Call for Nominations: CSC-SUTD Joint Scholarship

The China Scholarship Council and Singapore University of Technology and Design jointly sponsor outstanding PhD students to visit SUTD for an extended period (6-24 months) to carry out joint projects. The China Scholarship Fund provides for a round-trip international trip and a scholarship within the funding period. Scholarships are used to subsidize the basic study and living expenses while in Singapore.

Interested applicants should first contact a prospective faculty supervisor at SUTD to secure a nomination and letter of invitation before 1st March, 2026. More information is available here.

Quantum Computing Summer School at Los Alamos National Laboratory

The Quantum Computing Summer School is an immersive 10-week curriculum that includes tutorials from world-leading experts in quantum computation as well as one-on-one mentoring from Los Alamos National Laboratory staff scientists who are conducting cutting-edge quantum computing research. Summer school fellowship recipients will be exposed to the theoretical foundations of quantum computation and will become skilled at programming commercial quantum computers, such as those developed by IBM, Quera, IonQ, Quantinuum, DWave. All students (undergraduate and graduate) are encouraged to apply.  

In the first 2 weeks, students will attend lectures given by world-leading experts – from academia, industry and national laboratories – in quantum computing research. Following the 2-week lecture period, each student will work on a research project in quantum computing for the remaining 8 weeks. For this research project, each student will be paired with a LANL mentor who will propose project topics and provide guidance. Each project will involve some hands-on programming of a quantum computer (IBM’s, Quantinuum’s, Quera, D-Wave’s, as available). If time permits, the students will begin preparing their results for publication.

This is an fantastic opportunity with so many of the student projects delivering important findings on various hot topics related to quantum computing. This is a testament to the quality of the mentorship provided by the staff scientists involved in the school.

More information including how to apply is available here. The application deadline is January 11th, 2026.

Asian Network Mini-School on Quantum Materials 2024

Last week I visited the University of Indonesia to present two lectures on topological photonics at the Asian Network Mini-School on Quantum Materials 2024. This is one of a series of events held in South East Asian countries held by the ICTP Asian Network. The school attracted 95 participants from Indonesian universities, the majority being advanced undergraduates or graduate students. Meetings such as these provide valuable opportunities for early career scientists to learn about cutting-edge research areas and build collaborations with others in the region. I was impressed by the level of engagement from the audience - even though I ended my first lecture 20 minutes early, the remaining time was fully occupied by questions! Many thanks to the local organizers for putting together such an enjoyable meeting! Two more schools will be held this year, both in Thailand, on complex condensed matter systems and magnetism and spectroscopy, with more planned for next year.

PhD openings at Singapore University of Technology and Design

I have just joined Singapore University of Technology and Design (SUTD) as a faculty member. I will be slowly building up a research group as grant funding accumulates.

Presently there is an open call for applications for the SUTD PhD Programme for commencement in January 2025. The application deadline is 30th September. Successful scholarship applicants will receive a monthly stipend of S$3,500 (for Singapore citizens) or S$2,700 (for foreigners), with an additional $500 per month after passing the PhD Qualifying Exam.

Since these scholarships are not tied to any particular grant, I welcome applicants interested in working with me on theoretical projects related to any of the following topics:

• Photonics
• Topological materials
• Quantum technologies
• Quantum simulation
• Nonlinear waves
• Flat band systems
• Machine learning and physics
• Topological data analysis
  

Prospective applicants are invited to send me a CV and a short statement of research interests for more information on possible projects and the application process.

Reading the right papers

Students often find it particularly hard to tell which papers are worth an in-depth reading, which can be skimmed, and which are not essential to the current research project. Since this is something that is usually only learned through experience, examples can be helpful for building intuition.

Consider the first paper from my PhD research, Pseudospin and nonlinear conical diffraction in Lieb lattices, published in Physical Review A. With the benefit of hindsight, this turned out to be a Good Paper, with multiple experimental groups exploring some of the ideas in the following years. Why did it have an impact?

The research project didn't start by reading a bunch of papers and getting a new idea. The idea arose from talking to people - experimental collaborators, and one of the eventual co-authors (Omri), who had recently finished his PhD on the theory of wave propagation in graphene-like honeycomb photonic lattices. 

I was asked to see whether any of the ideas in his thesis could be feasibly investigated by our experimental collaborators. Honeycomb lattices being hard to do in their setup at the time, they wanted to know whether similar phenomena might be observable in a square lattice. Similar to how one can remove a period-doubled lattice from the triangular lattice to create a honeycomb lattice, removing sites from an ordinary square lattice yields a face-centred square lattice with intersecting bands. Great!

As is so often the case in research, we were not the first to have this idea, and actually in the preceding few years several groups had been exploring the properties of this lattice, motivated by huge interest in the electronic properties of graphene (Refs. [7,8,10,11,12,13] in the paper). These works were all published in the Physical Review, not "high impact" venues such as Nature / PRL, probably because referees thought it would be difficult to reproduce this model in an experiment. Being background material, an in-depth reading of all these papers was not required - we just needed to know roughly what they did and how they did it to understand how novel our results were.

