Re: [WiML] Digest for women-in-machine-learning@googlegroups.com - 6 updates in 6 topics

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On Tue, 27 Sep 2022 at 5:51 AM, <women-in-mac...@googlegroups.com> wrote:
Mary Mulkeen <mulk...@bc.edu>: Sep 26 12:49PM -0700

Tenure Track Faculty Positions in Computer Science
 
The Computer Science Department of Boston College seeks to fill two
tenure-track positions beginning in the 2023-2024 academic year. Rank is
open. Successful candidates for these positions will be expected to develop
or currently possess strong research programs that can attract external
funding, in an environment that also values high-quality undergraduate
teaching. Outstanding candidates in all areas of Computer Science will be
considered. For one of the positions, a preference may be shown toward
those who can enhance existing departmental strengths in Artificial
Intelligence with applications to the Sciences, and in the mathematical
foundations of Computer Science.
 
A Ph.D in Computer Science or a closely related discipline is required.
Application review is ongoing.
 
Applicants should submit a cover letter, a detailed CV, and teaching and
research statements, and should arrange for three confidential letters of
recommendation to be uploaded directly to Interfolio. *To apply go to:
http://apply.interfolio.com/113772 <http://apply.interfolio.com/113772>.*
 
Boston College conducts background checks as part of the hiring process and
requires all its employees to be fully vaccinated and boosted for COVID-19.
Information about the University and our department is available at bc.edu
and cs.bc.edu.
 
Boston College is a Jesuit, Catholic university that strives to integrate
research excellence with a foundational commitment to formative liberal
arts education. We encourage applicants from candidates who are committed
to fostering a diverse and inclusive academic community. Boston College is
an affirmative action/equal opportunity employer.
Shailee Jain <shaile...@gmail.com>: Sep 26 09:03AM -0500

*Final Deadline: Thurs | Sept 29th, 2022 | 23:59 hrs AOE*
Hello!
 
We invite submissions to the NeurIPS 2022 workshop on Memory in Artificial
and Real Intelligence (MemARI). One of the key challenges for AI systems is
to understand, predict, and model data over time. Pretrained networks
should be able to temporally generalize, or adapt to shifts in data
distributions that occur over time. Our current state-of-the-art (SOTA)
still struggles to model and understand data over long temporal durations –
for example, SOTA models are limited to processing several seconds of
video, and powerful transformer models are still fundamentally limited by
their attention spans. By contrast, humans and other biological systems are
able to flexibly store and update information in memory to comprehend and
manipulate streams of input.
 
How should memory mechanisms be designed in deep learning, and what can
this field learn from biological memory systems? MemARI aims to facilitate
progress on these topics by bringing together researchers from machine
learning, neuroscience, reinforcement learning, computer vision, natural
language processing and other adjacent fields. We invite submissions
presenting new and original research on topics including but not limited to
the following:
 
1.
 
Computational models of biological memory
2.
 
Role of different biological memory systems in cognitive tasks, with
implications for AI algorithms/architectures
3.
 
Biologically-inspired architectures to improve memory/temporal
generalization
4.
 
New approaches to improving memory in artificial systems
5.
 
Domain-specific uses of memory mechanisms (for ex., for lifelong
learning, NLP, RL etc.)
6.
 
Empirical and theoretical analyses of limitations in current artificial
systems
7.
 
Datasets and tasks to evaluate memory mechanisms of artificial networks
 
Important dates:
 
-
 
Paper submission deadline: Thurs September 29, 2022 23:59 [Anywhere on
Earth]
-
 
Decision Notification: Fri October 14th, 2022 [17:00 hrs PT]
-
 
Workshop date: Dec 2, 2022 in-person @ New Orleans
 
Submission instructions:
 
-
 
Submission Portal: MemARI OpenReview
<https://openreview.net/group?id=NeurIPS.cc/2022/Workshop/MemARI&referrer=%5BHomepage%5D(%2F)>
(currently accepting submissions)
-
 
All submissions must be in PDF format.
-
 
Submissions are limited to four content pages, including all figures and
tables; additional pages containing only references are allowed.
-
 
Please use the NeurIPS 2022 LaTeX style file
<https://neurips.cc/Conferences/2022/PaperInformation/StyleFiles>. Style
or page limit violations (e.g., by decreasing margins or font sizes) may
lead to automatic rejection
-
 
All submissions should be anonymous.
-
 
Per NeurIPS guidelines, previously published work is not acceptable for
submission.
 
