Dear all,
Several faculty members of the Vector Institute in Canada work on reinforcement learning, sequential decision making, and closely related research areas. Each of us is affiliated with one of the Canadian universities, but we are all affiliated with the Vector Institute and often collaborate with each other. Most of our students are located at the Vector Institute, which is a thriving environment for machine learning research.
We plan to recruit several graduate students this year. If you are interested in reinforcement learning research, please apply through our respective departments. We welcome both domestic and international students.
The name of faculty members who work on RL-related topics are as follows. Please check their webpages to find the best match based on your interests, and particular instructions that they may have for prospective students.
Amir-massoud Farahmand (Department of Computer Science, University of Toronto). Interests: Theoretical RL, Model-based RL, Risk and Robustness (Prospective Students)
Angela Schoellig (Institute for Aerospace Studies, University of Toronto). Interests: Robot Control and Learning; Reinforcement Learning for Robotics; Mobile Manipulation; Self Driving and Flying
Animesh Garg (Department of Computer Science, University of Toronto). Interests: Generalizable Autonomy for Robotics, Reinforcement Learning, Optimal Control, Causal Decision Making, Neural Architectures for Decision Making (Prospective Students)
Florian Shkurti (Department of Computer Science, University of Toronto). Interests: Machine learning for planning and control, Robotics, Inverse RL, Imitation Learning
Jeff Clune (Computer Science, University of British Columbia). Interests: Deep Reinforcement Learning, AI-Generating Algorithms
Joseph J. Williams (Departments of Computer Science, Statistical Sciences, and Psychology, University of Toronto). Interests: Multi-armed bandits for healthcare and education (Prospective Students)
Pascal Poupart (School of Computer Science, University of Waterloo). Interests: Partially Observable Reinforcement Learning, Bayesian Reinforcement Learning, Causal Reinforcement Learning, Federated Reinforcement Learning, Object-Oriented Reinforcement Learning, Reinforcement Learning in Natural Language Processing
Daniel M. Roy (Department of Statistical Sciences and Computer Science, University of Toronto). Interests: Theory for worst case and adaptive online learning, bandits, and, in the future, RL. Prospective students should apply to both CS and Statistics, if appropriate.
Scott Sanner (Department of Mechanical and Industrial Engineering, Cross-appointed in Department of Computer Science, University of Toronto). Interests: Data-driven Decision Making, Sequential Decision Optimization
Sheila McIlraith (Department of Computer Science, University of Toronto). Interests: Sequential decision making and reinforcement learning, Program synthesis, Human-compatible AI
Please note that, because of the high volume of inquiries, some of the listed faculty may not be able to respond to individual emails from prospective students. This should not be interpreted as a lack of interest. It is sufficient to mention their names in your application, and they will closely look at your application.
Each department has its own webpage, admission deadline and requirements. Please check them for the updated information.
University of Toronto
Computer Science (Admission – Deadline: December 1)
Mechanical and Industrial Engineering (Admission – Deadline: Jan 1, 2022)
University of Toronto Institute for Aerospace Studies (Admission – Deadline: December 15, 2021 (fee); January 15, 2022 (application material))
University of Waterloo (Computer Science) (Admission – Deadline: December 15)
University of British Columbia (Computer Science) (Admission – Deadline: December 15)
Several faculty members of the Vector Institute in Canada work on reinforcement learning, sequential decision making, and closely related research areas. Each of us is affiliated with one of the Canadian universities, but we are all affiliated with the Vector Institute and often collaborate with each other. Most of our students are located at the Vector Institute, which is a thriving environment for machine learning research.
We plan to recruit several graduate students this year. If you are interested in reinforcement learning research, please apply through our respective departments. We welcome both domestic and international students.
The name of faculty members who work on RL-related topics are as follows. Please check their webpages to find the best match based on your interests, and particular instructions that they may have for prospective students.
