Vlad is a developmental cognitive neuroscientist who is interested in understanding the mechanisms that support early developing perceptual abilities in human infants. Because measuring the underlying processes of the infant brain is incredibly challenging, Vlad uses biologically plausible computational models to explore what kinds of processes and developmental constraints may be sufficient to support infant perception. As a postdoctoral fellow with Dr. Michael Arcaro, he is starting to explore how early developing anatomical structures in the neonate visual system may scaffold perceptual abilities later in life.

Andrew is an applied developmental psychologist studying how young children learn to manage their attention and behavior in the classroom. He uses wearable accelerometers to capture children’s inattentive and impulsive behaviors, leveraging the objective and continuous recording of movement to understand daily fluctuations in children’s behavior. With support from Penn’s Data Driven Discovery Initiative, Andrew will apply supervised machine learning methods to evaluate which features of children’s movement (i.e., amount, intensity, onset, duration) best predict teacher ratings of children’s impulsivity and inattention. Andrew also looks forward to using unsupervised clustering techniques to identify temporal patterns of children’s behavior and network analysis to study social dynamics in the classroom.

Adrien is an astronomer interested in understanding the mechanisms that drove the formation of galaxies throughout the history of the Universe. Thanks to his multidisciplinary career with a background in telecommunication and data processing engineering, he routinely uses data science, high performance computing and software development in his research. Adrien holds a PhD in Computational Cosmology from the Astrophysics Research Institute of Liverpool John Moores University in the UK, and joined the University of Pennsylvania as a postdoctoral researcher after a first postdoctoral position at the University of Washington.

Noëmi is a computational linguist driven by a deep fascination with dialects, language varieties, and low-resource languages. Her research addresses the challenges of linguistic diversity, particularly in non-standardized dialects. Her dissertation explored how text-based computational models can better handle linguistic variation. Her current work focuses on speech-based language models to advance NLP in diverse linguistic contexts and gain insights into how linguistic information is encoded in neural networks.

Yahav is a computer scientist interested in developing principled techniques to ensure reliable machine learning for consequential decision-making. To this end, he designs algorithms that aim to guarantee fairness in prediction, be incentive-aware, and address bias along the different parts of the ML training pipeline — from data collection, to the feedback structure, to incorporating constraints and characterizing their costs and impacts. His work draws on and combines techniques and concepts from machine learning theory, optimization, and algorithmic game theory. He holds a PhD from the Hebrew University, during which he was also an Apple Scholar in AI/ML.

Sourav Dey is a computational chemist working in the intersection of chemistry and machine learning. His current interest is to automate chemical reaction discovery and contribute towards the development of self-driving labs. He uses principles of theoretical chemistry and machine learning to understand molecular properties and their interaction with each other in complex environments. His prior work involves electronic structure theory and multiscale modeling.

Marcelo Guzmán is a physicist working at the frontier of machine learning and soft-matter physics. He studies physical networks as new platforms for the emergence of learning and adaptation. By combining theory and simulations, he takes a theoretical perspective on the fundamental differences between machine learning and physical learning, such as interpretability, scaling laws, efficiency, and robustness. His prior work involves the characterization and design of physical networks with topologically protected responses.

Xinquan Huang is currently working with Prof. Paris Perdikaris and Prof. Nat Trask on generative models in science and engineering. His research interests span the areas of physics-informed machine learning, operator learning, generative modeling using diffusion models and their applications to fluid simulation, uncertainty quantification, and inverse problems. He completed his Ph.D. at King Abdullah University of Science and Technology and has interned at Microsoft Research AI4Science.

Hancheng Min is a machine learning theorist whose research centers around building mathematical principles that facilitate the interplay between machine learning and dynamical systems, working with Prof. René Vidal. His recent research focus has been on understanding the inductive bias of the training algorithms on promoting certain structural properties in the neural networks and connecting these theoretical findings to practical issues in ML such as the adversarial robustness of neural networks.

Melanie Segado is a postdoctoral fellow in Bioengineering advised by Konrad Kording at University of Pennsylvania. Her research is currently focused on building computational models of complex human movement using data-driven artificial intelligence approaches. Specifically, she is developing foundation movement models that can be fine-tuned for specialized applications in human health and performance, including low-cost video-based diagnostics for infant movement disorders. She holds a PhD in Neuroscience from McGill University where she studied sensorimotor integration during skilled motor execution. Following her PhD she worked as a research scientist  at the National Research Council Canada, integrating neural interfaces into custom-developed virtual reality environments.

Yan Sun is a statistician interested in developing trustworthy machine learning (ML) models. His research focuses on uncertainty quantification and model reliability. He develops rigorous methods to evaluate and improve model calibration, enhancing the accuracy of probabilistic forecasts. His work in sparse neural network models identifies key predictive features, making ML models more interpretable and efficient.