Reviewing process & Acceptance:
 
-
 
The review process is double-blind.
-
 
Accepted papers will be presented during a poster session, with
spotlight oral presentations for exceptional submissions. (with
accommodation for virtual attendees)
-
 
Accepted papers will be made publicly available as non-archival reports,
allowing future submissions to archival conferences or journals.
 
Please direct questions and all future correspondence to
memari-...@googlegroups.com. More information on the workshop can be
found on the MemARI website <https://memari-workshop.github.io/>.
 
Organizers:
 
Uri Hasson, Princeton University
 
Ken Norman, Princeton University
 
Alex Huth, UT Austin
 
Mariya Toneva, MPI for Software Systems
 
Shailee Jain, UT Austin
 
Javier Turek, Intel Labs
 
Vy Vo, Intel Labs
 
Mihai Capotă, Intel Labs
I-Chun <email...@gmail.com>: Sep 26 06:58AM -0700

*Applications for 2023 entry to the 4-Year Gatsby Unit PhD Programme are
Open!*
 
*Deadline: 13 November 2022*
 
The Gatsby Computational Neuroscience Unit
<https://www.ucl.ac.uk/gatsby/gatsby-computational-neuroscience-unit> is a
leading research centre focused on theoretical neuroscience and machine
learning. We study (un)supervised and reinforcement learning in brains and
machines; inference, coding and neural dynamics; Bayesian and kernel
methods, and deep learning; with applications to the analysis of perceptual
processing and cognition, neural data, signal and image processing, machine
vision, network data and nonparametric hypothesis testing.
 
The unit provides a unique opportunity for a critical mass of theoreticians
to interact closely with one another, with the Sainsbury WellcomeCentre for
Neural Circuits and Behaviour (SWC), with the Centre for Computational
Statistics and Machine Learning (CSML), and with research groups in related
UCL departments such as Computer Science; Statistical Science; Artificial
Intelligence; the ELLIS Unit at UCL; Functional Imaging; Neuroscience; and
the nearby Alan Turing and Francis Crick Institutes.
 
Our PhD programme provides a rigorous preparation for a research career.
Students complete a 4-year PhD in either machine learning or theoretical
neuroscience, with minor emphasis in the complementary field. Courses in
the first year provide a comprehensive introduction to both fields and
systems neuroscience. Students are encouraged to work and interact closely
with SWC/CSML researchers to take advantage of this uniquely
multidisciplinary research environment.
 
Applicants should have a strong analytical and quantitative background, a
keen interest in neuroscience, machine learning or both, and a relevant
first degree, for example in Mathematics, Statistics, Computer Science,
Engineering, Physics, Neuroscience or Cognitive Psychology.
 
Full funding is available regardless of nationality and current residence.
The unit also welcomes applicants who have secured or are seeking funding
from other sources.
 
Applications should be submitted directly to the Gatsby Unit via our online
portal. For more information on the programme and how to apply, see
www.ucl.ac.uk/gatsby/study-and-work/phd-programme
* Information on our research: www.ucl.ac.uk/gatsby/research-0
* Read more about our faculty members: www.ucl.ac.uk/gatsby/people
 

--
I-Chun Lin, PhD
Scientific Programme Manager
Gatsby Computational Neuroscience Unit, UCL
www.ucl.ac.uk/gatsby | @GatsbyUCL
Carlos Martin-Vide <carlos.m...@gmail.com>: Sep 24 01:04AM -0700

