Ben Grimmer
@profgrimmer.bsky.social
Assistant Professor @JohnsHopkinsAMS, Works in Mathematical Optimization,
Mostly here to share pretty maths/3D prints, sometimes sharing my research
Mostly here to share pretty maths/3D prints, sometimes sharing my research
Lately, non-crossing partitions have shown up out of nowhere in my research, which have a lovely duality structure. This inspired some good art and fractals :)
Wanted to share the fun here (just sharing the pretty art for now, the research story will come in due time)
1/4
Wanted to share the fun here (just sharing the pretty art for now, the research story will come in due time)
1/4
February 12, 2026 at 6:06 PM
Lately, non-crossing partitions have shown up out of nowhere in my research, which have a lovely duality structure. This inspired some good art and fractals :)
Wanted to share the fun here (just sharing the pretty art for now, the research story will come in due time)
1/4
Wanted to share the fun here (just sharing the pretty art for now, the research story will come in due time)
1/4
Happy to announce that my work "On optimal universal first-order methods for minimizing heterogeneous sums" just received the Optimization Letters Best Paper Prize.
link.springer.com/journal/1159...
This work is part of a larger trend, fighting the brittleness of classic smooth/nonsmooth models.
link.springer.com/journal/1159...
This work is part of a larger trend, fighting the brittleness of classic smooth/nonsmooth models.
Optimization Letters
Optimization Letters covers all aspects of optimization, including theory, algorithms, computational studies, and applications. This journal provides an ...
link.springer.com
February 2, 2026 at 3:42 PM
Happy to announce that my work "On optimal universal first-order methods for minimizing heterogeneous sums" just received the Optimization Letters Best Paper Prize.
link.springer.com/journal/1159...
This work is part of a larger trend, fighting the brittleness of classic smooth/nonsmooth models.
link.springer.com/journal/1159...
This work is part of a larger trend, fighting the brittleness of classic smooth/nonsmooth models.
Sunday morning spent setting up my office in the new @hopkinsdsai.bsky.social building. I gained a good amount more wall space, so I have the freedom to grow my collections again
January 11, 2026 at 5:42 PM
Sunday morning spent setting up my office in the new @hopkinsdsai.bsky.social building. I gained a good amount more wall space, so I have the freedom to grow my collections again
Reposted by Ben Grimmer
Join us in advancing data science and AI research! The Johns Hopkins Data Science and AI Institute Postdoctoral Fellowship Program is now accepting applications for the 2026–2027 academic year. Apply now! Deadline: Jan 23, 2026. Details and apply: apply.interfolio.com/179059
December 19, 2025 at 1:29 PM
Join us in advancing data science and AI research! The Johns Hopkins Data Science and AI Institute Postdoctoral Fellowship Program is now accepting applications for the 2026–2027 academic year. Apply now! Deadline: Jan 23, 2026. Details and apply: apply.interfolio.com/179059
My student Thabo Samakhoana and I have been obsessed with smoothings lately. The softmax/logSumExp smoothing seems to be the standard everywhere in ML and optimization.
So, in what sense is this choice "optimal"?
We found some "elementary" answers, both good and bad news (1/4)
So, in what sense is this choice "optimal"?
We found some "elementary" answers, both good and bad news (1/4)
December 13, 2025 at 4:35 AM
My student Thabo Samakhoana and I have been obsessed with smoothings lately. The softmax/logSumExp smoothing seems to be the standard everywhere in ML and optimization.
So, in what sense is this choice "optimal"?
We found some "elementary" answers, both good and bad news (1/4)
So, in what sense is this choice "optimal"?
We found some "elementary" answers, both good and bad news (1/4)
A new paper out with TaeHo Yoon and Ernest Ryu:
We looked at the design of optimal fixed-point algorithms.
That is, seeking to approximately solve T(y)=y using as few evaluations of the operator T() as possible. Maximally efficient methods are "minimax optimal" 1/
We looked at the design of optimal fixed-point algorithms.
That is, seeking to approximately solve T(y)=y using as few evaluations of the operator T() as possible. Maximally efficient methods are "minimax optimal" 1/
November 20, 2025 at 3:46 AM
A new paper out with TaeHo Yoon and Ernest Ryu:
We looked at the design of optimal fixed-point algorithms.
