icsabai
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4 years ago Dear Colleagues,
The abstract deadline for Machine Learning in Computational Biology (MLCB) meeting is fast approaching (Sept 30th). Can you please forward the CfP below to relevant mailing lists?
Best wishes,
MLCB organizers
Machine Learning in Computational Biology (MLCB), Nov 23-24, 2020 We are excited to be holding the 15th Machine Learning in Computational Biology (MLCB) meeting. In its 2020 reincarnation, MLCB will be a two day virtual conference November 23 and 24, 9am-5pm PST.
Since its inception in 2004, and until 2017, MLCB was an official NeurIPS workshop. Given the growth and maturity of the field, MLCB is now an independent meeting.
More details can be found on the website: https://mlcb.github.io
Registration: MailScanner has detected definite fraud in the website at "forms.gle". Do not trust this website: https://forms.gle/ZSwPFoLQ3iWBedsj8
Papers from MLCB 2019 can be viewed here: https://mlcb.github.io/mlcb2019_proceedings/
Important dates:
Submissions due: Sept 30th, 11:59pm (PST) at https://cmt3.research.microsoft.com/mlcb2020
Decision notification: Oct 30th
Meeting: Nov 23-24th.
Keynote speakers Eran Segal (Weizmann Institute of Science)
Aviv Regev (MIT, Broad Institute, Genentech)
Regina Barzilay (MIT)
Marinka Zitnik (Harvard Medical School)
Organizers Anshul Kundaje - Stanford University (USA)
Su-In Lee - University of Washington (USA)
Sara Mostafavi - University of British Columbia (Canada)
Gerald Quon - UC Davis (USA)
James Zou - Stanford University (USA)
David A Knowles - Columbia University & New York Genome Center (USA)
Meeting description The field of computational biology has seen dramatic growth over the past few years. A wide range of high-throughput omics and imaging technologies developed in the last decade now enable us to measure parts of a biological system at various resolutions—at the genome, epigenome, transcriptome, and proteome levels. These diverse technologies are now being used to study questions relevant to basic biology and human health. Fully realizing the scientific and clinical potential of these data requires developing novel supervised and unsupervised learning methods that are scalable, can accommodate heterogeneity, are robust to systematic noise and confounding factors, and provide mechanistic insights.
The goals of the MLCB meeting are to i) present emerging problems and innovative machine learning techniques in computational biology, and ii) generate discussion on how to best model the intricacies of biological data and synthesize and interpret results in light of the current work in the field.
Submission instructions We will accept submission in two tracks:
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Research on novel learning approaches in computational biology. We encourage contributions describing either progress on new problems or work on established problems using methods that are substantially different from established alternatives.
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Perspective on important but controversial problems. Examples include (but are not limited to) different ways of conceptualizing the supposed dichotomy between interpretation and accuracy, over-interpretation from visual representations of high dimensional data, lack of statistical rigour in inferring conclusions from data and models.
Researchers interested in contributing should upload a short paper (4 pages) in PDF format to the MLCB submission web site:
https://cmt3.research.microsoft.com/mlcb2020
by Sept 30th, 11:59 pm (PST). Both tracks will undergo rigorous peer review as in previous years.
No special style is required. Authors may use the NeurIPS style file, but are also free to use other styles as long as they use standard font size (10 or 11pt) and margins (1 in).
Papers need not be anonymized (but you can choose to anonymize your submission if you wish).
All submissions to the Research track will be peer reviewed and will be evaluated on the basis of their technical content. A strong submission to the meeting typically presents a new learning method that yields new biological insights, or applies an existing learning method to a new biological problem. However, submissions that improve upon existing methods for solving previously studied problems will also be considered. Examples of research presented in previous years can be found online at http://raetschlab.org:10080/nipscompbio/previous and https://mlcb.github.io/mlcb2019_proceedings/.
