Dr. Amir Koushyar Ziabari

Amir K Ziabari

Senior R&D Staff Data Scientist

Contact

865.576.2371 | ziabariak@ornl.gov

Bio

Dr. Amir Koushyar Ziabari is a senior R&D staff scientist at the Oak Ridge National Laboratory (ORNL), within the Electrification and Energy Infrastructure Division's Multimodal Sensor Analytics group. His academic journey includes a tenure as a Postdoc at Purdue University's Integrated Imaging group, Department of Electrical and Computer Engineering (ECE), where he also completed his PhD in August 2016. Dr. Ziabari's research has been at the intersection of physics, signal/image processing, computational imaging and machine/deep learning to unravel complex multiscale experimental physics phenomena. His work is pivotal in data analytics, driven by a synergy of data-driven and physics-based methodologies for image reconstruction and segmentation for various scientific imaging applications in domains including advanced manufacturing, medical imaging, nuclear, and materials science.

Dr. Ziabari's research philosophy succinctly can be characterized as "data science for science", where he has been leading efforts on developing innovative physics-based computational imaging, computer vision, and machine/deep learning algorithms for scientific imaging applications. These tools are designed to meticulously process and analyze multiscale scientific and medical imaging data, enhancing the capabilities of imaging systems and fostering advancements in image reconstruction, processing, detection, segmentation, and classification. His recent initiatives involve leveraging Generative AI models to synthesize realistic experimental data from physics-based simulations, thereby generating essential multi-modal training data for scientific imaging applications.

Currently leading projects on Advanced Materials & Manufacturing Technologies (AMMT) and DOE  Advanced Materials & Manufacturing Technologies Office (AMMTO) programs at ORNL,  Dr. Ziabari works closely with a dynamic team engaged in developing cutting-edge computational imaging and machine learning algorithms for a variety of scientific imaging applications. Beyond his R&D and leadership responsibilities at ORNL, Dr. Ziabari involves in three major societies—IEEE, ASTM, and OSA—where his recent promotion to IEEE senior member and technical committee member of the IEEE Computational Imaging allows him to more meaningfully contribute to the society.

His recent work in rapid X-ray CT characterization for additive manufacturing, leveraging deep learning-based reconstruction to significantly enhance the throughput and quality of imaging for dense metal parts, has resulted in collaborative efforts and in turn a licensing agreement and partnership with ZEISS, which in turn demonstrates  his commitment to bridging the gap between academic research and practical, real-world applications.

Dr. Ziabari has directly contributed to recruiting, mentoring and the growth of postdocs at ORNL, focused on creating a diverse and inclusive research environment. His vision for expanding research into non-destructive material characterization and multi-modal data analysis aims to further advance materials science applications at ORNL and beyond.

Dr. Ziabari has successfully secured funding as both principal investigator (PI) and co-PI, totaling over $3.5 million. His current and future endeavors will be focused on physics-based Computational imaging and Machine/Deep Learning for multi-modal scientific imaging applications in various scientific domains.

 

Professional Experience

Oak Ridge National Laboratory
 

R&D Scientist, Multimodal Sensor Analytic Group (formerly known as IM&SL) Dec. 2021 - Present

** PI for 7 internal and external proposals from which 5 are funded (Total funded: $3,443,689). 
** Co-PI in 5 proposals from which 3 is funded (total funded: $930,000)
** Secured 3 R&D licensing agreement with ZEISS, INL and NIST for our software SIMURGH.