That is, seeking to approximately solve T(y)=y using as few evaluations of the operator T() as possible. Maximally efficient methods are "minimax optimal" 1/
We looked at the design of optimal fixed-point algorithms.
That is, seeking to approximately solve T(y)=y using as few evaluations of the operator T() as possible. Maximally efficient methods are "minimax optimal" 1/
Lately, I have been obsessed with developing theoretically based optimization algorithms that actually attain the best practical performance.
Alas, the classic model of minimax optimal methods is overly conservative; it overfits to tune its worst-case.
We found a path forward 1/
Alas, the classic model of minimax optimal methods is overly conservative; it overfits to tune its worst-case.
We found a path forward 1/
November 18, 2025 at 2:59 PM
Lately, I have been obsessed with developing theoretically based optimization algorithms that actually attain the best practical performance.
Alas, the classic model of minimax optimal methods is overly conservative; it overfits to tune its worst-case.
We found a path forward 1/
Alas, the classic model of minimax optimal methods is overly conservative; it overfits to tune its worst-case.
We found a path forward 1/
Enjoyed being part of the Brin Mathematical Research Center's summer school on Scientific Machine Learning last week. Many very good talks and always nice to visit UMD!
August 14, 2025 at 8:29 PM
Enjoyed being part of the Brin Mathematical Research Center's summer school on Scientific Machine Learning last week. Many very good talks and always nice to visit UMD!
📢 Excited to share a new paper with PhD student Thabo Samakhoana. Nonsmooth optimization often uses smoothings, nearby smooth functions or sets. Often chosen in an ad hoc fashion.
We do away with ad hoc, characterizing optimal smoothings for convex cones and sublinear functions
We do away with ad hoc, characterizing optimal smoothings for convex cones and sublinear functions
August 12, 2025 at 2:40 PM
📢 Excited to share a new paper with PhD student Thabo Samakhoana. Nonsmooth optimization often uses smoothings, nearby smooth functions or sets. Often chosen in an ad hoc fashion.
We do away with ad hoc, characterizing optimal smoothings for convex cones and sublinear functions
We do away with ad hoc, characterizing optimal smoothings for convex cones and sublinear functions
Reposted by Ben Grimmer
Oh, that’s so satisfying! I stopped at the 4-norm ball thinking I had the solution as it fits the hole like a pot lid (has a perfect circle as an intersection).
August 5, 2025 at 7:36 PM
Oh, that’s so satisfying! I stopped at the 4-norm ball thinking I had the solution as it fits the hole like a pot lid (has a perfect circle as an intersection).
Yesterday I posted a maths puzzle that AIs all failed at (thanks for running the premium versions @xy-han.bsky.social and Ernest Ryu). The puzzle just needs elementary reasoning about p-norm balls (third row on my shelf below).
This thread gives the puzzle, solution, and a 3D printed demo :)
This thread gives the puzzle, solution, and a 3D printed demo :)
August 5, 2025 at 2:37 PM
Yesterday I posted a maths puzzle that AIs all failed at (thanks for running the premium versions @xy-han.bsky.social and Ernest Ryu). The puzzle just needs elementary reasoning about p-norm balls (third row on my shelf below).
This thread gives the puzzle, solution, and a 3D printed demo :)
This thread gives the puzzle, solution, and a 3D printed demo :)
I've invented a simple, lovely math puzzle I expect every AI fails:
Suppose you're a mathematical sailor at sea on a boat that has a perfectly cylindrical hole in the floor. All you brought is a collection of every p norm ball except p=2 (drat!). What do you do to cork the hole and save yourself?
Suppose you're a mathematical sailor at sea on a boat that has a perfectly cylindrical hole in the floor. All you brought is a collection of every p norm ball except p=2 (drat!). What do you do to cork the hole and save yourself?
August 4, 2025 at 11:57 PM
I've invented a simple, lovely math puzzle I expect every AI fails:
Suppose you're a mathematical sailor at sea on a boat that has a perfectly cylindrical hole in the floor. All you brought is a collection of every p norm ball except p=2 (drat!). What do you do to cork the hole and save yourself?
Suppose you're a mathematical sailor at sea on a boat that has a perfectly cylindrical hole in the floor. All you brought is a collection of every p norm ball except p=2 (drat!). What do you do to cork the hole and save yourself?
