The MANDA Lab Odyssey

A few quick observations.  When talking to MS or PhD students during seminar presentations or other scientific meetings, I sometimes notice that they answer questions about very concrete topics (e.g., mathematical models of memory) quite confidently and fluently .  However, fluency is primarily verbal in nature. A lot of fluent sequences of words but without a genuine sense of understanding. I wonder if that is partly an LLM effect. All of us use AI these days. When you ask AI to explain something, it does so fluently but not always in the simplest words.  The only solution to this that I can think of is the old-school pen-and-paper approach to reading papers, writing down equations and struggling to make sense of them, drawing simple pictures of how you think matrices transform through the equations of the models, etc. This is, of course, time-consuming. An LLM will spit out the answers in seconds, and you read the answer and go away with the feeling that you've understo...

 I was asked this question recently in a podcast done by the institute. This is a podcast that replaces "reflection talks" that faculty used to give before their 5y contract renewals or promotions.  The reflections podcast, with Richa Chadda, senior manager at the faculty affairs office. I used to accept people to the lab simply on the basis of an interview to gauge interest. I thought the rest could be discerned from their CV. Unfortunately, this quickly backfired because CVs don't reflect reality. More often than not, CVs are exaggerated. In other cases, they are filled with internship experiences but each lasting a month or two. In other words, there is a distinct lack of depth and commitment shown in most CVs but people are quite articulate these days and a simple interview is insufficient to gauge preparedness to take on research in cognitive science. The other issue is people think research in cognitive science is easier than say in quantum physics. So many people w...

Universities and teachers around the world are being forced to rethink their approach to teaching given the rapid advancement of AI. Most homework and assignments can be done by AI far better than even the instructors of the courses. AI-detection tools are not the answer since they will always be gamed, and moreover, preventing people from using AI isn't really training them for the real world because AI is here to stay. We have AI on our phones as pocket companions. Google searches have been replaced with AI answers to queries.  Viva voce to test students instead of take-home assignments, handwritten term papers in class, etc. have all been suggested, and I've tried them myself. However, these are interventions at the point of the test and not something that really encourages students to value learning. AI-driven grade inflation  does not imply better learning; in fact, the opposite is most likely true .  I happened to come across this short but interesting piece on Medi...

I will keep this short.  The sign of a great leader is their willingness and ability to lend wings to junior members of their team so that they can fly and achieve their potential.  Unfortunately, what I see all around me are overwhelmed people in leadership positions who keep resources and opportunities to themselves despite their inability to take more on and be able to do a good job with any of it. This behaviour may not even be intentional. This is what they probably had to do to survive in a low-resource environment. However, when everyone does this, we collectively stifle scientific progress by not allowing meritorious young researchers to do the best possible work. Academia is not meant to be a dog-eat-dog world, but that is what it has turned into.  I do see rare people who are true leaders in academia. Without exception, they all give support and resources to people who are junior to themselves and pass on opportunities, especially when they themselves are not fu...

I have had many frustrating experiences with mentees and others who are smart but overrely on AI for their work these days. The reliance on AI for coding is not the issue. The problem is that they set their human brains (which are typically quite adept!) aside when they work with AI, and over time, this sets in real rot of critical and deep thinking. When candidates interview confidently and answer hard interview questions, a second round comprising a data analysis challenge usually reveals the issues. During this second interview, I ask conceptual questions. Only if the applicant has taken some minimal effort to understand the code and the concepts involved in the analysis and has thought a bit about the cognitive questions driving the data analysis challenge can they pass the bar for entry to the lab. Ten years ago, most people with master's degrees (and sometimes research experience in the same domain!) from IITs and other reputed colleges and universities would have been able t...

On June 30, 2026, I will complete 5 years of running a human memory research lab. Here is a summary of our research efforts so far and vision for the upcoming 5 years. Overview of our research program Our main area of focus is human memory. However, we are investigating memory at multiple levels: 1) Memory at the stimulus level (averaged across many people)— memorability 2) Memory at the neurocognitive mechanistic level— temporal context and event segmentation 3) Neural propagating waves as the basis for multiscale neuronal communication (and therefore, cognition)   in the brain. Figure 1. An overview of our program (thanks to ChatGPT) for those without the patience to read the text (written without ChatGPT). :)  1) Memorability We have developed a body of work describing what makes experiences memorable. For instance, the word " PINEAPPLE " may be more memorable to people in general, regardless of their individual memory capacities, than a word like " FOOT . "  ...

