Mahmut Kandemir

AI-related Expertise Topics

  • Architecture and system support for AI and ML
  • Automated ML application-to-hardware mapping strategies
  • Compiler-directed code optimizations for ML applications
  • Optimizations for Graph Neural Networks
  • Drug Discovery

Externally Funded AI Projects

  • NSF SHF: Small: Software and Hardware Optimizations for Learning over Graphs; Award Number:2008398; PI: Mehrdad Mahdavi; Co-PI: Mahmut Taylan Kandemir; Date:10/01/2020; Award Amount:$500,000.00

AI-related Courses

  • CSE598: Hand-On Deep Learning
  • CSE598: Machine Learning for Health Analytics


Related Publications

  • Weilin Cong, Rana Forsati, Mahmut T. Kandemir, Mehrdad Mahdavi: Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks. KDD 2020: 1393-1403
  • Morteza Ramezani, Weilin Cong, Mehrdad Mahdavi, Anand Sivasubramaniam, Mahmut T. Kandemir: GCN meets GPU: Decoupling "When to Sample" from "How to Sample". NeurIPS 2020
  • Huaipan Jiang, Mengran Fan, Jian Wang, Anup Sarma, Shruti Mohanty, Nikolay V. Dokholyan, Mehrdad Mahdavi, Mahmut T. Kandemir: Guiding Conventional Protein-Ligand Docking Software with Convolutional Neural Networks. J. Chem. Inf. Model. 60(10): 4594-4602 (2020)
  • Mengran Fan, Jian Wang, Huaipan Jiang, Yilin Feng, Mehrdad Mahdavi, Kamesh Madduri, Mahmut T. Kandemir, and Nikolay V. Dokholyan: GPU-Accelerated Flexible Molecular Docking:  The Journal of Physical Chemistry B 2021 125 (4), 1049-1060, DOI: 10.1021/acs.jpcb.0c09051
  • Diana Guttman, Mahmut Taylan Kandemir, Meena Arunachalam, Rahul Khanna:Machine learning techniques for improved data prefetching. In Proc. ICEAC 2015: 1-4ashwant Raj Gunasekaran, Prashanth Thinakaran, Cyan Subhra Mishra, Mahmut Taylan Kandemir, Chita R. Das: Towards Designing a Self-Managed Machine Learning Inference Serving System in Public Cloud. under revision, 2021

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Mahmut Kandemir
Distinguished Professor of Computer Science and Engineering



The Center for Artificial Intelligence Foundations and Engineered Systems (CAFE), pronounced café, brings together expertise from 75 researchers representing 24 academic units across Penn State with the goal of developing cross-disciplinary interactions. The center’s focus is on accelerating advances by synergistically advancing AI foundations and the techniques to deploy them efficiently toward applications focused on engineered and defense systems. CAFE provides opportunities for research partnerships, faculty/student recruitment, and technology transition to practice.

Center for Artificial Intelligence Foundations and Engineered Systems

The Pennsylvania State University

W323 Westgate Building

University Park, PA 16802