s!As_V8E495C9xoHqmmugAjytEwxAX?e=UYot3a> by February 4, 2024. If you would like me to notify your supervisor about your participation in the fair, please also email me the Supervisor Form<https://1drv.ms/b/s!As_V8E495C9xoHn5XHS5lH5aPE5M?e=3NkJ02>. Each form needs to be downloaded to be filled out. Prior to the science fair, I will email you a judge’s packet with the judging manual, directions to the school, and a link to the science fair abstracts. The entire Science Fair Committee looks forward to working with you. Please forward this email to anyone else that you think would be interested in being a judge. Please let me know if you have any questions. Sincerely, Christine K. F. Hermann Co-Director, Blue Ridge Highlands Science Fair Dr. Christine Hermann, Professor of Chemistry, Chair of the Chemistry Department, Box 6949 Center for the Sciences 381 Radford University, Radford, VA 24142 540-831-5413 (office); 540-831-6615 (fax) chermann(a)radford.edu<https://rumail.radford.edu/owa/redir.aspx?C=60c55cd2773e4be09fcd9bccac56de0…>; http://chermann.asp.radford.edu/ Co-Director, Blue Ridge Highlands Regional Science Fair -- http://sciencefair.asp.radford.edu/ Vice-President of Iota Sigma Pi, National Honor Society for Women in Chemistry https://www.iotasigmapi.org/

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Blacksburg Graduate Office operations for the week of 12/18
by Mayo, Trey 18 Dec '23

18 Dec '23

As the semester comes to an end, we would like to let you know that the Blacksburg Graduate Programs Office will be working remotely the week of December 18th. Staff will be available via phone and email to assist you with your needs. Please note that the University will close at 12 noon on Friday, December 22nd and will reopen on Tuesday, January 2nd. On a more personal note, I will be leaving my position effective January 1st to take a new position at William & Mary starting January 2nd. I will still have access to my VT email and phone up until then, but my responses may be delayed due to moving and some other transitional matters. I would like to thank our great colleagues in Blacksburg and Northern Virginia for their support. It has been a pleasure working with you for the past three years and I wish you the best in your graduate studies. Trey Mayo ============================================================== Trey Mayo, Ed.D. Director of Graduate Programs Virginia Tech | Department of Computer Science Torgersen Hall Suite 3210 620 Drillfield Drive (MC 0106) | Blacksburg, VA 24061 (540) 231-0780 | treymayo(a)vt.edu<mailto:treymayo@vt.edu> http://cs.vt.edu<http://cs.vt.edu/>

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M.S. Thesis Defense for Samuel Williams - Friday, December 15 at 10:00am-noon in Gilbert Place 3211
by Denis Gracanin 15 Dec '23

15 Dec '23

You are cordially invited to the M.S. final defense of Samuel Williams this Friday, December 15 at 10:00am. Location: Gilbert Place 3211 Zoom: https://virginiatech.zoom.us/j/81196958311 Title: Exploring the Usability of Non-verbal Vocal Interaction (NVVI) and a Pitch Based Implementation Abstract: Natural user interfaces, including verbal vocal interactions like speech processing, are ubiquitous and commonly used in both industry and academic settings. However, this field is limited by the speech and language components. Non-verbal vocal interaction (NVVI) provides further opportunities for people to use their vocals as an input modality. Despite the many possibilities of NVVI input modalities, such as whistling, humming, and tongue clicking, the field is niche and literature is few and far between. This work attempts to address these gaps, as well as the small sample sizes of performed studies of prior work. The problem definition is defined as to perform a large-scale study exploring a pitch-based NVVI modality that uses a relative pitch interaction technique to offer a continuous mode of one-dimensional interaction. A user study is outlined and performed via an ecosystem comprising of Amazon Mechanical Turk for recruitment and study access, a modularized study website, and a secure server that stores the study results, tasks users with controlling a slider with the NVVI tool by humming and whistling, in addition to using the computer mouse to perform these tasks as a baseline. In total, 72 participants' results are considered for analysis. Results show that the pitch based NVVI technique used in this study does not follow Fitts' Law, is not as performant as the computer mouse, humming is a more performant modality with the NVVI tool than whistling, and that participants experienced a significantly higher task workload using the NVVI tool than the computer mouse. Using the results of this study and from reviewed literature, an NVVI framework is developed and implemented as a contribution of this work. Thanks, Denis — Denis Gracanin Department of Computer Science Virginia Tech 220 Gilbert Street, Room 3207 Blacksburg, VA 24061, USA Phone: (540) 231-2060

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M.S. Thesis Defense for Ayush Chaturvedi - Friday, December 15 at 10:00-11:30am in Torgersen 3160A
by Wuchun Feng 15 Dec '23