On June 30, 2026, I will complete 5 years as faculty in Indian academia. Here are some quick reflections. The good (great) The students. With its population, India is blessed with plenty of talent. Finding the right people to nurture and to see them develop into well-rounded scientists ready for the next step is a privilege that words cannot truly express. Now, the students we train are quite unlike the students who end up in the best universities in the US or elsewhere. These are people who haven't had that kind of exposure. For example, people who get admitted to PhD programs in the US typically already have significant research experience with a few publications to their name. So it is especially fulfilling when our students get into top PhD programs or do well otherwise after a stint in the lab. Our PhD scholars tend to start from scratch, and that also requires significant early investment of time for training the basics. Most of them are ambitious and passionate about what t...

 https://x.com/MakrandParanspe/status/2041313900533096692 Unfortunately, I have to agree with this take. I can speak from my experience as someone who does both experimental and computational work in cognitive science. If your work can be done on a normal computer using existing datasets, it is possible to publish steadily. But such work is less likely to be truly groundbreaking. Usually, datasets are shared after the original authors have already addressed the most immediate and critical questions that can be answered using them. This is why institutional support for experimental science matters so much. Setting up an ambitious experimental program takes time, space, infrastructure, administrative clarity, and patience. Early-career faculty who are trying to build something genuinely new often have to spend years bringing in grants, designing facilities, planning equipment, developing protocols, hiring and training people, and building analytical pipelines before the most exciting...

From Running Analyses to Doing Research: Lessons from My First Paper Anuska Maity Introduction When I entered IIIT Hyderabad, I knew that I wanted to pursue research. I believed that even small contributions could have lasting impact, but my understanding of what research involved was limited. I assumed it primarily meant reading prior work, proposing something novel, and validating ideas through analyses. This post reflects on how that understanding evolved during the process of working on my first research paper. In particular, I discuss how I learned that research is not only about running analyses or achieving statistically significant results, but about carefully testing assumptions, diagnosing failures, curating data responsibly, and using visualization as a tool for reasoning and debugging. I hope this reflection is useful for students who are just beginning their research journeys. Phase 1: Learning the Task Without Understanding the Process My initial months in the lab were la...

... but life gets better when we shift our attention to the positives in our life. This may sound   clichéd, but  the sheer number of people (including myself sometimes) who suffer due to focusing on "what could have been" is astounding. For most of us, if we could take a moment to think of all the very plausible ways in which life could have been much worse than it is right now, we would be much happier!  For me, so many things could have gone wrong, right from college when, in the initial two years, I was NOT doing well at academics. Soon, I had a mentor who agreed to guide me on physics research. I complained about his unavailability; there was even an entire year when he was gone and didn't respond to emails, but in hindsight, the fact that back in 2006-7, I even got to do research as a clueless undergrad in India was not something a lot of others had access to and I should have been thankful to that adviser whose courses actually taught me quite a bit about nonlinear...

I have made an observation in the last 4.5 years of working in Indian academia. Visionary leadership in academia is a scarce commodity. However, I have found some good examples of academic leadership in my limited experience and will talk about that.  One such experience was the IIT Mandi HCI Foundation/iHub call for proposals. I was shortlisted but when I got to IIT Mandi, the iHub leadership (their CEO, the institute director, etc.) said, "You all were shortlisted because you were the strongest on paper. We are not particularly interested in the specific ideas you wrote in your proposals but our aim was to get the best people together and connect you all so that you can come up with ambitious ideas that you wouldn't have been able to in isolation." And thus was born a consortium project with a startup and 3 other institutions (IIT Rourkee, IIIT Gwalior, and BITS Pilani Hyderabad) with a grand vision that was very different from each of our individual proposals. The DST ...

This year has been the most productive one for our lab so far, but of course, the groundwork for it was laid in the preceding 3 years. This semester, I had a high teaching load but also managed to make significant progress on my postdoc paper that still needs to be submitted. This is the first year, really, where we are able to share with the world some of the research we'd been working away on in the past few years. While we have published some decent conference papers already, we are beginning to submit some substantial journal papers (see the preprints below). I am confident that these will be published sometime next year. Note that most of this work was done with no funding other than institute support for students. Some of our folks are also applying to grad school, and I'm curious to see how their training and work here translate into PhD offers. After my talk at FAIL? '25 and the institute talk I gave about my work on traveling waves (in preparation for my Wave Club...