About Joining My Group

Long story short, I am always looking for motivated and talented students. I also receive hundreds of emails a day and cannot respond to all inquiries about joining my group. This document is not meant to be unfriendly, but just to save both of us time (many of these things are things I'd say in an email back).

• If you are a UM student:
  Until Fall 2022, (a) I will likely not have capacity for new mentees except through a small number of undergraduate research programs and (b) I will almost certainly not be taking on new MS students. Feel free to email other faculty in vision and learning, as well as graduate students.
  
  
• If you are not in a UM degree program and would like to be my student:
  Please apply directly and mention my name (as well as others that interest you) in your research statement. That's all that is required! Here are answers to common questions:
  • I am not taking students in the December 2022 application cycle, but keep in mind there are many people who do vision at Michigan
  • Admissions in anything related to computer vision and machine learning is currently extraordinarily competitive and I cannot assess your chances for you -- it depends highly on my group's needs at the moment and the full pool of applicants. However, you can look at current vision PhD students at Michigan to get a sense of typical profiles.
  • Emailing me genuinely does not improve your chances.
  • I genuinely read all PhD applications that mention my name, but applications are read and decided on by a central committee (I encourage reading Mor Harchol-Balter's guide to graduate school admissions in CS for a good sense of how this process works)
  • I am not involved in the MS process at all
  • I am delighted to talk to people who have been admitted; I cannot talk to people before they are admitted due to time constraints (in 2020, I was listed on > 200 of the applications to the UM CSE PhD program).
  • I cannot reveal information about the process or give status updates.
  • I do not respond to most emails about this.
  
  
• If you are not in a UM degree program and would like to visit:
  I do host visitors occasionally but have obligations to my students first and foremost, as well as to UM students. Here are answers to common questions:
  • I am not looking for visitors without a recommendation from somebody I personally know or know of. If you are currently working with someone, please tell me who.
  • I do not advise capstone projects
  • I do not take any short-term (e.g., less-than-three month) undergraduate visitors except in highly exceptional circumstances.
  • I do not respond to the overwhelming majority of emails about this.

If you've read this page and think you have a question that has not been answered, please feel free to email and put ``(JJ Gibson)'' in the subject line. I will always do my best to answer these.

Executive Summary

Research: I work on learning-based computer vision. I want to develop autonomous systems that can learn to build representations of the underlying state and dynamics of the world through observation. I am particularly interested in the following three areas:

• Understanding 3D from pictorial cues. One of my goals for the past ten years has been enabling computers to perceive the 3D world with the same ease that humans do. I'm broadly interested in this area, and as our ability to extract 3D from images has gotten better, I've become increasingly interested in how to optimally use this information in the context of multiple views.
  
  Representative projects: (1) Sparse View Planar Reconstruction investigates a wide-baseline problem that falls in between areas that computer vision knows how to do well; (2) Direct Ray Distance Functions shows how to produce full-3D reconstructions while learning on realistic non-watertight 3D data.
  
  
• Understanding the Interactive World. While images are themselves static, they depict a world of possibility in which we can move about and manipulate the world. I would like to give computers the ability to perceive this world and have focused on understanding interactions as they occur, from the perspective of hands and 3D.
  
  Representative projects: (1) 100 Days of Hands, a system that aims to provide basic information about hands that just works on fairly unconstrained data. Our system has been adopted by EPIC-KITCHENS; (2) Understanding Articulation in Videos shows how to extract a quantitative understanding of interacting 3D from ordinary videos for a common class of objects.
  
  
• Measurement systems for basic sciences, especially solar physics. I have collaboratated with experts in other fields to build ML-based systems for measurements for basic science. One focus has been virtual instruments for solar physics that enhance the value of space missions. Developing and verifying systems that collaborators like requires long-term investment, and is intensely satisfying.
  
  Representative projects: (1) SynthIA, a best-of-both worlds learning-based magnetogram system currently being integrated into the SDO pipeline for distribution as a data product; (2) Skelevision, a deep learning approach to measuring museum skeletal specimens, which we hope will let us ask questions about the relationship between temperature and bird morphology at unprecedented scales.
  
  

Teaching and mentoring: I've been able to work on these projects via collaboration with a wonderful group of student collaborators, ranging from the PhD to the BS level. In addition to current students, I'm proud of a large number of MS and BS students now off doing a wide variety of great things in both industry and academia. As a classroom instructor, I've been able to share computer vision with over 1,000 students over the past few years, including over 925 undergraduate students.

