
Dr. Max Ehrlich
I am currently on sabbatical until August when I will join NVIDIA hardware engineering
as a research scientist. I am still affiliated with the University of Maryland Computer Science
Department where I lead the Computational Entomology group, part of the
Perception and
Intelligence lab.
My current research combines machine learning and
computational
imaging to solve real problems. My focus is on breaking down and
understanding the first principles of the problem and then building
these principles back up into a machine learning solution rather than treating the model
as a black box.
In the past I have sucessfully applied this idea to image enhancement.
The broader
impact of this
is
to improve participation from underrepresented groups For example, by creating better
multimedia
compression algorithms which incorporate simple deep learning based techniques, people
operating in
underinvested locations (e.g., rural areas, native american reservations, 3rd world
countries) are able to participate in an increasingly media-focused internet.
I am grateful to have had recognition of the
importance of this work by many funding partners over
the years including government agencies: DARPA
and
IARPA, and private companies:
Facebook AI,
Adobe DIL,
and NVIDIA ADLR (where I currently
work).
I received my Ph.D. in Computer Science from the
University of Maryland
where
I was co-advised by Professor Larry
Davis
and
Professor Abhinav Shrivastava.
I received an M.S. in Computer Science from
Stevens
Institute of Technology. where I was advised by Professor Philippos Mordohai and a B.S. in
Computer Science
from Rutgers University.
I am a member of the Association for the Advancement of Artificial Intelligence (AAAI).
3/22 - Successfully defended my dissertation.
8/21 - One paper accepted to the MELEX workshop at ICCV 2021.
7/21 - Started as a Research Intern at NVIDIA.
7/21 - Serving as a Research Mentor at the Summer STEM Institute.
7/20 - One paper accepted to ECCV 2020.
7/20 - Started as a Visiting Resarch Engineer at Facebook AI.
7/19 - One paper accepted to ICCV 2019.
3/19 - One paper accepted to IGARS 2019.
Service
Conference Reviewer: AAAI 2020, ICLR 2020, ECCV {2020, 2022}, IJCAI 2021, CVPR {2021, 2022}, ICML 2021, ICCV 2021
Journal Reviewer: Transactions on Image Processing (TIP), International Journal of Artifical Intelligence (IJAI)
Contact
Contact me by email at
maxehr {at} umiacs {dot} umd {dot} edu
Send regular mail to
Max Ehrlich c/o Department of Computer Science 5109 Iribe Center 8125 Paint Branch Drive College Park, MD 20742
Students
- Shishira R Maiya (Ph.D. Student)
- Lillian Huang (Ph.D. Student)
- Chuong Minh Huynh (Ph.D. Student)
- Sazan Mahbub (Ph.D. Student)
- Vatsal Agarwal (Ph.D. Student)
- Namitha Padmanabhan (Masters Student)
- Evan Wen (High School)
Teaching
Spring 2022 CMSC422 Intro to Machine Learning
My Research
My research emphasizes broad impact and collaboration with outside agencies. Aside from these research programs, I have participated in many other published research projects, please see my full list of papers and patents below for more information.Video Compression
Video sharing is increasingly popular and quickly becoming the primary method for interaction on the internet. With the globlal pandemic, video conferencing has become mandatory for many people to work or attend school. This causes major problems for people who lack a broadband connection. In this ongoing paper series on video compression, I am developing ways to incorporate deep learning models which run on commodity hardware and can be used in the near term. This research is conducted in collaboration with NVIDIA.
JPEG Compression
JPEG compression is the most popular image compression algorithm and currently powers image sharing on the internet and mobile phones. In this paper series on JPEG compression, I advanced theoretical knowledge about the interaction between JPEG compression and deep learning and used these theoretical results to improve the fidelity of JPEG images both for human and machine consumption. This research was primarly funded by a three year academic grant awarded to me by Facebook (Meta) AI, allowing me to work autonomously, and led to collaborations with Facebook.

