Chenghong Wang

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Assistant Professor
Department of Computer Science
Indiana University Bloomington
3054 Luddy Hall, Bloomington, IN 47408
Contact: cw166 AT iu (dOt) edu

I am an assistant professor in the Department of Computer Science at Indiana University. I am also affiliated with Luddy’s Security & Privacy in Informatics, Computing, and Engineering (SPICE) Center. I received my Ph.D. in Computer Science from Duke University under the supervision of Ashwin Machanavajjhala and Kartik Nayak.

I am looking for Ph.D. students and Postdocs to join our team and contribute to research on ML accelerators and accelerator TEEs. If you are interested in architecture research and developing new hardware designs, please drop me an email.

Research

My current research focuses on trustworthy, data-centric AI infrastructures. Some current research projects are:

  • Data systems for trustworthy AI
    • ParsecDB (On-going): A highly efficient, full-fledged secure outsourced database that enables critical domain organizations to offload private data to the public cloud for analytics and ML ETL pipelines.
    • Picachv (Security25): A novel security monitor that formally and automatically enforces data use policies within data analytics and ML data pipelines.
    • DPAR (SC25): Private collective-communication AllReduce primitives for HPC-scale ML.
    • SPECIAL (VLDB24): The first secure workload planner designed for complex analytics over private data federations.
  • Confidential accelerators
    • BOLT (CCS25): A high-performance, secure, and data-oblivious KVS accelerator (the foundation for secure retrieval-based AI).
    • LinGCN (NeurIPS23):An accelerator architecture designed to reduce multiplication depth of homomorphic encryption based GCN inference.
    • AQ2PNN (MICRO23): An ultra-fast 2PC-DNN accelerator built on FPGAs.
  • Learning-augumented private algorithms
    • DPidx: The first private learned index for secure outsourced databases and private data federations.
    • Private PoS (Security23): Use PAC-based learning tools, such as noisy binary trees and Bayesian learners, to quantify privacy leakages in PoS blockchains.

My research has received generous support from the NSF (2419821, 2207231), IU IAS, Intel, and AMD

Students

Ph.D

Alumni