Speakers

**Stay tuned for more speakers!

Jennifer Redmon

Chief Data & Analytics Evangelist, Cisco | Forbes Technology Council Member

Jennifer Redmon joined Cisco in 2009 and serves as its, and industry’s 1st, Chief Data & Analytics Evangelist as well as sits on Forbes’ Technology Council. Jennifer is a leader in developing data-driven cultures and organizational analytical maturity through qualitative and quantitative methods. Her approach to fostering a data-driven workforce is taught at multiple higher ed. institutions around the world. Jennifer is passionate about giving back and consequently, founded Cisco’s Data Science and AI for Good initiative. She leads the company’s award-winning Data Science and AI for Suicide Prevention Team, which, inspired by and in collaboration with WHO researchers, focus on de-stigmatizing mental illness through cultural change. Her authorship focuses on data- and analytics-driven cultures, mental health in the workplace, suicide prevention, as well as innovative applications of data science and AI including social good. Jennifer holds an international MBA from Duke University with a concentration in Strategy, Bachelor’s Degrees in Economics and Art History from UC Davis, is in Georgia Tech’s Master of Science in Analytics Spring 2021 class, and is a certified Suicide First Responder.

Bhaskar Ghosh

Partner & Chief Technology Officer, 8VC

Bhaskar Ghosh (BG) is Partner and CTO at Silicon Valley venture capital firm 8VC, where he focuses on investing in early-phase start-ups building B2B SaaS, enterprise infrastructure and data-driven businesses, and helping incubate companies with tech-entrepreneurs. Prior to becoming an accidental venture capitalist, BG had a first career as a builder and engineering executive in Silicon Valley through various interesting gigs over two decades. He ran engineering at NerdWallet, helping scale the company to a market leader in consumer finance marketplaces and prior to that, as the founding Head of Data Engineering at LinkedIn, he and his team built and ran the data platforms that helped  enable LinkedIn’s meteoric growth from 50MM to 550MM+ members. Previously, BG also ran engineering at Yahoo! RightMedia (the largest real-time display-ads exchange of its time), and learnt about “big data” while writing parts of the Oracle and Informix RDBMS kernels.  BG has a PhD in Computer Science from Yale and a holder of 22 US patents.

Devavrat Shah

Co-founder and CTO of IkigaiLabs

Devavrat Shah is a Professor with the department of Electrical Engineering and Computer Science at Massachusetts Institute of Technology. He is the founding director of Statistics and Data Science at MIT. He is also a member of IDSS, LIDS, CSAIL and ORC at MIT. He co-founded Celect, Inc. (now part of Nike) in 2013 to help retailers decide what to put where by accurately predicting demand using omni-channel data. He is a co-founder and CTO of IkigaiLabs with the mission to build self-driving organizations by enabling data-driven operations with human-in-the-loop.

He is interested in developing large-scale machine learning algorithms for unstructured data. He has made foundational contributions to the development of “gossip” protocols and “message-passing” algorithms for statistical inference which have been the building blocks of modern distributed data processing systems.

His work has received broad recognition, including prize paper awards in Machine Learning, Operations Research and Computer Science, and career prizes including 2010 Erlang prize from the INFORMS Applied Probability Society, awarded bi-annually to a young researcher who has made outstanding contributions to applied probability. He is a distinguished alumni of his alma mater IIT Bombay from where he graduated with the honor of President of India Gold Medal. His work has been covered in popular press including NY Times, Forbes, Wired and Reddit

Amir Yazdanbakhsh

Research Scientist, Google Brain

Amir Yazdanbakhsh is the founder and co-lead of the learn to design accelerator team at Google Research that targets to leverage advances in machine learning to design better accelerators. Amir joined Google Brain as a Research Scientist in 2019 following a one year AI residency. He obtained his PhD in Computer Science from the Georgia Institute of Technology. His research interests include machine learning, computer architecture and systems, and programming language for hardware design. He has received the Microsoft Research PhD Fellowship Award (2016) and Qualcomm Innovation Fellowship (2015).

Jonathan Apple

Principal Data Scientist, Microsoft

Jonathan Apple joined Microsoft in early 2020 as a Data Scientist in the Azure Reliability organization. His work focuses on building scalable analytics solutions that “move the needle” in the realm of cloud reliability and platform risk management. Jonathan’s interests include distributed computing and developing insightful statistical models. His career has been focused on both research and development in Engineering (satellites, green energy, Industrial IoT, geospatial analytics), as well as building low-latency trading systems while working in FinTech. Jonathan has a B.S. and an M.S. in Physics from Drexel University and University of Pennsylvania (respectively) and is an avid chess player.