Remote Sensing
In this program, we developed novel methods for improving land cover segmentation in sattelite images. This is a challenging and important problem with wide application from national defense to planning and surveying. This research was funded by the IARPA Core3D program.
Video Compression
Video sharing is increasingly popular and quickly becoming the primary method for interaction on the internet. With the globlal pandemic, video conferencing has become mandatory for many people to work or attend school. This causes major problems for people who lack a broadband connection. In this ongoing paper series on video compression, I am developing ways to incorporate deep learning models which run on commodity hardware and can be used in the near term. This research is conducted in collaboration with NVIDIA.
JPEG Compression
JPEG compression is the most popular image compression algorithm and currently powers image sharing on the internet and mobile phones. In this paper series on JPEG compression, I advanced theoretical knowledge about the interaction between JPEG compression and deep learning and used these theoretical results to improve the fidelity of JPEG images both for human and machine consumption. This research was primarly funded by a three year academic grant awarded to me by Facebook (Meta) AI, allowing me to work autonomously, and led to collaborations with Facebook.
- Prior works train an ensemble of models, one for each JPEG quality. We use a single network parameterized by the JPEG quantization matrix.
- Prior works deal with grayscale images only, with the assumption that their models can be applied channel-wise. We show that single-channel networks have trouble generalizing and design a network for color correction.
- Prior works focus on CNN regression which causes blurry and textureless results. We introduce a novel GAN loss that includes an explicit texture restoring term, this yields a more realistic result.
Remote Sensing
In this program, we developed novel methods for improving land cover segmentation in sattelite images. This is a challenging and important problem with wide application from national defense to planning and surveying. This research was funded by the IARPA Core3D program.


Full List of Papers and Patents
2022
Max Ehrlich
Doctoral Dissertation, 2022
arXiv Cite It!
Max Ehrlich, Jon Barker, Namitha Padmanabhan, Larry S. Davis, Andrew Tao, Bryan Catanzaro, Abhinav Shrivastava
Under Submission, 2022 (arXiv available)
arXiv Cite It!
2021
Shishira R. Maiya, Max Ehrlich, Vatsal Agarwal, Ser-Nam Lim, Tom Goldstein, Abhinav Shrivastava
Under Submission, 2021 (arXiv available)
arXiv Cite It!
Evan Wen and Max Ehrlich
Under Submission, 2021 (arXiv available)
arXiv Cite It!
Max Ehrlich, Larry Davis, Ser-Nam Lim, Abhinav Shrivastava
In Proceedings of the IEEE International Conference on Computer Vision Workshops, 2021
arXiv CVF Cite It!
2020
Max Ehrlich, Larry Davis, Ser-Nam Lim, Abhinav Shrivastava
In Proceedings of the European Conference on Computer Vision, 2020
arXiv ECVA Cite It!
2019
Arthita Ghosh, Max Ehrlich, Larry Davis, Rama Chellappa
In IEEE International Geoscience and Remote Sensing Symposium, 2019
arXiv IEEE Cite It!
Max Ehrlich and Larry S. Davis
In Proceedings of the IEEE International Conference on Computer Vision, 2019
arXiv CVF Cite It!
Mohamed R. Amer, Timothy J. Shields, Amir Tamrakar, Max Ehrlich, Timur Almaev
U.S. Patent Application 16/085,859 filed January 31, 2019
Google Cite It!
2018
Arthita Ghosh, Max Ehrlich, Sohil Shah, Larry Davis, Rama Chellappa
In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2018
CVF Cite It!
2017
Timothy J. Shields, Mohamed R. Amer, Max Ehrlich, Amir Tamrakar
In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017
CVF Cite It!
2016
Max Ehrlich and Philippos Mordohai
In Proceedings of the IEEE Symposium on 3D User Interfaces, 2016
IEEE Direct Cite It!
Max Ehrlich, Timothy J. Shields, Timur Almaev, Mohamed R. Amer
In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2016
CVF Cite It!
2015
Max Ehrlich
Master's Thesis, Stevens Institute of Technology, 2015
Direct