Toshiya Hachisuka (Google Scholar) Associate Professor David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1 (affiliated with Computer Graphics Lab, Computational Mathematics, and Scientific Computation Group) toshiya.hachisuka@uwaterloo.ca Pronunciation of my name: "Hachisuka" has silent "u" (e.g., "Ha-chee-ska"). "Toshiya" has stress on "To" (e.g., "Toe-she-ya", *not* like "Toshiba", but like "Joshua"). I will be away on sabbatical during January 2024 to June 2024 and my email response might be delayed during this period. I am currently not looking for a new student. However, if you are a current student at UWaterloo and would like to work with me, feel free to reach out to me. By default, I will decline to write a recommendation letter if you just took one of my courses and never worked with me outside of it. If you are interested in working with me as a new student later than 2025 or so, please first read this (say that you read it in your email) and contact me by stating your interest in rendering or relevant subjects within computer graphics. In case you apply for a graduate study, please also send me a one-page summary of your understanding of one of our papers. I might not reply to your email if you failed to follow any of the above. In particular, I may delete your email immediately if it is apparent that you are not really interested in working with me on the topics that I work on within computer graphics. Applications without any contact in advance may not be given full consideration.

• Sep/20/2023: Two papers are accepted at SIGGRAPH Asia 2023.
• Apr/29/2023: Two papers are accepted at SIGGRAPH North America 2023.
• Jan/31/2023: Perhaps nobody cares, but I fixed minor bugs in glslppm that prevented it running on RTX 3000 series. It reaches 50M to 60M photon paths per second on RTX 3070 and coverges under several seconds for the example scene.

• Rendering
• Light transport simulation
• Numerical analysis
• Computational statistics
• Physics simulation

• Ege Ciklabakkal (PhD)
• Haochen Gan (PhD)
• Ryusuke Sugimoto (PhD)
• Wenyou Wang (PhD)
• Weijie Zhou (PhD)
• Maxwell Fang (MMath)
• Xiaochun Tong (MMath)

UWaterloo
• Logan Mosier (MMath)
• Matthew Georgy (Undergrad RA)
• Hongfei Huang (Undergrad RA)
• Alex Wang (Undergrad RA)
• Mengyu Yuan (Undergrad RA)
• Sean Zhang (Undergrad RA)
• Ryan Zhu (Undergrad RA)
• Jack Cui (Undergrad RA)
• Terry Chen (Undergrad RA)
• Aryan Abed-Esfahani (Undergrad RA)
• Sina Hojjati (MMath)
• Wesley Chang (Undergrad RA)
• Yiti Jiang (Undergrad RA)
• Leo Wang (Undergrad RA)
• Naoharu Shimada (PhD)
• Linwan Song (Undergrad RA)
• Ben Zhang (Undergrad RA)
• Eve Li (Undergrad RA)
• Ji Ho Yoo (MS)

UTokyo
• Daniel Meister (Postdoc)
• Sabyasachi Mukherjee (MS then PhD)
• Rex West (MS then PhD)
• Lan Gao (MS)
• Miyu Hashimoto (MS)
• Yuki Kaji (MS)
• Yuji Moroto (MS)
• Yuki Tanaka (MS)
• Aaryaman Vasishta (MS)
• Weijie Zhou (MS)
• Nobuo Nakagawa (PhD)
• Jamorn Sriwasansak (MS then PhD)
• Ronan Cailleau (Visiting MS Student from the University of Rennes 1)
• Masayuki Honda (MS)
• Kazuhiro Kobayashi (MS)
• Taiga Mizuide (MS)
• Haruka Otake (MS)
• Hiroshi Takano (MS)
• Binh-Son Hua (Postdoc)
• Haokun Yang (Research Student)
• Kentaro Hirai (MS)
• Maria Ximena Bastidas Rodriguez (Visiting PhD Student from the National University of Colombia)
• Adrien Gruson (Postdoc)
• Hui Liu (Research Student)
• Hisanari Otsu (PhD)
• Shin Fujieda (MS)
• Masafumi Yamamoto (MS)
• Igor Zavialov (Research Student)
• Kevin Galim (Visitng MS Student from the Technical University in Munich)
• Sadashige Ishida (Visiting Researcher from Nikon)
• Chaitanya Reddy (Visiting PhD student from McGill University)
• Alessandro Dal Corso (Visiting PhD Student from Technical University of Denmark)
• Yuanming Hu (Visitng Student from Tsinghua University)
• Sigurd Ofstad (Visitng MS Student from Norwegian University of Science and Technology)
• Issei Takarada (MS)
• Pierre Poulin (Visiting Professor from Université de Montréal)

