Hello, I'm Daniele Germano, a Solutionist who is passionate about innovation and problem-solving. I have a blend of creativity and analytical skills that enables me to tackle challenges and uncover new opportunities.

With my background in IT consulting, I specialize in leveraging Big Data Analysis and Machine Learning techniques to improve business outcomes. I have a strong foundation in statistics and computer science, allowing me to identify and resolve complex problems.

Throughout my career, I have designed and implemented Machine Learning and Deep Learning models, and I have experience managing small teams of data scientists. I'm also fascinated by the field of Neuroscience and stay up to date with the latest research developments.

In summary, I bring a unique blend of technical skills, creativity, and a deep passion for innovation to every project I undertake. I'm eager to apply my expertise to help solve complex challenges and drive meaningful results.


Research Associate @Department of Molecular Medicine - Sapienza

Since December 2022

The research focused on detecting "windows of responsiveness" in Minimally Conscious State patients, using Artificial Intelligence models.

Teacher @Boolean

Since September 2022

Teacher in the Data Analytics course, I am in charge of Statistics, SQL and Python & ML modules.

External Data Scientist @WISHINNOVATION

Since June 2022

I am engaged in the study and implementation of Artificial Intelligence models for creating algorithms that can improve indoor environmental quality (IEQ) and energy efficiency of buildings using IoT sensors.

External Data Scientist @P4Future

May 2022 - July 2022

I contributed to the creation of neural network algorithms to identify and classify fashion objects.

External Data Scientist @BrainSigns

September 2020 - August 2022

I finalized research projects that stemmed from my master's thesis on Neuroscience, by taking part in the research and development of models and methodologies for the classification of mental states. I am currently collaborating on the creation of a portable device able to classify mental states in different contexts, using machine learning algorithms.

Senior Data Scientist @Be

October 2020 - January 2022

I managed and lead a team of Data Scientists, and supervised the development of Machine and Deep Learning models. I collaborated in several projects and independently carried out a project on behavioural analysis, habits and points of interest of drivers for an insurance group.

Intership @Fondazione Santa Lucia

September 2019 - July 2020

I carried out thesis research aimed at the analysis and creation of Machine Learning methods for the classification of mental states (such as Stress, Workload and Vigilance) through the analysis of bioelectric signals such as EEG, ECG and GSR.

Senior Data Scientist @Jakala

September 2018 - September 2020

I was Referent for the realisation of an internal Project focused on the creation of a data platform, in order to enrich the partners' customer data in an anonymous way using Statistical Matching functions and Advanced Data Anonymization and Privacy Tool.

Data Scientist @Accenture

April 2015 - August 2018

As part of a team of 5 people, I participated in the realisation of 3 projects, each lasting one year. In the first project, for a banking group, I was responsible for creating a loyalty model for classifying users using the Cloudera platform with Spark Scala. In the second Project, Energy Group, I was in charge of creating Python scripts for the transformation of data included in an ETL process. In the third Project, Telecommunications Group, I worked on the creation of python scripts for data analysis and the creation of churn models on Google cloud Platform technology.


PhD in Bioengineering @Università la Sapienza

Since November 2023

My PhD research in bioengineering focuses on the implementation of artificial intelligence models for biosignal analysis in the context of passive brain-computer interfaces (pBCI). The main objective is the development of artificial intelligence models that enable continuous interaction and analysis with brain signals, under different working conditions, especially in out-of-laboratory contexts.

Master's Degree in Data Science @Università la Sapienza

September 2015 - July 2020

The Master's programme in Data Science provided me with an interdisciplinary approach that incorporates contributions from Computer Science, Statistics, Bioinformatics, and Neuroscience. Through this programme, I gained the necessary professional knowledge to develop big data collection, management, processing, and analysis technologies, and translate this data into valuable insights for innovative business and social sectors. In addition, elective courses such as Bioinformatics, Digital Epidemiology, and Neuroscience provided me with a better understanding of how Data Science can be applied in the medical field. Overall, this programme equipped me with a broad range of skills and knowledge to succeed in the dynamic and rapidly evolving field of Data Science.

UIIP Consultant Academy @BIOGEM

January 2015 - April 2015

The UIIP course provided me with a comprehensive set of skills and competencies in the field of ICT. Through this program, I gained expertise in the latest technologies and trends in the industry, including data analytics, machine learning, and software development. The program also equipped me with valuable project management skills, enabling me to effectively lead teams and oversee complex projects. Additionally, I developed strong problem-solving and critical thinking skills, which allow me to analyze complex situations and identify optimal solutions. Finally, I gained a deep understanding of the importance of collaboration and effective communication in the field of ICT, which is crucial for success in today's fast-paced and interconnected world. Overall, the UIIP program has provided me with the skills and competencies necessary to succeed in the dynamic and rapidly evolving field of ICT.