  • DiffusiveINR: Energy-Efficient Foundation Model for 3D Inverse Scientific Imaging Problems 
  • Foundation model for multi-modal image segmentation in additive manufacturing
  • High-Performance AI-Enhanced X-ray CT for Large-Scale DED Components
  • Super-resolution Deep Learning for X-ray CT reconstruction
  • Iterative Model-Based Deep Learning Framework for Accelerated Industrial Cone-beam X-ray CT
  • Semi-Supervised (GAN+CNN) CAD and Physics-based 3D X-ray CT reconstruction software (SIMURGH).
  • Generative AI for microstructure generation.
  • Data Analytic and 3D Image Reconstruction for Real Time Monitoring in Additive Manufacturing (AM)
  • CI and DL-based algorithms for High Annular Angular Dark Field (HAADF) STEM
  • Model Based Iterative Reconstruction (MBIR) for Limited View 3D Coded Aperture Imaging with Gamma rays 
  • DL-based Semantic Segmentation for Non-Destructive Characterization of Concretes
  • CI for X-ray CT of Nuclear TRISO Particles
  • Non-iterative deep learning based algorithms for 3D neutron CT imaging
  • Developed in-house 3D CycleGAN for cell microscopy data generation for our YOLO2U-Net software
  • Multi-Modal Detection and Instance Segmentation for Nephrons in MRI and Histo-pathological Data
  • DL-based Superresolution from low field & resolution to high field & resolution MRI data leveraging T1/T2 mappings
  • Maximum Likelihood Expectation Maximization (MLEM) algorithm for Coded Source Neutron Imaging.

Purdue University West Lafayette, IN 

Postdoctoral Researcher, Integrated Imaging Group (prof. Charles Bouman) Oct. 2016 - Oct. 2018

  • Plug-n-Play (ADMM) with a non-convex physics-based regularizer for image reconstruction in electron microscopy 
  • MBIR With Spatially Adaptive Sinogram Weights for GE’s Wide-Cone Cardiac CTs. Patent granted for the work. 
  • 2.5D DL-MBIR for X-ray CT reconstruction for GE Healthcare.

Graduate Research Assistant June 2012 - Aug. 2016

  • Physics-Informed Computational Imaging for sub-diffraction thermal imaging at nanoscale (100 nm) with visible light 
  • Finite element, analytical modeling along with experimental investigation of non-Fourier nanoscale thermal transport
  • 3D Power Blurring as an image processing based tool for fast and accurate thermal modeling in 3D integrated circuits (3D ICs) considering thermal vias
  • Implemented Power Blurring using CUDA programming.
     

University of California, Santa Cruz Santa Cruz, CA 

Graduate Research Assistant Sep. 2009 - May. 2012

  • Developed an image processing based tool for fast and accurate static and dynamic thermal modeling in integrated circuits (ICs) considering change in material properties (Adaptive Power Blurring)
  • Implementation of a Moving Gradient Method for Plausible Video Reconstruction

Awards

News highlights

Honors and Awards

  • R&D 100 Finalist 
  • Promoted to IEEE Computational Imaging Technical Committee Member 2023 
  • Promoted to IEEE Senior Member 2022 
  • Paper for calcification of breast imaging highlighted in Electronic Imaging Symposium 2023 
  • Won AAPM TrueCT Challenge Award 2022 
  • Promoted to R&D staff Scientist at ORNL 2021 
  • ORNL Innovation Award 2021 
  • ICONs Best Paper Award 2021 
  • Promoted to R&D Associate staff at ORNL 2019 
  • DAAD PostdoctNeT Award on: Artificial Intelligence Coming of Age - R & D in Germany 2018 
  • IMAPS Conference Best Paper Award 2010 
  • University of California at Santa Cruz Fellowship

Education

Purdue University, PhD in Electrical and Computer Engineering (ECE), 2012 - 2016

University of California Santa Cruz (UCSC), Master of Science in ECE, 2009 - 2012

Sharif University of Technology, Master of Science in ECE, 2006 - 2008

Amirkabir University of Technology, Bachelor of Science in ECE, 2001 - 2005

Professional Service

Trademarks and Patents

  1. L. Fu. Amirkoushyar Ziabari, et al., 2017. Tomographic Reconstruction with Spatially Adaptive Sinogram Weights. US Patent App. 15/840,953.
  2. Amirkoushyar Ziabari, et al., 2020. System And Method For Artifact Reduction Of Computed Tomography Reconstruction Leveraging Artificial Intelligence And A Priori Known Model For The Object Of Interest. U.S. Appl. No. 63/060,450.
  3. Amirkoushyar Ziabari, et al., 2022. Simurgh. CERTIFICATE OF U.S. COPYRIGHT REGISTRATION Copyright, Copyright Number: 90000193.