AarhusU
• Jacob Toft Pedersen (PhD)
• Stig Rohde Døssing (MS)
• Heine Stokholm (MS)
• Troels Mortensen (MS)
• Kristian Theilgaard Hansen (MS)
• Troels Ugilt Jensen (MS)
• Marc Ilsø Poulsen (MS)
• Nikolaj Kobek Thorsen (MS)
• Tommy de Blanck (MS)
• Lasse Staal (MS)
• Horea Ioan Trinca (Visiting PhD Student from Babeș-Bolyai University)

UWaterloo
• Advanced Topics in Computer Graphics - Rendering (CS888), F2023
• Introduction to Computer Graphics (CS 488/688), S2023
• Advanced Topics in Computer Graphics - Rendering (CS888), W2023
• Numerical Computation (CS 370), S2022
• Introduction to Computer Graphics (CS 488/688), F2021
• Advanced Topics in Computer Graphics - Rendering (CS888), W2021

UTokyo
• Realistic Image Synthesis, S2020
• Introduction to Computer Graphics, S2020
• Physically Based Animation, S2018
• Introduction to Computer Graphics, S2018
• Realistic Image Synthesis, S2017
• Introduction to Computer Graphics, S2017
• Physically Based Animation, S2016
• Introduction to Computer Graphics, S2016
• Realistic Image Synthesis, S2015
• Introduction to Computer Graphics, S2015

AarhusU
• Advanced Graphics Effects, S2013
• Rendering Algorithms, W2013
• Rendering Algorithms, W2012

