Founder and CEO at Deep Forest Sciences 

Bharath is the founder and CEO of Deep Forest Sciences, which builds AI for deep technology applications, and is the lead developer of the DeepChem open-source project. Previously, He was the co-founder and CTO of Computable Labs, a venture-backed data engineering startup. Bharath founded DeepChem while doing his PhD in computer science at Stanford University where he studied the application of deep learning methods to drug discovery. He was also the co-lead creator of the widely used MoleculeNet benchmark suite. Bharath’s graduate education was supported by a Hertz Fellowship. He received his BA and BS from UC Berkeley in EECS and Mathematics and was valedictorian of his graduating class in mathematics.

Bharath is the lead author of “TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning”, and "Deep Learning for the Life Sciences." 

Deep Learning Researcher @ Shell 

As a Deep Learning Researcher at Shell, Achint applies his machine learning expertise to solve challenging problems in the energy sector. He works with a multidisciplinary team of data scientists, engineers, and domain experts to design, develop, and deploy reliable and robust AI systems that enhance Shell's operations, products, and services.

He has a strong background in physics, with a PhD from Duke University and a Master's and Bachelor's degree from Birla Institute of Technology and Science, Pilani. During his PhD, he focused on using variational autoencoders, independent component analysis, and maximum mean discrepancy analysis to model neuroscience data. He has also worked on theoretical models of learning and memory formation in the brain, especially olfactory memory. He has published multiple papers in peer-reviewed journals and conferences, and obtained a certification in Designing Reliable and Robust AI Systems from Stanford Online.

PhD Student at MIT EECS

He is a PhD student at MIT EECS in Professor Tess Smidt’s research group, the Atomic Architects. He was previously a Pre-Doctoral Researcher at Google Research, India. He majored in Computer Science and Engineering (with a minor in Mathematics) at IIT Guwahati. He was one of 3 undergraduates from all over the world to win the ACM SIGBED Scholars' Award in 2020.

He received the Caltech Summer Undergraduate Research Fellowship (SURF) in 2019 spending the summer at NASA Jet Propulsion Laboratory where he worked on event detection algorithms for time-series data as recorded by the Plasma Instrument for Magnetic Sounding on the Europa Clipper mission. 

Lecturer (Assistant Professor) at Royal Holloway, University of London

He is a Lecturer (Assistant Professor) at the Department of Computer Science at Royal Holloway, University of London. He was a post-doc at the Institut für Neuroinformatik at the Ruhr-Universität Bochum. He finished his  PhD with Prof. Wolfgang Maass at the Institute for Theoretical Computer Science at Technische Universität Graz, working on biologically plausible learning and meta-learning in spiking neural networks.

He has a Masters in computer science from the University of Texas at Austin where he worked with Prof. Risto Miikkulainen on using neuro-evolution and task-decomposition to learn complex tasks. He has also worked as a Software Development Engineer at Amazon.com in the DynamoDB team for a couple of years right after his Masters.

He received his undergraduate degree at IIT Madras and worked at Indian Institute of Science, Bangalore as a Research Assistant with Prof. K Gopinath right after his undergrad.

CS PhD at UCLA | IITD'20

She is a Computer Science Ph.D. student at UCLA passionate about interpretable ML for healthcare and genomics with experience in cross-domain application of machine learning, with expertise in ML for options trading at a quant firm and LLM research for big tech. 

She received her undergraduate degree at IIT Delhi. She has interned at Amazon Research, Morgan Stanley, Belvedere Trading, Harvard University, and UCLA and is the recipient of several awards, including the Amazon Fellowship, the Jane Street Graduate Research Fellowship, the UCLA Departmental Fellowship, and the Charpak Scholarship from the Embassy of France. She has also published multiple papers in peer-reviewed journals and conferences.

Graduate Student at Mila/UdeM

He is a Ph.D. student at Mila and Université de Montréal supervised by Yoshua Bengio. His work focuses on the probabilistic inference framework of GFlowNets. At Mila, he leads various efforts to develop novel machine learning approaches in the context of drug discovery. He is also a ML Research Intern with Jason Hartford at Recursion (Valence Labs) working on experimental design for gene knockouts.

Before joining Mila as a graduate student, he was a visiting researcher working with Yoshua Bengio on uncertainty estimation and drug discovery. He also spent a year at Microsoft Turing working on compressing and optimizing LLMs for deployment across Bing and Office.

He completed his bachelor’s degree at the National Institute of Technology Karnataka, Surathkal. To work on his thesis, he spent a semester at Microsoft Research. He also spent a summer at the Machine Learning group at Leuphana Universitat Lüneburg, supervised by Uwe Dick and Ulf Brefeld.

Research Scientist in Computational Neuroscience

She is a research scientist specializing in Computational Neuroscience with a Ph.D. and +6 years of research experience.  Her work utilizes fMRI imaging and analysis, machine learning (ML), deep learning (DL), and data science methodologies to model and analyze intricate interactions within large-scale brain networks.  She is a doctoral researcher at the National Brain Research Centre where they developed a novel mathematical framework and unsupervised algorithm to track temporal stability of functional connectivity patterns in large-scale resting-state brain networks using multidimensional (4D) fMRI data and also identified a resting-state fMRI biomarker capable of classifying common mental health disorders at the single-subject level.

 She contributed to a collaborative project funded by The Bill & Melinda Gates Foundation, developing an image quality transfer technique that achieved a resolution enhancement of up to 3 times in low-resolution hyperfine (< 1T) T1 MRI scans through the development and implementation of an image quality transfer technique utilizing convolutional neural networks.