About me:

Born in Naples, 28.05.1985

I am a PhD student in the Astroinformatics group at the HITS – Heidelberg Institute for Theoretical Studies


Mobile: +49(0)17635788930

Office: +49(0)6221533315

Email: antonio.disanto@h-its.org




Brief Curriculum Vitae

2004: High School Diploma at “Liceo Scientifico Statale E. Majorana”, Pozzuoli (NA), with 100/100

2009: Bachelor Degree in Physics at “University of Naples Federico II”, with 104/110

2014: Master Degree in Astrophysics and Space Science at “University of Naples Federico II” with 110/110 cum laude

2015: PhD student at University of Heidelberg with HITS scholarship.




Extension to intermediate redshift of a method for galaxies photometric redshifts determination in multiband surveys,  Supervisors Prof. G. Longo, Dr. Raffaele D’Abrusco

Abstract: The main purpose of this thesis work was the determination of photometric redshifts of galaxies using data obtained with synoptic surveys, using neural networks and Virtual Observatory tools.



Classification of astronomical transients with machine learning methods,  Supervisors Prof. G. Longo, Dr. M. Brescia, Dr. S. Cavuoti

Abstract: The huge data burst which took place over the last years and the new generation of instruments have increased the request for new techniques and methods of data analysis and classification, that, if possible, must be fully automatized and efficient. In this light, a new method for classification of transients has been tested: MLPQNA, a neural network algorithm available in the DAMEWARE platform. The main purposes were to increase the efficiency of classification and to implement the first step of a more general workflow, based on the construction of a decision tree constituted by different classifiers.  



D’Isanto, A., Cavuoti, S., Brescia, M., Donalek, C., Longo, G., Riccio, G., Djorgovski, S.G., 2016. An analysis of feature relevance in the classification of astronomical transients with machine learning methods. MNRAS, 457, 3, 3119-3132

Polsterer, K.L., D’Isanto, A., Gieseke, F., 2016, Uncertain Photometric Redshifts. (only arXiv, provisional)

D’Isanto, A., 2016. Uncertain Photometric Redshifts with Deep Learning Methods. Proceedings IAU Symposium No. 325, 2016

D’Isanto, A., Polsterer, K.L., 2017. Uncertain Photometric Redshifts via Combining Deep Convolutional and Mixture Density Networks. ESANN 2017 Proceedings

A. D’Isanto and K. L. Polsterer, 2018, A&A, 609, A111. Photometric redshift estimation via deep learning – Generalized and pre-classification-less, image based, fully probabilistic redshifts

A. D’Isanto, S. Cavuoti, F. Gieseke and K. L. Polsterer, 2018, A&A, 616, A97, Return of the features – Efficient feature selection and interpretation for photometric redshifts.


My research interests

I am mainly interested in the application of machine learning, deep learning and data mining techniques to Astrophysical problems, in particular in the regime of Big Data.
Currently I am working on the cosmological field, being interested in the problem of photometric redshifts determination in the form of density distributions.


Current projects

  • Probabilistic photometric redshifts via deep learning on COSMOS data
  • Efficient feature selection for photometric redshift estimation on galaxies and automatically generated features interpretation (provisional)


Conferences & Summer Schools

  • Astroinformatics 2016 (Sorrento): poster presentation
  • Astrostatistics and Data Mining Summer School 2016 (Heidelberg)
  • European Symposium on Artificial Neural Networks 2017 (Bruges): poster presentation
  • DeepLearn Summer School 2017 (Bilbao)
  • AG 2017 – Meeting of the German Astronomical Society (Goettingen): contributed talk
  • Astroinformatics 2017 (Cape Town): contributed talk
  • ASTRON Hackathon 2018 (Dwingeloo)
  • Astroinformatics 2018 (Heidelberg): contributed talk


Invited talks

  • 04.04.2017: Deep Learning in Astronomy – MPIA Heidelberg StatMeet
  • 20.12.2017: The two worlds of photometric redshift estimation: fully automatic vs feature based models – Lecture for the Astroinformatics course at the University of Naples Federico II, Physics Department
  • 19.12.2018: Convolutional neural networks: Theory and application in astronomy – Lecture for the Astroinformatics course at the University of Naples Federico II, Physics Department


Public Outreach

I am collaborating with Tom’s Hardware Italy for the production of scientific contents in the Science section of the website. Actually I have published more than 40 articles. The full list of them (only in Italian) is available HERE.


Other interests

I always loved sport and in particular martial arts. For about 15 years I used to practice Taekwondo, in which I am 2nd dan black belt. Since three years I am studying also Kung Fu Wing Chun. Furthermore, I have a passion for reading, mainly fantasy and science fiction, cinema and role-playing games. Obviously Astronomy (and astronomical outreach)  is not just my job but also one of my hobbies.

Another big passion that occupied an important period of my life was politics. I have been leader of the city section of a party for a year  and actively participate to the political life of my home town for about  three years.