	"Quantum Ray Marching for Reformulating Light Transport Simulation" [supplemental] L. Mosier, M. McGuire, and T. Hachisuka ACM SIGGRAPH Asia 2023 (Conference Track), 2023 (First scalable light transport algorithm on quantum computers.)
	"Input-Dependent Uncorrelated Weighting for Monte Carlo Denoising" [supplemental] [project] J. Back, B. Hua, T. Hachisuka, and B. Moon ACM SIGGRAPH Asia 2023 (Conference Track), 2023 (Intrinsically low-bias denoising for Monte Carlo rendering.)
	"A Practical Walk-on-Boundary Method for Boundary Value Problems" [project] R. Sugimoto, T. Chen, Y. Jiang, C. Batty, and T. Hachisuka ACM Transactions on Graphics (SIGGRAPH North America 2023), 2023 (Versatile Monte Carlo ray tracing solver for boundary value problems.)
	"Parameter-space ReSTIR for Differentiable and Inverse Rendering" [project] W. Chang, V. Sivaram, D. Nowrouzezahrai, T. Hachisuka, R. Ramamoorthi, and T. Li ACM SIGGRAPH North America 2023 (Conference Track), 2023 (Resampled path reuse for differentiable rendering.)
	"A Monte Carlo Method for Fluid Simulation" [project] D. Rioux-Lavoie*, R. Sugimoto* (* joint first authors), T. Özdemir, N. H. Shimada, C. Batty, D. Nowrouzezahrai, and T. Hachisuka ACM Transactions on Graphics (SIGGRAPH Asia 2022), 2022 (Monte Carlo framework for solving Navier-Stokes equations.)
	"Marginal Multiple Importance Sampling" [supplemental] R. West, I. Georgiev, and T. Hachisuka ACM SIGGRAPH Asia 2022 (Conference Track), 2022 (Unbiased approximation of MIS when one or more of the sampling techniques has an intractable marginal PDF.)
	"Surface-Only Dynamic Deformables using a Boundary Element Method" [project] R. Sugimoto, C. Batty, and T. Hachisuka ACM SIGGRAPH/Eurographics Symposium on Computer Animation 2022 (received "Best Paper Award"), 2022 (A practical extension of BEM for surface-only deformables.)
	"Single-pass Stratified Importance Resampling" [project] E. Ciklabakkal, A. Gruson, I. Georgiev, D. Nowrouzezahrai, and T. Hachisuka Eurographics Symposium on Rendering 2022 (CGF track), 2022 (Stratification made available within resampled importance sampling.)
	"Lightweight Multidimensional Adaptive Sampling for GPU Ray Tracing" D. Meister and T. Hachisuka Journal of Computer Graphics Techniques, 2022 (GPU-friendly MDAS.)
	"Regression-based Monte Carlo Integration" [project] C. Salaun, A. Gruson, B. Hua, T. Hachisuka, and G. Singh ACM Transactions on Graphics (SIGGRAPH North America 2022), 2022 (Connecting regression and MC integration to have a drop-in improvement of MC integration.)
	"Self-Supervised Post-Correction for Monte Carlo Denoising" [project] J. Back, B. Hua, T. Hachisuka, and B. Moon ACM SIGGRAPH North America 2022 (Conference Track), 2022 (Improving MC denoising just based on run-time rendered images.)
	"Fast Polygonal Splatting using Directional Kernel Difference" [talk] Y. Moroto, T. Hachisuka, N. Umetani Eurographics Symposium on Rendering 2021 (DL-only track), 2021 (Efficient blur with polygonal kernels using directional differences.)
	"Deep Combiner for Independent and Correlated Pixel Estimates" [project] J. Back, B. Hua, T, Hachisuka, and B. Moon ACM Transactions on Graphics (SIGGRAPH Asia 2020), 2020 (Effective combination of independent and correlated sampling via deep learning.)
	"Continuous Multiple Importance Sampling" [project] R. West, I. Georgiev, A. Gruson, and T. Hachisuka ACM Transactions on Graphics (SIGGRAPH North America 2020), 2020 (Continuous generalization of multiple importance sampling.)
	"A Model for Soap Film Dynamics with Evolving Thickness" [project] S. Ishida*, P. Synak* (* joint first authors), F. Narita, T. Hachisuka, and C. Wojtan ACM Transactions on Graphics (SIGGRAPH North America 2020), 2020 (Bubble dynamics simulation with evolving thickness.)
	"Quantum Coin Method for Numerical Integration" [slides] [code] N. H. Shimada and T. Hachisuka Computer Graphics Forum, 2020 (Practical numerical integration with quantum computing.)
	"Delayed Rejection Metropolis Light Transport"   [project] D. Rioux-Lavoie*, J. Litalien* (* joint first authors), A. Gruson, T. Hachisuka, and D. Nowrouzezahrai ACM Transactions on Graphics (Volume 39, Issue 3, presented at SIGGRAPH North America 2020), 2020 (Delayed rejection in MCMC applied to rendering.)
	"Stratified Markov Chain Monte Carlo Light Transport"   [comparisons] A. Gruson, R. West, and T. Hachisuka Eurographics 2020, 2020 (Perfect image-space stratification with MCMC.)
	"Deep Adaptive Wavelet Network" M. X. B. Rodriguez, A. Gruson, L. F. Polania, S. Fujieda, F. P. Ortiz, K. Takayama, and T. Hachisuka IEEE Winter Conference on Applications of Computer Vision (WACV) 2020, 2020 (Trainable wavelets within convolutional neural networks.)
	"Integral Formulations of Volumetric Transmittance"   [slides] I. Georgiev*, Z. Misso* (*joint first authors), T. Hachisuka, D. Nowrouzezahrai, J. Křivánek, and W. Jarosz ACM Transactions on Graphics (SIGGRAPH Asia 2019), 2019 (Integral formulations of transmittance that are suitable for unbiased Monte Carlo integration.)
