{"id":1172,"date":"2020-12-07T19:42:20","date_gmt":"2020-12-07T19:42:20","guid":{"rendered":"http:\/\/yingzhenli.net\/home\/en\/?page_id=1172"},"modified":"2026-06-07T01:08:36","modified_gmt":"2026-06-07T01:08:36","slug":"working-with-me","status":"publish","type":"page","link":"https:\/\/yingzhenli.net\/home\/en\/?page_id=1172","title":{"rendered":"Working with me"},"content":{"rendered":"<p><em>[<a href=\"http:\/\/yingzhenli.net\/home\/en\/?page_id=1769\">Why human learning &amp; research when people claim \"AI can automate everything\"?<\/a>]<\/em><\/p>\n<h4><span style=\"color: #800000;\"><strong>Working with me as a Post-Doctoral Researcher<\/strong><\/span><\/h4>\n<p><span style=\"color: #993300;\"><strong>Update Jun 2025: 2 post-doc positions available at <span style=\"color: #ff0000;\">Nangyang Technological University (NTU, Singapore)<\/span>, starting June 2026.<\/strong><\/span><\/p>\n<ul>\n<li>Topic 1: Bayesian Deep Learning: New Architecture Design in LLM Era\n<ul>\n<li>Examples of aligned interests: Bayesian deep learning, uncertainty quantification, approximate Bayesian inference, predictive Bayesian inference, attention\/RNN architecture design, meta-learning &amp; in-context learning (not pure LLM prompting type), online Bayesian inference, continual learning, speed-up methods for Bayesian computation and\/or attention\/RNN architectures, etc.<\/li>\n<\/ul>\n<\/li>\n<li>Topic 2: Scaling Structured\/Causal Representation Learning for Deep Generative Models\n<ul>\n<li>Examples of aligned interests: structural latent variable modelling, causal discovery, causal representation learning, modern deep generative models (diffusion\/flow\/auto-regressive models etc.), disentangled representations, dynamical systems, unsupervised learning, self-supervised learning, etc.<\/li>\n<\/ul>\n<\/li>\n<li>Requirements:\n<ul>\n<li>PhD in computer science\/statistics or relevant AI\/ML domains.\n<ul>\n<li>Have graduated\/submitted your PhD by the starting date.<\/li>\n<\/ul>\n<\/li>\n<li>Good publication records in top ML conferences and\/or statistics journals.<\/li>\n<li><strong>Aligned research interest (see topics) - please don't waste your time contacting me if this is not the case - sorry!<\/strong><\/li>\n<\/ul>\n<\/li>\n<li>The positions are based at NTU. Short visits to Imperial will be arranged subject to funding availability.<\/li>\n<li>How to apply: Apply by emailing me your CV, and a link to your representative paper. Selected candidates will be requested for a reference letter from their PhD supervisor and invited for an interview.<\/li>\n<\/ul>\n<h4><span style=\"color: #800000;\"><strong>Working with me for a PhD<\/strong><\/span><\/h4>\n<p style=\"font-weight: 400;\"><span style=\"color: #993300;\"><strong>Update Jun 2026: Three (3) fully-funded PhD studentships for Jan 2027 entry at NTU, Singapore. <br \/><\/strong><\/span><span style=\"color: #993300;\"><strong>Position at Imperial, UK - status pending, come back in Nov 2026.<\/strong><\/span><\/p>\n<p>You can apply one or multiple positions at one or both universities. However, if passing the interview, you'll only be accepted by one of the positions at one university.\u00a0<\/p>\n<p>Subject to funding availability: short-term research visits will be arranged between universities (Imperial students to NTU or NTU students to Imperial).<\/p>\n<p><strong>I won't be able to advise on pure \"vibe LLM prompting\/agent\" style research - sorry! Please don't waste your time contacting me if that's the case.<\/strong><\/p>\n<p style=\"font-weight: 400;\"><span style=\"color: #0000ff;\"><strong>Imperial College London (Imperial, UK) - Oct 2027 entry<\/strong><\/span><\/p>\n<ul style=\"font-weight: 400;\">\n<li>At most one (1) General PhD subject to funding availability - update TBD in Nov 2026.