The MANDA Lab Odyssey

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...