Bachelor's Degree in Statistics for Business @Università della Calabria

September 2011 - October 2014

My Bachelor's degree in Statistics for Business has equipped me with a strong foundation in methodological skills in Statistics and Actuarial Science. The program's focus on Statistical Science has also provided me with a comprehensive understanding of Computational, Data Managment, and Algorithmic skills, as well as Financial and Insurance skills. Through this program, I gained a deep understanding of the underlying theories and principles of statistical analysis, and developed proficiency in advanced statistical techniques such as regression analysis, time series analysis, and hypothesis testing. Furthermore, I honed my skills in data management, visualization, and interpretation, which are essential for generating actionable insights in a business setting. Overall, my Bachelor's degree has provided me with a rigorous and comprehensive education in Statistics for Business, giving me the skills and knowledge necessary to succeed in a variety of data-driven roles.


Unsupervised Detection of Covariate Shift Due to Changes in EEG Headset Position: Towards an Effective Out-of-Lab Use of Passive Brain–Computer Interface

D. Germano, N. Sciaraffa, V. Ronca, A. Giorgi, G. Trulli, G. Borghini, G. Di Flumeri, F. Babiloni, P. Aricò
Applied Sciences (MDPI) 2023
View the abstract

In the field of passive Brain–computer Interfaces (BCI), the need to develop systems that require rapid setup, suitable for use outside of laboratories is a fundamental challenge, especially now, that the market is flooded with novel EEG headsets with a good quality. However, the lack of control in operational conditions can compromise the performance of the machine learning model behind the BCI system. First, this study focuses on evaluating the performance loss of the BCI system, induced by a different positioning of the EEG headset (and of course sensors), so generating a variation in the control features used to calibrate the machine learning algorithm. This phenomenon is called covariate shift. Detecting covariate shift occurrences in advance allows for preventive measures, such as informing the user to adjust the position of the headset or applying specific corrections in new coming data. We used in this study an unsupervised Machine Learning model, the Isolation Forest, to detect covariate shift occurrence in new coming data. We tested the method on two different datasets, one in a controlled setting (9 participants), and the other in a more realistic setting (10 participants). In the controlled dataset, we simulated the movement of the EEG cap using different channel and reference configurations. For each test configuration, we selected a set of electrodes near the control electrodes. Regarding the realistic dataset, we aimed to simulate the use of the cap outside the laboratory, mimicking the removal and repositioning of the cap by a non-expert user. In both datasets, we recorded multiple test sessions for each configuration while executing a set of Workload tasks. The results obtained using the Isolation Forest model allowed the identification of covariate shift in the data, even with a 15-s recording sample. Moreover, the results showed a strong and significant negative correlation between the percentage of covariate shift detected by the method, and the accuracy of the passive BCI system (p-value < 0.01). This novel approach opens new perspectives for developing more robust and flexible BCI systems, with the potential to move these technologies towards out-of-the-lab use, without the need for supervision for use by a non-expert user.

AIR SAFE: Leveraging IoT Sensors and AI Models to Foster Optimal Indoor Conditions

M. Viviani, S. Colace, D. Germano, S. Laurita, G. Papuzzo, A. Forestiero
Numerical Computations: Theory and Algorithms - 4th International Conference, NUMTA 2023
In Press
View the abstract

The need of safe and livable indoor environments has intensified recently, given the great amount of time people spend indoor. In addition, the recent COVID-19 pandemic has moved the interest from the outdoor to indoor spaces. To guarantee an optimal indoor environmental quality, monitoring and regulating many variables (such as indoor and outdoor temperature, pollutants concentration, noise, and brightness) is necessary. In this context, we have developed AIR SAFE, an IoT and AI based infrastructure to monitor and control environmental quality in closed spaces. AIR SAFE uses Machine Learning models to make predictions of temperature, relative humidity, and CO2 concentration. These predictions, together with data from a network of IoT sensors, are used to take actions on windows and air conditioning system with the aim of modifying for the better the room environment. We show the results of the AI model we have developed for predicting indoor concentration of CO2, relative humidity, and temperature. Our Long Short Term Memory (LSTM) model has been tested against literature models using simulated data at first, and then testing the best models on real data. Using real data, the LSTM network performs best at forecasting temperature and relative humidity, while Random Forest is the best CO2 concentration predictor.