Publications

View All

Other Publications

   Journals

  1. L. Amichi, ..., Amirkoushyar Ziabari, “A 3D Nanoscale View of Electrocatalyst Degradation in Hydrogen Fuel Cells,” Advanced Energy Materials, 2024.
  2. O. Rahman, ..., Amirkoushyar Ziabari, “Dual X-ray CT-aided classification of melt pool boundaries and flaws in crept additively manufactured parts,” Journal of Materials Characterization, 2024.
  3. Amirkoushyar Ziabari, et al., “YOLO2U-Net: 3D Localization, Detection and Instance Segmentation of Cells,” Journal of Pattern Recognition Letters, 024.
  4. J. Rakhmonov, ..., Amirkoushyar Ziabari, et al., “Creep deformation and cavitation in an additively manufactured Al-8.6Cu- 0.4Mn-0.9Zr (wt%), Journal of Additive manufacturing, 2024.
  5. R.Kannan,...,Amirkoushyar Ziabari, et al., “Reduction kinetics of hematite powder using argon/hydrogen plasma with prospects for near net shaping of sustainable iron,” Sustainable Materials and Technologies, 2024.
  6. A Cheniour, Amirkoushyar Ziabari, Y Le Pape, “A mesoscale 3D model of irradiated concrete informed via a 2.5 U-Net semantic segmentation,” Construction and Building Materials 412, 134392, 2024.
  7. P. Fernandez, ..., Amirkoushyar Ziabari, M. M. Kirka, “Digital polycrystalline microstructure generation using diffusion proba- bilistic models,” Materialia 33, 101976, 2024.
  8. Fernandez-Zelaia, Patxi, Sebastien N. Dryepondt, Amir Koushyar Ziabari, and Michael M. Kirka. “Self-supervised learning of spatiotemporal thermal signatures in additive manufacturing using reduced order physics models and transformers,” Computational Materials Science 232 (2024): 112603.
  9. Snow, Zackary, Luke Scime, Amir Koushyar Ziabari, Brian Fisher, and Vincent Paquit. “Scalable in situ non-destructive evaluation of additively manufactured components using process monitoring, sensor fusion, and machine learning.” Additive Man- ufacturing 78 (2023): 103817.
  10. Amirkoushyar Ziabari, et al., “2.5D Deep Learning Model Based Iterative Reconstruction with CAD Based Beam Hardening Correction for High-Throughput X-ray Computed Tomography Characterization in Metal Additive Manufacturing,” Nature Com- putational Materials, vol.9, pp. 91, 2023.
  11. Z. Snow, L. Scime, Amirkoushyar Ziabari, et al., “Observation of Spatter-Induced Stochastic Lack-of–Fusion in Laser Powder Bed Fusion Using In Situ Process Monitoring,” Additive Manufacturing, pp. 103298, 2022.
  12. Z. Y. Wu, P. Zhu, ..., Amirkoushyar Ziabari, et al., “A general synthesis of single atom catalysts with controllable atomic and mesoporous structures,” Nature Synthesis 1.8 (2022): 658-667.
  13. S. V. Venkatakrishnan, Amirkoushyar Ziabari, et al. Model-based Reconstruction for Enhanced X-ray CT of Tri-structural Isotropic (TRISO) Particles. Applied Optics 61.6 (2022): C73-C79.
  14. S. V. Venkatakrishnan, Amirkoushyar Ziabari, et al. “Algorithm-driven Advances for Scientific CT Instruments: From Model- based to Deep Learning-based Approaches.” IEEE Signal Processing Magazine, vol. 39, no. 1, pp. 32-43, Jan. 2022, doi: 10.1109/MSP.2021.3123594.
  15. S. V. Venkatakrishnan, Amirkoushyar Ziabari, et al. “Convolutional neural network based non-iterative reconstruction for accelerating neutron tomography”. Machine Learning: Science and Technology, 2(2), 025031, 2021.
  16. S. Alajlouni, S., A. Beardo, L. Sendra, Amirkoushyar Ziabari, et al. Geometrical quasi-ballistic effects on thermal transport in nanostructured devices. Nano Research, 14(4), 945-952, 2021.
  17. Amirkoushyar Ziabari, M. Parsa, Y. Xuan, J. H. Bahk, K. Yazawa, F. X. Alvarez, & A. Shakouri, ”Far-field thermal imaging below diffraction limit”. Optics express, 28(5), 7036-7050, 2020.
  18. G. W. Helmreich, D. Richardson , S. Venkatakrishnan, & Amirkoushyar Ziabari, (2020). Method for measurement of TRISO kernel and layer volumes by X-ray computed tomography. Journal of Nuclear Materials, 539, 152255.
