Prof. Nikolai Slavov ✓

@slavov_n · Cambridge, USA · joined 09 Apr 2014

Mentor, scientist & engineer. Director of @ParallelSqTech. Having fun in @SlavovLab with single-cell proteomics & ribosomes. Organizer of @SCP_meeting

56 032Followers

270Following

11 090Posts total

105.3KViews on collected posts

Ultimi post

Prof. Nikolai Slavov ✓@slavov_n11 Sep

I am looking forward to the Chicago BioEngineering Conference next week. I will talk about scaling up deep and sensitive protein analysis by multiplexing in the mass and time domains and ... about a new topic to be announced next Thursday. See you next week in Chicago ! https:/

1.6K views · 8 likes · 2 reposts · 0 replies Open on X →

Prof. Nikolai Slavov ✓@slavov_n9 Sep

Nature just published a tech feature on single-cell proteomics. It highlights our journey from skepticism to robust technology and biological discoveries. I love that the feature starts with a biological discovery enabled by the technology: https://t.co/LqzouYfg9x 1/ https://t

4.4K views · 41 likes · 16 reposts · 1 replies Open on X →

Prof. Nikolai Slavov ✓@slavov_n8 Sep

A first order result from this study is that perturbation effects are specific to cell states: Rested and stimulated T-cells have distinct responses to purturbations. ==> If you build and test perturbation models, do it with the relevant cell types and cell states. https://t.c

9.2K views · 128 likes · 15 reposts · 2 replies Open on X →

Prof. Nikolai Slavov ✓@slavov_n7 Sep

Every time computers get better at something, they end up sharpening our sense of what makes us human. The success of today's AI is a case in point: it shows just how good we are at the things AI still struggles with, such as dealing with open-ended, ill-defined problems, 2K views · 4 likes · 0 reposts · 1 replies Open on X →

Prof. Nikolai Slavov ✓@slavov_n7 Sep

My personal experience is different. I always felt motivated to take risks, and my most recognized success is based upon taking risks. I recently talked about this experience: https://t.co/AbS3ZKV9MK 31.6K views · 33 likes · 4 reposts · 0 replies Open on X →

Prof. Nikolai Slavov ✓@slavov_n16 Jan

@slavov_n AI hype is in full swing in research circles right now. Worth keeping a clear view of what these systems actually do. Transformers predict tokens. That’s the entire mechanism. “Reasoning” is mostly a story repeated often enough to start sounding empirical. The exciteme 182 views · 1 likes · 0 reposts · 1 replies Open on X →

Prof. Nikolai Slavov ✓@slavov_n27 Dec 2025

@slavov_n Spot on, Nikolai. Computer simulation, mostly based in physics models, has existed as a scientific tool for more than 75 years. AI is just a newer, data based, incarnation with very similar epistemics. 341 views · 2 likes · 0 reposts · 0 replies Open on X →

Prof. Nikolai Slavov ✓@slavov_n27 Dec 2025

@slavov_n will you call (microscope, x-ray d, electrophoresis, RDT, sequencing , pcr, gfp, cas9) tools evolutionary or revolutionary? 682 views · 1 likes · 0 reposts · 1 replies Open on X →

Prof. Nikolai Slavov ✓@slavov_n27 Dec 2025

The use of AI in scientific research is evolutionary, not revolutionary. Ever since the pocket calculator, we have been delegating to machines tasks at which they excel: From arithmetic and simulation to database search, information retrieval and data analysis. Modern AI tools 55.2K views · 60 likes · 18 reposts · 7 replies Open on X →

Rispetto ad account della stessa dimensione

5 post degli ultimi 90 giorni, accanto alla fascia di 10K–100K follower. arriva a molti, ma pochi di loro reagiscono.

Visualizzazioni mediane4 371questo account1 079mediana per 10K–100K

Copertura, %7.80%questo account3.82%mediana per 10K–100K

Interazione, %0.61%questo account1.65%mediana per 10K–100K

Metrica	Questo account	Mediana per 10K–100K	Rapporto
Visualizzazioni mediane per post	4 371	1 079	4.05×
Copertura (visualizzazioni ÷ follower)	7.80%	3.82%	2.04×
Tasso di interazione	0.61%	1.65%	0.37×

Altri account di questa fascia → Confronta con un altro account → Come sono costruiti questi parametri →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

55.2K27 Dec

682

341

18216 Jan

31.6K7 Sep

9.2K8 Sep

4.4K9 Sep

1.6K11 Sep

Last 9 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.15%27 Dec

0.29%

0.59%

1.10%16 Jan

0.12%7 Sep

0.25%

1.57%8 Sep

1.33%9 Sep

0.61%11 Sep

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.65%.

What the audience does

Likes57.0%278 in total

Reposts11.3%55 in total

Replies2.7%13 in total

Bookmarks29.1%142 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

Ultimi post

Rispetto ad account della stessa dimensione

Growth & engagement

Views per post

Engagement rate per post

What the audience does

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