<\/li>\n<\/ul>\n<p style=\"font-weight: 400;\"><span style=\"color: #ff0000;\"><strong>Nangyang Technological University (NTU, Singapore) - Jan 2027 entry<\/strong><\/span><\/p>\n<p style=\"font-weight: 400;\">One (1) PhD studentship for each of the following topics (fully funded):<\/p>\n<ul style=\"font-weight: 400;\">\n<li>Bayesian Deep Learning: New Architecture Design in LLM Era;<\/li>\n<li>MLSys for Bayesian Computation (with\u00a0<a href=\"https:\/\/os-hxfan.github.io\/\">Hongxiang Fan<\/a>);<\/li>\n<li>Scaling Structured\/Causal Representation Learning for Deep Generative Models.<\/li>\n<\/ul>\n<p style=\"font-weight: 400;\">Application process:<\/p>\n<ul style=\"font-weight: 400;\">\n<li>Email me your CV, transcript and a research statement, also include English and GRE test results when applicable. Selected candidates will be invited to submit official applications <a href=\"https:\/\/www.ntu.edu.sg\/education\/graduate-programme\/ccds-phd-cs-engineering\">via university<\/a>, and get interviews after checks by admission admin (on transcripts etc).\u00a0<\/li>\n<li>The entire application process (including interview) will be finished by the end of Jan 2026 or early Feb 2026.<\/li>\n<\/ul>\n<p>======<\/p>\n<p>Before contacting me, think about <a href=\"http:\/\/yingzhenli.net\/home\/en\/?page_id=1083\">whether you should (not) do a PhD in machine learning<\/a>, read about my research topics, and think about whether we are a good match.<\/p>\n<p>If you want to do a PhD with me then, I expect you to have a solid skill set in both math and coding. This might be demonstrated by a 1st-class UK degree (or equivalent) in math, physics, computer science or related subjects. I also provide <a href=\"http:\/\/yingzhenli.net\/home\/en\/?page_id=1394\">a self-assessment here<\/a> if you want a bit more reassurance. Publication is a plus factor but not necessary, as long as you can demonstrate your potential for research innovations.<\/p>\n<p>When you contact me, attach your CV, plus a short statement (1 page maximum) about the potential ML research problems you want to work on with me and your expectations about PhD research. Also see <a href=\"https:\/\/www.imperial.ac.uk\/computing\/prospective-students\/courses\/phd\/phd-application-guidelines\/\">the PhD application guideline<\/a> for submitting an application to Imperial Computing.<\/p>\n<h4><strong><span style=\"color: #800000;\"><span style=\"caret-color: #800000;\">Working with me as an MRes student (Imperial)<\/span><\/span><\/strong><\/h4>\n<p><span style=\"color: #993300;\"><strong>Update Nov 2024: <\/strong>We are changing the admission process for 2025\/26 entry and onwards, interested applicants should just apply via Imperial application portal when it opens. Project selection will be done **after** admission.<\/span><\/p>\n<p>Imperial Computing also has an <a href=\"https:\/\/www.imperial.ac.uk\/computing\/prospective-students\/pg\/mres-artificial-intelligence-and-machine-learning\/\" target=\"_blank\" rel=\"noopener noreferrer\">MRes in AI &amp; ML degree<\/a> program where I am one of the supervisors there. This is a research master program where the degree will be awarded based on finishing a research thesis. Depending on the research results and interview, MRes students can continue for a PhD in AI &amp; ML, or join AI industry for ML engineering roles.<\/p>\n<p>Normally I expect to get 1 MRes students per year on projects proposed by myself and\/or other faculty members in collaboration (i.e., you might get co-supervisors). I treat MRes students in almost the same way as 1st-year PhD students (the only difference is that they need to finish in 1 year), and I expect them to participate in the group activities of my research group.