José Fernando Maya-Vetencourt – Network Science- Top Researcher Award

Published on by Network Science

José Fernando Maya-Vetencourt – Network Science- Top Researcher Award

Assoc Prof Dr. José Fernando Maya-Vetencourt distinguished academic and researcher in the field Network science.

🌐 Professional Profile

Educations📚📚📚

Dr. José Fernando Maya-Vetencourt obtained his Bachelor’s degree in Biology from the University of the Andes in Merida. He then pursued a Master’s degree in Biochemistry and Cellular Biology at the Ecole Normale Supérieure in Paris, where he contributed to an international research project. He earned his PhD in Neurobiology at the Scuola Normale Superiore of Pisa under the mentorship of Lamberto Maffei and continued there as a Postdoctoral fellow. His postdoctoral work extended to the NEST laboratory.

Research Interests

Dr. Maya-Vetencourt’s research interests encompass:

  1. Neuronal Interfaces: Developing devices to substitute damaged tissues in the nervous system.
  2. Optogenetics and Neuronal Plasticity: Investigating mechanisms underlying sensory perception and behavior.
  3. Retinal Circuitries: Studying physiological and pathological conditions in the retina.

Publications and Academic Contributions

Dr. Maya-Vetencourt has published 34 scientific manuscripts in esteemed peer-reviewed journals, with 29 indexed in PubMed, including Science, Nature Neuroscience, and Nature Nanotechnology. He has edited one neuroscience book, authored four book chapters, and co-invented four patents. His work has garnered over 3400 citations, an H-index of 22 on Google Scholar, and an average impact factor per manuscript of 13.06.

Teaching and Mentorship

He has supervised over 35 Master’s thesis students in Neuroscience and Biotechnology. Dr. Maya-Vetencourt is a member of the Italian Society of Neuroscience (SINS), the American Society of Neuroscience (SfN), and the Italian Physiological Society (SIF). He serves as an associate review editor for “Frontiers in Molecular Neuroscience” and “Frontiers in Cellular Neuroscience”.

Awards and Recognitions

In 2017, Dr. Maya-Vetencourt received two prestigious “Young Investigator” awards: one from the Italian Physiological Society in Pavia and another from the Federation of European Physiological Societies in Vienna.

Pioneering Research

Dr. Maya-Vetencourt pioneered approaches to reactivate juvenile-like plasticity in the adult visual system (Nat Neurosc 2007; Science 2008). He explored chromatin susceptibility mechanisms for reinstating plasticity in the adult brain (Eur J Neurosci 2011). At IIT, he set up an electrophysiology lab to apply photovoltaic interfaces in rodent models of retinitis pigmentosa, leading to the development of a fully organic prosthetic device for subretinal implantation in RCS rats (Nat Mat 2017). This research demonstrated significant recovery of light sensitivity and visual acuity, accompanied by increased metabolic activity in the visual cortex.

Current Projects

Dr. Maya-Vetencourt is currently working on adapting surgical implant procedures for pig models to evaluate biocompatibility and visual function recovery (Front Bioeng Biotech 2020). He is also developing innovative “liquid” retinal prostheses to address age-related macular degeneration (Nat Nanotech 2020a, 2020b).

Future Directions

Dr. Maya-Vetencourt continues to advance the field of neuroscience by developing novel, biocompatible, and cost-effective solutions for neurodegenerative diseases, aiming to improve therapeutic outcomes for patients with degenerative blindness.

 

 