******************************************************************
 
7th INTERNATIONAL SCHOOL ON DEEP LEARNING
 
DeepLearn 2022 Autumn
 
Luleå, Sweden
 
October 17-21, 2022
 
https://irdta.eu/deeplearn/2022au/
 
*****************
 
Co-organized by:
 
Luleå University of Technology
EISLAB Machine Learning
 
Institute for Research Development, Training and Advice – IRDTA
Brussels/London
 
******************************************************************
 
Regular registration: October 14, 2022
 
******************************************************************
 
SCOPE:
 
DeepLearn 2022 Autumn will be a research training event with a global scope
aiming at updating participants on the most recent advances in the critical
and fast developing area of deep learning. Previous events were held in
Bilbao, Genova, Warsaw, Las Palmas de Gran Canaria, Guimarães and Las
Palmas de Gran Canaria.
 
Deep learning is a branch of artificial intelligence covering a spectrum of
current frontier research and industrial innovation that provides more
efficient algorithms to deal with large-scale data in a huge variety of
environments: computer vision, neurosciences, speech recognition, language
processing, human-computer interaction, drug discovery, health informatics,
medical image analysis, recommender systems, advertising, fraud detection,
robotics, games, finance, biotechnology, physics experiments, biometrics,
communications, climate sciences, bioinformatics, etc. etc. Renowned
academics and industry pioneers will lecture and share their views with the
audience.
 
Most deep learning subareas will be displayed, and main challenges
identified through 21 four-hour and a half courses and 2 keynote lectures,
which will tackle the most active and promising topics. The organizers are
convinced that outstanding speakers will attract the brightest and most
motivated students. Face to face interaction and networking will be main
ingredients of the event. It will be also possible to fully participate in
vivo remotely.
 
An open session will give participants the opportunity to present their own
work in progress in 5 minutes. Moreover, there will be two special sessions
with industrial and recruitment profiles.
 
ADDRESSED TO:
 
Graduate students, postgraduate students and industry practitioners will be
typical profiles of participants. However, there are no formal
pre-requisites for attendance in terms of academic degrees, so people less
or more advanced in their career will be welcome as well. Since there will
be a variety of levels, specific knowledge background may be assumed for
some of the courses. Overall, DeepLearn 2022 Autumn is addressed to
students, researchers and practitioners who want to keep themselves updated
about recent developments and future trends. All will surely find it
fruitful to listen to and discuss with major researchers, industry leaders
and innovators.
 
VENUE:
 
DeepLearn 2022 Autumn will take place in Luleå, on the coast of northern
Sweden, hosting a large steel industry and the northernmost university in
the country. The venue will be:
 
Luleå University of Technology
 
https://www.ltu.se/?l=en
 
STRUCTURE:
 
3 courses will run in parallel during the whole event. Participants will be
able to freely choose the courses they wish to attend as well as to move
from one to another.
 
Full live online participation will be possible. However, the organizers
highlight the importance of face to face interaction and networking in this
kind of research training event.
 
KEYNOTE SPEAKERS:
 
Tommaso Dorigo (Italian National Institute for Nuclear Physics),
Deep-Learning-Optimized Design of Experiments: Challenges and Opportunities
 
Elaine O. Nsoesie (Boston University), AI and Health Equity
 
PROFESSORS AND COURSES:
 
Sean Benson (Netherlands Cancer Institute), [intermediate] Deep Learning
for a Better Understanding of Cancer
 
Thomas Breuel (Nvidia), [intermediate/advanced] Large Scale Deep Learning
and Self-Supervision in Vision and NLP
 
Hao Chen (Hong Kong University of Science and Technology),
[introductory/intermediate] Label-Efficient Deep Learning for Medical Image
Analysis [virtual]
 
Jianlin Cheng (University of Missouri), [introductory/intermediate] Deep
Learning for Bioinformatics
 
Nadya Chernyavskaya (European Organization for Nuclear Research),
[intermediate] Graph Networks for Scientific Applications with Examples
from Particle Physics
 
