Name: Kwesi Appau Ohene-Obeng
Pronouns: he/him/his
Institution: University of Texas at El Paso
Department: Mathematical Sciences
Biography:
I am a data scientist driven by a profound passion for leveraging data to tackle real-world challenges. My expertise spans the realms of machine learning, data analysis, and statistical modeling, encompassing both supervised and unsupervised learning techniques. My track record showcases a proven ability to translate data-driven insights into tangible enhancements for business processes and informed decision-making. I excel in collaborating seamlessly within diverse teams and possess adeptness in conveying intricate findings to audiences with varying technical backgrounds. Currently, I am pursing my Ph.D. in Data Science at the University of Texas at El Paso. My academic foundation includes a master’s degree in Mathematics and Finance earned at the University of Essex, United Kingdom. Notably, my master’s thesis delved into Bayesian mortality forecasting, where I employed a non-informative prior alongside time series analysis to refine the model. My educational background, coupled with practical experience, has significantly shaped my problem-solving approach, enabling me to deliver timely and effective solutions. I am adept at employing diverse statistical methodologies and visualization techniques to craft compelling data narratives, forming the bedrock of preliminary analysis. Furthermore, I harness my programming prowess in Python and R to facilitate the seamless execution of projects.
Academic Status: PhD Student
Year in program: 2nd
Research Area/Department: Data Science; Machine Learning/AI
Other, specify:
Major/Specialty: Major: Data Science Specialty: Time Series, Mathematical Statistics, Machine Learning
Degrees Earned or in Progress: PhD Data Science (in progress) – Dec 2025 MSc Mathematics and Finance – Nov 2020 BSc Statistics- May 2016
What courses or academic preparation have you completed to prepare for a summer internship experience?
Time Series Applied Regression Introduction to Data Mining Research collaborations Probability Inferential Statistics Computational Linear Algebra Introduction to Statistical Analysis Python programming R programming Introduction to Database Management
Have you published any research or worked on research/technical projects? No
Where has your research been published or where have you conducted research/technical projects?
Please describe your research/academic interests:
My research interests span supervised learning (training models to make predictions or classify data based on labeled data), unsupervised learning (finding patterns, structures, or representations in unlabeled data), time series and regression, and fairness, bias, and ethics in machine learning (ensuring that machine learning models and algorithms are fair and unbiased).
Computational and Data Science Areas:
Data Analytics and Visualization; Machine Learning and AI
Research Synergy:
I am interested in the computational, AI/machine learning, and data sciences research and technical areas because they have the potential to revolutionize how we address some of the world’s most pressing problems, such as climate change, energy security, and public health. I believe that computational science can be used to model and simulate complex physical and chemical systems, which can be used to develop new energy technologies, design more efficient manufacturing processes, and improve our understanding of climate change. AI/machine learning can be used to analyze large datasets to identify patterns and trends that would be difficult or impossible to see with the naked eye. This can be used to develop new diagnostic tools for diseases, predict the behavior of extreme weather events, and optimize the performance of energy systems. Data sciences can be used to collect, clean, and analyze data to gain insights and make predictions. This can be used to improve the efficiency of energy production and distribution, develop new methods for carbon capture and storage, and improve the accuracy of weather forecasts. I am currently in the concluding stages of my research project, where I used three deep learning techniques to predict the stock market. I also deployed a hybrid model where I combined wavelet transformations before fitting the models. This combination of machine learning and computational science has shown to be an effective way of making meaning out of data. I am excited to contribute to the work of the DOE labs by applying my skills in computational, AI/machine learning, and data sciences research and technical areas. I believe that these technologies have the potential to revolutionize the way we address some of the world’s most challenging problems. In addition to the specific examples above, I am also interested in collaborating with DOE lab staff to develop new and innovative ways to apply computational, AI/machine learning, and data sciences to the DOE’s mission. I am eager to learn from the expertise of the DOE lab staff and to contribute to the development of new technologies that can make a real difference in the world. I am particularly interested in applying computational, AI/machine learning, and data sciences to the DOE’s mission of advancing scientific research and developing innovative technologies to support its mission. For example, I am interested in using these technologies to develop new energy technologies, improve energy efficiency, and develop new methods for carbon capture and storage. I believe that my skills and experience in computational, AI/machine learning, and data sciences can make a significant contribution to the work of the DOE labs. I am eager to learn from the expertise of the DOE lab staff and to collaborate on new and innovative ways to apply these technologies to the DOE’s mission.
Motivation:
My passion for the fields of computational science, AI/machine learning, and data sciences is driven by their profound potential to address some of the most pressing challenges our world faces today. These domains offer powerful tools that can bring about transformative changes in various aspects of life. I am particularly excited about the opportunity to apply these technologies in diverse settings, where their unique capabilities align with specific missions and objectives. In my view, computational science empowers us to simulate complex systems, fostering innovation in a wide range of fields. AI/machine learning, on the other hand, enables us to extract valuable insights from vast and complex data, leading to improved decision-making and problem-solving. As I approach the culmination of my current research project, where I’ve employed cutting-edge techniques to gain insights from data, I’ve witnessed firsthand the tremendous potential of these fields to make meaningful contributions to various domains. I am motivated to continue exploring and contributing to these fields, firmly believing that they hold the keys to addressing many of our world’s challenges. Moreover, I am eager to collaborate with experts and peers to pioneer innovative approaches that can drive positive change and make a lasting impact on society. Together, we can harness the power of computational science, AI/machine learning, and data sciences to create a brighter and more promising future for all.
Lightning Talk Title: HARMONIZING MACHINE LEARNING METHODS FOR TIME SERIES FORESCASTING