Reposted by Ben Grimmer
very cool talk:
youtu.be/K_dhTP2I2uo?...
"Near-Linear Runtime for a Classical Matrix Preconditioning Algorithm"
- Jason Altschuler
youtu.be/K_dhTP2I2uo?...
"Near-Linear Runtime for a Classical Matrix Preconditioning Algorithm"
- Jason Altschuler
Jason Altschuler - Near-Linear Runtime for a Classical Matrix Preconditioning Algorithm
YouTube video by Institute for Pure & Applied Mathematics (IPAM)
youtu.be
May 26, 2025 at 3:24 PM
very cool talk:
youtu.be/K_dhTP2I2uo?...
"Near-Linear Runtime for a Classical Matrix Preconditioning Algorithm"
- Jason Altschuler
youtu.be/K_dhTP2I2uo?...
"Near-Linear Runtime for a Classical Matrix Preconditioning Algorithm"
- Jason Altschuler
Lots of great questions and engagement from Wisconsin folk! They were quick at turning around and getting it online. See below:
www.youtube.com/watch?v=QNfq...
www.youtube.com/watch?v=QNfq...
April 30, 2025 at 8:57 PM
Lots of great questions and engagement from Wisconsin folk! They were quick at turning around and getting it online. See below:
www.youtube.com/watch?v=QNfq...
www.youtube.com/watch?v=QNfq...
Just landed in Madison! Tomorrow, I'll be sharing my work optimizing optimization methods, to and beyond minimax optimality in their SILO seminar.
Will share a link to the talk on YouTube after
Will share a link to the talk on YouTube after
April 30, 2025 at 3:33 AM
Just landed in Madison! Tomorrow, I'll be sharing my work optimizing optimization methods, to and beyond minimax optimality in their SILO seminar.
Will share a link to the talk on YouTube after
Will share a link to the talk on YouTube after
Reposted by Ben Grimmer
The optimization technique of gradient descent is like feeling your way down a mountain in the dark. You may not be able to see the way, but you’ll eventually reach the lowest point in the area. (From the archive)
www.quantamagazine.org/risky-giant-...
www.quantamagazine.org/risky-giant-...
March 29, 2025 at 8:04 PM
The optimization technique of gradient descent is like feeling your way down a mountain in the dark. You may not be able to see the way, but you’ll eventually reach the lowest point in the area. (From the archive)
www.quantamagazine.org/risky-giant-...
www.quantamagazine.org/risky-giant-...
My PhD students are awesome. They gave my fiancee(wife) and I this gorgeous cherry blossom card for our wedding and soon honeymoon in Japan <3
March 15, 2025 at 12:54 PM
My PhD students are awesome. They gave my fiancee(wife) and I this gorgeous cherry blossom card for our wedding and soon honeymoon in Japan <3
As an early wedding present (happening this Saturday!), my dad made me a custom shelf to hold my collection of unit norm balls!
Rockafellar+Wets's thick textbook is included for reference.
Rockafellar+Wets's thick textbook is included for reference.
March 12, 2025 at 7:52 PM
As an early wedding present (happening this Saturday!), my dad made me a custom shelf to hold my collection of unit norm balls!
Rockafellar+Wets's thick textbook is included for reference.
Rockafellar+Wets's thick textbook is included for reference.
New (first) paper with my student Aaron Zoll :)
We consider first-order methods for a ridiculously general model: minimizing a convex composition of functions g_j(x) that vary heterogeneously in whether they are smooth, nonsmooth, convex, strongly convex or anything in between.
We consider first-order methods for a ridiculously general model: minimizing a convex composition of functions g_j(x) that vary heterogeneously in whether they are smooth, nonsmooth, convex, strongly convex or anything in between.
March 11, 2025 at 2:00 PM
New (first) paper with my student Aaron Zoll :)
We consider first-order methods for a ridiculously general model: minimizing a convex composition of functions g_j(x) that vary heterogeneously in whether they are smooth, nonsmooth, convex, strongly convex or anything in between.
We consider first-order methods for a ridiculously general model: minimizing a convex composition of functions g_j(x) that vary heterogeneously in whether they are smooth, nonsmooth, convex, strongly convex or anything in between.
