Events
The Data Driven Discovery Initiative regularly hosts data-science related events open to the Penn community. These events bring together faculty, staff, and students from diverse fields to explore the intersection of data science, artificial intelligence, and the natural and social sciences. Please see below for more information on what’s happening this spring.
March
Data Science Lunch
Tuesday, March 4, from 12 p.m. – 1:30 p.m.
Amy Gutmann Hall, Room 615
About the speaker:
Data Science Lunch
Monday, March 17 from 12 p.m. – 1:30 p.m.
Amy Gutmann Hall, Room 615
About the speaker:
Hamsa Bastani is an Associate Professor of Operations, Information, and Decisions at the Wharton School, with a secondary appointment in Statistics and Data Science. She also serves as co-director of the Wharton Healthcare Analytics Lab. Her research focuses on developing innovative machine learning algorithms for learning and optimization, utilizing methods such as reinforcement learning, active learning, and behavioral nudges. Passionate about addressing critical societal challenges, Hamsa has collaborated with national governments to implement large-scale algorithms that enhance public health outcomes.
AI For Science Seminar: “Learning by Manifold Packing”
Tuesday, March 25
Amy Gutmann Hall, Room 414
Contrastive self-supervised learning based on point-wise comparisons has been widely studied for vision tasks. In the neural cortex, neuronal responses to distinct stimulus classes are organized into geometric structures known as neural manifolds. Accurate classification of stimuli can be achieved by effectively separating these manifolds, akin to solving a packing problem. Despite its intuitive appeal, neurobiological relevance, and potential for enhancing interpretability, the perspective of neural manifold packing in contrastive learning remains largely unexplored. In this talk, Stefano Martiniani will discuss how concepts from statistical and soft matter physics can be leveraged to analyze neural manifold packing dynamics under stochastic gradient descent and related optimization algorithms. This perspective not only informs the development of highly interpretable self-supervised learning methods but also reveals striking parallels between the energy landscapes of sphere packings and the loss landscapes of neural networks. Stefano will present a combination of numerical experiments and analytical theory demonstrating the depth of these analogies.
Careers in Data Science Panel
Tuesday, March 27 from 5:30 p.m. – 7 p.m.
Arch 208
Penn Arts & Sciences College Alumni Mentoring Series (CAMS) and the Data-Driven Discovery Initiative (DDDI) are excited to present a second career mentorship event focused on Data Science Careers. Undergraduate students will have an opportunity to hear from and connect with a diverse group of professionals working in a variety of data-driven careers.
April
Data Science Lunch
Tuesday, April 1, from 12 p.m. – 1:30 p.m.
Amy Gutmann Hall, Room 615
About the speaker:
Dolores Albarracín is a Penn Integrates Knowledge Professor and the director of the Social Action Lab. She studies the impact of communication and persuasion on human behavior and the formation of beliefs, attitudes, and goals, particularly those that are socially beneficial. Beyond studying basic attitudinal processes, she also focuses on developing interventions to foster positive social interactions and inform effective public policies.
Data Science Lunch
Tuesday, April 8, from 12 p.m. – 1:30 p.m.
Amy Gutmann Hall, Room 615
About the speaker:
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. Hancheng is also an AI x Science Fellow.
Data Science Lunch
Wednesday, April 15, from 12 p.m. – 1:30 p.m.
Amy Gutmann Hall, Room 615
About the speaker:
Vlad Ayzenberg 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.
Data Science Lunch
Tuesday, April 22, from 12 p.m. – 1:30 p.m.
Amy Gutmann Hall, Room 615
About the speaker:
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. Xinquan is also an AI x Science Fellow.
Scientific AI
Thursday, April 24, from 1:30 p.m. – 3:30 p.m.
Singh Center for Nanotechnology, Glandt Forum
Join researchers from SAS as they explore how advancements in AI are transforming their work and unlocking new frontiers in their fields.
Panelists:
- Andrew Zahrt (Chemistry)
- Sudeep Bhatia (Psychology)
- Jésus Fernandez-Villaverde (Economics)
- Carlos Santana (Philosophy)
- Maria Cuellar (Criminology)
- Irina Marinov (Earth and Environmental Science)
Data Science Lunch
Tuesday, April 29, from 12 p.m. – 1:30 p.m.
Amy Gutmann Hall, Room 615
About the speaker:
Stefano Puntoni is the Sebastian S. Kresge Professor of Marketing at the Wharton School, as well as the co-director of AI at Wharton. His research explores how emerging technologies, particularly AI, are transforming consumption and society. Focusing on the psychology of AI, he examines how people interact with and adopt artificial intelligence in their day-to-day lives, shedding light on consumer behavior and adoption patterns.
May
Data Science Lunch
Tuesday, May 6, from 12 p.m. – 1:30 p.m.
Amy Gutmann Hall, Room 615
About the speaker:
Paris Perdikaris is an Associate Professor in Mechanical Engineering and Applied Mechanics. His research interests span a range of topics at the interface of computational science and machine learning. Current efforts are focused on the development of foundation models for accelerating physical simulations, physics-informed machine learning, neural operators, and uncertainty quantification. By bridging the gap between data-driven approaches and scientific knowledge, his group’s work is paving the way for more accurate, efficient, and interpretable simulations across fields like Earth system modeling, fluid dynamics, and materials science.
AI For Science Seminar
Wednesday, May 28, from 2 p.m. – 3 p.m.
Amy Gutmann Hall, Room 414
Please check back for more information.