	"Scalable Virtual Ray Lights Rendering for Participating Media "   [slides] N. Vibert, A. Gruson, H. Stokholm, T. Mortensen, W. Jarosz, T. Hachisuka, and D. Nowrouzezahrai Eurographics Symposium on Rendering 2019 (CGF track), 2019 (Lightcuts-like algorithm for VRLs with a proper error bound.)
	"A Frequency Analysis and Dual Hierarchy for Efficient Rendering of Subsurface Scattering"   [slides] D. Milaenen, L. Belcour, J. P. Guertin, T. Hachisuka, and D. Nowrouzezahrai Graphics Interface 2019 (received "Best Student Paper Award"), 2019 (Accelerating rendering of translucent objects via dual-tree traversal)
	"A Survey on Gradient-Domain Rendering" B. Hua, A. Gruson, V. Petitjean, M. Zwicker, D. Nowrouzezahrai, E. Eisemann, and T. Hachisuka Eurographics 2019 STARs, 2019 (Survey on gradient-domain rendering)
	"Light Transport Simulation in the Gradient Domain" B. Hua, A. Gruson, M. Zwicker, and T. Hachisuka SIGGRAPH Asia 2018 Courses, 2018 (Course on gradient-domain rendering)
	"Geometry-Aware Metropolis Light Transport"   [project] H. Otsu, J. Hanika, T. Hachisuka, and C. Dachsbacher ACM Transactions on Graphics (SIGGRAPH Asia 2018), 2018 (MCMC rendering which efficiently takes the visibility term into accout)
	"Matrix Bidirectional Path Tracing" C. R. A. Chaitanya, L. Belcour, T. Hachisuka, S. Premože, J. Pantaleoni, and D. Nowrouzezahrai Eurographics Symposium on Rendering 2018 (EI & I track), 2018 (Matrix reformulation and stratified subsampling of light subpaths in bidirectional path tracing)
	"Gradient-Domain Volumetric Photon Density Estimation"   [project] A. Gruson, B. Hua, N. Vibert, D. Nowrouzezahrai, and T. Hachisuka ACM Transactions on Graphics (SIGGRAPH North America 2018), 2018 (Gradient-domain formulation of volumetric photon density estimation including beams and planes)
	"Efficient Energy-Compensated VPLs using Photon Splatting"   [project] J. Sriwasansak, A. Gruson, and T. Hachisuka ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (long papers) 2018, 2018 (More accurate VPL rendering using photon splatting)
	"Reproducing Spectral Reflectances from Tristimulus Colors"   [project] H. Otsu, M. Yamamoto, and T. Hachisuka Computer Graphics Forum, 2018 (Data-driven approach for converting RGB to spectrum)
	"Thinking Outside the Cornell Box: Non-rendering Research by a Rendering Guy" T. Hachisuka Invited talk, MEIS 2017, 2017 (My recent attempts on non-rendering research)
	"A Hyperbolic Geometric Flow for Evolving Films and Foams"   [project] S. Ishida, M. Yamamoto, R. Ando, and T. Hachisuka ACM Transactions on Graphics (SIGGRAPH Asia 2017), 2017 (Geometric reformulation of physically based film animation)
	"Supervised Learning of How to Blend Light Transport Simulations" H. Otsu, S. Kinuwaki, and T. Hachisuka The Proceedings of the MCQMC 2016, 2017 (General framework for automatically blending light transport algorithms via machine learning)
	"Extended Path Integral Formulation for Volumetric Transport"   [slides] T. Hachisuka, I. Georgiev, W. Jarosz, J. Křivánek, and D. Nowrouzezahrai Eurographics Symposium on Rendering 2017 (EI & I track), 2017 (Alternative formulation that unifies path integration and photon density estimation including beams)
	"Fusing State Spaces for Markov Chain Monte Carlo Rendering"   [project] H. Otsu, A. S. Kaplanyan, J. Hanika, C. Dachsbacher, and T. Hachisuka ACM Transactions on Graphics (SIGGRAPH North America 2017), 2017 (How to use PSS-style MLT and Veach-style MLT simultaneously)
	"Parameter Estimation of BSSRDF for Heterogeneous Materials"   [slides] H. Sone, T. Hachisuka, and T. Koike Eurographics 2017 Short Papers, 2017 (Technique to use homogeneous BSSRDF models for rendering heterogeneous media)
	"Gradient-Domain Photon Density Estimation"   [project] B. Hua, A. Gruson, D. Nowrouzezahrai, and T. Hachisuka Computer Graphics Forum (Eurographics 2017), 2017 (Gradient-domain formulation of photon density estimation)
	"Secrets of Parthenon Renderer" T. Hachisuka Invited seminar, Tokyo Demo Fest, 2017 (Technical bits behind one of the first, publicly available, GPGPU offline rendering systems)
	"Robust Light Transport Simulation via Metropolized Bidirectional Estimators" M. Šik, H. Otsu, T. Hachisuka, and J. Křivánek ACM Transactions on Graphics (SIGGRAPH Asia 2016), 2016 (VCM/UPS with Markov chain Monte Carlo)
	"Rise of the Machines in Monte Carlo Integration for Rendering" T. Hachisuka Talk in the special session: Graphics and Transport, MCQMC2016, 2016 (On-going work on deep learning in Monte Carlo integration)
	"Implementing a Practical Rendering System using GLSL"   [code]   [arXiv] T. Hachisuka Invited seminar, Tokyo Demo Fest, 2015 (Various topics on how to implement SPPM and BVH using only GLSL)
	"Directional Dipole Model for Subsurface Scattering"   [slides]   [code]   [demo] J. R. Frisvad, T. Hachisuka, and T. K. Kjeldsen ACM Transactions on Graphics (Volume 34 Issue 1, presented at SIGGRAPH North America 2015), 2014 (Fully analytical BSSRDF that takes directions of light into account)
	"Multiplexed Metropolis Light Transport"   [slides]   [code - mmlt]   [code - pssmlt] T. Hachisuka, A. S. Kaplanyan, and C. Dachsbacher ACM Transactions on Graphics (SIGGRAPH North America 2014), 2014 (Fusion of Markov chain Monte Carlo and multiple importance sampling)