Service and outreach: In addition to internal service and one-off outreach efforts, I have run UM's AI4All Summer Program since 2019, which has taught 75 high schoolers over the past three years. I also regularly serve the vision community as an area chair for conferences (CVPR 2019 — 2022, NeurIPS 2020 — 2022, ICLR 2021, ECCV 2022, among other) and as an action editor for TMLR. I've also provided my expertise for the public interest, for example doing pro-bono work for the Michigan Innocence Clinic.

Funding: These endeavours have been generously supported by both public and private funding, including a NSF CAREER Award (2022). My group's work has been sponsored by The National Science Foundation, DARPA, Toyota Research Institute, The Procter & Gamble Company, Nokia Networks, NASA, and the Lockheed Martin Solar and Astrophysics Laboratory as well as the University of Michigan's Precision Health Initiative and the Michigan Institute for Data Science.

Student Collaborators

My current PhD student collaborators are:

• Richard Higgins, Sep 2019 — present
• Nilesh Kulkarni, Sep 2019 — present, (co-advised with Justin Johnson)
• Shengyi Qian, Sep 2019 — present
• Dandan Shan, Sep 2020 — present, previously ECE MS Student with me, Rackham International Student Fellowship winner
• Christopher Rockwell, Sep 2020 — present, (co-advised with Justin Johnson), previously CSE MS Student with me
• Sarah Jabbour, Sep 2020 — present, (co-advised with Jenna Wiens)
• Linyi Jin, May 2021 — present, previously Robotics MS student with me

Past Student Collaborators

I am proud of my past student collaborators, now off doing great things elsewhere!

MS students/Visitors
•   Yinwei Dai	(UM CSE BS+MS)		Next: PhD Student, Princeton
•   Max Hamilton	(UM CSE BS+MS)		Next: Andrew Owen's Group, then PhD Student, UMass Amherst
•   Zhaoheng Zheng	(UM CSE MS)		Next: PhD Student, USC CS
•   Vihang Agarwal	(UM ECE MS)		Next: Research Asistant, UM Medicine
•   Ravi Sundaram	(UM ECE MS)		Next: R&D Research Engineer, Playstation
BS students/Visitors
•   Siyi Chen	(UM CSE BSE)		Next: PhD Student, University of Michigan ECE
•   Dichang Zhang	(UM CSE BSE)		Next: PhD Student, Stony Brook University CS
•   Samir Agarwala	(UM CSE BSE)		Next: MS Student, Stanford CS
•   Ruiyu Li	(UM CSE BSE)		Next: MS Student, CMU MLD
•   Malet Haile	(AAU BS)
•   Tibebu Wassie	(AAU BS)
•   Zhizhuo Zhou	(UM CSE BSE)		Next: MS Student, CMU Robotics, NSF GRFP Winner
•   Gemmechu Mohammed	(AAU BS)		Next: PhD Student, Cornell University CS
•   Alexander Raistrick	(UM CSE BSE)		Next: PhD Student, Princeton CS
•   Xiao Song	(UM CSE BS)		Next: MEng Student, UC Berkeley CS
•   Justin Bi	(UM CSE BS)
•   Yige Kristina Liu	(UM CSE BS)		Next: MS Student, Stanford CS
•   Michelle Shu	(JHU CS BS)		Next: PhD Student, Cornell CS
•   Jiaqi Geng	(UM CSE BSE)		Next: MS Student, CMU Robotics
•   Qichen Fu	(UM CSE BSE)		Next: MS Student, CMU Robotics
•   Yue Wu	(UM CSE BSE)
•   Zhengyuan Dong	(UM CSE BSE)

AAU: Addis Ababa University; JHU: Johns Hopkins University

Teaching

University of Michigan:

• EECS 542 (Advanced Topics in Computer Vision — i.e., let's read papers and discuss where vision is heading)
  • Fall 2021 (53 students), Fall 2020 (50 students)
  EECS 442 (Computer Vision — i.e., let's learn about computer vision):
  • Winter 2022 (300 students), Winter 2021 (With J. Johnson) (329 students), Fall 2019 (155 students), Winter 2019 (151 students)
• EECS 598 (Special Topics: The Ecological Approach to Visual Perception — i.e., let's talk about how embodiment and vision relate):
  • Winter 2020 (31 students)
• AI4All (a two-week residential program for high schoolers):
  • Summer 2021, 2020, 2019