  19. M. Kirka, W. Halsey, D. Rose, Amirkoushyar Ziabari, V. Paquit, D. Ryan, P. Brackman, “Analysis of Data Streams for Qual- ification and Certification of Inconel 738 Airfoils Processed Through Electron Beam Melting,” ASTM: Selected Technical Papers (STP), 2020.
  20. Amirkoushyar Ziabari, J. Rickman, L. Drummy, J. Simmons, and C. Bouman, “Physics-Based Regularizer for Joint Soft Seg- mentation and Reconstruction of Electron Microscopy Images,” IEEE Transaction on Computation Imaging, Vol. 5, No. 4, DOI: 10.1109/TCI.2019.2899499, pp. 660-674, 2019.
  21. Amirkoushyar Ziabari, P. Torres, B. Vermeersch, X. Cartoixa, A. Torello, Y. Xuan, J-H Bahk, Y. R. Koh, M. Parsa, P. D. Ye, F. X. Alvarez and A. Shakouri, “Full-field thermal imaging of hydrodynamic heat flow in nanoscale devices,” Nature Communications, 10.1038/s41467-017-02652-4, Vol. 9 No. 1, Jan. 2018.
  22. P. Torres, Amirkoushyar Ziabari, A. Torello, J. Bafaluy, J. Camacho, X. Cartoixa, A. Shakouri, and X. Alvarez, “Emergence of hydrodynamic heat transport in semiconductors at the nanoscale,” Physical Review Materials, Vol. Issue 7, 2018.
  23. S. Sadeque, A. Candadai, Y. Gong, K.Maize, Amirkoushyar Ziabari, A. Mohammed, A. Shakouri, T. Fisher, and D. Janes, “Tran- sient Thermal Response of Microscopic Hotspots in Silver Nanowire Network Conductors,” IEEE Transaction On Nanotechnology, 10.1109/TNANO.2018.2794782, 2018.
  24. S. Sadeque, Y. Gong, K. Maize Amirkoushyar Ziabari, A. Mohammed, A. Shakouri, and D. Janes, “Transient Thermal Response of Hot-spots in Graphene-Silver Nanowire Hybrid Transparent Conducting Electrodes,” submitted, 2018.
  25. Amirkoushyar Ziabari, M. Zebarjadi, D. Vashaee, and A. Shakouri, “Nanoscale Solid State Cooling: A Review,” in Reports on Progress in Physics, 79(9), 095901, 2016.
  26. S. Jin, Amirkoushyar Ziabari, Y. R. Koh, M. Saei, X. Wang, B. Deng, Y. Hu, J-H Bahk, A. Shakouri, and G. J. Cheng. “Enhanced thermoelectric performance of P-type nanowires with pulsed laser assisted electrochemical deposition.” Extreme Mechanics Letters 9, pp. 386-396, (2016).
  27. H. Pajouhi, A. Y. Jou , R. Jain , Amirkoushyar Ziabari, A. Shakouri , C. A. Savran , S. Mohammadi, “Flexible CMOS microelectrode arrays with applications in single cell characterization,” in Journal of Applied Physics, Vol. 17, Issue 20, Nov 2015.
  28. T. Favaloro*, Amirkoushyar Ziabari*, J-H. Bahk, P. Burke, J. Bowers, A. Gossard, Z. Bian, and A. Shakouri, “High temperature thermoreflectance imaging and transient Harman characterization of thermoelectric energy conversion devices,” in Journal of Applied Physics, Vol. 116, No. 3, 2014; ISSN: 10897550; DOI: 10.1063/1.4885198.
    *Authors contributed equally to this work.
  29. K. Maize, Amirkoushyar Ziabari, W. French, P. Lindorfer, B. OConnel, and A. Shakouri, “Thermoreflectance CCD Imaging of Self Heating in Power MOSFET Arrays,” in IEEE Transaction On Electron Devices, Vol. 61, No. 9, pp. 3047-3053, 2014; ISSN: 0018-9383; DOI: 10.1109/TED.2014.2332466;
  30. Amirkoushyar Ziabari, J.H. Park, E. K. Ardestani, J. Renau, S. M. Kang, and A. Shakouri,“Power Blurring: Fast Chip-Level Static and Transient Thermal Analysis Method for Packaged Integrated Circuits,” IEEE Transaction on Very Large Scale Integration (TVLSI) Systems, 2014; ISSN: 10638210; DOI: 10.1109/TVLSI.2013.2293422.
  31. Amirkoushyar Ziabari, E. Suhir, A. Shakouri,“Minimizing Thermally Induced Interfacial Shearing Stress in a Thermoelectric Module with Low Fractional Area Coverage”, Microelectronic Journal, (2014);
    http://dx.doi.org/10.1016/j.mejo.2013.12.004.
  32. M. Ziabari, A. M. Kassai, Amirkoushyar Ziabari, and S. E. Maklavani, “Designing a Hamming Coder/Decoder using QCAs”, in Journal of Applied Science, 2008.