	"Unifying Points, Beams, and Paths in Volumetric Light Transport Simulation"   [slides]   [poster]   [code]   [comparison]   [supplemental] J. Křivánek, I. Georgiev, T. Hachisuka, P. Vévoda, M. Šik, D. Nowrouzezahrai, and W. Jarosz ACM Transactions on Graphics (SIGGRAPH North America 2014), 2014 (Unified formulation of Monte Carlo path integration and photon density estimation with points and beams)
	"At the Boundary between Light Transport Simulation and Computational Statistics" T. Hachisuka Talk in the special session: Advanced light transport simulation, MCQMC2014, 2014 (Overview of connections between some of my work in rendering and more general problems in computational statistics)
	"State of the Art in Photon-Density Estimation" T. Hachisuka and W. Jarosz (organizers) (check out the course website for the full list of speakers.) SIGGRAPH Asia 2013 Courses, 2013 (Course on recent advancements in photon density estimation)
	"Joint Importance Sampling of Low-Order Volumetric Scattering"   [comparison]   [slides] I. Georgiev, J. Křivánek, T. Hachisuka, D. Nowrouzezahrai, and W. Jarosz ACM Transactions on Graphics (SIGGRAPH Asia 2013), 2013 (Importance sampling of multiple geometry terms and phase functions in participating media)
	"Robust Image Denoising using a Virtual Flash Image for Monte Carlo Ray Tracing" B. Moon, J. Jun, J. Lee, K. Kim, T. Hachisuka, and S. Yoon Computer Graphics Forum (Volume 32 Number 1, presented at EGSR 2014), 2012 (Denoising method with a new image feature)
	"A Path Space Extension for Robust Light Transport Simulation"   [supplemental]   [slides]   [code (refer to SmallVCM. UPS = VCM in implementation.) ] T. Hachisuka, J. Pantaleoni, and H. W. Jensen ACM Transactions on Graphics (SIGGRAPH Asia 2012), 2012 (Extended path integral formulation for Monte Carlo path integration and photon density estimation)
	"Randomized Coherent Sampling for Reducing the Perceptual Error of Rendered Images"   [poster] L. Staal and T. Hachisuka SIGGRAPH North America 2012 Posters, 2012 (New uniform sampling method that reduces perceptual noise in Monte Carlo ray tracing)
	"State of the Art in Photon-Density Estimation" T. Hachisuka and W. Jarosz (organizers) (check out the course website for the full list of speakers.) SIGGRAPH North America 2012 Courses, 2012 (Course on recent advancements in photon density estimation)
	"Robust Adaptive Photon Tracing using Photon Path Visibility"   [slides] T. Hachisuka and H. W. Jensen ACM Transactions on Graphics (Volume 30 Issue 5, presented at SIGGRAPH North America 2013), 2011 (Adaptive Markov chain Monte Carlo sampling for photon tracing with a simple target distribution)
	"A Progressive Error Estimation Framework for Photon Density Estimation" T. Hachisuka, W. Jarosz, and H. W. Jensen ACM Transactions on Graphics (SIGGRAPH Asia 2010), 2010 (Full paper of the talk at SIGGRAPH 2010)
	"Parallel Progressive Photon Mapping on GPUs"   [code]   [slides] T. Hachisuka and H. W. Jensen SIGGRAPH Asia 2010 Technical Sketches, 2010 (Simple and parallel range-query algorithm with randomization)
	"An Error Estimation Framework for Photon Density Estimation"   [slides] T. Hachisuka, W. Jarosz, and H. W. Jensen SIGGRAPH North America 2010 Talks: Rendering Intangibles, 2010 (Error estimation for progressive photon density estimation)
	"Stochastic Progressive Photon Mapping"   [slides]   [code]   [data0]   [data1] T. Hachisuka and H. W. Jensen ACM Transactions on Graphics (SIGGRAPH Asia 2009), 2009 (First progressive average density estimator for distribution effects)
	"An Empirical BSSRDF Model" C. Donner, J. Lawrence, R. Ramamoorthi, T. Hachisuka, H. W. Jensen, and S. Nayar ACM Transactions on Graphics (SIGGRAPH North America 2009), 2009 (Precomputed BSSRDFs for moderately scattering media)
	"Progressive Photon Mapping"   [slides]   [code]   [code with comments]   [data0]   [data1] T. Hachisuka, S. Ogaki, and H. W. Jensen ACM Transactions on Graphics (SIGGRAPH Asia 2008), 2008 (New progressive density estimator for light transport simulation)
	"Multidimensional Adaptive Sampling and Reconstruction for Ray Tracing"   [slides]   [code]   [data0]   [data1] T. Hachisuka, W. Jarosz, R. P. Weistroffer, K. Dale, G. Humphreys, M. Zwicker, and H. W. Jensen ACM Transactions on Graphics (SIGGRAPH North America 2008), 2008 (Adaptive sampling and reconstruction in the random number space)
	"Combined Lagrangian-Eulerian Approach for Accurate Advection"   [poster]   [code] T. Hachisuka SIGGRAPH North America 2005 Posters (2nd Place in SRC:Undergraduate division at SIGGRAPH), 2005 (Advection equation solver using mapping functions)
	"High-Quality Global Illumination Rendering Using Rasterization" T. Hachisuka GPU Gems 2: Programming Techniques for High Performance Graphics and General-Purpose Computation (Chapter 38), 2005 (Application of the poster at SIGGRAPH 2004)
	"Final Gathering on GPU"   [poster] T. Hachisuka GP2, ACM Workshop on General Purpose Computing on Graphics Processors (also presented at SIGGRAPH North America 2004 Posters), 2004 (Rasterization-based final gathering)

