Scholar
Tahseen Rabbani
Google Scholar ID: ufpvVrQAAAAJ
Postdoctoral Scholar, University of Chicago
Citations & Impact
Citations
185
H-index
7
i10-index
3
Publications
20
Co-authors
0
Publications
Resume (English only)
Academic Achievements
- - 2024: Selected as a 2024 RSA Security Scholar.
- - 2023: Published a paper at NeurIPS'23 titled Large-Scale Distributed Learning via Private On-Device Locality-Sensitive Hashing.
- - 2022: Published a paper at ICLR'23 titled SWIFT; Published a paper at NeurIPS'22 titled SketchGNN; Presented two papers at the GL Frontiers 2022 workshop; Presented a pre-print at FL-AAAI-22 titled Comfetch; Published a paper at MSML'22 titled Practical and Fast Momentum-Based Power Methods.
- - 2021: Nominated as a 2022 Apple Scholar in AI/ML; Awarded an NSF COMBINE Fellowship for 2021-2022.
Research Experience
- - 2025: Participated in the CFAgentic@ICML'25 panel discussion and presented at both MemFM and CFAgentic workshops at ICML 2025.
- - 2024: Presented a paper at the AAAI'25 Workshop AI2ASE; Co-hosted the watermarking workshop at NeurIPS'24; Presented the distributed learning platform DISCO at GDHF'24 in Nairobi, Kenya; Two works accepted to the NeurIPS'24 Workshop on Machine Learning and Compression; Competition proposal accepted by NeurIPS'24; Published a paper on benchmarking image watermarks at ICML'24.
- - 2023: Published a paper at NeurIPS'23; Presented a pre-print at SLowDNN'23 in Dubai, UAE.
- - 2022: Published a paper at ICLR'23 titled SWIFT; Published a paper at NeurIPS'22 titled SketchGNN; Presented a pre-print at FL-AAAI-22 titled Comfetch; Published a paper at MSML'22 titled Practical and Fast Momentum-Based Power Methods.
Education
- Completed a postdoc at Yale University in 2024, under the supervision of Dr. Mary-Anne Hartley. Subsequently joined the University of Chicago as a Postdoctoral Scholar, co-hosted by Tian Li and Ce Zhang.
Background
- AI/ML postdoc, primarily interested in efficiency, distributed learning, and privacy. Also interested in numerical methods, generative watermarking, optimization, and machine translation.
Miscellany
- Personal interests include low-resource, private, and distributed machine learning, particularly in the fields of proteomics and drug discovery.
Co-authors