  33. M. Ziabari, A. M. Kassai, S. E. Maklavani, and Amirkoushyar Ziabari, “A Prospect of Future Generation of Quantum Dots in Computers”, in Journal of Applied Science, 2008.

    Conferences

  34. Anika Tabbassum, Amirkoushyar Ziabari “Adapting Segment Anything Model (SAM) to Experimental Datasets via Fine-Tuning on GAN-based Simulation: A Case Study in Additive Manufacturing”, Accepted, Neurips Workshops, 2024.
  35. Amirkoushyar Ziabari, et al. “Combining Deep Learning and scatterControl for High-Throughput X-ray CT based Non- Destructive Characterization of Large-Scale Casted Metallic Components”, Accepted, Industrial CT Conference, (invited for Journal Publication), 2024.
  36. T. Kaarsberg, ... Amirkoushyar Ziabari, et al., “Energy Efficiency Scaling for 2 Decades (EES2) R&D Roadmap. V1.0 for Compute”, IEEE HPEC, 2024.
  37. Jingsong Li, ... Amirkoushyar Ziabari, et al., “Edge Projection-Based Adaptive View Selection for Cone-Beam CT”, accepted, Asilomar conference in Signal, Systems and Image Processing, 2024.
  38. Haley Sullivan, ... Amirkoushyar Ziabari, “2.5D Super-Resolution for Defect Detection in X-ray Computed Tomography of Additively Manufactured Parts”, accepted, Asilomar conference in Signal, Systems and Image Processing, 2024.
  39. Aniket Pramanik, ..., Amirkoushyar Ziabari, “A Learnt Half-Quadratic Splitting-Based Algorithm for Fast and High-Quality Industrial Cone-beam CT Reconstruction,” CT meeting 2024.
  40. Obaidullah Rahman, ..., Amirkoushyar Ziabari, “Deep learning based workflow for accelerated industrial X-ray Computed To- mography (CT),” IEEE International Conference on Image Processing (ICIP), 2023.
  41. Obaidullah Rahman, ..., Amirkoushyar Ziabari, “Neural Network-based Single-material Beam-hardening Correction for X-ray CT in Additive Manufacturing,” 17th Fully3D International Conference, 2023.
  42. M. Gopalakrishnan, Amirkoushyar Ziabari, et al., “Physics guided machine learning for image-based material decomposition of tissues from simulated breast models with calcifications,” Electronic Imaging Symposium, 2022.
  43. Amirkoushyar Ziabari, et al., “SIMURGH: A Framework for CAD-Driven Deep Learning Based X-ray CT Reconstruction,” in 2022 IEEE International Conference on Image Processing (ICIP) (pp. 3836-3867).
  44. Amirkoushyar Ziabari, et al., “High Throughput Deep Learning-Based X-ray CT Characterization for Process Optimization in Metal Additive Manufacturing,” American Society of Precision Engineering (ASPE), 2022 Summer Topical Meeting Advancing Precision in Additive Manufacturing, Vol. 77, pp. 160–164.
  45. Amirkoushyar Ziabari, et al. “Artificial Intelligence Based X-ray CT Reconstruction of Metal Additive Manufacturing Parts”, Micro-CT User Meeting 2021.