	"Deep Adaptive Wavelet Network" M. X. B. Rodriguez, A. Gruson, L. F. Polania, S. Fujieda, F. P. Ortiz, K. Takayama, and T. Hachisuka Manuscript, Dec 2019 (Trainable wavelets within convolutional neural networks.)
	"Quantum Coin Method for Numerical Integration" N. H. Shimada and T. Hachisuka Manuscript, Oct 2019 (Practical numerical integration with quantum computing.)
	"Wavelet Convolutional Neural Networks for Texture Classification"   [updated manuscript] S. Fujieda, K. Takayama, and T. Hachisuka Manuscript, July 2017 (Wavelets within convolutional neural networks.)
	"Five Common Misconceptions about Bias in Light Transport Simulation" T. Hachisuka Manuscript, November 2013 (Notes on five common misconceptions about bias in rendering.)
	"Directional Dipole for Subsurface Scattering in Translucent Materials"   [slides] J. R. Frisvad, T. Hachisuka, and T. K. Kjeldsen Manuscript, August 2013 (Analytical BSSRDF that takes directions of light into account.)
	"A Path Space Extension for Robust Light Transport Simulation" T. Hachisuka, J. Pantaleoni, and H. W. Jensen NVIDIA Technical Report NVR-2012-001, April 2012 (Unified sampling framework for Monte Carlo path integration and photon density estimation.)
	"Ray Tracing on Graphics Hardware" T. Hachisuka Research Exam, University of California at San Diego, 2009 (Comparative studies of GPU ray tracing algorithms.)
	"Generalized Polarization Ray Tracing using a Monte-Carlo Method" T. Hachisuka Final project in CSE 272, University of California at San Diego, 2007 (Monte Carlo formulation of polarization effects.)

