Name: Martha Asare
Pronouns: she/her/hers
Institution: The University of Texas Rio Grande Valley
Department: School of Mathematics and Statistics
Biography:
Martha Asare is a driven graduate student from the University of Texas Rio Grande Valley, specializing in applied statistics and data science. With a Bachelor’s in Statistics from Kwame Nkrumah University of Science and Technology in Ghana, Martha offers a global perspective to her studies. With three years of experience in statistical analysis and Python development, Martha excels in designing data analysis systems, conducting exploratory data analysis, and creating data visualizations. She collaborates with diverse teams to tackle business challenges using data-driven insights. Martha’s research interests span Machine Learning & AI, Big Data Analytics, Causal Inference, Spatial Statistics, and Time Series Analysis. Her impactful projects include AI applications in autism diagnosis, wine quality prediction, gene expression ddata and AI and high blood pressure analysis. Martha’s commitment to academia shines through her conference presentations and leadership roles, including Vice President of the International Society for Optics and Photonics UTRGV Chapter and Secretary of UTRGV’s Women in STEM Programs. Martha excels in Python, R, LaTeX, and statistical software. She’s also proficient in graphic design and office tools. Martha seeks opportunities in machine learning, data analysis, and Python development, ready to contribute her expertise to any organization’s success.
Academic Status: Masters Student
Year in program: 2nd
Research Area/Department: Applied Mathematics; Computer Science; Data Science; Machine Learning/AI; Mathematics
Other, specify:
Major/Specialty: Applied Statistics and Data Science
Degrees Earned or in Progress: BSc. Statistics , Kwame Nkrumah University of Science and Technology, 2017 Ms. Applied Statistics and Data Science, University of Texas Rio Grrande Valley
What courses or academic preparation have you completed to prepare for a summer internship experience?
To prepare for my upcoming summer internship experience, I have successfully completed several relevant academic courses in the fields of science and computer science. These courses include Advanced Statistical Learning, Probability and Statistics, Advanced Numerical Analysis, Stochastic Processes, Linear Algebra, and Neural Networks and Deep Learning during the fall 2022 and spring 2023 semesters. Currently, I am enrolled in Statistical Data Analysis with Python, Statistical Methods and thesis one which is one machine learning and gene exprression data. I am confident that the knowledge and skills acquired from these courses will equip me well for a productive and enriching summer internship.
Have you published any research or worked on research/technical projects? Yes
Where has your research been published or where have you conducted research/technical projects? 1. Linguistic Heritage and Mathematics Identity: A Study on Latin* Students’ Calculus 1 Experiences which has been accepted for the 53rd Annual International Bilingual Education Conference(NABE ) 2024 Presention at New Orleans. 2. Mathematics Identity and Specifications Grading on Calculus courses
Please describe your research/academic interests:
My academic and research interests encompass several areas within the realm of data science and analytics:
1. Machine Learning & AI: I have a strong inclination towards exploring advanced machine learning algorithms and artificial intelligence techniques. My goal is to harness the power of these technologies to improve data-driven decision-making processes in various domains.
2. Big Data Analytics: I am keen on leveraging large and complex datasets to extract meaningful insights and drive informed business intelligence. Analyzing and processing big data pose exciting challenges that I am eager to tackle.
3. Causal Inference: Understanding cause-and-effect relationships within complex data is a pivotal aspect of my research interests. I am committed to delving into methodologies and tools that can help uncover causal links and provide actionable insights.
4. Spatial Statistics: Spatial patterns and correlations within datasets fascinate me. I am intrigued by the opportunity to apply spatial statistical methods to analyze geospatial data and unveil valuable information for diverse applications.
5. Time Series Analysis: The study of data trends over time is a compelling area of interest for me. I am dedicated to exploring time series analysis techniques to create predictive models and gain a deeper understanding of temporal data dynamics.
In essence, my academic and research interests revolve around the intricate world of data, where I seek to apply innovative methodologies and technologies to unravel insights, solve complex problems, and contribute to advancements in the fields of machine learning, data analytics, and statistical inference.
Computational and Data Science Areas:
Applied Mathematics; National Security; Data Analytics and Visualization; Machine Learning and AI
Research Synergy:
Due to their revolutionary potential in resolving challenging real-world problems, computational, AI/machine learning, and data sciences studies are of particular interest to me. These technical fields support the notion of identifying synergies with DOE lab personnel interests and initiatives and are perfectly in line with the SRP’s emphasis on collaboration with students from various rresearch departments. The collaborative aspect of computational, AI, and data sciences lies at the heart of my interest for them. These professions frequently necessitate cross-disciplinary interaction, which closely resembles the SRP’s collaborative spirit. I can use these methods to address particular research difficulties by collaborating with the DOE lab team and utilizing their knowledge and resources.
Motivation:
I am motivated to participate in the Sustainable Research Pathways (SRP) internship program by a number of reasons, many of which are closely related to my academic and research experiences and are described below:
1. Passion for Research : My motivation for research is driven by my passion for computational, AI/machine learning, and data sciences. I sincerely enjoy exploring challenging issues, coming up with novel solutions, and making a difference in these domains.
2. Desire for Real-World Impact: The SRP is a rare chance to apply academic knowledge in real-world settings. The possibility of working on initiatives at DOE labs that have an immediate and significant influence on energy, the environment, and national security—problems of global importance truly motivates me.
3. Collaborative Environment: I firmly believe in the value of interdisciplinary teamwork, and the SRP’s collaborative culture is in line with this belief. I’m excited to work with the DOE lab personnel, leveraging my technical abilities and their subject-matter knowledge to produce beneficial results.
4. Alignment with Research Interests: The technological fields of machine learning, big data analytics, causal inference, spatial statistics, and time series analysis that I have described in my application are completely in line with the top areas of research for DOE labs. This alignment increases my desire to help with their projects.
5. Professional Development: The SRP offers participants the chance to advance both personally and professionally. This will give me the chance to learn from professionals in the sector, broaden my skill set, and acquire firsthand experience—experiences that will surely help me in my future academic and professional efforts.
To end, my drive to take part in the SRP stems from my love of research, my dedication to making a difference in critical global issues, and my enthusiasm for multidisciplinary, collaborative work. I see the SRP as a venue where I can combine my academic interests with real-world applications, all the while supporting the DOE’s mission and furthering scientific understanding.
Lightning Talk Title: Machine Learning Modeling for Ovarian Cancer Cells