The Mathematics + Computer Science Seminar is a biweekly seminar highlighting research activities within the MCS Department at 利记sbo.
The seminars will be held in a hybrid fashion in S321 and virtually via the Zoom Meeting .
ID: 988 0730 0288
Passcode: 557482
Attendance is a requirement of "MCS 2111: MCS Seminar"
For further information on the 2024 Seminars, please contact Matthew Johnston (mjohnsto1@86899805.com).
The schedule for the MCS Seminar is as follows.
April 23, 11:00 a.m. - 12:00 p.m.
Speaker: Yelena Vaynberg
Abstract: 3D printing technology has transformed the manufacturing landscape, making it possible to create complex shapes that were once challenging or unachievable with traditional methods. In this presentation, we will explore the mathematical underpinnings that are fundamental to 3D printing technologies, with a particular emphasis on the algorithms that translate digital models into executable printing instructions. We'll examine the role of mathematics in the slicing process, the significance of topology in enhancing structural integrity, and discuss how mathematical optimization can be utilized to minimize material use and reduce printing duration. Furthermore, this presentation will showcase how these principles are applied in practice by producing complex mathematical figures, including fractals and geometric sculptures.
April 9, 11:00 a.m. - 12:00 p.m. (Zoom only)
Speaker: Eric Martinson
Abstract: Repeated visual observations of an environment are common in big data: people capturing temporally separate video streams with phones; facility security combining fixed cameras with human patrols; robots cleaning or monitoring a home. The challenge, however, is effectively processing these large highly repetitive data to extract useful results. Event-based methods like object detection struggle with a lack of application specific training data, while anomaly-based methods have high false positive rates requiring significant human review. Indoor spaces further complicate the matter as they are often co-occupied by people, changing constantly, and have highly individual detection requirements. What is needed are new ways for incorporating context into the search, discarding that which a human observer would otherwise ignore. To address this challenge, we have developed a novel system for Meaningful Change Detection, integrating two recent advances in machine learning: Neural Radiance Fields (NeRF’s) and Contrastive Language-Image Pre-Training (CLIP). Combining these approaches allows us to generate before and after images from the same viewpoint with a NeRF model, then apply semantically meaningful queries to search for changes useful to the application. This talk will present early results from the first prototype system and discuss future directions for investigation.
March 26, 11:00 a.m. - 12:00 p.m. (S214 - note special room!)
Speaker: Bashkim Zendeli
Abstract: In this presentation, I will discuss trivial, yet pivotal obstacles encountered in basic algebra and calculus, which frequently lead to contradictions. Furthermore, I will introduce my approach to solving an applied physics problem. I will introduce known but not often used in calculus classes the FM techniques for evaluating non elementary integrals. I will provide an illustrative example from number theory and examine the integration of complex variable functions, emphasizing their connection and significance to Zeta functions and their implications for the Riemann Hypothesis.
March 12, 11:00 a.m. - 12:00 p.m. (online only)
Speaker: Wisam Bukaita
Abstract: The fundamental connection between data science and research is evident in that data plays with the research process. As researchers and students, our first step towards conducting impactful research is obtaining and utilizing data to support our hypotheses. Without data, research is merely based on ideas and thoughts, lacking real-world insights. In this sense, data forms the foundation of any meaningful research. However, simply having data is not enough; it needs to be managed, organized, and analyzed to derive meaningful conclusions. This is where the toolbox of data science comes into play. These powerful tools and techniques allow us to efficiently manage and manipulate data, enabling us to draw relevant insights and draw strong conclusions. The data science toolbox encompasses a range of essential components, from data extraction and organization to predictive modeling and visualization, all of which are vital for successful research. Whether conducting a literature review, hypothesis testing, or building predictive models, the data science toolbox is a valuable resource for researchers and students alike.
February 27
Speaker: Haissam Badih
Abstract: Recent advancements in malware necessitate a robust security solution, as traditional and hybrid malware detection approaches fall short of modern cyber-attack strategies. This paper introduces a novel method to detect malware targeting webcam protocols and addresses crypto-jacking threats within blockchain technology. Our approach enhances blockchain security by injecting a specialized application into each node, enabling the identification and protection of miners and nodes against crypto-jacking. The application monitors CPU usage to detect abnormal processes, utilizing a cuckoo process for further analysis without alerting the attacker, thereby preventing compromised miners from conducting transactions. This innovative method not only secures nodes but also contributes to creating a denylist of infected internet protocols and blockchain addresses, enhancing overall network safety. Tested against threat actors and regular users, our approach is an effective defense mechanism against malware, particularly crypto-jacking.