In these papers we not only found the now commonly-used name for this lattice (the Lieb lattice), but also learned about how its properties were of interest in the context of cold atoms / BECs and electronic properties of materials. Lucky for us, we could not find any papers studying this lattice from the point of view of photonics, meaning that we had something novel! But on the other hand, we clearly couldn't just take these existing results (based on tight binding models) and do exactly the same using a "photonic" tight binding model without our work ending up being merely incremental and forgettable. Therefore we considered a few photonics-specific extensions:

(1) Wave propagation dynamics in the nonlinear regime, translating the analysis in one of Omri's recent papers (Ref. [11]) to the Lieb lattice setting. This one I had to read and re-read in detail to fully understand the analytical and numerical simulation tools used.

(2) Understanding the coupling between the different angular momentum degrees of freedom in our system. This similarly involved an extension of previous results by others for the honeycomb lattice (Ref. [18]) to the Lieb lattice setting. We also had to carefully read and understand this paper.

(3) Photonics-specific simulations not limited to a tight binding approximation and using experimentally-feasible parameters similar to those used in our collaborators' recent work (Ref. [26]).

In summary:

• Talk to experts early on to find out what the real important problems are and whether they have any that you are in a position to solve.
• Once you have an approximate solution or plan of attack, you need to check the literature to understand its importance and relevance to other work. At this stage you will often encounter papers with ideas very similar to yours.
• Identify your niche and expand on the novel points of your work, usually building on a few specific related papers that need to be carefully read and understood.
• It is usually easier to first solve a specific problem a single expert is having, and then figure out how your solution generalizes. The reverse approach - solving a problem in generality before considering specific examples - should only be attempted with extreme caution.

Doing literature reviews the smart way

Despite literature surveys being a key component of research, strategies for reviewing the scientific literature and identifying promising avenues of research are rarely included in graduate student coursework. This means that students may be unaware of more powerful tools that are available.

It is useful to have a tiered search strategy, starting with resources aimed at a broad audience, for example technical magazines such as Optics & Photonics News, to identify interesting or promising directions to study in more detail. While wikipedia is a popular first choice, peer-reviewed alternatives such as Scholarpedia provide more reliable and trustworthy articles written by known experts.

Google Scholar is perhaps the most popular scholarly search engine, but its limitations mean it is most useful for exploring papers on highly specific lines of research, mainly by following citation trains and highly-cited papers. Subscription-based search engines such as Web of Science are usually available under university subscriptions and give much more powerful tools for exploring a research area and seeing the bigger picture, such as the ability to filter search results by journal or author affiliations and visualise how publication trends are evolving over time using citation reports

Thanks to covid, many academic talks can now be viewed online. These are a great alternative to reading the papers themselves, particularly because the speaker may reveal insights that didn't end up in the journal article. One should keep in mind differences between workshops and larger conferences - target audience, breadth and depth of individual talks and the programme as a whole, and sometimes the candour of the speakers, particularly if the talk will be made available online. This means that in-person conference attendance is still highly valuable, because speakers may be more willing to share unpublished work and future research ideas during smaller more informal discussions. Talking to the right person can save hours of time figuring out what the key references are!

The volume of publications in an area may shape your research strategy. If a given keyword has hundreds or thousands of articles coming out each year, it's usually a sign that you need to narrow your focus to find a niche in which you can shine. Publications often follow a hype cycle, that is, an initial surge of interest leading to a transient peak in activity, followed by a more stable plateau as the field matures. Sometimes a line ends up being infeasible, leading to interest dying off before such a plateau can form.

It is important to emphasize the number of publications in an area should not be used to judge whether a field is worthwhile to study. For example, one researcher might see a booming field and be put off, desiring to work in a smaller area with a better potential for growth. A short peak of activity followed by little interest may suggest a research line has a difficult problem that nobody knows how to solve, offering an opportunity for you to make your mark.

Does artificial intelligence have a place in reviewing the literature and deciding on promising lines of research? Yes and no. Artificial intelligence is more than just large language models and chatbots, encompassing a variety of other machine learning-based tools for enhancing productivity, for example by helping to analyse and visualise citation networks. Some experimental examples of these network analysis tools are available on arXiv through arXivlabs and are worth a try - even if their capabilities are limited or inaccessible today (e.g. requiring a subscription), in the coming years the best ones will become more widely available via university-wide subscriptions, similar to the growth of collaborative paper-writing tools such as Overleaf.

And what about large language models? In my opinion, it's best to avoid them when carrying out literature reviews. Language models are trained to favour fluency over accuracy, so rather than generating new knowledge they are better used for performing tasks where the end-user can verify the output. Even when asked to analyze specific papers, you can't be sure that the model missed or misunderstood an important point, for example when jargon used within a research area differs from the commonly-understood meaning of a word. And even if (or when) these issues are solved by new and improved models, at the end of the day large language models are designed to spit out probable-sounding sequence of tokens. On the other hand, scientific breakthroughs often come about through the pursuit of unlikely or unexpected avenues of investigation.

Finally, one should not read too much. Too much time spent reading what other people have done not only takes time away from your own research, but it can also sap your creativity and ability to pursue directions away from the groupthink. Richard Hamming explained this eloquently in famous lecture "You and Your Research" he gave at Bell Labs, available both as a text transcript and a video recording. I highly recommend reading or watching!

In summary:

1. You should use a variety of sources, search engines, and media types
2. Remember every source and search engine has a bias
3. Aggregated statistics are just as important as individual papers
4. Try emerging AI-powered search & visualization tools
5. Don't read too much!