Selected Publications

2022
	Nilesh Kulkarni, Justin Johnson, David F. Fouhey What's behind the couch? Directed Ray Distance Functions for 3D Scene Reconstruction Arxiv Preprint [Arxiv]   [PDF] We can produce high-quality 3D reconstructions from a single RGB image via implicit function by carefully analyzing what we expect networks to produce during training.
	Shengyi Qian, Linyi Jin, Chris Rockwell, Siyi Chen, David F. Fouhey Understanding 3D Object Articulation in Internet Videos CVPR 2022 [Arxiv]   [Arxiv PDF]   [Bibtex]   By training on both video data and 3D reconstructions in the right way, we can build models of articulations of 3D objects on ordinary video data.
	Brian C. Weeks, Zhizhuo Zhou, Bruce K. O'Brien, Rachel Darling, Morgan Dean, Tiffany Dias, Gemmechu Hassena, Mingyu Zhang, and David F. Fouhey A deep neural network for high throughput measurement of functional traits on museum skeletal specimens. Accepted in Methods in Ecology and Evolution. [Website]   Bird sizes correlate with temperature. We reduce measurement time of museum specimens by ≈10x, leading to datasets at previously unexplored scales.
	Richard E.L. Higgins, David F. Fouhey, Spiro K. Antiochos, Graham Barnes, Mark C.M. Cheung, J. Todd Hoeskema, K.D. Leka, Yang Liu, Peter W. Schuck, Tamas I. Gombosi SynthIA: A Synthetic Inversion Approximation for the Stokes Vector Fusing SDO and Hinode into a Virtual Observatory Accepted in The Astrophysics Journal Supplement Series [Arxiv]   [Open Access]   [Video of SynthIA outputs from May 5 to June 24, 2016]   Our system produces synthetic solar magnetograms that combine the best aspects of multiple instruments. This system formed the basis of a successful NASA Heliophysics Division Tools and Method grant to integrate the system into SDO/HMI's Joint Science Center.
2021
	Dandan Shan*, Richard E.L. Higgins*, David F. Fouhey COHESIV: Contrastive Object and Hand Embedding Segmentation In Video NeurIPS 2021 [PDF]   [Bibtex] By applying the Gestalt principle of common fate at scale, we can learn how to segment hand-held objects with fairly minimal supervision.
	Alexander Raistrick, Nilesh Kulkarni, David F. Fouhey Collision Replay: What Does Bumping Into Things Tell You About Scene Geometry? BMVC 2021 (Oral) [PDF]   [Supplement (PDF)]   [Supplement Video (MP4)]   [Bibtex] Collisions with the world are usually seen as a nuisance. At scale and with a random-walk-inspired formulation, they can be used to learn a depth sensor
	Linyi Jin, Shengyi Qian, Andrew Owens, David F. Fouhey Planar Surface Reconstruction from Sparse Views ICCV 2021 (Oral) [Arxiv]   [PDF]   [Project Page] We can learn to reconstruct scenes from a handful of views with an unknown relationship. Humans seem to do this fine, but it poses serious challenges for computers.
	Chris Rockwell, David F. Fouhey, Justin C. Johnson PixelSynth: Generating a 3D-Consistent Experience from a Single Image ICCV 2021 [Arxiv [PDF [Project Page]   [Bibtex]   [CSE News Piece] PixelSynth fuses the complementary strengths of 3D reasoning and autoregressive modeling to create an immersive experience from a single image.
	Zhizhuo Zhou, Gemmechu Hassena, Brian C. Weeks, David F. Fouhey Quantifying Bird Skeletons CV4Animals Workshop [PDF]   [Bibtex]   We can measure bird skeleton specimens extraordinarily accurately and quite fast with deep learning. This system can unlock datasets of birds at unprecedented scales.