📝🔬Publications📝🔬

  • 1. Edoardo Porzano, Benedetta Borghi, José Fernando Maya-Vetencourt. 2024.
    Environmental enrichment as an innovative therapeutic approach to treat neurodegenerative
    blindness (in preparation)
    2. Edoardo Porzano, Dominga Lapi, Benedetta Borghi, Silvia Marracci, Melecchi Alberto,
    Giovanni Casini, José Fernando Maya-Vetencourt. 2024. Enriched environment
    enhances visual responses of the inner retina in early-stage retinitis pigmentosa (in
    preparation)
    3. Dmytro Shmal, Giulia Mantero, Thomas Floss, Fabio Benfenati, José Fernando MayaVetencourt. 2024. Restoring vision in adult amblyopia by enhancing plasticity through
    deletion of the transcriptional repressor REST. iScience 27(4): 109507. IF 5.8
    4. Ilaria Piano, Arianna Votta, Patrizia Colucci, Francesca Corsi, Sara Vitolo, Chiara Cerri, Dario
    Puppi, Michele Lai, José Fernando Maya-Vetencourt, Massimiliano Leigheb, Chiara
    Gabellini, Elisabetta Ferraro. 2023. Anti-inflammatory reprogramming of microglia cells by
    metabolic modulators to counteract neurodegeneration: a new role for Ranolazine. Scientific
    Reports 13: 20138. IF 5.13
    5. Simona Francia, Stefano Di Marco, Mattia Lorenzo DiFrancesco, Davide Ferrari, Dmytro
    Shmal, Alessio Cavalli, Grazia Pertile, Marcella Attanasio, José Fernando MayaVetencourt, Giovanni Manfredi, Guglielmo Lanzani, Fabio Benfenati, Elisabetta Colombo.
    2023. P3HT-Graphene device for the restoration of visual properties in a model of retinitis
    pigmentosa. Advanced Materials Technologies 8: 2201467. IF 8.86
    6. Domenico Ventrella*, José Fernando Maya-Vetencourt*, Alberto Elmi, Francesca Barone,
    Camilla Aniballi, Luisa Vera Muscatello, Maurizio Mete, Grazia Pertile, Fabio Benfenati, Maria
    Laura Bacci. 2022. The p-ERG spatial acuity in the biomedical pig under physiological
    conditions. Scientific Reports 12: 15479. IF 5.13
    7. Simona Francia, Dmytro Shmal, Stefano Di Marco, Greta Chiaravalli, José Fernando MayaVetencourt, Giulia Mantero, Caterina Michetti, Sara Cupini, Giovanni Manfredi, Mattia
    Lorenzo DiFrancesco, Anna Rocchi, Sara Perotto, Marcella Attanasio, Riccardo Sacco, Silvia
    Bisti, Maurizio Mete, Grazia Pertile, Guglielmo Lanzani, Elisabetta Colombo, Fabio Benfenati.
    2022. Light induced charge generation in polymeric nanoparticles restores vision in
    advanced-stage retinitis pigmentosa rats. Nature Communications 13: 3677. IF 17.69
    8. Miguel Skirzewski, Stéphane Molotchnikoff, Luis F Hernandez, José Fernando MayaVetencourt. 2022. Multisensory integration: is medial prefrontal cortex signaling relevant for
    the treatment of higher-order visual dysfunctions? Frontiers in Molecular Neuroscience
    14: 806376. IF 5.63
    9. Stefano di Marco, Mattia Di Francesco, Elisabetta Colombo, Giovanni Manfredi, José
    Fernando Maya- Vetencourt, Guglielmo Lanzani, Fabio Benfenati. 2020. Modulation of
    neuronal firing: what role can nanotechnology play? Nanomedicine 8: 579141. IF 4.7
    10. José Fernando Maya-Vetencourt, Stefano Di Marco, Maurizio Mete, Mattia Di Paolo,
    Francesca Barone, Domenico Ventrella, Giovanni Manfredi, Silvia Bisti, Guglielmo Lanzani,
    Grazia Pertile, Maria Laura Bacci, Fabio Benfenati. 2020. Biocompatibility of a polymeric
    subretinal prosthesis in the domestic pig. Frontiers in Bioengineering and
    Biotechnology 8: 1188. IF 6.06
    11. José Fernando Maya-Vetencourt, Giovanni Manfredi, Maurizio Mete, Elisabetta Colombo,
    Mattia Bramini, Mattia DiFrancesco, Dmytro Shmal, Cyril Eleftheriou, Francesca Di Maria,
    Vanessa Cossu, Laura Emionite, Flavia Ticconi, Cecilia Marini, Gianmario Sambuceti, Grazia
    Pertile, Guglielmo Lanzani, Fabio Benfenati. 2020. Subretinal semiconducting polymer
    nanoparticles fully rescue vision in a rat model of retinal dystrophy. Nature
    Nanotechnology 15: 698. IF 40.5
    12. DiFrancesco Mattia, Elisabetta Colombo, Ermanno Papaleo, José Fernando MayaVetencourt, Giovanni Manfredi, Guglielmo Lanzani, Fabio Benfenati. 2020. A hybrid P3HTGraphene interface for efficient photostimulation of neurons. Carbon 162: 308. IF 11.3
    13. Francesca Barone, Luisa Vera Muscatello, Domenico Ventrella, Alberto Elmi, Noemi
    Romagnoli, Luciana Mandrioli, José Fernando Maya-Vetencourt, Cristiano Bombardi,
    Maurizio Mete, Giuseppe Sarli, Fabio Benfenati, Grazia Pertile, Maria Laura Bacci. 2020. The
    porcine iodoacetic acid model of retinal degeneration: morpho-functional characterization of
    the visual system. Experimental Eye Research 193 107979. IF 3.77
    14. Mattia DiFrancesco, Francesco Lodola, Elisabetta Colombo, Luca Maragliano, Giuseppe M.
    Paternò, Mattia Bramini, Simone Cimò, Letizia Collela, Daniele Fazzi, José Fernando MayaVetencourt, Chiara Bertarelli, Guglielmo Lanzani, Fabio Benfenati. 2020. Neuronal firing
    modulation by a membrane-targeted photoswitch. Nature Nanotechnology 15(4): 296. IF
    40.5
    15. Stéphane Molotchnikoff, Vishal Bharmauria, Nayan Chanauria, Lyes Bachatene, José
    Fernando Maya- Vetencourt. 2019. The function of connectomes in encoding sensory
    stimuli. Progress in Neurobiology 101659. IF 14.16
    16. Luisa Vera Muscatello, Francesca Barone, Domenico Ventrella, Alberto Elmi, Luciana
    Mandrioli, Noemi Romagnoli, José Fernando Maya-Vetencourt, Cristiano Bombardi,
    Maurizio Mete, Giuseppe Sarli, Fabio Benfenati, Grazia Pertile, Maria Laura Bacci. 2019.
    Retinal features in iodoacetic-treated biomedical pigs as a model for photoreceptor disorders.
    Journal of Comparative Pathology 166: 103. IF N.A.
    17. Francesca Barone, Eleonora Nannoni, Alberto Elmi, Domenico Ventrella, Marika Vitali,
    Giovanna Martelli, José Fernando Maya-Vetencourt, Fabio Benfenati, Maria Laura Bacci.
    2018. Behavioral assessment of vision in pigs. Journal of the American Association for
    Laboratory Animal Science 57(4): 350. IF 1.34