	"Robust Light Transport Simulation using Progressive Density Estimation" T. Hachisuka (Advisor: Prof. Henrik Wann Jensen) Dissertation for Doctor of Philosophy, University of California at San Diego, 2011 (Light transport simulation framework based on progressive density estimation. )
	"Advection Equation Solver using Mapping Functions" T. Hachisuka (Advisor: Prof. Seiichi Koshizuka) Thesis for Bachelor of Engineering, University of Tokyo, 2006 (Formulation of the advection equation as mapping.)

• Doctor of Philosophy in Computer Science, University of California at San Diego, September 2011
• Bachelor of Engineering, University of Tokyo, March 2006
• Associate of Engineering, Ibaraki National College of Technology (not a "college" as in the US - see here), March 2003

Academia
• Associate Professor (tenured), University of Waterloo, September 2020 to present (on leave from March to July 2021)
• Associate Professor ("junkyoju" in Japanese, tenured), University of Tokyo, April 2018 to August 2020
• Visiting Professor, McGill University, May 2019 to November 2019
• Junior Associate Professor ("koushi" in Japanese, tenured), University of Tokyo, August 2014 to March 2018
• Assistant Professor ("adjunkt" in Danish, without tenure), Aarhus University, October 2011 to July 2014

Industry
• Consultant, Aurora Innovation Inc., January 2022 to present
• Consultant, Xiaomi Japan, April 2020 to July 2020
• Consultant, Disney Research Zurich (research on light transport), December 2011 to December 2012
• Software Intern (contributed to OptiX SDK), Nvidia, August 2010 to September 2010
• Developer Intern (contributed to fluid/cloud rendering and animation), Weta Digital, June 2009 to September 2009
• Contract Developer (developed a GPU ray tracing library), nTechnology, September 2007 to October 2007
• Graduate Student Researcher, University of California at San Diego, September 2006 to August 2011
• Contract Researcher (consulted on fluid rendering and animation), Prometech Software, May 2005 to May 2006
• Contract Developer (developed an off-line rendering system on a GPU), Raia, June 2005 to September 2008
• Developer (developed a lightmap baking system on a GPU), Quat Technology, August 2003 to December 2003

• David R. Cheriton Faculty Fellowships in Computer Science, University of Waterloo, 2023 to 2026
• Best Paper Award (the first author: R. Sugimoto), ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 2022
• Best Paper Award (the first author: D. Milaenen), Graphics Interface, 2019
• NVIDIA Fellowship, 2010 (chosen as a recipient also in 2008)
• ATI/AMD Fellowship, 2008
• Powell Foundation Fellowship, 2007
• Dean award of the Department of Engineering (summa cum laude equivalent), University of Tokyo, 2006
• Award for Excellence in Science, JASSO, 2006
• 2nd Place at SIGGRAPH Undergraduate Division in ACM Student Research Competition, 2005
• Super Creator, IPA, 2003
• Exploratory Software Project for Youth (the MITOH Youth Program), IPA, 2002

• NSERC, Discovery Grant, 2020 to 2024
• CFI, John R. Evans Leaders Fund, 2020
• JSPS KAKENHI, Grant-in-Aid for Scientific Research (B), 2019 to 2020
• JSPS KAKENHI, Fostering Joint International Research (A), 2019
• Autodesk/Solid Angle, 2018 to 2022
• JSPS KAKENHI, Grant-in-Aid for Challenging Research (Exploratory), 2017 to 2019
• Nikon, 2016 to 2017
• JSPS KAKENHI, Grant-in-Aid for Young Scientists (A), 2015 to 2019
• Samsung Advanced Institute of Technology, 2012 to 2014

• Reviewer:
  • SIGGRAPH North America
  • SIGGRAPH Asia
  • Eurographics Symposium on Rendering
  • ACM Transaction on Graphics
  • Computers & Graphics
  • Computer Graphics Forum
  • Eurographics
  • Pacific Graphics
  • High Performance Graphics
  • IEEE Computer Graphics and Applications
  • IEEE Transactions on Visualization and Computer Graphics
  • IEEE Transactions on Signal Processing
  • Journal of Computer Science and Technology
  • Journal of Computer Graphics Techniques
  • Czech Science Foundation
  • Netherlands Organisation for Scientific Research
• Paper committee:
  • Visual Computing / Graphics and CAD Symposium 2014, 2019
  • High Performance Graphics 2020, 2023
  • Symposium on Interactive 3D Graphics and Games 2013, 2020
  • Eurographics Symposium on Rendering 2012, 2013, 2014, 2017, 2019, 2020, 2023
  • Pacific Graphics 2013, 2015, 2016, 2017, 2018
  • SIGGRAPH NA 2016, NA 2017, Asia 2018, Asia 2019, NA 2020 (COI), Asia 2020 (COI), NA 2022 (COI), Asia 2022, NA 2023 (COI)
  • Eurographics 2019, 2020, 2023, 2024
• Paper chair:
  • Eurographics Symposium on Rendering 2018
• Editorial board:
  • Computer Graphics Forum (associate editor, 2019 - 2022)