	Richard E.L. Higgins, David F. Fouhey, Dichang Zhang, Spiro K. Antiochos, Graham Barnes, J. Todd Hoeskema, K.D. Leka, Yang Liu, Peter W. Schuck, Tamas I. Gombosi Fast and Accurate Emulation of the SDO/HMI Stokes Inversion with Uncertainty Quantification The Astrophysical Journal (ApJ), Volume 911, Number 2, 2021 [Arxiv]   [Published PDF]   [Bibtex]   [Project Page]   [HMI Nugget] We can emulate the magnetogram production pipeline of SDO/HMI, a key NASA mission. This system lays the ground-work for SynthIA, which produces best-of-both-worlds style magnetograms
2020
	S. Qian*, L. Jin*, D. F. Fouhey Associative3D: Volumetric Reconstruction from Sparse Views ECCV 2020 [Arxiv]   [Project Page]   [Code]   [Bibtex] We can build a voxel-based reconstruction of images from two views, even without access to the relative camera positions
	C. Rockwell, D. F. Fouhey Full-Body Awareness from Partial Observations ECCV 2020 [Arxiv]   [Project Page]   [Bibtex]   [Code] Human 3D pose estimation systems work poorly on people as they are usually depicted in video. A self-training method works well at fixing this problem.
	S. Jabbour, D.F. Fouhey, E. Kazerooni, M.W. Sjoding, J. Wiens Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts MLHC 2020 [PDF]   [Bibtex]   [Code] Deep nets can easily exploit shortcuts (e.g., apparent bone density), but a simple transfer learning approach can help mitigate the use of shortcuts.
	D. Shan, J. Geng*, M. Shu*, D.F. Fouhey Understanding Human Hands in Contact at Internet Scale CVPR 2020 (Oral) [PDF]   [Bibtex]   [Project Page & Code] We built a new dataset and model that enables really accurate recognition of basic hand information. Since hands are key to interaction, this basic information unlocks tons of useful new problems.
	M. El Banani, J. Corso, D.F. Fouhey Novel Object Viewpoint Estimation through Reconstruction Alignment CVPR 2020 [PDF]   [Supp.]   [Bibtex]   [Code and Project Page] We can learn to do relative pose estimation by aligning reconstructions
	N. Kulkarni, A. Gupta, D.F. Fouhey, S. Tulsiani Articulation-aware Canonical Surface Mapping CVPR 2020 [Arxiv]   [PDF]   [Bibtex] We can build canonical surface maps for objects that articulate, such as elephants and horses
2019
	A. Szenicer*, D.F. Fouhey*, A. Munoz-Jaramillo, P.J. Wright, R. Thomas, R. Galvez, M. Jin, M.C.M. Cheung A Deep Learning Virtual Instrument for Monitoring Extreme UV Solar Spectral Irradiance Science Advances, Vol. 5, Number 10, 2019 [Open Acess]   [Bibtex]   [Prediction Video]   [Activations Video]   [Overview Video]   We built a virtual version of the EVE MEGS-A instrument that can serve as a replacement after its electrical short
	Press coverage/releases: (Inverse.com)   (SETI Institute) (theregister.co.uk)   (phys.org) (TechExplorist.com)   (ScientificAmerican.com) (Earth & Space Science News)   (hpcwire.com) (sciencedaily.com)
	D. Zhukov, J.-B. Alayrac, G. Cinbis, D.F. Fouhey, I. Laptev, J. Sivic Cross-task weakly-supervised learning from instructional videos CVPR 2019 [PDF]   [Project Page]   [Arxiv]   [Bibtex] By accounting for the compositional nature of language, we can learn better models from instructional videos
	R. Galvez*, D.F. Fouhey*, M. Jin, A. Szenicer, A. Munoz-Jaramillo, M.C.M. Cheung, P.J. Wright, M.G. Bobra, Y. Liu, J. Mason, R. Thomas A Machine Learning Dataset Prepared From the NASA Solar Dynamics Observatory Mission The Astrophysical Journal Supplement, 242:1, 2019 [PDF]   [Arxiv]   [Bibtex]   [Movie & Explanation]   [Small dataset + demo] We produced a machine-learning-ready dataset that merges the three instruments aboard the NASA SDO mission
Earlier
	A. Kumar, S. Gupta, D. F. Fouhey, S. Levine, J. Malik Visual Memory for Robust Path Following NeurIPS 2018 (Oral) [Project Page]   [PDF]   [Bibtex]
	D. F. Fouhey, W. Kuo, A. A. Efros, J. Malik From Lifestyle VLOGs to Everyday Interactions CVPR 2018 [Project Page]   [Arxiv]   [Bibtex]