Sajal Halder – Network Science – Best Researcher Award

Published on by Network Science

Sajal Halder – Network Science

Dr. Sajal Halder distinguished academic and researcher in the field Network science. He is currently working as a Research Fellow at Charles Sturt University, Australia. He has completed PhD in Computer Science at RMIT University and he has been appointed as an Assistant Professor (on study leave) at Jagannath University, Bangladesh since August 2017. His research interests are Personalized Itinerary Recommendation Systems, Machine Learning, Data Mining, Periodic Behavior Mining in Large-Scale Graph, Trajectory Behavior Analysis and Real Life Big Data Analysis.

Eduvation

He completed his academic journey with a stellar record, culminating in a Doctor of Philosophy (PhD) from the School of Computing Technologies at the Royal Melbourne Institute of Technology (RMIT) University in Melbourne, Victoria, Australia, with graduation in December 2022. His research focus during his doctoral studies was on the development of itinerary recommendation systems using deep learning methodologies. Under the guidance of A/Prof Jeffrey Chan and Prof. Xiuzhen Zhang, he contributed to the advancement of knowledge in this domain.

Prior to his doctoral pursuits, He earned a Master of Engineering in Computer Engineering from Kyung Hee University in South Korea, graduating with distinction in August 2013. His academic excellence is underscored by an impressive Cumulative Grade Point Average (CGPA) of 4.23/4.30 (95.25%). His master’s thesis, conducted under the supervision of Prof. Young-Koo Lee, delved into the exploration of supergraph-based periodic behaviors mining in dynamic social networks.