Tannya Jajal

Resource Manager, VMware | Technology Contributor, Forbes Middle East | AI Expert, AI Policy Exchange

Tannya is a technology evangelist, A.I. Expert, and futurist. Through her work, Tannya encourages current and future leaders to explore the ethical and practical implications of exponential technologies on business, life and society at large. Tannya is an advocate of radical innovation, the rise of meaningful work in a post-automation world, and a proponent of educational reform.

She currently works as the Resource Manager at VMware, a leader in cloud infrastructure, is a technology contributor at Forbes Middle East, serves as an AI Expert at the AI Policy Exchange, is the co-leader of the Dubai Women in Tech Lean In Circle, and is the Head of Partnerships at Awecademy, a future-focused edtech startup whose mission is to bring a sense of awe and wonder to education and prepare learners for a post-automation world.

Website: here

Bradley Rees

Sr. Manager and RAPIDS cuGraph Lead, NVIDIA

Brad Rees is a Sr. Manager in the AI Infrastructure group at NVIDIA and lead of the RAPIDS cuGraph team. Brad has been developing software for over 30 years and specializes in complex analytic systems, primarily using graph analytic techniques for social and cyber network analysis. His technical interests are in HPC, machine learning, deep learning, and graph. Brad holds a Ph.D. in Computer Science from the Florida Institute of Technology.

Zhaoyang Lv

Research Scientist, Facebook Reality Labs Research

Zhaoyang Lv is a research scientist with the Facebook Reality Labs Research, Machine Perception team in Redmond, WA. Zhaoyang finished his Ph.D. at Georgia Tech, jointly advised by Prof. James Rehg, and Prof. Frank Dellaert. During his Ph.D., he interned at Nvidia Research under Jan Kautz and at Max Planck Institute with Prof. Andreas Geiger. Before Zhaoyang started his Ph.D., he finished his Master thesis under the supervision of Prof. Andrew Davison at the Imperial College London.

Zhaoyang’s research interest is to explore how we can photorealistically digitalize the complex dynamic world and render anything at anytime and anywhere, by rethinking the system end-to-end, from sensing and image formation system to the virtual rendered novel view video.

Website: here

Ramakrishna Vedantam

Research Scientist, Facebook AI Research (FAIR)

Ramakrishna Vedantam is a Research Scientist at Facebook AI Research (FAIR) in New York. Previously, he obtained his Ph.D. at the Georgia Institute of Technology (2018), an MS from Virginia Tech (2016) and did his undergraduate studies at IIIT, Hyderabad (2013). His research interest is in machine learning that mimics the capabilities of human learning and reasoning. He has been awarded the Google Ph.D. fellowship in Machine Perception and has received best reviewer awards at ICCV, CVPR, and ICLR.

Brent Zucker

Engineering Manager, NCR Software Innovation Lab

Brent Zucker serves as the Engineering Manager for the NCR Software Innovation Lab where he leads teams and projects that focus on disrupting banks, restaurants, and stores by developing concepts that leverage Machine Learning, Computer Vision, and Distributed Ledger Technology. Recently, he was awarded the NCR 2020 Co-inventor of the Year for patents he created. Brent holds a Master of Science in Computer Science from Georgia Institute of Technology and a Bachelor of Science in Computer Science from Georgia College.

Ram Yalamanchili

Founder and CEO, Tilda Research

Ram Yalamanchili is the founder and CEO of Tilda Research, where he is focused on building data and operational infrastructure to better execute clinical trials. Prior to Tilda, he was a founder and CTO at Lexent Bio, which was acquired by Roche. Lexent built novel liquid biopsy technologies to help change the way we manage cancer. Prior to Lexent, he was the CEO/Founder of Push Computing, which pioneered predictive security and container technologies for mobile (acquired by MobileIron). Prior to that, he was an early engineer at VMware and helped architect the ESXi product. ESXi hypervisor powers a majority of the private cloud infrastructure today. Ram studied Electrical Engineering and Management Science at Carnegie Mellon and Stanford University respectively.

Henry Peter

Co-Founder & CTO, Ushur

As a Co-Founder & CTO of Ushur, Henry Peter brings both an entrepreneurial mindset and a corporate discipline to his role. Since its founding in 2014 Henry’s singular focus has been on building a broad automation platform that allows for specific applications targeted for industry verticals such as insurance, healthcare, and banking, as a starting point. His focus on building a robust platform that can provide industry-specific intelligent automation templates for their rapid adoption has been based on not just transactional automation but on cognitive automation, which can provide 10X gains in efficiencies and productivity when compared to the traditional robotic process automation (RPA), which is often transaction-based. Using the Applied AI techniques his Data Science team has been delivering breakthrough advances for their customers. 

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