Yousof

Computer Engineering (AI & Robotics)
@ Ferdowsi Univ
Anomaly Detection • Convex Hull • AMP/KAMP • Distributed Systems
Python & R & ML

[Antonio Lupetti @antoniolupetti]

"Algebra, Topology, Differential Calculus, and Optimization Theory for Computer Science and Machine Learning" is an excellent free university-level textbook on the mathematical foundations of computer science and machine learning.

Across more than 1,000 pages, it covers groups, rings, fields, vector spaces, bases, linear maps, matrices, direct sums, determinants, Gaussian elimination, LU and Cholesky decomposition, echelon forms, iterative methods for solving linear systems, differential calculus, topology, convexity, optimization, and many other topics.

It is a rigorous, broad, and modern reference with a clear focus on the mathematics behind AI, machine learning, and computer science.

https://t.co/ykCMrlmGCg

[Manolis Kellis @manoliskellis]

MIT Course announcement: Machine Learning for Computational Biology #MLCB25
Fall'24 Lecture Videos: https://t.co/tA3zeuIF7g
Fall'24 Lecture Notes: https://t.co/C3WmXZuQur
(a) Genomes: Statistical genomics, gene regulation, genome language models, chromatin structure, 3D genome topology, epigenomics, regulatory networks.
(b) Proteins: Protein language models, structure and folding, protein design, cryo-EM, AlphaFold2, transformers, multimodal joint representation learning.
(c) Therapeutics: Chemical landscapes, small-molecule representation, docking, structure-function embeddings, agentic drug discovery, disease circuitry, and target identification.
(d) Patients: Electronic health records, medical genomics, genetic variation, comparative genomics, evolutionary evidence, patient latent representation, AI-driven systems biology.
Foundations and frontiers of computational biology, combining theory with practice. Generative AI, foundation models, machine learning, algorithm design, influential problems and techniques, analysis of large-scale biological datasets, applications to human disease and drug discovery. 
First Lecture: Thu Sept 4 at 1pm in 32-144
With: Prof. Manolis Kellis @manoliskellis, Prof. Eric Alm @ejalm, TAs: Ananth Shyamal, Shitong Luo @luost26
Course website: https://t.co/ateGr6xKLM
@MIT @MITEECS @MITdeptofBE @MITCSBPhD @MIT_CSAIL @Harvard @HarvardMed @BroadInstitute

[Frank Nielsen @FrnkNlsn]

Book #OTD

"Learning Theory:
An Approximation Theory Viewpoint"

[Kirk Borne @KirkDBorne]

Download 698-page PDF eBook…

Everything You Always Wanted To Know About Mathematics* (*But didn’t even know to ask)

A Guided Journey Into the World of Abstract Mathematics, Theorems, and the Writing of Proofs: https://t.co/JLsDOmpP1q

[Frank Nielsen @FrnkNlsn]

Computational geometry in Hilbert simplex geometry:

https://t.co/9aMUFNkPGS

[Yousof @YousofLHC]

@nima_selected @aliramezani4747 @aliramezani4747 واقعا خجالت آوره همچین حرکتی زدن بجای پاسخگویی. چند هزار نفر سردرگم و گرفتار شدن بعد شما واکنشتون همچین چیزیه؟!!

[Pushmeet Kohli @pushmeet]

AI agents are advancing research-level math. 🚀

I’m thrilled to share @GoogleDeepMind’s AlphaProof Nexus - an agentic framework for formal proof search powered by Gemini.

When applied to a set of open formal math problems, our agent autonomously solved:
✅ 9 open Erdős problems (including two open for 56 years!)
✅ 44 Online Encyclopedia of Integer Sequences (OEIS) problems
✅ A 15-year-old open problem in algebraic geometry ✅ A 7-year-old open question in min-max optimization

We are collaborating with mathematicians across disciplines - from combinatorics and graph theory to quantum optics. Ultimately, these results show the massive potential of even simple agentic loops powered by Gemini.

Read the paper here: https://t.co/c5M9ZjRXU1

[Frank Nielsen @FrnkNlsn]

New Followers, tutorial

"An Elementary Introduction to Information Geometry"

👉 https://t.co/8KEScZpX1P

[Yousof @YousofLHC]

@srishticodes Hi!
I've recently get my master in Ai an I'm into bioinformatics

[Yousof @YousofLHC]

Building robust bioinformatics algorithms requires systematic, stage-by-stage validation. This disciplined approach ensures each component performs as designed before integration, transforming complex research into reliable, high-performance systems. #BioInformatics #CPP #Python

[Frank Nielsen @FrnkNlsn]

Dynamic convex hull of ellipses.

https://t.co/t6PGdyLzgG

[Yousof @YousofLHC]

@abakcus Nice proof 👌

[Yousof @YousofLHC]

@FazlBarez @Oxford Hi Dr. Barez, I’ve just finished my MSc. in AI and would love to collaborate and learn with your team. What skills are you looking for? I’m open to exploring different technologies and would be grateful for any remote opportunity (still improving my speaking skills!)

[Yousof @YousofLHC]

Love this! Using LLMs as the mutation operator in genetic programming is genius. 🔥

Evolving algorithms with AI instead of random changes; such a clever fusion of evolutionary search and modern AI. Inspiring work!
https://t.co/a0dt80DCIM
#AI #GeneticAlgorithms #LLM #Innovation

[Yousof @YousofLHC]

@Riazi_Cafe_en Fractional optimization + ML + Geometric Information Theory 😁 all together

[Yousof @YousofLHC]

@Naam_kafi_hai Yousof

[Yousof @YousofLHC]

@chris_juravich Nice mapping 😁👌

[Yousof @YousofLHC]

@FrnkNlsn Such an interesting topic👌 I'd never thought about it from this perspective before

[Yousof @YousofLHC]

@kareem_carr So comprehensive and precise!

[Yousof @YousofLHC]

@ScholarshipfPhd Nice topic😅👌