	S. Tulsiani, S. Gupta, D. F. Fouhey, A. A. Efros, J. Malik Factoring Shape, Pose, and Layout from the 2D Image of a 3D Scene CVPR 2018 [Project Page]   [Arxiv]   [Bibtex]
	M. Lescroart, D. F. Fouhey, J. Malik Convolutional neural networks represent shape dimensions -- but not as accurately as humans Abstract at VSS 2018 [Abstract]
	S. Gupta, D.F. Fouhey, S. Levine, J. Malik Unifying Map and Landmark Based Representations for Visual Navigation Arxiv 2017 [Project Page]   [Arxiv]   [Bibtex]
	D. F. Fouhey, A. Gupta, A. Zisserman From Images to 3D Shape Attributes TPAMI (Pre-print on Arxiv) The TPAMI version has ugly typesetting (full-width tables on the bottom?) that I was unable to change. Read the Arxiv one. [Arxiv]   [Bibtex]
	R. Girdhar, D. F. Fouhey, M. Rodriguez, A. Gupta Learning a Predictable and Generative Vector Representation for Objects ECCV 2016 (Spotlight) [Publication (PDF)]   [Bibtex] [Project Page]
	D. F. Fouhey Factoring Scenes into 3D Structure and Style Doctoral Dissertation [Dissertation (PDF)]   [Bibtex] [Defense Slides (PDF)]
	D. F. Fouhey, A. Gupta, A. Zisserman 3D Shape Attributes CVPR 2016 (Oral - Watch the presentation on Youtube) [Publication (PDF)]   [Bibtex] [Project Page]   [Poster (PDF)]   [Talk (PPTX)]   [Talk (PDF)]
	R. Girdhar, D. F. Fouhey, K. M. Kitani, A. Gupta, M. Hebert Cutting through the Clutter: Task-Relevant Features for Image Matching WACV 2016 [Publication (PDF)]   [Bibtex]
	D. F. Fouhey, W. Hussain, A. Gupta, M. Hebert Single Image 3D Without a Single 3D Image ICCV 2015 [Publication (PDF)]   [Bibtex] [Poster (PDF)]   [Supplemental (PDF)]   [Bonus Details (PDF)]
	X. Wang, D. F. Fouhey, A. Gupta Designing Deep Networks for Surface Normal Estimation CVPR 2015 [Publication (PDF)]   [Bibtex]
	D. F. Fouhey, A. Gupta, and M. Hebert Unfolding an Indoor Origami World ECCV 2014 (Oral - Watch the presentation on VideoLectures.net) [Publication (PDF)]   [Bibtex] [Project Page]   [Extended Results (PDF)]
	D. F. Fouhey and C.L. Zitnick Predicting Object Dynamics in Scenes CVPR 2014 [Publication (PDF)]   [Bibtex] [Poster (PDF)]   [Supplemental (PDF)]
	D. F. Fouhey, V. Delaitre, A. Gupta, A. Efros, I. Laptev, and J. Sivic. People Watching: Human Actions as a Cue for Single View Geometry. IJCV (extended version of ECCV 2012 paper) [Preprint (PDF)]   [Final version (via Springer)]
	D. F. Fouhey, A. Gupta, and M. Hebert Data-Driven 3D Primitives for Single Image Understanding ICCV 2013 [Publication (PDF)]   [Bibtex] [Project Page]   [Poster (PDF)]
	D. F. Fouhey, V. Delaitre, A. Gupta, A. Efros, I. Laptev, and J. Sivic. People Watching: Human Actions as a Cue for Single View Geometry. ECCV 2012 (Oral - Watch the presentation on VideoLectures.net ) [Publication (PDF)]   [Bibtex] [Project Page]
	V. Delaitre, D. F. Fouhey, I. Laptev, J. Sivic, A. Gupta, and A. Efros. Scene Semantics from Long-term Observation of People. ECCV 2012 [Publication (PDF)]   [Bibtex] [Project Page]
	D. F. Fouhey, A. Collet, M. Hebert, and S. Srinivasa Object Recognition Robust to Imperfect Depth Data. CDC4CV 2012 Workshop at ECCV 2012 [Publication (PDF)]   [Bibtex] [Supplemental(PDF)]   [Supp. Video 1]   [Supp. Video 2]
	M. Costanza-Robinson, B. Estabrook, and D. F. Fouhey Representative elementary volume estimation for porosity, moisture saturation, and air-water interfacial areas in unsaturated porous media: Data quality implications (Sorry for not posting a pre-print!) In Water Resources Research, Volume 47, 2011 [Official Version]   [Bibtex]
	D. F. Fouhey, D. Scharstein, and A. Briggs. Multiple plane detection in image pairs using J-linkage. ICPR 2010 [Publication (PDF)]   [Bibtex] Implementation (Python and C) [Code (Zip)]   [Poster (PDF)]