Evaluation of a New Lightweight EEG Technology for Translational Applications of Passive Brain-Computer Interfaces

N. Sciaraffa, G. Di Flumeri, D. Germano, A. Giorgi, A. Di Florio, G. Borghini, A. Vozzi, V. Ronca, F. Babiloni, P. Aricò
Frontiers in Human Neuroscience 16 (2022)
View the abstract

Technologies like passive brain-computer interfaces (BCI) can enhance human-machine interaction. Anyhow, there are still shortcomings in terms of easiness of use, reliability, and generalizability that prevent passive-BCI from entering real-life situations. The current work aimed to technologically and methodologically design a new gel-free passive-BCI system for out-of-the-lab employment. The choice of the water-based electrodes and the design of a new lightweight headset met the need for easy-to-wear, comfortable, and highly acceptable technology. The proposed system showed high reliability in both laboratory and realistic settings, performing not significantly different from the gold standard based on gel electrodes. In both cases, the proposed system allowed effective discrimination (AUC > 0.9) between low and high levels of workload, vigilance, and stress even for high temporal resolution (<10 s). Finally, the generalizability of the proposed system has been tested through a cross-task calibration. The system calibrated with the data recorded during the laboratory tasks was able to discriminate the targeted human factors during the realistic task reaching AUC values higher than 0.8 at 40 s of temporal resolution in case of vigilance and workload, and 20 s of temporal resolution for the stress monitoring. These results pave the way for ecologic use of the system, where calibration data of the realistic task are difficult to obtain.

Validation of a Light EEG-Based Measure for Real-Time Stress Monitoring during Realistic Driving

N. Sciaraffa, G. Di Flumeri, D. Germano, A. Giorgi, A. Di Florio, G. Borghini, A. Vozzi, V. Ronca, R. Varga, M. van Gasteren, F. Babiloni, P. Aricò
Brain sciences 12.3 (2022)
View the abstract

Driver's stress affects decision-making and the probability of risk occurrence, and it is therefore a key factor in road safety. This suggests the need for continuous stress monitoring. This work aims at validating a stress neurophysiological measure—a Neurometric—for out-of-the-lab use obtained from lightweight EEG relying on two wet sensors, in real-time, and without calibration. The Neurometric was tested during a multitasking experiment and validated with a realistic driving simulator. Twenty subjects participated in the experiment, and the resulting stress Neurometric was compared with the Random Forest (RF) model, calibrated by using EEG features and both intra-subject and cross-task approaches. The Neurometric was also compared with a measure based on skin conductance level (SCL), representing one of the physiological parameters investigated in the literature mostly correlated with stress variations. We found that during both multitasking and realistic driving experiments, the Neurometric was able to discriminate between low and high levels of stress with an average Area Under Curve (AUC) value higher than 0.9. Furthermore, the stress Neurometric showed higher AUC and stability than both the SCL measure and the RF calibrated with a cross-task approach. In conclusion, the Neurometric proposed in this work proved to be suitable for out-of-the-lab monitoring of stress levels.

Mental Effort Estimation by Passive BCI: A Cross-Subject Analysis

N. Sciaraffa, D. Germano, A. Giorgi, V. Ronca, A. Vozzi, G. Borghini, G. Di Flumeri, F. Babiloni, P. Aricò
43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2021)
View the abstract

Despite the technological advancements, the employment of passive brain computer interface (BCI) out of the laboratory context is still challenging. This is largely due to methodological reasons. On the one hand, machine learning methods have shown their potential in maximizing performance for user mental states classification. On the other hand, the issues related to the necessary and frequent calibration of algorithms and to the temporal resolution of the measurement (i.e. how long it takes to have a reliable state measure) are still unsolved. This work explores the performances of a passive BCI system for mental effort monitoring consisting of three frontal electroencephalographic (EEG) channels. In particular, three calibration approaches have been tested: an intra-subject approach, a cross-subject approach, and a free-calibration procedure based on the simple average of theta activity over the three employed channels. A Random Forest model has been employed in the first two cases. The results obtained during multi-tasking have shown that the cross-subject approach allows the classification of low and high mental effort with an AUC higher than 0.9, with a related time resolution of 45 seconds. Moreover, these performances are not significantly different from the intra-subject approach although they are significantly higher than the calibration-free approach. In conclusion, these results suggest that a light (three EEG channels) passive BCI system based on a Random Forest algorithm and cross-subject calibration could be a simple and reliable tool for out-of-the-lab employment.



Understanding how chemical and electric signals induce thoughts and actions.


Try and leave this world a little better than you found it. – Sir. Robert Baden-Powell


Fair consumption of resources and a balanced life is the goal to be achieved for a collective well-being.


Getting to know different cultures by experiencing local customs and traditions.


While I enjoy participating in a variety of sports, I find a dedicated hour of swimming very relaxing.


For more information about me and my work, please contact me.