\u00a0<\/p>\n<p>Please see the <a href=\"https:\/\/www.imperial.ac.uk\/computing\/prospective-students\/pg\/mres-artificial-intelligence-and-machine-learning\/\" target=\"_blank\" rel=\"noopener noreferrer\">degree program website<\/a> for application info. Once you get admitted, in the first month of the programme you can discuss with me on potentially working with me for your MRes project.\u00a0<\/p>\n<h4><span style=\"color: #800000;\"><span style=\"caret-color: #800000;\">Areas for PhD\/MRes research topics<\/span><\/span><\/h4>\n<p>We can be a good match if you want to research on the following topics:<\/p>\n<ol>\n<li>Bayesian computation, e.g. approximate inference, sampling;<\/li>\n<li>Uncertainty quantification &amp; decision theory;<\/li>\n<li>Training generative models for vision &amp; NLP;<\/li>\n<li>Sequential generative models &amp; stochastic dynamical models;<\/li>\n<li>Analysing behaviours of (stochastic) neural networks;<\/li>\n<li>Hierarchical\/compositional representations, disentangled representations,\u00a0generating counterfactuals, etc;<\/li>\n<li>Robust ML, e.g. security &amp; privacy issues, model diagnosis\/repair;<\/li>\n<li>Adaptive learning, transfer learning &amp; continual learning;<\/li>\n<li>Personalised few-shot learning with e.g. knowledge representations;<\/li>\n<li>Others, e.g. energy-based models, gradient estimation, Stein's method, etc.<\/li>\n<\/ol>\n<h4><span style=\"color: #800000;\"><span style=\"caret-color: #800000;\">Short-term projects\/internships\/participation<\/span><\/span><\/h4>\n<p>If you are an Imperial undergrad\/master\/PhD student, contact me for potential projects.\u00a0<\/p>\n<p>We will also host \"crash courses\" on advanced ML topics presented by our team members at the CSML reading group organised by people in Computing &amp; Statistics departments, so reach out if you would like to participate.\u00a0<\/p>\n<p>For other people, usually I do not offer paid short-term projects or internships. Still you are welcome to contact me for lightweight discussions and mentorships on related research topics.\u00a0<\/p>\n<h4><span style=\"color: #800000;\"><span style=\"caret-color: #800000;\">Collaborations<\/span><\/span><\/h4>\n<p>See <a href=\"http:\/\/yingzhenli.net\/home\/en\/?page_id=1087\">here<\/a> for a list of topics that we can potentially collaborate.<\/p>\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>[Why human learning &amp; research when people claim \"AI can automate everything\"?] Working with me as a Post-Doctoral Researcher Update Jun 2025: 2 post-doc positions available at Nangyang Technological University (NTU, Singapore), starting June 2026. Topic 1: Bayesian Deep Learning: New Architecture Design in LLM Era Examples of aligned interests: Bayesian deep learning, uncertainty quantification, ... <span class=\"more\"><a class=\"more-link\" href=\"https:\/\/yingzhenli.net\/home\/en\/?page_id=1172\">[Read more...]<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["entry","page","publish","author-admin","post-1172"],"_links":{"self":[{"href":"https:\/\/yingzhenli.net\/home\/en\/index.php?rest_route=\/wp\/v2\/pages\/1172","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/yingzhenli.net\/home\/en\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/yingzhenli.net\/home\/en\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/yingzhenli.net\/home\/en\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/yingzhenli.net\/home\/en\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1172"}],"version-history":[{"count":51,"href":"https:\/\/yingzhenli.net\/home\/en\/index.php?rest_route=\/wp\/v2\/pages\/1172\/revisions"}],"predecessor-version":[{"id":1824,"href":"https:\/\/yingzhenli.net\/home\/en\/index.php?rest_route=\/wp\/v2\/pages\/1172\/revisions\/1824"}],"wp:attachment":[{"href":"https:\/\/yingzhenli.net\/home\/en\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1172"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}