Amir-massoud Farahmand (Department of Computer Science, University of Toronto). Interests: Theoretical RL, Model-based RL, Risk and Robustness (Prospective Students)
Angela Schoellig (Institute for Aerospace Studies, University of Toronto). Interests: Robot Control and Learning; Reinforcement Learning for Robotics; Mobile Manipulation; Self Driving and Flying
Animesh Garg (Department of Computer Science, University of Toronto). Interests: Generalizable Autonomy for Robotics, Reinforcement Learning, Optimal Control, Causal Decision Making, Neural Architectures for Decision Making (Prospective Students)
Florian Shkurti (Department of Computer Science, University of Toronto). Interests: Machine learning for planning and control, Robotics, Inverse RL, Imitation Learning
Jeff Clune (Computer Science, University of British Columbia). Interests: Deep Reinforcement Learning, AI-Generating Algorithms
Joseph J. Williams (Departments of Computer Science, Statistical Sciences, and Psychology, University of Toronto). Interests: Multi-armed bandits for healthcare and education (Prospective Students)
Pascal Poupart (School of Computer Science, University of Waterloo). Interests: Partially Observable Reinforcement Learning, Bayesian Reinforcement Learning, Causal Reinforcement Learning, Federated Reinforcement Learning, Object-Oriented Reinforcement Learning, Reinforcement Learning in Natural Language Processing
Daniel M. Roy (Department of Statistical Sciences and Computer Science, University of Toronto). Interests: Theory for worst case and adaptive online learning, bandits, and, in the future, RL. Prospective students should apply to both CS and Statistics, if appropriate.
Scott Sanner (Department of Mechanical and Industrial Engineering, Cross-appointed in Department of Computer Science, University of Toronto). Interests: Data-driven Decision Making, Sequential Decision Optimization
Sheila McIlraith (Department of Computer Science, University of Toronto). Interests: Sequential decision making and reinforcement learning, Program synthesis, Human-compatible AI
Please note that, because of the high volume of inquiries, some of the listed faculty may not be able to respond to individual emails from prospective students. This should not be interpreted as a lack of interest. It is sufficient to mention their names in your application, and they will closely look at your application.
Each department has its own webpage, admission deadline and requirements. Please check them for the updated information.
University of Toronto
Computer Science (Admission – Deadline: December 1)
Mechanical and Industrial Engineering (Admission – Deadline: Jan 1, 2022)
University of Toronto Institute for Aerospace Studies (Admission – Deadline: December 15, 2021 (fee); January 15, 2022 (application material))
University of Waterloo (Computer Science) (Admission – Deadline: December 15)
University of British Columbia (Computer Science) (Admission – Deadline: December 15)
In addition to these faculty members whose work has a significant focus on RL, there are many other researchers at the Vector Institute who work on other aspects of machine learning and deep learning, including fundamental algorithm design, representation learning, generative models, optimization, theory of ML and DL, computer vision, privacy, fairness, intersection of quantum computing and ML, and applications in healthcare, material design, music, etc. You can find their names here.
About Vector Institute
The Vector Institute is an independent non-profit corporation, with Faculty Members and Affiliates from the University of Toronto, University of Waterloo, University of Guelph, Dalhousie University, and other Canadian universities. It is supported with generous funding from the provincial and federal governments, as well as Canadian industry sponsors. The Vector Institute is located in the MaRS Discovery District building, spanning nearly the entire 7th floor and overlooking downtown Toronto and the beautiful Queen’s Park. On any given day, the Vector Institute houses over a hundred students, dozens of Faculty Members, supported with state-of-the-art compute power, and dedicated professional staff. The daily life and concentration of expertise in the Institute fosters collaboration and the exchange of ideas among its members through talks, seminar series, visitors, and tutorials. The Vector Institute’s vision is to drive excellence in the creation of artificial intelligence, to use it to foster economic growth, and to improve the lives of Canadians. To that end, the Vector Institute has close ties to both academia and industry.
Best Regards,
Amir-massoud Farahmand on behalf of my colleagues
About Vector Institute
The Vector Institute is an independent non-profit corporation, with Faculty Members and Affiliates from the University of Toronto, University of Waterloo, University of Guelph, Dalhousie University, and other Canadian universities. It is supported with generous funding from the provincial and federal governments, as well as Canadian industry sponsors. The Vector Institute is located in the MaRS Discovery District building, spanning nearly the entire 7th floor and overlooking downtown Toronto and the beautiful Queen’s Park. On any given day, the Vector Institute houses over a hundred students, dozens of Faculty Members, supported with state-of-the-art compute power, and dedicated professional staff. The daily life and concentration of expertise in the Institute fosters collaboration and the exchange of ideas among its members through talks, seminar series, visitors, and tutorials. The Vector Institute’s vision is to drive excellence in the creation of artificial intelligence, to use it to foster economic growth, and to improve the lives of Canadians. To that end, the Vector Institute has close ties to both academia and industry.