15 Dec '23

Hi, everyone! You are cordially invited to the M.S. final defense of Ayush Chaturvedi this Friday, December 15 at 10:00am. *Location*: Torgersen 3160A *Zoom*: https://virginiatech.zoom/my/wfeng Title: Optimizations for Deep Learning-Based CT Image Enhancement Abstract: Computed tomography (CT) combined with deep learning (DL) has recently shown great potential in biomedical imaging. DL models, with varying architectures inspired by the human brain, are improving imaging software and diagnosis. However, the combination of the two approaches greatly depends on two factors. First, the accuracy of these DL models heavily relies on the datasets used for training, which often contain poor-quality images. Second, as DL models mimic the human brain, their deep neural architecture becomes complex and demands significant computational power. In contrast, the neural architecture of the brain is highly sparse, meaning it does not utilize its total capacity to perform day-to-day tasks. Therefore, in this work, we apply sparse optimizations to minimize the complexity of the DL models and then leverage architecture-aware optimization to reduce the total training time of these DL models. To that end, we leverage a DL model called DenseNet and Deconvolution Network (DDNet) to this end. DDNet is a deep convolutional neural network with high dimensionality. It enhances low-quality CT images into high-quality ones but requires many hours to train. To further improve its image enhancement capabilities, we first augment DDNet's architecture with a more robust loss function in order to achieve state-of-the-art chest CT image enhancement, but it comes at an even higher computational cost. Thus, to reduce the associated computational demand, we introduce three sparsification techniques and examine their impact on the performance and accuracy of state-of-the-art hardware resources. In addition, we apply three architecture-aware optimizations to efficiently utilize the underlying computing hardware and further reduce the training time for DDNet. Regards, Wu ~~ Wu FENG | Synergy Lab | Depts. of CS, ECE, BEAM | NSF SHREC Center & SEEC Center at Virginia Tech | Professor, Turner Faculty Fellow, and Director | 2050 Torgersen Hall | 620 Drillfield Drive | VT | Blacksburg VA 24061 | 540-231-1192 | wfeng(a)vt.edu

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Fwd: Invitation for Women’s Datathon for Computer Science Students
by Mohammed Seyam 11 Dec '23

11 Dec '23

---------- Forwarded message --------- My name is Beatrice, and I’m on the Business team at *Correlation One* <https://www.correlation-one.com/>. We are a New York based startup that hosts data science and engineering competitions for the world’s best STEM students. We are excited to announce that this upcoming Spring, we are partnering with Citadel and Citadel Securities to launch the next season of events! *Top performing students have the opportunity to win $15,000 in cash prizes along with exclusive recruiting opportunities with Citadel* (both internships and full-time roles). The events are completely free and invite only. One event that we would love to highlight is our second-ever *Women’s Datathon* <https://www.citadel.com/careers/students/datathons/?utm_source=correlationo…>. Seeing great success in its pilot season last year, we are excited to share that the event will be returning this upcoming February in Miami. It’s a data science competition open to all women students across North America. *Invited students will be flown out to Miami in an all-expense-paid trip for the two-day long event.* Could you share the information below with your students? We’d love to see students from Virginia Tech apply before we reach full capacity. Please don’t hesitate to reach out if you have any questions - I would be happy to assist! Thank you and I look forward to hearing from you! * Women’s Datathon * Hosted by Citadel and Citadel Securities in partnership with Correlation One, this event is open to top students across North America who identify as women. Women’s Datathon takes place this February, and *invited students will be flown out to Miami in an all-expense-paid trip for the two-day long event.* Our Datathons challenge students from the world's best universities to tackle real-world social challenges in topics such as urban traffic, renewable energy, and education. Women’s Datathon is a national data science competition, bigger and more exciting than our regional events! Check out *this video* <https://www.youtube.com/watch?v=sO8T_CDXPxQ> on last year’s event. *When*: February 9th & 10th *Where*: Miami, FL *Who*: Undergraduates, graduates, Ph.D., and post-doc researchers who identify as women *Prizes*: $15,000 + Exclusive recruiting opportunities with Citadel and Citadel Securities *APPLY FOR WOMEN'S DATATHON* <https://www.citadel.com/careers/students/datathons/?utm_source=correlationo…> -- Beatrice Khoo Business Operations Manager Correlation One <https://www.correlation-one.com/> 1330 Avenue of the Americas 12th Floor New York, NY 10019 <http://www.correlation-one.com/> -- *Mohammed Seyam <https://website.cs.vt.edu/people/faculty/mohammed-seyam.html>, *PhD Collegiate Assistant Professor Coordinator for Experiential Learning <https://xl.vt.edu/> Department of Computer Science <http://cs.vt.edu/> Virginia Polytechnic Institute and State University (VirginiaTech <http://www.vt.edu/>). Website <http://people.cs.vt.edu/seyam>|LinkedIn <https://www.linkedin.com/in/msseyam/> | Facebook <https://www.facebook.com/MSSeyam>

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Advanced Topics Course on Compilers
by Kirshanthan Sundararajah 08 Dec '23