February 20
Speaker: Christopher Cartwright
Abstract: The intertwining of mathematics, physics, and music has a long and convoluted history. We will trace some of the significant developments in music with input from math and physics related to musical scales and tonality, string theory ancient and modern, overtones and frequencies. The journey will see contributions from Pythagorus to Fourier via Euler, Galileo to Einstein via Helmholtz, and Bach to Stravinsky via Beethoven.
February 6
1 - 2 p.m. in S321
Speaker: Bruce Pell
Abstract: In this talk, I’ll present an overview of my past, present and future research projects related to modeling the spread of infectious diseases. Along the way, we’ll discuss reasons why such a task is essential and what types of mathematical tools can be used to understand the dynamic spread of diseases. Specific case studies will be presented from previous research projects (Ebola, Zika and Plague) and current and future projects (COVID-19, wastewater-based epidemiological surveillance, pathogen fitness, and thermal mismatch curves).
January 16
Speaker: Matthew D. Johnston (Associate Professor, MCS)
Abstract: We investigate how a population's natural and vaccine immunity affects the competitive balance between two strains of an infectious disease with different epidemiological characteristics. Specifically, we consider the case where one strain is more transmissible and the other strain is more immune-resistant. Our analysis shows that vaccination has a significant effect on the competitive balance between two strains, potentially leading to dramatic flips from one strain dominating in the population to the other. It also shows that which strain gains an advantage as a population's immunity level increases depends upon the integration between the mechanisms of natural and vaccine immunity.
April 11, 11:00 a.m. - 12:00 p.m.
Speaker: Destiny Anyaiwe
Abstract: Resourcefulness and well-informed decision-making rely heavily on the availability and effective use of data. With the advent and prolific use of computers in every aspect of human endeavors, data generation, its rate, and influx have been re-engineered. Therefore, it is crucial to adequately train students in research in a data-centric world. This is the focus of my talk, which will cover topics such as data science sources of data, the reasons for collecting data, how to collect data, how to analyze data, and how to use analysis results.
March 28
Speaker: Matthew D. Johnston
Abstract: In this talk, I will present some recent joint work with Drs. Pell and Nelson on the mathematics of COVID-19 spread. We introduce an n-stage vaccination model and corresponding system of differential equations which can simulate a disease outbreak by breaking the population down according to their vaccination status. This allows the mitigation effects of vaccination and accelerating effects of variants such as delta to be uncoupled from one another, and offers valuable insight for the future course of the COVID-19 pandemic. We fit the model to 2021 data from the Virginia Department of Health.
March 14
Speaker: Wisam Bukaita
Abstract: Many real-world events and occurrences happen every day, and explaining these events is the scientist's desire. Events produce data, and the data illustrate the trends and patterns of the event. The data can speak and narrate the events' story through the research work. However, without data, science is voiceless. The scientists draw the story of events in one simple and directive step by asking WHY. My academic journey in research focused on illustrating conclusions based on data and science. My demonstration will focus on two perspectives: The first perspective is my current research, and The second perspective is how to prepare and help students to put their first steps on the research path.
February 28
Speaker: Paula Lauren
Abstract: In this talk, I will provide an overview of Generative A.I. Assistants with a focus on the one that took the world by storm a few months ago. It’s a computer program that goes by the name of ChatGPT. The talk will be anchored around a media interview recorded last month at Lawrence Tech on ChatGPT that delved into key Natural Language Processing (NLP) tasks, but did not air due to time constraints. Several of the various NLP tasks for training the system will be conveyed via conversations with ChatGPT in the context of a novel cake recipe. I will also talk about a special topics course taught at Lawrence Tech, which delves into the programmatic and mathematical aspects of these key NLP tasks for students interested in learning more.
February 14
Speaker: Bruce Pell
Abstract: In this talk, I’ll present an overview of my past, present and future research projects related to modeling the spread of infectious diseases. Along the way, we’ll discuss reasons why such a task is essential and what types of mathematical tools can be used to understand the dynamic spread of diseases. Specific case studies will be presented from previous research projects (Ebola, Zika and Plague) and current and future projects (COVID-19, wastewater-based epidemiological surveillance, pathogen fitness, and thermal mismatch curves).
January 31
Speaker: Yelena Vaynberg
Abstract: In this talk, I will introduce students to actuarial mathematics. I will explain what actuary science is and show the different mathematical computations involved. I will also talk about my Geometry in Art class and an interesting application. We will gain an understanding of the kind of research that is done in this area and how it is used to help Archeologists and Historians determine the age of excavated objects.
January 17
Speaker: Tao Liu
Abstract: In this talk, I will share my own experiences of studying, working, and researching in the field of Computer Science and Engineering. I will use my own story as a clue to discuss some common problems in study and work, as well as opportunities for research projects. Through this talk, I hope to brighten the path for our students to move forward in the world of Computer Science.