Best Regards,
Amir-massoud Farahmand on behalf of my colleagues
PhD in Reinforcement Learning, Differential Privacy or Fairness
We are looking for a PhD student to join our group on reinforcement
learning and decision making under uncertainty more generally, at the
University of Neuchatel, Switzerland ( https://www.unine.ch/ ). We
are particularly interested in candidates with a strong mathematical
background. Prior research experience as documented by your Masters
thesis is required. Within the area, we are looking for candidates
with a strong research interest in the following fields
- Reinforcement learning and decision making under uncertainty:
1. Exploration in reinforcement learning.
2. Decision making nuder partial information.
3. Representations of uncertainty in decision making.
4. Theory of reinforcement learning (e.g. PAC/regret bounds)
5. Bayesian inference and approximate Bayesian methods.
- Social aspect of machine learning
1. Theory of differntial privacy.
2. Algorithms for differentially private machine learning.
3. Algorithms for fairness in machine learning.
4. Interactions between machine learning and game theory.
5. Inference of human models of fairness or privacy.
The main supervisor will be Christos Dimitrakakis <
https://sites.google.com/site/christosdimitrakakis >
Examples of our group's past and current research can be found on arxiv:
https://arxiv.org/search/?searchtype=author&query=Dimitrakakis%2C+C.
The student will have the opportunity to visit and work with other group
members at the University of Oslo, Norway (
https://www.mn.uio.no/ifi/english/people/aca/chridim/index.html ) and
Chalmers University of Technology, Sweden (
http://www.cse.chalmers.se/~chrdimi/ ). While the group is currently
geographically distributed, there will be plenty of opportunities for
exchanges.
The PhD candidate must have a strong technical background, including:
1. Thorough knowledge of calculus and linear algebra.
2. A good theoretical background in probability and statistics/machine
learning.
3. Practical experience with at least one programming language.
The candidate's background will be mainly assessed through their MSc
thesis and trannoscripts, and secondarily through an interview.
>>>> Application Information <<<<<
*Starting date* 1 Februrary 2022 or soon afterwards.
*Application deadline* 30 November 2021.
To apply sen an email to christos.dimitrakakis@gmail.com with the
subject 'PhD Neuchatel'.
An application must include:
1. A statement of research interests and motivation relevant to the
position.
2. A CV with a list of references.
3. Your MSc thesis or another research work demonstrating your academic
writing.
4. A degree trannoscript.
Feel free to include any other additional information.
We are looking for a PhD student to join our group on reinforcement
learning and decision making under uncertainty more generally, at the
University of Neuchatel, Switzerland ( https://www.unine.ch/ ). We
are particularly interested in candidates with a strong mathematical
background. Prior research experience as documented by your Masters
thesis is required. Within the area, we are looking for candidates
with a strong research interest in the following fields
- Reinforcement learning and decision making under uncertainty:
1. Exploration in reinforcement learning.
2. Decision making nuder partial information.
3. Representations of uncertainty in decision making.
4. Theory of reinforcement learning (e.g. PAC/regret bounds)
5. Bayesian inference and approximate Bayesian methods.
- Social aspect of machine learning
1. Theory of differntial privacy.
2. Algorithms for differentially private machine learning.
3. Algorithms for fairness in machine learning.
4. Interactions between machine learning and game theory.
5. Inference of human models of fairness or privacy.
The main supervisor will be Christos Dimitrakakis <
https://sites.google.com/site/christosdimitrakakis >
Examples of our group's past and current research can be found on arxiv:
https://arxiv.org/search/?searchtype=author&query=Dimitrakakis%2C+C.
The student will have the opportunity to visit and work with other group
members at the University of Oslo, Norway (
https://www.mn.uio.no/ifi/english/people/aca/chridim/index.html ) and
Chalmers University of Technology, Sweden (
http://www.cse.chalmers.se/~chrdimi/ ). While the group is currently
geographically distributed, there will be plenty of opportunities for
exchanges.
The PhD candidate must have a strong technical background, including:
1. Thorough knowledge of calculus and linear algebra.
2. A good theoretical background in probability and statistics/machine
learning.
3. Practical experience with at least one programming language.
The candidate's background will be mainly assessed through their MSc
thesis and trannoscripts, and secondarily through an interview.
>>>> Application Information <<<<<
*Starting date* 1 Februrary 2022 or soon afterwards.
*Application deadline* 30 November 2021.
To apply sen an email to christos.dimitrakakis@gmail.com with the
subject 'PhD Neuchatel'.
An application must include:
1. A statement of research interests and motivation relevant to the
position.
2. A CV with a list of references.
3. Your MSc thesis or another research work demonstrating your academic
writing.
4. A degree trannoscript.
Feel free to include any other additional information.
Université de Neuchâtel
Venez étudier à l'Université de Neuchâtel. Découvrez nos quatre facultés et nos nombreuses formations en bachelor et en master.