Reposted by Ben Grimmer
🎉Congrats to the 126 early-career scientists who have been awarded a Sloan Research Fellowship this year! These exceptional scholars are drawn from 51 institutions across the US and Canada, and represent the next generation of groundbreaking researchers. sloan.org/fellowships/...
February 18, 2025 at 3:14 PM
🎉Congrats to the 126 early-career scientists who have been awarded a Sloan Research Fellowship this year! These exceptional scholars are drawn from 51 institutions across the US and Canada, and represent the next generation of groundbreaking researchers. sloan.org/fellowships/...
PhD students set up arts and crafts to make Valentine's mailboxes and collect cards. They (slide) rule :)
February 15, 2025 at 9:01 PM
PhD students set up arts and crafts to make Valentine's mailboxes and collect cards. They (slide) rule :)
Newest office addition might be the biggest computer in my department! (Assuming compute is measured by length)
January 29, 2025 at 12:17 PM
Newest office addition might be the biggest computer in my department! (Assuming compute is measured by length)
Reposted by Ben Grimmer
Postdoctoral position at @cornelluniversity.bsky.social, "from all areas of research that advance the state of the art in data science and the health sciences, extending the reach of data-driven research into novel medical application domains"
Happy to chat!
academicjobsonline.org/ajo/jobs/29529
Happy to chat!
academicjobsonline.org/ajo/jobs/29529
Cornell University, Center for Data Science for Enterprise and Society
Full service online faculty recruitment and application management system for academic institutions worldwide. We offer unique solutions tailored for academic communities.
academicjobsonline.org
January 25, 2025 at 1:21 PM
Postdoctoral position at @cornelluniversity.bsky.social, "from all areas of research that advance the state of the art in data science and the health sciences, extending the reach of data-driven research into novel medical application domains"
Happy to chat!
academicjobsonline.org/ajo/jobs/29529
Happy to chat!
academicjobsonline.org/ajo/jobs/29529
NeurIPS has released recordings of talks!
I had the privilege to speak in the OPT-ML Workshop: neurips.cc/virtual/2024...
My talk presents a solution to the ''minimization game'' for smooth convex problems (ie a subgame perfect method that optimally adapts to any gradients seen)
I had the privilege to speak in the OPT-ML Workshop: neurips.cc/virtual/2024...
My talk presents a solution to the ''minimization game'' for smooth convex problems (ie a subgame perfect method that optimally adapts to any gradients seen)
Optimization for ML WorkshopNeurIPS 2024
neurips.cc
January 16, 2025 at 9:26 PM
NeurIPS has released recordings of talks!
I had the privilege to speak in the OPT-ML Workshop: neurips.cc/virtual/2024...
My talk presents a solution to the ''minimization game'' for smooth convex problems (ie a subgame perfect method that optimally adapts to any gradients seen)
I had the privilege to speak in the OPT-ML Workshop: neurips.cc/virtual/2024...
My talk presents a solution to the ''minimization game'' for smooth convex problems (ie a subgame perfect method that optimally adapts to any gradients seen)
Proud Advisor Moment: One of my first advisees, Danlin Li, now a PhD at Georgia Tech, just had our paper extending her Masters thesis accepted to Math Programming!
She gives a novel primal-dual way to understand the classic (primal) subgradient method
Check it out: arxiv.org/abs/2305.17323
She gives a novel primal-dual way to understand the classic (primal) subgradient method
Check it out: arxiv.org/abs/2305.17323
Some Primal-Dual Theory for Subgradient Methods for Strongly Convex Optimization
We consider (stochastic) subgradient methods for strongly convex but potentially nonsmooth non-Lipschitz optimization. We provide new equivalent dual descriptions (in the style of dual averaging) for ...
arxiv.org
January 16, 2025 at 2:48 PM
Proud Advisor Moment: One of my first advisees, Danlin Li, now a PhD at Georgia Tech, just had our paper extending her Masters thesis accepted to Math Programming!
She gives a novel primal-dual way to understand the classic (primal) subgradient method
Check it out: arxiv.org/abs/2305.17323
She gives a novel primal-dual way to understand the classic (primal) subgradient method
Check it out: arxiv.org/abs/2305.17323