I am Ashish, a Senior Research Data Scientist at RCAC. My professional journey is marked by a solid statistics and machine learning foundation underpinning my data science expertise. I specialize in analyzing complex data sets, applying statistical models, and leveraging machine learning techniques to extract meaningful insights and drive innovation in my field.

As a Senior Research Data Scientist, I am responsible for designing and leading data analysis projects that require a deep understanding of statistics and machine learning. My job involves developing advanced predictive models, performing rigorous statistical analysis, and interpreting data to inform strategic decisions. I also stay at the forefront of advancements in AI and data science, ensuring that the latest research and best practices in these domains guide my work.

Individuals often seek my expertise for my extensive statistics and machine learning background, which is crucial for tackling complex data science challenges. Whether it's for advice on statistical methodologies, assistance in building and optimizing machine learning models, or insights into data-driven decision making, my comprehensive knowledge and practical experience make me a valuable resource. Moreover, my ability to clearly communicate complex technical concepts to diverse audiences further enhances my effectiveness as a mentor and collaborator in the field of data science.

Currently, I am focusing on enhancing my deep learning skills, particularly in neural network architecture design. While I have a robust foundation in machine learning and statistics, the evolving landscape of deep learning presents new challenges and opportunities. By deepening my knowledge and practical expertise in this area, I aim to apply these advanced techniques to more complex and nuanced data problems, thereby expanding my capabilities in cutting-edge data science research and applications.

My go-to productivity trick is time blocking. I allocate specific blocks of time for different tasks and focus solely on one task during its allotted time. This approach helps me avoid multitasking and distractions, allowing me to dive deeply into complex data analysis or research without interruption. By dedicating focused time to each task, I ensure that I am productive and efficient, making the most of my workday.

The trait I most attribute my success to is my curiosity. In the ever-evolving field of data science and AI, staying curious has driven me to learn and adapt continuously. My curiosity leads me to explore new methodologies, experiment with different techniques, and remain open to innovative ideas. This has not only kept my work dynamic and engaging but has also allowed me to stay ahead in a field where technological advancements happen rapidly. My success in data science is largely due to this relentless pursuit of knowledge and understanding, fueled by my inherent curiosity.

1. Academic Background and Interest in Mathematics and Statistics: My interest in data science began with a strong academic background in mathematics and statistics. I was always fascinated by the power of numbers and the insights they can reveal when analyzed correctly. This led me to pursue higher education in fields closely related to data analysis and statistical modeling.
2. Curiosity and Passion for Technology and Data: A natural curiosity and passion for technology and its applications in data drove me toward a career in data science. I was intrigued by how data could be used to solve real-world problems, predict outcomes, and inform decision-making processes. The interdisciplinary nature of data science, blending computer science with statistics, was particularly appealing to me.

I am particularly excited about the emerging technologies in AI and machine learning. The rapid advancements in areas like deep learning, neural networks, and natural language processing are opening up new possibilities for data analysis and predictive modeling. It's thrilling to explore these cutting-edge technologies and think about how they can be applied to solve complex problems.

A common misconception about my role as a Senior Research Data Scientist is that it's solely focused on crunching numbers and running algorithms. While data analysis and algorithm development are certainly key aspects of the job, there's much more to it. People often overlook the importance of understanding the business context, formulating the right questions, and interpreting the results in a meaningful way. Data science is not just about applying statistical models or machine learning techniques; it's about using these tools to extract insights that can inform decision-making and solve real-world problems. Additionally, effective communication and collaboration with stakeholders and team members are crucial components of the role, which go beyond the technical aspects of data analysis.

I have been on the RCAC team full time for 4 years, with several years before that as a student employee.

I have a high school diploma, some college education, many industry technology certificates and lots of skills gained from hands-on job experience and home-labbing. I have published a paper and poster on utilizing cloud bursting in HPC environments and am engaged in several professional groups focused on the research community, composable computing on campuses and various storage technologies and their applications. CaRCC, OpenZFS user group, campus k8s/openshift are the organizations I am involved with.

1. Passion for Teaching and Sharing Knowledge: I have always been passionate about teaching and sharing my knowledge with others. Becoming a mentor for the REU program allows me to guide and inspire undergraduates who are just beginning their journey in data science and research. It's an opportunity for me to impart the lessons I've learned and help students navigate the complexities of this field.
2. Contributing to the Growth of the Field: Data science is a rapidly evolving field, and by mentoring young researchers, I feel I can contribute to its growth. Nurturing the next generation of data scientists is crucial for the continued advancement of our field, and I take pride in being able to play a part in that.
3. Personal Fulfillment and Professional Development: Mentoring is incredibly fulfilling for me. It challenges me to stay current with the latest developments and think critically about the fundamentals of data science and machine learning. This not only benefits my mentees but also aids my own professional development.
4. Building a Stronger Research Community: I believe in the power of collaboration and community in research. By mentoring in the REU program, I can help build a stronger, more interconnected research community. It's rewarding to see students grow and contribute to the field, and I enjoy being a part of their journey.
5. Giving Back to the Academic Community: Throughout my career, I've been fortunate to have mentors who have significantly influenced my professional path. Becoming a mentor myself is a way of giving back to the academic community that has supported me. It's my way of paying it forward to the next generation of researchers.

1. Personal Satisfaction and Fulfillment: Mentoring in the REU program offers a great sense of personal satisfaction and fulfillment. Seeing students grow, learn, and develop their research skills is incredibly rewarding. It's gratifying to know that I am contributing to the development of future scientists and researchers.
2. Professional Development and Learning: Being a mentor keeps me engaged with the latest trends and developments in data science and machine learning. It challenges me to stay current and deepens my understanding of the field. Additionally, explaining complex concepts to students helps reinforce my own knowledge and sometimes provides new insights or perspectives.
3. Networking and Collaboration Opportunities: The REU program allows me to connect with other professionals and researchers in my field. These connections can lead to collaborative research projects, joint publications, and other professional opportunities. Networking with peers and being part of a community focused on research and education is highly beneficial.
4. Enhancing Teaching and Communication Skills: Mentoring students helps improve my teaching and communication skills. It requires me to explain complex topics clearly and effectively, tailor my communication to different learning styles, and provide constructive feedback. These skills are valuable in my professional life, especially when collaborating with teams or presenting research findings.
5. Contributing to the Field: By mentoring in the REU program, I am able to contribute to the field of data science in a meaningful way. Helping to train the next generation of researchers ensures the continued growth and advancement of the field. It's rewarding to be a part of shaping the future of data science and machine learning.

The students will gain hands-on experience in NLP, dtat engineering, and software integration while working collaboratively with mentors and advisors to deliver impactful solutions.

1. Requirements Gathering:
   • Assist in interviewing user support staff and analyzing current documentation to identify information gaps and define criteria for automated documentation tools.
2. Data Pipeline Development:
   • Develop and test ETL pipelines for extracting and preprocessing data from TicketHub, including insights from past user support requests.
3. NLP Model Development:
   • Fine-tune and optimize an open-source LLM to process HPC-related technical language.
   • Train the model to generate FAQs and support documents by identifying key information from processed data.
4. Software Development:
   • Collaborate on designing the integration of the NLP model with TicketHub, ensuring logical and efficient automation triggers.
5. User Interface Development:
   • Contribute to designing and testing user-friendly interfaces for trigerring and interacting with the document generation features.
6. Testing and Validation:
   • Conduct software testing and validation of generated documents.
   • Assist in creating reports to evaluate the performance and accuracy of the documentation tool.
7. Documentation and User Guide:
   • Help prepare detailed user guides and technical documentation for the newly integrated tools.