Doctoral studies in Computational/Theoretical Neuroscience at NYU
New York University is home to a thriving interdisciplinary community of researchers using computational and theoretical approaches in neuroscience. We are interested in exceptional PhD candidates with strong quantitative training (e.g., physics, mathematics, engineering) coupled with a clear interest in scientific study of the brain.
A listing of faculty, sorted by their primary departmental affiliation, is given below. Doctoral programs are flexible, allowing students to pursue research across departmental boundaries. Nevertheless, admissions are handled separately by each department, and students interested in pursuing graduate studies should submit an application to the program that best fits their goals and interests.
Center for Neural Science (CNS), Graduate School of Arts & Sciences (deadline: 1 December)
[https://neuroscience.nyu.edu/program.html, and https://as.nyu.edu/cns/DoctoralProgram.html]
* SueYeon Chung (starting Sep 2022) - NeuroAI and geometry.
* Andre A. Fenton - Molecular, neural, behavioral, and computational aspects of memory.
* Paul W. Glimcher - Decision-making in humans and animals. Neuroeconomics.
* David Heeger (also in Psychology) - Computational neuroscience, vision, attention.
* Roozbeh Kiani - Vision and decision-making.
* Wei Ji Ma (also in Psychology) - Perception, working memory, and decision-making.
* Tony Movshon - Vision and visual development.
* Bijan Pesaran - Neuronal dynamics and decision-making.
* Alex Reyes - Functional interactions of neurons in a network.
* John Rinzel (also in Mathematics) - Biophysical mechanisms and theory of neural computation.
* Cristina Savin (also in the Center for Data Science) - Computational models of learning and memory, machine learning.
* Robert Shapley - Visual physiology and perception.
* Eero Simoncelli - Computational vision and audition.
* Xiao-Jing Wang - Computational neuroscience, decision-making and working memory, neural circuits.
* Alex Williams - Statistical analysis of neural data.
Neuroscience Institute, School of Medicine (deadline: 1 December)
[https://neuroscience.nyu.edu/program.html, and https://med.nyu.edu/departments-institutes/neuroscience/]
* Gyorgy Buzsaki - Rhythms in neural networks.
* Dmitri Chklovskii (also in the Simons Foundation) - Neural computation and connectomics.
* Biyu He - Large-scale brain dynamics underlying human cognition.
* Dmitry Rinberg - Sensory information processing in the behaving animal.
* Shy Shoham - Methods for controlling, imaging, and analyzing neural systems.
* Mario Svirsky - Auditory neural prostheses; experimental/computational studies of speech production/perception.
Psychology, Cognition & Perception program (deadline: 1 December)
[http://as.nyu.edu/psychology/graduate/phd-cognition-perception.html]
* Todd Gureckis - Memory, learning, and decision processes.
* Brendan Lake (also in the Center for Data Science) - Computational modeling of cognition, deep learning.
* Michael Landy - Computational approaches to vision.
* Laurence Maloney - Mathematical approaches to psychology and neuroscience.
* Denis Pelli - Visual object recognition.
* Jonathan Winawer - Visual perception and memory.
Mathematics (deadline: 4 January)
[http://math.nyu.edu/degree/phd/]
* David McLaughlin - Nonlinear wave equations, computational visual neuroscience.
* Aaditya Rangan - Computational neurobiology, numerical analysis.
* Charles Peskin - Mathematical biology.
* Daniel Tranchina - Information processing in the retina.
* Lai-Sang Young - Dynamical systems, statistical physics, computational modeling and theoretical neuroscience.
Data Science (deadline: 12 December)
[https://cds.nyu.edu/phd-program/]
* Joan Bruna (also in Computer Science) - Machine learning, signal/image processing.
* Kyungyun Cho (also in Computer Science) - Machine learning, natural language processing.
* Carlos Fernandez-Granda (also in Mathematics) - Optimization methods for medical imaging, neuroscience, computer vision.
New York University is home to a thriving interdisciplinary community of researchers using computational and theoretical approaches in neuroscience. We are interested in exceptional PhD candidates with strong quantitative training (e.g., physics, mathematics, engineering) coupled with a clear interest in scientific study of the brain.
A listing of faculty, sorted by their primary departmental affiliation, is given below. Doctoral programs are flexible, allowing students to pursue research across departmental boundaries. Nevertheless, admissions are handled separately by each department, and students interested in pursuing graduate studies should submit an application to the program that best fits their goals and interests.
Center for Neural Science (CNS), Graduate School of Arts & Sciences (deadline: 1 December)
[https://neuroscience.nyu.edu/program.html, and https://as.nyu.edu/cns/DoctoralProgram.html]
* SueYeon Chung (starting Sep 2022) - NeuroAI and geometry.
