About Me

I am an Assistant Professor in the University of Nottingham with a secondment in the Trustworthy Autonomous Systems research hub. This site serves as a point of contact to find me.

Throughout my admittely short career I have had the chance to work in several teams and interact with people of many backgrounds: software engineers, computer scientists, petroleum geologists, education researchers, linguists, and many I am forgetting. This has given me a strong taste for interdisciplinary, collaborative research. If you share this taste and are looking for a collaboration, feel free to get in touch with me. That is the case whether you are an academic looking for a collaboration or an industry professional - I do not discriminate.

Research

My scientific interests revolve around applied interdisciplinary research involving machine learning. I am particularly interested and open to collaborative work in the following topics: interpretable machine learning, digital humanities (argument mining, social media mining, disinformation detection), and digital health (physiological data, mental health). I am currently involved in multiple projects:

• I am the PI in the TAS-funded Privacy-Preserving Detection of Online Misinformation project.
• I oversee the NLP component of the TRIPOD project, which is focused on modernising therapeutic practice using natural language processing.
• In collaboration with Dr. Pepita Barnard, we are investigating socio-cultural aspects influencing the adoption of trust assessments tools in AI projects.

I also take part in the Brain Data Group through joint work with Johann Benerradi, as well as the Cyber-physical Health and Assistive Robotics Technologies (CHART) Research Group through collaborations with Prof. Praminda Caleb-Solly.

I am currently co-supervising the following PhD students:

• Johann Benerradi (started in 2019) - machine learning and fNIRS data for mental workload classification.
• Dan Heaton (started in 2020) - hybrid qualitative/quantitative approaches to investigate public discourse around automated decision-making algorithms.
• Giovanni Schiazza (started in 2020) - political internet memes and their effects on users.
• Xin Yu Liew (started in 2021) - human-in-the-loop approaches to misinformation detection in social media.
• Jialin Chen (started in 2022) - digital twins for human-assistive robotics.

About letters of recommendation

I have a bad case of the referral fatigue so I will look for any excuse to avoid writing them. Here are a few tips you can use to trick me into writing them:

• Politely ask for them. If I receive a request from a university about an application I never heard about before, I will ignore it and you will look bad.
• Don’t ask for too many of them. I take pride in my work, whether it is teaching or research, so letters of recommendation take time. Show me that you respect my time by asking for a couple of references instead of bombarding my name on a dozen of them.
• Make sure that I know you. Me teaching a module is not enough, even if you got a good grade (a good grade is not indicative that you will be fit for a Masters or a PhD). Sending me your previous transcripts from high school is also not very convincing.
• Show me that you know me. If you are asking me to write a reference letter for an engineering school, I will know that you have not even spent a minute researching what I do and what I am qualified to comment on. If you can’t do that little amount of research, how can I recommend you for something as significant as a postgraduate degree?