Sébastien Fabbro (University of Victoria), [introductory/intermediate]
Learning with Astronomical Data
 
Efstratios Gavves (University of Amsterdam), [advanced] Advanced Deep
Learning [virtual]
 
Quanquan Gu (University of California Los Angeles), [intermediate/advanced]
Benign Overfitting in Machine Learning: From Linear Models to Neural
Networks
 
Jiawei Han (University of Illinois Urbana-Champaign), [advanced] Text
Mining and Deep Learning: Exploring the Power of Pretrained Language Models
 
Awni Hannun (Zoom), [intermediate] An Introduction to Weighted Finite-State
Automata in Machine Learning [virtual]
 
Tin Kam Ho (IBM Thomas J. Watson Research Center),
[introductory/intermediate] Deep Learning Applications in Natural Language
Understanding
 
Timothy Hospedales (University of Edinburgh), [intermediate/advanced] Deep
Meta-Learning
 
Shih-Chieh Hsu (University of Washington), [intermediate/advanced]
Real-Time Artificial Intelligence for Science and Engineering
 
Andrew Laine (Columbia University), [introductory/intermediate]
Applications of AI in Medical Imaging
 
Tatiana Likhomanenko (Apple), [intermediate/advanced] Self-, Weakly-,
Semi-Supervised Learning in Speech Recognition
 
Peter Richtárik (King Abdullah University of Science and Technology),
[intermediate/advanced] Introduction to Federated Learning
 
Othmane Rifki (Spectrum Labs), [introductory/advanced] Speech and Language
Processing in Modern Applications
 
Mayank Vatsa (Indian Institute of Technology Jodhpur),
[introductory/intermediate] Small Sample Size Deep Learning
 
Yao Wang (New York University), [introductory/intermediate] Deep Learning
for Computer Vision
 
Zichen Wang (Amazon Web Services), [introductory/intermediate] Graph
Machine Learning for Healthcare and Life Sciences
 
Alper Yilmaz (Ohio State University), [introductory/intermediate] Deep
Learning and Deep Reinforcement Learning for Geospatial Localization
 
OPEN SESSION:
 
An open session will collect 5-minute voluntary presentations of work in
progress by participants. They should submit a half-page abstract
containing the title, authors, and summary of the research to
da...@irdta.eu by October 9, 2022.
 
INDUSTRIAL SESSION:
 
A session will be devoted to 10-minute demonstrations of practical
applications of deep learning in industry. Companies interested in
contributing are welcome to submit a 1-page abstract containing the program
of the demonstration and the logistics needed. People in charge of the
demonstration must register for the event. Expressions of interest have to
be submitted to da...@irdta.eu by October 9, 2022.
 
EMPLOYER SESSION:
 
Organizations searching for personnel well skilled in deep learning will
have a space reserved for one-to-one contacts. It is recommended to produce
a 1-page .pdf leaflet with a brief description of the organization and the
profiles looked for to be circulated among the participants prior to the
event. People in charge of the search must register for the event.
Expressions of interest have to be submitted to da...@irdta.eu by October
9, 2022.
 
ORGANIZING COMMITTEE:
 
Nosheen Abid (Luleå)
Sana Sabah Al-Azzawi (Luleå)
Lama Alkhaled (Luleå)
Prakash Chandra Chhipa (Luleå)
Saleha Javed (Luleå)
Marcus Liwicki (Luleå, local chair)
Carlos Martín-Vide (Tarragona, program chair)
Hamam Mokayed (Luleå)
Sara Morales (Brussels)
Mia Oldenburg (Luleå)
Maryam Pahlavan (Luleå)
David Silva (London, organization chair)
Richa Upadhyay (Luleå)
 
REGISTRATION:
 
It has to be done at
 
https://irdta.eu/deeplearn/2022au/registration/
 
The selection of 8 courses requested in the registration template is only
tentative and non-binding. For logistical reasons, it will be helpful to
have an estimation of the respective demand for each course. During the
event, participants will be free to attend the courses they wish.
 
