QuantNet Admission Predictor

[Andy Nguyen]

We’re thinking about building something new — the QuantNet Admission Predictor.

Over the years, the Tracker has collected more than 10,000 verified data points from applicants across all top quant programs. That’s a goldmine of information — and we’re exploring how to use it to help future applicants make smarter, data-driven decisions.

The idea is simple:
You enter your profile once (GPA, GRE, experience, education background, etc.), and the tool predicts your chance of admission at each program — based on real historical data, not random internet opinions.

We could also show things like:

• How your profile compares to admitted students
• Which programs are realistic, reach, or safety
• Suggestions on what would improve your odds

Before we move forward, I’d love to get your feedback:

1. Would you find this useful?
2. What kind of features would make it most valuable?
3. What price point would you consider reasonable (one-time or subscription)?

Your input will help us decide how to build and price it — and maybe even shape the next big QuantNet tool.
Drop your thoughts below — or DM me if you’d be interested in early access when it’s ready.

 

[Andy Nguyen]

Any comment would be great for us to know how useful this would be for our members.

 

[fabiantum]

For 1.: Yes, I would find this tool useful, as for my research, I basically had to use ChatGPT and had no reliable prediction.

For 2.: I would love a feature, such as given the suggested programs, what should be the optimal way to prepare the SOP or CV for them, and what they valued for previous applicants.

For 3.: I guess a one-time price would be better, as most students would only use it for one admission cycle, otherwise like 5-10 $ per month would be reasonable for a subscription.

That being said, I would love to have an additional feature on the tracker, namely, having an option to make the GPA optional, but if you don't display it, use comparative metrics, such as e.g. top 10 percent of my class (as some European universities have extremely harsh GPA policies, e.g. at mine the average passing grade is often like a GPA of 1.0)

 

[Yuvraj Chauhan]

Hey Andy, this looks pretty amazing, and I don't want to be the devil's advocate here but I feel I want to throw some light on the below:

There are majorly two types of candidates, one fits the category one doesn't. Once someone fits the bill, the process does become random. There are too many variables at play here which an outsider would find it very tough to gauge or account for. Things like one's interpersonal skills, the strength of references, SoP's, prevailing macro-econ conditions, immigration rules, et cetera would be very hard to properly account for. There's also the angle of other qualitative issues surrounding a program, such as internal decisions to vary the intake, or any practices to temper the yield rate. These are all incredibly hard to account for.

While what you've set out to do is nothing short of pioneering, it runs two primary risks:
a. Demoralizing candidates who aren't able to properly self-reflect on their strengths
b. Lend more confidence than acceptable to candidates because of selection bias

Both of these are potentially highly destructive to someone's candidature. In my experience, similar programs have often been attempted in India to "predict" admissions into Indian MBA's (this is a very toxic industry, I've witnessed the pitfalls of this firsthand. r/catpreparation would give you a decent idea as a non-Indian about what I'm referring to) and these have been highly problematic.

The above additionally introduces the risk of people negatively viewing QuantNet (this possibility is highly undesirable for folks like me and thousands of others who have gained amazing insights from this resource) because of this feature since they maybe didn't get the course the predictor said it would, or they learnt from elsewhere they could've gotten into a course the predictor said they can't get into. While I agree people need to own their decisions and the predictor is just supposed to be viewed as a crutch, things like grad school applications are major life decisions for people and they tend to get emotional and logic tends to go out the window.

I'm pretty sure you've already gamed out what I've outlined before and I'm by no means saying something which you haven't thought of before, but these are my two cents on it.

If I may recommend, focusing on trying to get more insights about quant-adjacent fields such as AM, or Data Analytics or Credit Modelling may fulfil an existing gap in QuantNet's present offerings.

 

[Andy Nguyen]

Appreciate the input and different perspective guys. Hope to hear more feedback from other members as we plan our future offerings.

 

[ZizheZhou]

We’re thinking about building something new — the QuantNet Admission Predictor.

Over the years, the Tracker has collected more than 10,000 verified data points from applicants across all top quant programs. That’s a goldmine of information — and we’re exploring how to use it to help future applicants make smarter, data-driven decisions.

The idea is simple:
You enter your profile once (GPA, GRE, experience, education background, etc.), and the tool predicts your chance of admission at each program — based on real historical data, not random internet opinions.

We could also show things like:

• How your profile compares to admitted students
• Which programs are realistic, reach, or safety
• Suggestions on what would improve your odds

Before we move forward, I’d love to get your feedback:

1. Would you find this useful?
2. What kind of features would make it most valuable?
3. What price point would you consider reasonable (one-time or subscription)?

Your input will help us decide how to build and price it — and maybe even shape the next big QuantNet tool.
Drop your thoughts below — or DM me if you’d be interested in early access when it’s ready.

