Category: PEOPLE

Find out about the researchers who are part of the Odor Objects project team

Bio: Mario Pannunzi

Mario Pannunzi is a post doctoral research fellow at the Unversity of Sussex. After obtaining his Ph.D. in Rome, he moved to Barcelona, where he worked until 2017 with Professor Gustavo Deco, in the Computational Neuroscience Group at the Universitat Pompeu Fabra.

His approach is multidisciplinary and his research experience spans from data analysis (behavioral, neuroimaging, and neurophysiological data), experiments, and computational modeling. His main interests, until now, have been modeling human and mammals decision-making through biologically plausible neural networks, but he recently switched to a research more focused on smaller-scale systems, like bees and Drosophila.

Bio: Mr. Ho Ka Chan

photo_Chan Ho Ka

Ho Ka Chan is a PhD student in the University of Sussex. He obtained his undergraduate degree in physics in the Chinese University of Hong Kong. He then obtained his M. Phil degree in Hong Kong Baptist University. Supervised by Prof. Changsong Zhou, he studied how transfer of spike correlation of neurons is affected by synaptic properties.

Bio: Dr. Alan Diamond

Alan Diamond is a post doctoral research fellow at  the Unversity of Sussex. He will be joining this project in February 2016. He obtained his PhD researching the control and modelling of bio-inspired humanoid robots. More recently he has worked on several computational neuroscience projects buidling bio-inspired spiking neural models based on the insect olfactory system.

Bio: Prof. Thomas Nowotny

Prof Thomas Nowotny is leading the team at the University of Sussex focusing on the computational modelling aspects of the project.

Thomas obtained his PhD in Theoretical Physics from the University of Leipzig in 2001 and after working at UCSD for 5 years moved to the University of Sussex in 2007 where he is now a Professor of Informatics.

His interest include Chemical Sensing, Computational Neuroscience, Dynamic Clamp, Electronic Nose, GPU Computing, High Performance Computing, Insects, Ion channels, Machine Learning (AI), Neural networks, Novel Computing Paradigms, Olfaction, Robotics, and Systems Neuroscience.

Bio: Aarti Sehdev

Aarti Sehdev is PhD student at the University of Konstanz, under the supervision of Dr. Paul Szyszka. She studied for both her undergraduate and Master’s degree in Biological Sciences at University College London, where she worked on genetic variation in the Drosophila auditory system.

Here she focuses on how honey bees perform target odorant segregation from an odour mixture, using behavioural assays and calcium imaging.

Bio: Dr. Paul Szyszka

photo_dr Paul SzyszkaInsects live in a complicated olfactory environment. Airborne odors distribute in small packages, intermingle and fluctuate at different time scales. Therefore, olfactory stimuli contain much less information about identity and location of their sources than visual or auditory scenes. Paul Szyszka is interested in how the insect olfactory system copes with such complicated stimuli. He uses behavioral experiments to probe the limits of insects’ olfaction and to test how olfactory performance depends on experience. He uses electrophysiological and optical imaging methods to investigate the neuronal mechanisms of odor perception and experience-dependent plasticity.

Bio: Prof. Brian H Smith

Brian Smith is a behavioral neuroscientist who studies how animals learn about odors in order to predict important events, such as an encounter with food, a mate or predator. His research employs detailed behavioral studies of learning and memory. He and his research team also use a combination of electrophysiological, bioimaging, molecular and computational techniques to directly link changes in behavior to changes in the brain.

Smith’s research focuses on learning and memory systems in both insects and mammals. His work is being applied to studies of human diseases, such as Alzheimer’s and Parkinson’s, as well as to the negative effects of heavy metal poisoning on learning and memory.