Stanford University
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Sharad Singhani
Si Instructor, Stanford Pre-Collegiate Studies
Staff, Stanford Pre-Collegiate StudiesBioSharad Singhani brings over two decades of combined experience in technology leadership and Computer Science education. A dedicated educator for more than 12 years, he previously spent 15 years in the software industry, holding technical leadership positions at prominent Silicon Valley organizations, including Oracle and BMC Software.
His expertise spans Artificial Intelligence, Machine Learning, Computer Vision, Large Language Models (LLMs), Intelligent Robotics, Unity development, full-stack software engineering, relational and non-relational database systems, and programming in Python, Java, and C++. Drawing on extensive industry and academic experience, he equips students with the knowledge, skills, and mindset needed to thrive in an increasingly technology-driven world.
Known for his student-centered approach, Sharad creates an engaging and intellectually stimulating learning environment where students are encouraged to think critically, collaborate effectively, and solve complex real-world problems. He has mentored students across academic, research, and competitive settings, guiding them toward excellence while preparing them to become the next generation of innovators and technology leaders.
Sharad earned an M.S. with Distinction from ICSEI, DAVV Indore, and an M.Sc. from Indian Institute of Technology Roorkee. -
Alyson Singleton
Ph.D. Student in Environment and Resources, admitted Autumn 2021
BioAly is a PhD student in the Emmett Interdisciplinary Program in Environment & Resources, investigating the impact of large-scale global change on infectious disease transmission and broader health dynamics. Based on the concepts of One Health and Planetary Health, she focuses on the design and evaluation of win-win solutions that can synergistically benefit human and environmental health. As we anticipate widening disease disparities under increasing climate and land-use change, her research aims to identify opportunities to prevent and mitigate these compounding harms. She approaches these topics by integrating novel computational methods, field-data collection, and epidemiologic techniques.
Prior to coming to Stanford, Aly was a Data Science Fellow at the Centers for Disease Control and Prevention where she developed analytic tools for outbreak detection and triage of multiple pathogens and supported the CDC’s Novel Coronavirus (COVID-19) Response. She also worked at the People, Place & Health Collective at the Brown University School of Public Health while earning her undergraduate (BS, Applied Mathematics) and master's degrees (MA, Biostatistics).