* Andre A. Fenton - Molecular, neural, behavioral, and computational aspects of memory.
* Paul W. Glimcher - Decision-making in humans and animals. Neuroeconomics.
* David Heeger (also in Psychology) - Computational neuroscience, vision, attention.
* Roozbeh Kiani - Vision and decision-making.
* Wei Ji Ma (also in Psychology) - Perception, working memory, and decision-making.
* Tony Movshon - Vision and visual development.
* Bijan Pesaran - Neuronal dynamics and decision-making.
* Alex Reyes - Functional interactions of neurons in a network.
* John Rinzel (also in Mathematics) - Biophysical mechanisms and theory of neural computation.
* Cristina Savin (also in the Center for Data Science) - Computational models of learning and memory, machine learning.
* Robert Shapley - Visual physiology and perception.
* Eero Simoncelli - Computational vision and audition.
* Xiao-Jing Wang - Computational neuroscience, decision-making and working memory, neural circuits.
* Alex Williams - Statistical analysis of neural data.
Neuroscience Institute, School of Medicine (deadline: 1 December)
[https://neuroscience.nyu.edu/program.html, and https://med.nyu.edu/departments-institutes/neuroscience/]
* Gyorgy Buzsaki - Rhythms in neural networks.
* Dmitri Chklovskii (also in the Simons Foundation) - Neural computation and connectomics.
* Biyu He - Large-scale brain dynamics underlying human cognition.
* Dmitry Rinberg - Sensory information processing in the behaving animal.
* Shy Shoham - Methods for controlling, imaging, and analyzing neural systems.
* Mario Svirsky - Auditory neural prostheses; experimental/computational studies of speech production/perception.
Psychology, Cognition & Perception program (deadline: 1 December)
[http://as.nyu.edu/psychology/graduate/phd-cognition-perception.html]
* Todd Gureckis - Memory, learning, and decision processes.
* Brendan Lake (also in the Center for Data Science) - Computational modeling of cognition, deep learning.
* Michael Landy - Computational approaches to vision.
* Laurence Maloney - Mathematical approaches to psychology and neuroscience.
* Denis Pelli - Visual object recognition.
* Jonathan Winawer - Visual perception and memory.
Mathematics (deadline: 4 January)
[http://math.nyu.edu/degree/phd/]
* David McLaughlin - Nonlinear wave equations, computational visual neuroscience.
* Aaditya Rangan - Computational neurobiology, numerical analysis.
* Charles Peskin - Mathematical biology.
* Daniel Tranchina - Information processing in the retina.
* Lai-Sang Young - Dynamical systems, statistical physics, computational modeling and theoretical neuroscience.
Data Science (deadline: 12 December)
[https://cds.nyu.edu/phd-program/]
* Joan Bruna (also in Computer Science) - Machine learning, signal/image processing.
* Kyungyun Cho (also in Computer Science) - Machine learning, natural language processing.
* Carlos Fernandez-Granda (also in Mathematics) - Optimization methods for medical imaging, neuroscience, computer vision.
NYU Langone Health
Neuroscience Institute | NYU Langone Health
Scientists at NYU Langone’s Neuroscience Institute are committed to research and education that drive discoveries about the brain.
Physics (deadline: 18 December)
[https://as.nyu.edu/physics/programs/graduate.html]
* Marc Gershow - Perception, decision-making, and learning in neural circuits.
Computer Science (deadline: 12 December)
[http://www.cs.nyu.edu/home/phd/]
* Davi Geiger - Computational vision and learning.
* Yann LeCun - Machine learning, computer vision, robotics, computational neuroscience.
Economics (deadline: 18 December)
[https://as.nyu.edu/econ/graduate/phd.html]
* Andrew Caplin - Economic theory, neurobiology of decision.
* Andrew Schotter - Experimental economics, game theory, neurobiology of decision.
✔️ @ApplyTime
[https://as.nyu.edu/physics/programs/graduate.html]
* Marc Gershow - Perception, decision-making, and learning in neural circuits.
Computer Science (deadline: 12 December)
[http://www.cs.nyu.edu/home/phd/]
* Davi Geiger - Computational vision and learning.
* Yann LeCun - Machine learning, computer vision, robotics, computational neuroscience.
Economics (deadline: 18 December)
[https://as.nyu.edu/econ/graduate/phd.html]
* Andrew Caplin - Economic theory, neurobiology of decision.
* Andrew Schotter - Experimental economics, game theory, neurobiology of decision.
✔️ @ApplyTime
as.nyu.edu
Graduate Program