Since the capacity of the venue is limited, registration requests will be
processed on a first come first served basis. The registration period will
be closed and the on-line registration tool disabled when the capacity of
the venue will have got exhausted. It is highly recommended to register
prior to the event.
 
FEES:
 
Fees comprise access to all courses and lunches. There are several early
registration deadlines. Fees depend on the registration deadline. The fees
for on site and for online participants are the same.
 
ACCOMMODATION:
 
Accommodation suggestions are available at
 
https://irdta.eu/deeplearn/2022au/accommodation/
 
CERTIFICATE:
 
A certificate of successful participation in the event will be delivered
indicating the number of hours of lectures.
 
QUESTIONS AND FURTHER INFORMATION:
 
da...@irdta.eu
 
ACKNOWLEDGMENTS:
 
Luleå University of Technology, EISLAB Machine Learning
 
Rovira i Virgili University
 
Institute for Research Development, Training and Advice – IRDTA,
Brussels/London
"masha...@gmail.com" <masha...@gmail.com>: Sep 23 06:39PM -0700

The deadline for workshop paper submission has been extended to this
Monday, Sept. 26th!
 
=======================================================================
 
## Call for Papers ##
 
NeurIPS'22 5th Robot Learning Workshop: Trustworthy Robotics
 
http://www.robot-learning.ml/ <http://www.robot-learning.ml/2022/>
 
Submission deadline: 26 September 2022 (AoE)
 
## Overview ##
 
Machine learning (ML) has been one of the premier drivers of recent
advances in robotics research and has made its way into impacting several
real-world robotic applications in unstructured and human-centric
environments, such as transportation, healthcare, and manufacturing. At the
same time, robotics is a key motivation for numerous research problems in
artificial intelligence research, from data-efficient algorithms to robust
generalization of decision models. However, there are still considerable
obstacles to fully leveraging state-of-the-art ML in real-world robotics.
For capable ML-equipped robots, guarantees on the robustness and analysis
of the social implications of these tools are required for their
utilization in human-facing robotic domains (e.g. autonomous vehicles, and
tele-operated or assistive robots).
 
To support the development of robots that are safely deployable among
humans, the field must consider trustworthiness as a central aspect in the
development of robot learning systems. Unlike many other applications of
ML, the combined complexity of physical robotic platforms and
learning-based perception-action loops presents unique technical
challenges. These challenges include concrete problems such as very high
performance requirements, explainability, predictability, verification,
uncertainty quantification, and robust operation in dynamically
distributed, open-set domains. Since robots are developed for use in human
environments, in addition to these technical challenges, we must also
consider the social aspects of robotics such as privacy, transparency,
fairness, and algorithmic bias. Both technical and social challenges also
present opportunities for robotics and ML researchers alike. Contributing
to advances in the aforementioned sub-fields promises to have an important
impact on real-world robot deployment in human environments, building
towards robots that use human feedback, indicate when their model is
uncertain, and are safe to operate autonomously in safety-critical settings
such as healthcare and transportation.
 
This year’s robot learning workshop aims at discussing unique research
challenges from the lens of trustworthy robotics. We adopt a broad
definition of trustworthiness that highlights different application domains
and the responsibility of the robotics and ML research communities to
develop “robots for social good.” Bringing together experts with diverse
backgrounds from the ML and robotics communities, the workshop will discuss
new perspectives on trust in the context of ML-driven robot systems.
 
## Topics and Objectives ##
 
Topics of interest include but are not limited to:
 
-

uncertainty estimation in robotics;
-

explainable robot learning;
-

domain adaptation and distribution shift in robot learning;
-

multi-modal trustworthy sensing and sensor fusion;
-

safe deployment for applications such as agriculture, space, science,
and healthcare;
-

privacy aware robotic perception;
-

information system security in robot learning;
-

learning from offline data and safe on-line learning;
-

simulation-to-reality transfer for safe deployment;
-

robustness and safety evaluation;
-

certifiability and performance guarantees;
-

safe robot learning with humans in the loop;
-

algorithmic bias in robot learning;
-

quantification and adherence to social norms;
-

robotics for social good;
-

ethical robotics.