  46. Amirkoushyar Ziabari, et al. “High Resolution X-Ray CT Reconstruction of Additively Manufactured Metal Parts using Gener- ative Adversarial Network-based Domain Adaptation in AI-CT.” Microscopy and Microanalysis 27.S1 (2021): 2940-2942.
  47. Amirkoushyar Ziabari, et al. “Beam hardening artifact reduction in x-ray CT reconstruction of 3D printed metal parts leveraging deep learning and CAD models.” ASME International Mechanical Engineering Congress and Exposition. Vol. 84492. American Society of Mechanical Engineers, 2020.
  48. M. Parsa, C. Schuman, N. Rathi, Amirkoushyar Ziabari, et al. “Accurate and Accelerated Neuromorphic Network Design Leveraging A Bayesian Hyperparameter Pareto Optimization Approach.” International Conference on Neuromorphic Systems 2021, pp. 1-8.
  49. Amirkoushyar Ziabari, et al., “A Two-Tier Convolutional Neural Network for Combined Detection and Segmentation in Biological Imagery,” IEEE GlobalSIP, 2019.
  50. Amirkoushyar Ziabari, D. Rose, M. Eicholtz, D. Solecki, A. Shirinifard , “A 2.5D YOLO-Based Fusion Algorithm for 3D Local- ization of Cells,” Asilomar Conf. on Signals, Systems and Computers, 2019.
  51. M. Parsa, A. Ankit, Amirkoushyar Ziabari, K. Roy, “PABO: Pseudo Agent-Based Multi-Objective Bayesian Hyperparameter Optimization for Efficient Neural Accelerator Design,” ICCAD 2019.
  52. Amirkoushyar Ziabari, V. Paquit, M. Kirka, P. Bingham, and S. Venkatakrishnan, “X-Ray CT Reconstruction of Additively Manufactured Parts using 2.5 D Deep Learning MBIR,” Microscopy and Microanalysis Conference, Aug. 2019.
  53. Amirkoushyar Ziabari, D. Ye, S. Srivastava, K. D. Sauer, J. B. Thibault, and C. Bouman , “2.5D Deep Learning for CT Image Reconstruction with Multi-GPU implementation,” Asilomar Conf. on Signals, Systems and Computers, pp. 2044-2049, Monterrey, Oct. 2018.
  54. J. Simmons, Amirkoushyar Ziabari, J. Rickman, C. Bouman,“Physics-Based Regularization for Denoising Polycrystalline Material Images,” SIAM Conference on Imaging Science, Bologna, Italy, June. 2018.
  55. Amirkoushyar Ziabari, D. Ye, L. Fu, S. Srivastava, K. D. Sauer, J. B. Thibault, and C. Bouman , “Model Based Iterative Reconstruction With Spatially Adaptive Sinogram Weights For Computed Tomography,” CT Meeting, Salt Lake City, Utah, May 2018.
  56. Amirkoushyar Ziabari, J. Rickman, C. Bouman and J. Simmons, “Physics Based Modeling for the Development of Soft Seg- mentation and Reconstruction Algorithms,” Asilomar Conference on Signals, Systems and Computers, Monterey, California, Nov. 2017.
  57. Amirkoushyar Ziabari, J. Rickman, J. Simmons and C. Bouman, “Physic-Based Image Reconstruction of SiC Grain Boundaries,” Microscopy and Microanalysis Conference, Aug. 2017.
  58. Amirkoushyar Ziabari, Y. Xuan, J-H Bahk, M. Parsa, P. D. Ye, and A. Shakouri, “Sub-Diffraction Thermoreflectance Thermal Imaging using Image Reconstruction,” Proc. IEEE ITherm, Invited paper, June 2017.
