Internships help Sanghani Center graduate students enrich their academic experience. While putting their knowledge to work, they gain on-the-job work skills, contribute to solving real-world problems, and what they learn often enhances their particular research interests.

“AI and machine learning are becoming central in all fields,” said Sanghani Center Director Naren Ramakrishnan. “Our students’ proficiency in these areas makes them valuable to industries in a range of verticals and, in turn, our students benefit greatly from internship experiences."

This summer, students are interning at Microsoft, Google, Capital One, Amazon, and the Chronicle of Higher Education, among other companies.

Following is a roundup of who they are, where they are, and the kind of work they are doing:

Hani Alomari, a Ph.D. student in computer science, is a machine learning engineer intern at Adobe in San Jose, California. He is advised by Chris Thomas.   

Hasan Arif, a Ph.D. student in computer science, is a Ph.D. research intern at Byte Dance/TikTok in Bellevue, Washington.  He is working on improving the efficiency and robustness of speculative decoding. He is advised by Bo Ji.  

Ali Asgarov, a Ph.D. student in computer science, is interning as an applied scientist II at Amazon in Bellevue/Seattle, Washington. He is working on multimodal Large Language Models (LLMs) for Amazon’s global returns platform, building agentic conversational systems and vision-language models to improve return reasoning, damage assessment, and large-scale decision-making. His advisor is Chris Thomas.

Md. Atabuzzaman, a Ph.D. student in computer science, is a research intern, Advanced Solution Development, at Futurewei Technologies, Inc., in Framingham, Massachusetts. He is working on data platform and Large Language Model (LLM)-related enterprise system solutions, with a focus on improving the robustness of Large Vision-Language Models (LVLMs) for question answering on multi-sheet engineering image understanding. He is advised by Chris Thomas.

Kevin Chang, a master’s degree student in computer science, is a generative AI engineering intern at Peraton in Herndon, Virginia. He is contributing to the development of enterprise-grade generative AI systems, focusing on agentic workflow orchestration and Retrieval-Augmented Generation (RAG) pipelines. He is also developing evaluation frameworks, optimizing prompt libraries, and implementing responsible AI controls -- such as hallucination mitigation and validation gates -- to ensure reliable, auditable decision-support workflows. His advisor is Chang-Tien Lu.

Chen-Wei (Wilson) Chang, a master’s degree student in computer science, is a software development engineer intern at Amazon in Austin, Texas. He is working on agentic AI workflow automation. His advisor is Chang-Tien Lu.

Jianpeng Chen, a Ph.D. student in computer science, is a Ph.D. software engineer intern at Google in Mountain View, California. He is working on a research/engineering hybrid project aimed on optimizing the training and inference efficiency for large scale ranking model of YouTube Shorts foundations. His advisor is Dawei Zhou.

Yusuf Dalva, a Ph.D. student in computer science, is a research intern at Google Research in Mountain View, California. He is working on developing a content-adaptive tokenizer that allocates tokens based on image complexity and that spans multiple resolutions within a single model. His advisor is Pinar Yanardag.

Amartya Dutta, a Ph.D. student in computer science, is an AI research intern at Genentech in South San Francisco, California. The goal of the internship is to build a robust multimodal biomedical foundation model. She is co-advised by Anuj Karpatne and T. M. Murali.

Wei Fan, a Ph.D. student in computer science, is a machine learning engineer intern at Apple in Cupertino, California. She is working on a project related to agentic AI. Her advisor is Bo Ji.  

Maryam Haghani, a Ph.D. student in computer science, is interning as a data enablement and analytics engineer at Regeneron Biopharmaceutical Company in Tarrytown, New York. She is developing applications to support biomedical data replatforming, automated reporting, and scalable access to scientific data. Her advisor is Song Li.

Mridul Khurana, a Ph.D. student in computer science, is an intern at Apple in Seattle, Washington, where he is working on photorealistic image editing. His advisor is Anuj Karpatne.

Harith Laxman, a master’s degree student in computer science, is a machine learning intern at The Chronicle of Higher Education in Washington, D.C. He is working on building -- and deploying to production -- a chat-based tool designed to extract analytical from the  Integrated Postsecondary Education Data System (IPEDS). His advisor is Naren Ramakrishnan.

Pin-Jie Lin, a Ph.D. student in computer science, is an applied scientist intern at the Amazon AI Lab in Santa Clara, California, where he is working on multilingual reasoning for Large Language Models (LLMs). His advisor is Tu Vu.

Kazi Sajeed Mehrab, a Ph.D. student in computer science, is an AI research engineer intern at LinkedIn, in Mountain View, California. He is working on Large Language Model (LLM)-based generative recommenders. He is co-advised by Anuj Karpatne and Chris Thomas.

Abhilash Neog, a Ph.D. student in computer science, is an applied scientist intern at Microsoft in Redmond, Washington. He is working on generative artificial intelligence models for image editing tasks, with a focus on improving the quality and consistency of generated content. His advisor is Anuj Karpatne.

Priya Pitre, a Ph.D. student in computer science, is an AI engineering intern on the Emerging AI Patterns team at Capital One in San Jose, California. She is working to develop an open-weight agentic AI system capable of autonomously managing the end-to-end model development lifecycle, including training, evaluation, refinement, and deployment. She is co-advised by Xuan Wang and Naren Ramakrishnan.

Gaurab Pokharel, a Ph.D. student in computer science, is interning as an associate AI scientist at Pearson, located in London, England. He is looking at how machine learning can support large-scale human scoring of text. Specifically, he’s designing an analysis framework to compare training materials generated with model assistance against those built entirely by human experts — measuring quality, effectiveness, and efficiency — and characterizing where the model-assisted approach reaches parity and where it falls short. His advisor is Sanmay Das.

Colby Stakun-Pickering, a Ph.D. student in statistics, is interning as a biostatistician at Vertex, a pharmaceutical company in Boston, Massachusetts. He is building an AI document retrieval model to identify relevant clinical trial documentation requested by the U.S. Food and Drug Administration (FDA) to expedite the regulatory drug approval process. He is co-advised by Leanna House and David Higdon.

Umid Suleymanov, a Ph.D. student in computer science, is a data science intern for Foundation Security and Science at Amazon in Seattle, Washington. He is focused on the intersection of artificial intelligence and cybersecurity, applying Large Language Models (LLMs) and advanced machine learning techniques to develop and enhance security frameworks. His advisor is Murat Kantarcioglu.

Yu-Min Tseng, a Ph.D. student in computer science, is interning as a student researcher at Google in Seattle, Washington. She is working on Large Language Model (LLM) auto-rater evaluation. Her advisor is Tu Vu.

Kavana Venkatesh, a Ph.D. student in computer science, is an AI/machine learning intern at Apple, Inc., in Seattle, Washington. She is researching machine learning-driven approaches for enhanced personalization and recommendations.  She is advised by Jiaming Cui.

Hidir Yesiltepe, a Ph.D. student in computer science, is a research intern at Meta in New York City, where he is working on generative video modeling. His advisor is Pinar Yanardag.

Xinyue (Susan) Zeng, a Ph.D. student in computer science, is a research intern at Microsoft Research in Seattle/Redmond, Washington. She is working on multimodal long-horizon reasoning, focusing on training and adapting models to improve their reasoning skills while developing dynamic test-time search methods that help them solve complex multi-step tasks more efficiently and effectively. Her advisor is Dawei Zhou.