## Submissions ##
 
Submission website: https://cmt3.research.microsoft.com/RLW2022/
<https://cmt3.research.microsoft.com/NeurIPSWRL2021/>
 
Email: neurips...@robot-learning.ml <https://groups.google.com/u/1/>
 
Submissions should use the NeurIPS template
<https://neurips.cc/Conferences/2022/PaperInformation/StyleFiles>, and be 4
pages (plus as many pages as necessary for references). The reviewing
process will be double blind following the same standards as the main
conference.
 
Accepted papers and eventual supplementary material will be made available
on the workshop website. However, this does not constitute an archival
publication and no formal workshop proceedings will be made available,
meaning contributors are free to publish their work in archival journals or
conferences. In the spirit of providing useful feedback, we will not accept
submissions accepted to other conference or journal proceedings at the time
of submission.
 
## Awards and Funding ##
 
We will likely be able to award prizes for the best papers. In addition, we
hope to sponsor registration fees for presenting authors and some
attendees, focussing on participants from underrepresented minorities in
the field.
 
Apply for funding: https://forms.gle/G8zm6mtSY4r85fsf7
 
## Deadlines and Dates ##
 
-

*Submission deadline: 22 September 2022 (Anywhere on Earth)*
-

*Notification: 14 October 2022 (Anywhere on Earth)*
-

*Funding Request Deadline: 21 October 2022 (Anywhere on Earth)*
-

*Workshop (virtual): 9 December 2022*

## Organizers ##
 
Alex Bewley <https://alex.bewley.ai/> (Google Research, Zurich), Anca Dragan
<http://people.eecs.berkeley.edu/~anca/> (UC Berkeley), Igor Gilitschenski
<https://www.gilitschenski.org/> (University of Toronto), Emily Hannigan
<https://www.linkedin.com/in/emilyjhannigan/> (Columbia University), Masha
Itkina <https://mashaitkina.weebly.com/> (Stanford University, Toyota
Research Institute (TRI)), Hamidreza Kasaei <https://www.ai.rug.nl/hkasaei> (University
of Groningen, Netherlands), Nathan Lambert <https://www.natolambert.com/>
(HuggingFace), Julien Perez <https://perezjln.github.io/> (Naver Labs
Europe), Ransalu Senanayake <http://www.ransalu.com/> (Stanford
University), Jonathan Tompson <https://jonathantompson.com/> (Google
Research, Mountain View), Markus Wulfmeier <https://markusrw.github.io/> (Google
DeepMind, London)
 
## Advisory Board ##
 
Roberto Calandra <https://www.robertocalandra.com/about/> (Facebook AI
Research), Jens Kober <http://www.jenskober.de/> (TU Delft, Netherlands), Danica
Kragic <https://www.csc.kth.se/~danik/> (KTH), Fabio Ramos
<https://fabioramos.github.io/Home.html> (NVIDIA, University of Sydney), Vincent
Vanhoucke <https://vincent.vanhoucke.com/> (Google Research, Mountain View)
 
Best,
 
Masha Itkina,
 
On behalf of the Organizing Committee
Chen-Nee Chuah <ch...@ucdavis.edu>: Sep 26 11:56AM -0700

the ECE Department of UC Davis has a faculty opening in Data Science and
Machine Learning. We are looking for candidates with strong interest and
leadership skills to bridge data science research with multiple
technical disciplines as well as application domains such as health and
connected and autonomous systems. The full description of the position
can be found at our recruitment page below,
https://recruit.ucdavis.edu/JPF05160 <https://recruit.ucdavis.edu/JPF05160>
For full consideration, applicants should apply by December 1, 2022. We
particularly welcome applications from members of under-represented groups.
 
Please help share the search ad with your students/postdocs or
colleagues who may be interested.
 
Thank you,
Chen-Nee
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