April 26
Speaker: Tao Liu
Abstract: In this talk, I will first introduce research topics on the intersection between Machine Learning and Cybersecurity, including Machine Learning for Security, and Security for Machine Learning. Research projects include Machine Learning based Malware Analysis, Adversarial Machine Learning, and Machine Learning powered Malware will be discussed to provide students with an in-depth understanding of these topics. I will then briefly introduce my other research works, supervised student projects, and cybersecurity teaching projects.
April 19
Speaker: Destiny Anyaiwe
Abstract: An important parameter in getting scholars to engage in research is basically their genuine interest in the subject. For undergraduate students, such interest could stem from affinity for a profession, area of interest for further studies, or type of job the student is aiming to get. Student advisors & supervisors also play a huge role in influencing students' interests. In this talk, I will take a moment to talk about myself as an advisor, my teaching ideology, my research and classroom environments. I will also take a look at areas of my research interest and some topics of recent students CRE and capstone projects. The talk will be concluded with a description of who 'an ideal research/senior project student' is to me, what I expect from them and what the future demands from our working together.
April 12
Speaker: Abdollah Kavousifard
Abstract: The threat of cyberattacks have motivated researchers to use Big Data and Artificial Intelligence (AI) to detect malicious activity and ensure the preservation of privacy and security. Within the smart grid and smart city concepts, AI techniques can be used to identify transactions that are likely to be fraudulent or compromised, as well as automate manually intensive data management tasks. My recent research activities involve applications of AI in electric grids to transportation systems, smart city, microgrids, electric vehicles, electric arc furnaces, industrial control systems (IDSs), renewable energy sources, and energy hubs. My talk will familiarize students with my research interests in the areas of advanced AI, cyber security, IDSs, and big data mining to facilitate possible future research collaborations.
April 5
Speaker: Yelena Vaynberg
Abstract: In this talk, I will introduce students to actuarial mathematics. I will explain what actuary science is and show the different mathematical computations involved. I will also talk about my Geometry in Art class and an interesting application. We will gain an understanding of the kind of research that is done in this area and how it is used to help Archeologists and Historians determine the age of excavated objects.
Speaker: Sharon M. Carter
Abstract: In this presentation I will discuss: who I am outside of the classroom, the evolution of my teaching career, my philosophy of Math Education, and projects with my students.
March 22
Speaker: CJ Chung
Abstract: In this talk, I will introduce research & development project experiences in areas such as Embedded Systems/Software Engineering (ESE), Autonomous Robotics, Internet of Things (IoT), Evolutionary Computation (EC) including Cultural Algorithms, Evolutionary Neuro Fuzzy Systems, Deep Learning (DL), and STEM education since 1980 for over 40 years. Then future opportunities as well as in-class project ideas in those fields will be introduced.
March 1
Speaker: Wisam Bukaita
Abstract: The scope of the presentation incorporates a brief review of the research path and future research in addition to the in-class projects and modeling. A second-order non-homogenous differential equation is employed in my research papers to add the aesthetical and architectural views to the structural system and deliver the art of math in a real-life structural building. The modified differential equation provides a strong alternative to the most recent American Institute Steel Construction, AISC codes for structural engineers through a new derived alignment chart to facilitate the design process. Coding skills and 3D printing are functionalized to enhance learning in the classroom. Other alternative teaching methods are presented to combine playing games and practicing some of the theoretical concepts using virtual reality.
February 15
Speaker: Paula Lauren
Abstract: In this talk, I will explain the use of word embeddings and how they are used to derive meaning from text. Word embeddings are a numerical representation of words (also known as distributional word vectors) based on word pair co-occurrences from a corpus. In addition, I will present an overview of some of my past, recent, and current research projects leveraging word embeddings in various computing tasks. Since this seminar series is geared towards MCS2111 students, I will also incorporate a teaching part at the end of my talk to discuss my text mining and analytics course along with methodology towards senior projects and directed study.
February 1
Speaker: Bruce Pell
Abstract: In this talk, I’ll present an overview of my past, present and future research projects that relate to modeling the spread of infectious diseases. Along the way we’ll discuss reasons for why such a task is important and what types of mathematical tools can be used to understand the dynamic spread of diseases. Specific case studies will be presented from previous research projects (Ebola, Zika and Plague) along with current and future projects (COVID-19, pathogen fitness and thermal mismatch curves).
January 18
Speaker: Matthew D. Johnston
Abstract: In this talk, I will present some recent joint work with Drs. Pell and Nelson on the mathematics of COVID-19 spread. We introduce an n-stage vaccination model and corresponding system of differential equations which can simulate a disease outbreak by breaking the population down according to their vaccination status. This allows the mitigation effects of vaccination and accelerating effects of variants such as delta to be uncoupled from one another, and offers valuable insight for the future course of the COVID-19 pandemic. We fit the model to 2021 data from the Virginia Department of Health.