Commencing his academic journey,  he achieved a Bachelor of Science in Computer Science and Engineering from the University of Dhaka, Bangladesh, with graduation in November 2010. Displaying academic prowess, he secured a CGPA of 3.60/4.00, securing the 5th position out of 59 students. His final undergraduate project, conducted under the guidance of Supervisor Ashis Kumar Biswas, focused on the classification of multiple protein sequences through the analysis of irredundant patterns.

In summary, his educational background reflects a trajectory marked by academic excellence and a commitment to cutting-edge research in the fields of computer science and engineering.

Professional Profiles:

EXPERIENCE

• We developed a metadata-based malicious and benign package detection model for the NPM repository.
• We introduced two sets of features, easy to manipulate (ETM) and difficult to manipulate (DTM),
where manipulating DTM depends on long-term planning and monotonic properties. Then, we verify
our feature selection effectiveness based on four well known machine learning and one deep learning
techniques.
• We also analyse algorithms’ performance using metadata manipulation and recommend better metadata adversarial attack-resistant algorithms.
• Experiment analysis shows that our proposed model reduces 97.56% False Positive and 80.35 % False
Negative number.
• Achievement 1: Sajal Halder et al. ”Malicious Package Detection using Metadata Information”, In
submission (A* Conference).
• Achievement 2: Sajal Halder et al. ”Install Time Malicious Package Detection on NPM Repository”,
In process.
Skills: Feature Engineering, Data Scraping, Open Source Software, Machine Learning Model, Deep Learning,
Python, Report/Article Writing, Data Visualization and Teamw

PhD Researcher, RMIT University Melbourne, Australia.

i. Project Title: Capacity aware Fair POI Recommendation using Users Satisfaction.
Innovation: Design a novel fair POI recommendation model considering POI capacity and users preferences.
Language and Tools: Python, Actor-critic network, GCN, Tensorflow, Neural Network, Graph Allocation and Optimization
Duration: July 2021 to date.
ii. Project Title: Deep reinforcement learning of dynamic POI generation and optimization for itinerary
recommendation
Innovation: Propose and implement deep actor-critic network based itinerary recommendation model
named DRLIR.
Language and Tools: Python, AWS, Actor-critic network, GCN, Tensorflow, Neural Network
Duration: January 2021 to June 2021.
iii. Project Title: POI Recommendation with Queuing Time and User Interest Awareness
Innovation: Improve users interest based multi-tasking based transformer architecture models (TLR U
and TLR-M UI) for best POI recommendation.
Language and Tools: Python, PyCharm, Jupyter, Tensorflow, Keras, Matplotlib, Transformer, Lucid
Chart, Overleaf
Duration: March 2020 to December 2020.
iv. Project Title: Transformer-based Multi-tasking for Queuing Time Aware Next POI Recommendation
Innovation: Design and implement multi-tasking based transformer architecture models (TLR and
TLR-M) for better POI recommendation.
Language and Tools: Python, PyCharm, Jupyter, Tensorflow, Keras, Matplotlib, Transformer, Lucid
Chart, Overleaf
Duration: August 2019 to February 2020.
v. Project Title: Efficient Itinerary Recommendation via Personalized POI Selection and Pruning
Innovation: Develop efficient personalized itinerary recommendation model (EffiTourRec).
Language and Tools: R-studio, Lucid Chart, Overleaf, MatLab.
Duration: August 2018 to July 2019.

