Venkat Dasari

Degree and Graduation Year: MS in DACSS, 2026

Tell us a little bit about yourself: I come from a computer science background with a focus on web development and machine learning. After completing my undergraduate degree in Computer Science, I was fortunate to explore both corporate and non-profit sectors during my gap year—working as a Software Engineer Intern at Tata Elxsi and contributing to the operations side of The Bharat Scouts and Guides. These diverse experiences helped shape both my technical abilities and my understanding of organizational dynamics.

What made you choose DACSS when thinking about your graduate education? I've always had a passion for understanding human behavior, so I was looking for programs that blend data science with the soft sciences. While several PhD programs integrate computational methods with other disciplines, there were very few programs at the master's level, and DACSS was one of them. As I aspire to use my technical skillset to understand social phenomena, I made DACSS my top priority and never looked back.

What data skills are you gaining/have you gained from your time at DACSS? I entered the program comfortable with technical methods, but I often overlooked the importance of interpretability and communicating significance. DACSS has taught me to interpret numbers within social contexts—making results meaningful and accessible to broader audiences. I've developed a keen interest in computational discourse analysis and belief formation, and I've gained confidence in designing end-to-end research projects and communicating findings to diverse audiences, whether academic, industry, or public-facing.

What kind of work have you been doing while at DACSS? Since the beginning of my program, I've been actively involved in research with Professor Justin Gross, where we conduct large-context text analysis on political book corpora using large language models and develop statistically defensible, theory-driven methods. At the Institute for Social Science Research (ISSR), I provide consultations on quantitative text analysis—from traditional NLP techniques to state-of-the-art AI/ML approaches—to support researchers across UMass with their projects. Additionally, I volunteer as AI Architecture Assurance Team Lead at GenAI Global, focusing on AI 2027 risks and promoting human-aligned policy development in AI.

Where do you see yourself in 5-10 years? Although I'm open to entering the job market following my master's, I plan to pursue a PhD within the next two to three years to achieve the intellectual mastery I aspire to. After completing my doctorate, I hope to work as a data scientist in research-focused industry roles while also teaching part-time to fulfill my passion for mentoring and sharing knowledge.

What advice would you have for people thinking about data science/analytics for their career and/or graduate school? "Data science never dies—it just evolves." My advice to aspiring data scientists is to stay both passionate and compassionate. The world is changing rapidly since the AI revolution, and the only way to keep up is to stay motivated by your purpose and never stop learning. Technical skills will evolve, tools will change, but a genuine commitment to understanding the dimensions of data—and using it responsibly—will always remain relevant.