08 Dec '23

Hello Everyone, I will be teaching *CS 6304: Advanced Topics in Language and Translation *in Spring 2024. You can find more information about this course here: https://kirshanthans.github.io/cs6304/. If you are interested in pursuing research in topics related to compilers or systems in general, I highly recommend taking this course. If you have any questions regarding the course, please do not hesitate to contact me. Best, ~Krish -- *Kirshanthan "Krish" Sundararajah, PhD.* *Assistant Professor* Virginia Tech | Computer Science 220 Gilbert St, Suite 4103 Blacksburg, VA 24060 (540) 231-1779 | kirshanthans(a)vt.edu kirshanthans.github.io

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Invitation to MS thesis defense Thu 1:15pm (Chongyu He), Ph.D. research defense Fri 4pm (Ola Karajeh)
by Fox, Edward 07 Dec '23

07 Dec '23

You are invited to 2030 Torgersen Hall, or my Zoom room, for each of 2 defenses this week. See details below. Warm regards! -- Edward A. Fox, Ph.D., Professor Virginia Tech | Computer Science (0106) 1160 Torgersen Hall (CS dept. office) Blacksburg, VA 24061 (540) 231-5113 | fox(a)vt.edu<mailto:fox@vt.edu> | http://fox.cs.vt.edu<http://fox.cs.vt.edu/> Room: 2030 Torgersen Hall Zoom: https://virginiatech.zoom.us/my/ed.fox Thursday 12/7 at 1:15pm Name: Chongyu He Title: Deep Learning Approach for Cell Nuclear Pore Detection and Quantification over High Resolution 3D Data Abstract: The intricate task of segmenting and quantifying cell Nuclear pores in high-resolution 3D microscopy data is critical for cellular biology and disease research. This thesis introduces a deep learning pipeline crafted to automate the segmentation and quantification of Nuclear pores from high-resolution 3D cell organelle images. Our aim is to refine computational methods capable of handling the data's complexity and size, thus improving accuracy and reducing manual labor in biological image analysis. The developed pipeline incorporates data preprocessing, augmentation strategies, random block sampling, and a three-stage post-processing algorithm. It utilizes a 3D U-Net with a VGG-16 backbone, optimized through cyclical data augmentation and random block sampling to tackle the challenges posed by limited labeled data and the processing of large-scale 3D images. The pipeline has demonstrated its capability to effectively learn and predict Nuclear pore structures, achieving some improvements in validation metrics compared to baseline models. Our experiments suggest that cyclical augmentation helps prevent overfitting, and random block sampling contributes to managing data imbalance. The post-processing phase successfully automates the quantification of Nuclear pores without the need for manual intervention. The proposed pipeline offers an efficient and scalable approach to segmenting and quantifying Nuclear pores in 3D microscopy images. Despite the ongoing challenges of computational intensity and data volume, the techniques developed in this study provide insights into the automation of complex biological image analysis tasks, with potential applications extending beyond the detection of Nuclear pores. Friday 12/8 at 4pm Name: Ola Karajeh Title: Improving Text Classification Using Graph-based Methods Abstract: Text classification is a fundamental natural language processing task. However, in real-world applications, class distributions are usually skewed, e.g., due to inherent class imbalance. In addition, the task difficulty changes based on the underlying language. When rich morphological structure and high ambiguity are exhibited, natural language understanding can become challenging. For example, Arabic, ranked the fifth most widely used language, has a rich morphological structure and high ambiguity that result from Arabic orthography. Thus, Arabic natural language processing is challenging. Several studies employ Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), but Graph Convolutional Networks (GCNs) have not yet been investigated for the task. Sequence-based models can successfully capture semantics in local consecutive text sequences. On the other hand, graph-based models can preserve global co-occurrences that capture non-consecutive and long-distance semantics. A text representation approach that combines local and global information can enhance performance in practical class imbalance text classification scenarios. Yet, multi-view graph-based text representations have received limited attention. In this research, first we introduce Multi-view Minority Class Text Graph Convolutional Network (MMCT-GCN), a transductive multi-view text classification model that captures textual graph representations for the minority class alongside sequence-based text representations. Experimental results show that MMCT-GCN obtains consistent improvements over baselines. Second, we develop an Arabic BERT Graph Convolutional Network (AraBERT-GCN), a hybrid model that combines the large-scale pre-trained models that encode the local context and semantics alongside graph-based features that are capable of extracting the global word co-occurrences in non-consecutive extended semantics by only one or two hops. Experimental results show that AraBERT-GCN outperforms the state-of-the-art (SOTA) on our Arabic text datasets. Finally, we propose an Arabic Multidimensional Edge Graph Convolutional Network (AraMEGraph) designed for text classification that encapsulates richer and context-aware representations of word and phrase relationships, thus mitigating the impact of the complexity and ambiguity of the Arabic language.

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good luck and thank you
by Cal Ribbens 06 Dec '23

06 Dec '23

Dear CS students, Congratulations on having reached the last day of classes for the semester. We wish you all great stamina and good health as you finish out projects and exams for the semester. Thank you for all the work you put in over these last months. Please enjoy the upcoming break. And to those who are completing their degrees --- we wish you all the best in all the days and years ahead! Go Hokies! Dr. Ribbens -- Head, Department of Computer Science Virginia Tech

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