• Research Topic Generator
  • Click the window to instantly generate a new research topic.
  • Edit nouns.txt and adjectives.txt to customize for your needs.
  • Currently does not support automatic generation of a paper and technical validation of generated topics.
• My awesome coolest totally-wicked strategy for producing many papers on the same topic (modification of an anonymous post - note: this is *supposed to be* a joke):
  1. Preparation - Discover some new application domain.
  2. Paper #1 - Introduce the problem. Model it as a ray tracing problem and render images using pbrt. Minimal implementation effort is needed.
  3. Paper #2 - Solve the problem with importance sampling. Because you know something about your application domain, you can find a good PDF to reduce variance a bit compared to #1.
  4. Paper #3 - Importance sampling is not working well in difficult cases. Now let's solve it with a Markov Chain Monte Carlo method such as the Metropolis-Hastings algorithm.
  5. Paper #4 - Speed is important. Implement the algorithm on a GPU.
  6. Paper #5 - #4 is still too slow. You've found an interactive application of the problem, so now you need to focus on speed rather than image quality. Rasterization.
  7. Paper #6 - You've analyzed your application domain, and found that 50-70% of all problem instances belong to a special class that can be rendered more efficiently. New paper.
  8. Paper #7 - It's time for an approximation algorithm. Another new paper.
  9. Papers #8 to #14 - Replace heuristic parts of each one of the papers above by deep learning. Doubled the number of papers.
  10. Now, repeat the process from the step 2 by slightly changing the topic.
• Some of Pixar's 22 rules of storytelling applied to paper writing (originally tweets by Emma Coats):
  1. #2: You gotta keep in mind what's interesting to you as a reader, not what's interesting to say as an author. They can be very different.
  2. #5: Simplify. Focus. Hop over detours. You'll feel like you're losing valuable stuff but you aren't.
  3. #8: Finish your paper, let go even if it's not perfect. In an ideal world you have both, but move on. Do better next time.
  4. #11: Writing a paper lets you start refining your ideas. If it stays in your head, a perfect idea, you'll never share it with anyone.
  5. #20: Exercise: take the building blocks of a paper you dislike. How d'you revise them into what you DO like?
• Too general (and potentially practical) research hints:
  • Look for hints in a different, but related area (dipole BSSRDF, MLT).
  • Use a different representation (point-based rendering).
  • Think about an opposite/inverse way to solve the same problem (light tracing).
  • Use a hierarchical data structure if you need to improve performance (lightcuts, fast multipole method).
  • Precompute as much as possible (precomputed radiance transfer).
  • Reuse information that you already have as much as possible (adaptive sampling).
  • Project the solution space onto another space (spherical harmonics).
  • Find and exploit symmetries/periodicity in your problem (bidirectional path tracing).
  • Try numerical solutions instead of complex analytic solutions (Monte Carlo ray tracing).
  • Increase or decrease the number of dimensions (photon beams and planes).
  • Combine and/or generalize seemingly different components (UPS/VCM).
  • See if limitations and assumptions of existing solutions are fundamental or not (PPM).
  • Approximate a complex phenomenon via analytical computation with simplification (microfacets BSDF).
  • Use product instead of summation and vice versa (factorized BRDFs, log/exp tricks).
  • Think about a parametric space (Hough transform).
  • Decompose a complex problem into simpler subproblems (operator splitting).
  • Start from the goal and see how to reach this goal (position-based physics).
  • Exploit coherency in your problem (many-light methods, acceleration data structures).
  • Try a brute-force approach if the problem is not well-defined (artificial neural networks).
  • Find another, mathematically equivalent, representation of the problem (differentiable rendering).
• I prefer a new approach to a known problem than a known approach to a new problem.
• I like bulleted lists.