Research/Project Member
i Research Group: VLSI Research Group, Department o f Computer Science and Engineering, University of Dhaka, Bangladesh
Supervisor: Professor Dr. Hafiz Md. Hasan Babu
Duration: July 2016 to June 2018.
ii Project Name: Establishment of Basic Campus Network in Jagannath University
Source of Fund: HEQEP, UGC, Government of Bangladesh.
Duration: January 2017 to July 2018.
iii Project Name: Establishment of Basic Campus Network on Bangabandhu Sheikh Mujibur Rahman
Science & Technology University,
Source of Fund: HEQEP, UGC, Government of Bangladesh.
Duration: June 2015 to July 2016

Journal/ Conference Reviewers

• Engineering Applications of Artificial Intelligence (Q1 Ranked Journal, IF: 7.8)
• Information Process and Management (Q1 Ranked Journal, IF: 7.47)
• Applied Soft Computing (Q1 Ranked Journal, IF: 8.7)
• Scientific Reports (Q1 Ranked Journal, IF: 4.6)
• IEEE Transactions on Computational Social Systems (Q1 Ranked Journal, IF: 4.74)
• Transactions on Knowledge Discovery from Data (Q1 Ranked Journal, IF: 4.2)
• IEEE Access (Q1 Ranked Journal, IF 3.367)
• Expert System with Applications, (Q1 Ranked Journal, IF 6.954)
• World Wide Web (Q1 Ranked Journal, IF: 3.00)
• Knowledge and Information Systems (Q1 Ranked Journal, IF: 2.82)
• Neural Processing Letters (IF: 3.1)

SELECTED PUBLICATIONS

1. Sajal Halder, Kwan Hui Lim, Jeffrey Chan, and Xiuzhen Zhang.A Survey on Personalized Itinerary
Recommendation: From Optimisation to Deep Learning. In Applied Soft Computing Journal, 111200,
2023, Q1 Ranked Journal, (Impact Factor – 8.7). Publisher: Elsevier.

2. Sajal Halder, Kwan Hui Lim, Jeffrey Chan, and Xiuzhen Zhang. Capacity-aware fair POI recommendation combining transformer neural networks and resource allocation policy. In Applied Soft
Computing Journal, pages 110720, 2023, Q1 Ranked Journal, (Impact Factor – 8.7). Publisher: Elsevier.

3. Sajal Halder, Kwan Hui Lim, Jeffrey Chan, and Xiuzhen Zhang. POI Recommendation with Queuing
Time and User Interest Awareness. In Data Mining and Knowledge Discovery, 2022, Q1 Ranked
Journal, (Impact Factor – 5.406). Publisher: Springer.

4. Sajal Halder, Kwan Hui Lim, Jeffrey Chan, and Xiuzhen Zhang. Efficient Itinerary Recommendation
via Personalised POI Selection and Pruning. In Knowledge and Information Systems, Volume 64, Pages
963–993, 2022. Q1 Ranked Journal, (Impact Factor – 2.53), Publisher: Springer.

5. Sajal Halder, Kwan Hui Lim, Jeffrey Chan and Xiuzhen Zhang. Transformer-based Multi-task
Learning for Queuing Time Aware Next POI Recommendation. In the 25th Pacific-Asia Conference
on Knowledge Discovery and Data Mining (PAKDD-2021), A Ranked Conference.

6. Sajal Halder, Md. Samiullah and Young-Koo Lee. Supergraph based Periodic Patterns Mining in
Dynamic Social Networks. Expert Systems with Applications-An International Journal. volume 72,
pages 430-442, 2017, Q1 Ranked Journal, (Impact Factor – 6.954). Publisher: Elsevier.
7. MS Rahman, Sajal Halder, MA Uddin, UK Acharjee, An efficient hybrid system for anomaly detection in social networks. In Cybersecurity, Q1 ranked Journal, (Impact Factor – 6.27), 2021, Volume 4,
Pages 1-11, Publisher: Springer.

8. Iram Fatima, Sajal Halder, Muhammad Aamir Saleem, Rabia Batool, Muhammad Fahim, YoungKoo Lee, Sungyoung Lee. Smart CDSS: Integration of Social Media Interaction Engine (SMIE) in
Healthcare for Chronic Disease Patients. Multimedia Tools and Applications, pages 1-21, 2013, Q1
Ranked Journal, (Impact Factor – 2.757), Publisher: Springer.

9. Yongkoo Han, Kisung Park, Donghai Guan, Sajal Halder, and Young-Koo Lee. Topological Similarity
based Feature Selection for Graph Classification. In the Computer Journal, bxt123, 2013, Q2 Ranked
Journal, (Impact Factor – 0.755), Publisher: Oxford University Press.