  59. Amirkoushyar Ziabari, Alex Shakouri, Y. Xuan, J-H Bahk, Y. .R. Koh, M. Si, P. D. Ye, and A. Shakouri, “Non-Diffusive Heat Transport in Twin Nanoheater Lines On Silicon,” Proc. IEEE ITherm, June 2017.
  60. Alex Shakouri, Amirkoushyar Ziabari, et al., “Stable Thermoreflectance Thermal Imaging Microscopy with Piezoelectric Position Control,” Proc. IEEE SemiTherm, pp. 128-132, 2016.
  61. Kazuaki Yazawa, D. Kendig, Amirkoushyar Ziabari, et al., “Thermal imaging of nanometer features,” Proc. IEEE ITherm, pp. 495-500, 2016.
  62. S. H. Shin, M. A. Wahab, W. Ahn, Amirkoushyar Ziabari, K. Maize, A. Shakouri, and Muhammad. A. Alam, “Fundamental trade- off between Short-Channel Control and Hot Carrier Degradation in an Extremely-thin Silicon-on-Insulator (ETSOI) Technology,” Electron Devices Meeting (IEEE International IEDM), pp. 20.3.1-20.3.4, 2015.
  63. Amirkoushyar Ziabari, et al., “Sub-diffraction Limit Thermal Imaging for HEMT Devices,” Proc. IEEE SemiTherm, San Jose, CA, USA, March 2015.
  64. Alex Shakouri, Amirkoushyar Ziabari, et al., “Temperature Sensitivity and Noise in Thermoreflectance Thermal Imaging,” accepted for Proc. IEEE SemiTherm, San Jose, CA, USA, March 2015.
  65. Amirkoushyar Ziabari, K. Yazawa, and A. Shakouri, “Designing a Mechanically Robust Thermoelectric for High Temperature Applications,” 32nd International Thermal Conductivity Conference and 20th International Thermal Expansion Symposium, May 2014.
  66. Amirkoushyar Ziabari, E. Suhir, A. Shakouri, “Minimizing Thermally Induced Interfacial Shearing Stress in a Thermoelectric Module”, in Therminic, Budapest, Hungary, Sept. 2012.
  67. Amirkoushyar Ziabari, A. Shakouri, ”Fast Thermal Simulations of Vertically Integrated Circuits (3D ICs) including Thermal Vias”, in ITherm, San Diego, USA, May 2012.
  68. K. Yazawa, Amirkoushyar Ziabari, Y. R. Koh, V. Sahu, Y. Joshi, A. Fedorov, and A. Shakouri “Cooling Power Optimization for Hybrid Solid-State and Liquid Cooling in Integrated Circuit Chips with Hotspots ”, in ITherm, San Diego, USA, May 2012.
  69. V. Sahu, K. Yazawa, Amirkoushyar Ziabari, Y. Joshi, A. Fedorov, and A. Shakouri “Energy Efficient Liquid-Thermoelectric Hybrid Cooling for Hot-Spot Removal ”, in Proc. IEEE SemiTherm, San Jose, CA, USA, 2012.
  70. E. K. Ardestani, Amirkoushyar Ziabari, J. Renau, and A. Shakouri, “Enabling Reliability-Aware Floorplanning With Fast Thermal Simulations”, in Proc. IEEE SemiTherm, San Jose, CA, USA, 2012.
  71. Amirkoushyar Ziabari, E. K. Ardestani, J. Renau, and A. Shakouri, “Fast Thermal Simulators for Architectural Level Circuit Design”, in Proc. IEEE SemiTherm, San Jose, CA, USA, 2011.

  72. Amirkoushyar Ziabari, Z. Bian, and A. Shakouri, “Adaptive Power Blurring Techniques to Calculate Temperature Profiles Under Large Temperature Variations”, in IMAPS, San Jose, CA, USA, 2010.

     

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