The meeting allows submissions of papers that are under review or have been recently published in a conference or a journal. This is done to encourage presentation of mature research projects that are interesting to the community. The authors should clearly state any overlapping published work at the time of submission (e.g. as a footnote on the first page).
abiricz
qbeer
AAAI Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physics Sciences (MLPS)
March 22-24, 2021 @Stanford University, Palo Alto, California, USA (or virtual meeting depending on the COVID-19 situation)
https://sites.google.com/view/aaai-mlps
Key Dates Submission: November 20, 2020, 23:59 GMT Notification: December 18, 2020 Symposium: March 22-24, 2021
With recent advances in scientific data acquisition and high-performance computing, Artificial Intelligence (AI) and Machine Learning (ML) have received significant attention from applied mathematics and physics science community. From successes reported by industry, academia, and research communities, we observe that AI and ML have great potential to leverage scientific domain knowledge to support new scientific discoveries and enhance the development of physical models for complex natural and engineering systems.
For example, deep learning supports discovery of new materials and high-energy physics from numerous computer simulations and experiments and let us learn low-dimensional manifolds underlying the acquired data in order to represent the system of interest parsimoniously and effectively. ML has offered new insights on adaptive numerical discretization schemes and numerical solvers, which are clearly distinct from traditional mathematical theories. AI also provides a new way of generalizing constitutive physics laws based on big scientific data sets.
Despite the progress, there are still many open questions. Our current understanding is limited regarding how and why AI/ML work and why they can be predictive. AI has been shown to outperform traditional methods in many cases especially with high-dimensional, inhomogeneous data sets. However, a rigorous understanding of when AI/ML is the right approach is largely lacking: for what class of problems, underlying assumptions, available data sets, and constraints are these new methods best suited? The lack of interpretability in AI-based modeling and related scientific theories makes them insufficient for high-impact, safety-critical applications such as medical diagnoses, national security, and environmental contamination. With transparency and a clear understanding of the data-driven mechanism, desirable properties of AI should be best utilized to extend current methods in physical and engineering modeling. Handling expensive training costs and large memory requirements for ever-increasing scientific data sets becomes also important to guarantee scalable science machine learning.
This symposium will aim to present the current state of the art and identify opportunities and gaps in AI/ML-based physics science. The symposium will focus on challenges and opportunities for increasing the scale, rigor, robustness, and reliability of physics-informed AI necessary for routine use in science and engineering applications and discuss potential researcher-AI collaborations to significantly advance diverse scientific areas and transform the way science is done.
Topics: Authors are strongly encouraged to present papers that combine and blend physical knowledge and artificial intelligence/machine learning algorithms. Topics of interest include but are not limited to the following:
Submission guideline:
We solicit extended abstracts, full papers, and poster abstracts on topics related to the above and can include recent or ongoing research, surveys, and business/use cases. Extended abstracts (2 to 4 pages) and full papers (up to 6 pages) will be peer-reviewed. Posters can be proposed by submitting an abstract (1 to 2 pages).
All submissions should follow the AAAI format in the Author Kit, will be handled through EasyChair (https://easychair.org/conferences/?conf=sss21) and the review will be double-blind to ensure academic integrity. Accepted extended abstracts and full papers shall be published in an open access proceedings site. More details can be found in https://easychair.org/cfp/AAAI_MLPS_2021
Invited Speakers: Animashree Anandkumar, Caltech/NVIDIA Nathan Kutz, University of Washington More invited speakers are to be announced
Organizing Committee: Jonghyun Harry Lee, University of Hawai'i at Manoa Eric F. Darve, Stanford University Peter K. Kitanidis, Stanford University Michael W. Mahoney, University of California, Berkeley Anuj Karpatne, Virginia Tech Matthew W. Farthing, U.S. Army Engineer Research and Development Center Tyler Hesser, U.S. Army Engineer Research and Development Center
Program Committee: Kevin Carlberg, University of Washington Nathaniel Trask, Sandia National Laboratories Paris Perdikaris, University of Pennsylvania Peter Sadowski, University of Hawai'i at Manoa Hongkyu Yoon, Sandia National Laboratories