Hi Andy,
I just saw your post about the QuantNet Admission Predictor — and honestly, I got really excited reading it. It’s almost surreal, because my team and I actually built something quite similar a while ago — EdVance.pro — an AI-assisted platform for grad and study-abroad admissions.
We worked on matching applicants to programs using real historical data, analyzing profile competitiveness, and even generating personalized improvement suggestions. So when I read your idea, I immediately thought, “Wow, this is exactly the kind of direction we’ve been passionate about too.”
I think your concept has huge potential. Many applicants don’t know how to interpret data from the Tracker — turning that into real predictive insights could be transformative. I’d personally love to see features like profile benchmarking and scenario testing (“what if I raise my GRE quant by 3 points?”).
For pricing, I’d imagine something lightweight — maybe a small one-time fee or a seasonal plan during the application cycle. Most people would happily pay for accurate guidance that saves them weeks of uncertainty.
If you’re open to it, I’d love to chat or share what we learned from building EdVance.pro — especially around data modeling and user behavior. It’s really inspiring to see QuantNet moving in this direction.
Best,
Zizhe Zhou

 

[kombuchalover]

I think this would be helpful. However, I think that to avoid taking advantage of people, the algorithm must be as accurate as possible. Thus, I think it should account for the courses that the student is taking as well and their work experience (in some capacity) and not just the GRE and GPA. I think what would be most reasonable would be a 1 time payment of $20 to see the admission predictions for 4 schools of your choice (and other pricing for less or more schools based upon this).

 

[Andy Nguyen]

This is amazing to know that you were working on this before @ZizheZhou. We definitely should chat.
@Yuvraj Chauhan very valid points you have there. Our goal for this is not to make money, we will make it very affordable to whoever wants to utilize the insights to make better decision.
@kombuchalover has similar advice. We are not going to rely on GPA, GRE, TOEFL and some numerics and come up with a magic number. We all know there are more nuances than the numbers. I have seen time and again people have good stats fail to get in while other got it.

In order to fully benefit from this, members would have to provide work experience, internship, target jobs, etc. You have to give in order to get.
I think at the end of the day, this is another tool for serious members to gain an edge.

I appreciate all the inputs, guys. Keep them coming if you have any. If you think this is not going to work, let me know why as well.
I'm curious to hear what people on the other side of the admission have to say @Ash Cross @Fiona Taft

 

[mokline.iyed]

Yes, I think this would be extremely useful. Every year, applicants waste a lot of time trying to estimate their chances based on anecdotal posts or outdated forum threads. A data-driven predictor built from verified QuantNet profiles would finally bring transparency and structure to the process.


Being able to compare our profile directly with past admitted students and see which programs are realistic, reach, or safety would help applicants focus their preparation and allocate their application budget much more efficiently.


One very important factor to include is the quality and rigor of the applicant’s previous university. Two students with identical GPAs and GRE scores may have very different levels of preparation depending on where they studied. It might be useful to categorize universities into broad tiers, for example five levels, to capture this effect and make predictions more accurate and fair.


It could also motivate students to improve key areas such as GRE, GPA, or programming experience once they understand how much these factors influence admission probabilities. I would definitely use such a tool and I think many future applicants would find it invaluable.

 

[Andy Nguyen]

This is very useful feedback @mokline.iyed
This discussion indicates there is a need and great utility for the tool, however we will name it. Depends on how we will frame and implement it, it can be an effective tool to allow applicants to discover their area of weakness to improve on.

 

[ZizheZhou]

This is very useful feedback @mokline.iyed
This discussion indicates there is a need and great utility for the tool, however we will name it. Depends on how we will frame and implement it, it can be an effective tool to allow applicants to discover their area of weakness to improve on.

I believe it’s crucial to quantify every evaluation dimension. Each Financial Engineering program has its own focus, so it’s important to understand what each school values most. For instance, Princeton may place a stronger emphasis on the ranking of your undergraduate institution, meaning that this factor could play a dominant role in their admissions decision. Then it's time for comparing the undergraduate institution. Who is better? Tsinghua University or Peking University? I think we can pick a university ranking(Hopefully not QS that's really bias).
The real challenge lies in evaluating candidates’ internship and research experience, since these vary widely in nature and quality. AI could be particularly helpful here — we can design tailored prompts that assess and score these experiences, similar to how EdVance operates. Crafting these prompts will be challenging, as they must capture multiple dimensions such as the scale of the internship company and the impact factor of the publication.
Finally, regarding the name of this predictor, QuantAdmit could be a strong and fitting choice!

 

[mokline.iyed]

This is very useful feedback @mokline.iyed
This discussion indicates there is a need and great utility for the tool, however we will name it. Depends on how we will frame and implement it, it can be an effective tool to allow applicants to discover their area of weakness to improve on.

@Andy Nguyen Yes, there’s a real need for it, would be a great addition to the platform.

 

[mokline.iyed]

@ZizheZhou Totally agree.. quantifying each dimension would make the tool far more insightful. QuantAdmit sounds great too.

 

[ZizheZhou]

@ZizheZhou Totally agree.. quantifying each dimension would make the tool far more insightful. QuantAdmit sounds great too.