Upcoming 2024 Speakers

Victor Fung from Georgia Institute of Technology [Oct 21] Meeting link: Teams

Victor Fung currently works at Georgia Tech. Previously he worked at Oak Ridge National Laboratory where he was a Eugene P. Wigner Fellow in the Center for Nanophase Materials Sciences (CNMS). He obtained his B.A. in Chemistry from Cornell University and his Ph.D. in Chemistry from the University of California, Riverside. His research seeks to harness the power of computing and machine learning to accelerate the chemical discovery process, with the eventual goal of fully realizing materials by inverse design. This includes developing novel methods and tools which incorporate chemical information to model phenomena at the atomic scale, as well as design new materials from the ground up, atom-by-atom. His work also involves establishing automated, data-driven and domain-informed ecosystems for materials and chemical discovery which can be deployed on the latest supercomputers.

2024 List of Past Speakers

Dr. Prasad Iyer from Sandia National Laboratories [July 29]

Prof. Emma Alexander from Northwestern University [Aug 12]

2023 List of Past Speakers

Dr. Zhantao Chen from SLAC National Accelerator Laboratory [May 8]

Prof. Pinshane Huang from University of Illinois at Urbana-Champaign [June 5]

Dr. Steven Torrisi from Toyota Research Institute [July 10]

Dr. Chris Rackauckas from Massachusetts Institute of Technology [Aug 14]

Dr. Sam Dillavou from University of Pennsylvania [Sept 18]

Dr. Peter Lu from University of Chicago [Oct 23]

Prof. Tess Smidt Massachusetts Institute of Technology [Nov 15]

Prof. Aditi Krishnapriyan from University of California, Berkeley [Dec 11]

Advancing Atom-by-Atom Characterization Using Generative Networks by Pinshane Huang Tutorial link: Youtube

Machine Learning for Intelligent Data Collection and Analysis by Zhantao Chen Tutorial link: Youtube

Accelerating mesoscale predictions via surrogate models trained by machine learning methods by Remi Dingreville Tutorial link: Youtube

Exploring Energy-Efficiency in Neural Systems with Spike-based Machine Intelligence by Priya Pandey Tutorial link: Youtube

Autonomous experiments in the age of computing, machine learning and automation by Rama Vasudhevan Tutorial link: Youtube

Date and Time: 03-06-2022 ; 3 PM - 4 PM CST

Title: Accelerating mesoscale predictions via surrogate models trained by machine learning methods

Speaker: Dr. Rémi Dingreville

Download the abstract here (PDF).

Meeting link: Teams

Rémi Dingreville is a Distinguished Member of the Technical Staff at Sandia National Laboratories and staff scientist at the Center for Integrated Nanotechnologies (CINT) a DOE Office of Science user facility. His current research is at the intersection of computational materials and data sciences to understand and characterize process-structure-properties for materials reliability across scales. He leads a few research programs at Sandia focused on the discovery of resilient materials and manufacturing processes via AI-guided approaches. Rémi holds a Ph.D. in Mechanical Engineering from the Georgia Institute of Technology in Atlanta GA, and a B.S./M.S. in Materials Science and Engineering from École Nationale Supérieure des Techniques Avancées in France.

Date and Time: 11-28-2022 ; 3 PM - 4 PM CST

Title: Exploring Energy-Efficiency in Neural Systems with Spike-based Machine Intelligence

Speaker: Dr. Priya Panda

Download the abstract here (PDF).

Meeting link: Teams

Dr. Panda is an assistant professor in the electrical engineering department at Yale University, USA. She received her B.E. and Master’s degree from BITS, Pilani, India in 2013 and her PhD from Purdue University, USA in 2019. During her PhD, she interned in Intel Labs where she developed large scale spiking neural network algorithms for benchmarking the Loihi chip. She is the recipient of the 2019 Amazon Research Award, 2022 Google Research Scholar Award, 2022 DARPA Riser Award. Her research interests lie in Neuromorphic Computing, energy-efficient accelerators, and in-memory processing.

Date and Time: 08-29-2022 ; 3 PM - 4 PM CST

Title: Autonomous experiments in the age of computing, machine learning and automation: progress and challenges

Speaker: Dr. Rama Vasudevan

Download the abstract here (PDF).

Meeting link: Teams

Dr. Vasudevan is the group Leader of the Data NanoAnalytics (DNA) Group at Center for Nanophase Materials Sciences, Oak Ridge National Laboratory. His research is focused on smart, autonomous synthesis and characterization tools driven by improvements in machine learning and tight integration between theory, automation and individual instruments. He has a specific sub-focus is on applications and development of scalable reinforcement learning for scanning probe microscopy, to optimize, manipulate and better characterize ferroic materials at the nanoscale, and upgrade scanning probe microscopy from a standard characterization tool to one capable of autonomous physics discovery by connecting algorithms, edge computing and theory in end-to-end automated workflows.

Date and Time: 04-25-2022 ; 3 PM - 4 PM CST

Title: AI Applications with Spiking Neural Networks and Neuromorphic Computing

Speaker: Dr. Shruti R. Kulkarni

Download the abstract here (PDF).

Meeting link: Teams

Dr. Kulkarni is a research scientist at the Oak Ridge National Laboratory in the Learning Systems group. Her research spans different aspects of neuromorphic computing including algorithms, applications, simulators, and hardware codesign. She was formerly a postdoc at ORNL advised by Dr. Catherine Schuman, working on different techniques to implement learning to learn framework for neuromorphic computing. She received her PhD in 2019 from New Jersey Institute of Technology with a major in Electrical Engineering under the guidance of Dr. Bipin Rajendran. Her dissertation was on bio-inspired learning and hardware acceleration with emerging memories. She has 19 peer reviewed journal and conference publications.

Date and Time: 03-07-2022 ; 3 PM - 4 PM CST

Title: Understanding Trajectories: From Electron Microscopes to Atomistic Simulations

Speaker: Dr. Ayana Ghosh

Download the abstract here (PDF).

Meeting link: Teams

Dr. Ghosh’s research focuses on data-driven and machine learning approaches combined with the state-of-the-art first principles methods to study complex material systems. In particular, she has interestsin developing physics-based machine learning frameworks to investigate causal mechanisms in a wide range of materials ranging from inorganic perovskites, actinides, 2D systems to organic crystals and polymers. Bridging the gap between appropriately utilizing data generated by simulations and experiments require a great deal of understanding the nuances present in both fields, which is the goal of her efforts. She received her MS, PhD in Materials Science and Engineering from the University of Connecticut in the summer of2020 and BS in Physics, Abstract Mathematics from the University of Michigan-Flint in spring of 2015.

Date and Time: 02-16-2022 ; 2 PM - 3 PM CST

Title: AI-Driven Design of High Entropy Halide Perovskite Alloys

Speaker: Dr. Arun Mannodi-Kanakkithodi

Download the abstract here (PDF).

Meeting link: Teams

Dr. Kanakkithodi is an assistant professor in Materials Engineering at Purdue University. He received his PhD in Materials Science and Engineering from the University of Connecticut in 2017 and worked as a postdoctoral researcher at the Center for Nanoscale Materials in Argonne National Laboratory from 2017 to 2020. His research involves using first principles computational modeling, machine learning, and materials informatics to drive the design of new materials for energy-relevant applications. He is a resident associate in the Nanoscience and Technology Division at Argonne, a regular attendee, presenter, and organizer at the Materials Research Society (MRS) spring and fall meetings, and a co-organizer of the data science and machine learning workshop series as part of the NSF-funded nanoHUB.org.