Miscellaneous

You may be interested in the following.

Writing (arranged in chronological order):

• My Ph.D. dissertation, Factoring Scenes into 3D Structure and Style (Dissertation and Defense Slides)
  
• A note on some practical considerations when evaluating surface normals
• My A.B. thesis, Multi-Model Estimation in the Presence of Outliers (Thesis and Poster and Presentation)

Miscellaneous

• CMU RI Thesis Template (zip) based off of a CMU RI tech report template from Daniel Morris.

Joke Papers

Sometimes when I feel a creative itch, the end result is a joke publication (which despite the name often have a serious point to make). These are all done with the one and only Daniel Maturana.

• Keras4Kindergartners.com: Check out our secret backup plan for if research doesn't work out.
• Deep Excel: Everybody knows that deep learning brings about synergy and so does Excel, so Daniel Maturana and I released ExcelNet, a break-through technology that merges the power of Deep Learning with Excel.
  The spreadsheet   The whitepaper   The pitch slides   Protips (actually quite helpful)  
• Visual Rank Estimation: Visually Identifying Rank with Daniel Maturana proves that linear algebra can be replaced with machine learning. It also shows that if you are a CNN, the much-hated jet colormap is actually the best colormap. Winner of the ``Most Frighteningly Like Real Research'' award at SIGBOVIK 2016.
• Cat Basis Purrsuit: I've been told that this is the highlight of my research career and it can only go downhill from here: Cat Basis Pursuit
• Celebrity Learning: You may also know my award-winning work with Daniel Maturana on celebrity-themed learning, making money at home from Hilbert's Nulstellensatz, and more from OneWeirdKernelTrick.com
• Kardashian Kernel: The original, rarely imitated, never duplicated. Originator of the alphabetically-related-work section: The Kardashian Kernel

Fun & Games

• The LaCroix Flavors, Ranked: Uh, yeah
• Visualizing AUROCs: I could never figure out what an AUROC of 60% actually means. So I made a fun visualization to play with. Play with it here.
• Venus Transit Poster: I wanted a poster of the transit of venus, and didn't like what I could find. So I made one myself with the original JSOC SDO/AIA data. Here: Highish-res (5k) Higher-res (8k) Padded to 30"x40"
• Paris 20/20: The only way to see Paris is to walk through all 20 arrondissements in a single journey by foot. Co-conspirators (so far!): Mathieu Aubry Andrew Owens.
  Possible variants: here another the originally planned one (and 20 miles too).
• Warning: Deep Nets! As mentioned in the New Yorker! You too can have a warning sign for your door.
• Free parking in Berkeley: Parking people hate him. Find out how to park on Berkeley's campus for free with this 1 weird trick.
• Caffe64: dependency-free deep learning in < 12KB via the magic of assembly. Clone and star it here
• miniml: Everybody these days has a deep learning toolkit that takes forever to download, has tons of dependencies, and produces inscrutable models. In the spirit of simplicity and transparency, I wrote a new package that's only 2172 bytes when compiled, has zero dependencies (not even the C standard library -- that's total bloat), and fits a hidden-layer-free network aka logistic regression. Meet miniml here: miniml .
• Revisiting Monet in Light of that Other Painter of light: Monet is so booooring and tranquil. Click here to see him spiced up to look like Thomas Kinkade
• Academic Ancestry and Erdös Number: See where I fit in!
• Sculpture at Berkeley: 3D shapes are fun and Berkeley has a lot of them
• #1 Messiest Desk: My desk was voted #1 messiest desk in the second floor of Smith Hall at Carnegie Mellon during the RI's open house for 2016.
  If a cluttered desk is an indication of a cluttered mind, then what does an empty desk indicate?
• Award-Winning Optimization Sheet: I wrote an award-winning one page cheat-sheet for Convex Optimization at CMU (10-725). Be sure to check the watermark!