Openings

Sian Jin from the Department of Computer and Information Sciences at Temple University is recruiting Ph.D. students starting in Spring or Fall 2027. Prospective students interested in high-performance computing, scientific data compression, efficient AI systems, and computing systems are encouraged to apply.

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Main Research Areas

1. High-Performance Computing (HPC) and data-intensive systems

2. Scientific data compression, representation, analysis, and visualization

3. Efficient AI and large language model (LLM) systems

4. GPU-accelerated and compression-aware computing

5. Domain-specific data reduction for scientific and biomedical applications

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Biography

Dr. Sian Jin is an Assistant Professor in the Department of Computer and Information Sciences at Temple University. He received his Ph.D. in Computer Engineering from Indiana University in 2023 under the supervision of Prof. Dingwen Tao, and his B.S. in Physics from Beijing Normal University in 2018. His research lies at the intersection of high-performance computing, data compression, and efficient AI systems. His recent work focuses on scientific data reduction and representation, compression-aware computing systems, GPU-accelerated data processing, and efficient AI/LLM inference. His research has been published in leading conferences and journals including SC, PPoPP, VLDB, EuroSys, ICDE, HPDC, ICS, and IPDPS.

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Research Environment

Our research involves interdisciplinary and multi-institutional collaborations and provides students with opportunities to work on both fundamental research and large-scale computing applications. Students may have opportunities to collaborate with researchers at U.S. national laboratories, pursue research internships, attend major academic conferences, and evaluate research solutions on large-scale computing systems and supercomputers.

Ph.D. students are provided with financial support covering tuition, stipend, and health insurance, as well as support for research-related conference travel when appropriate.

The group values independent thinking, open communication, and close research mentorship. Students are encouraged to develop their own research interests while working collaboratively on challenging problems in high-performance computing, data compression, and AI systems. The lab is located in Temple University's Science Education and Research Center (SERC), which provides a modern research and working environment.

About Temple University

Temple University is an R1 research university located in Philadelphia, Pennsylvania. The Department of Computer and Information Sciences has active research programs in computer systems, high-performance computing, artificial intelligence, data science, and related areas, providing opportunities for collaboration across research groups.

Philadelphia offers convenient access to academic, industrial, and research institutions throughout the U.S. East Coast. New York City and Washington, D.C. are both easily accessible by train.

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Basic Requirements

Applicants should:

1. Have a strong foundation in computer science, computer engineering, or a related field such as mathematics or physics, with solid programming skills.

2. Be interested in systems, HPC, data compression, AI/ML systems, or related research areas.

3. Have strong motivation for research and the ability to learn and work independently.

Prior research experience in systems, parallel programming, GPU computing, machine learning, or data compression is helpful but not required.

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Application

The expected start date is Spring or Fall 2027.

Interested students are encouraged to contact me directly at sian.jin@temple.edu.

Please use the following email subject:

PhD Application [Spring/Fall] 2027 – [Your Name]

Please include:

1. A brief personal statement describing your research experience, research interests, and motivation for pursuing a Ph.D.

2. CV/resume.

3. Academic transcripts.

4. Any additional materials that may help demonstrate your research experience or technical background (optional).

Please combine the materials into a single PDF file when possible.