I’m currently a Product Manager at Google Cloud AI. Previously, I was the lead Product Manager for systems and software at Cerebras Systems, where we built the industry's fastest Deep Learning acceleration solution.

I have an MBA from the University of Texas-McCombs School of Business, where I was a Venture Fellow at BuildGroup and Mercury Fund; here I gained hands-on experience in developing investment themes, conducting due diligence, valuation, and assisting portfolio companies, and worked with internal data science teams to conceive and deploy machine learning projects at scale.

Prior to my MBA, I received a Masters' in Computer Engineering from the University of Wisconsin-Madison and held leadership positions within product and research groups within the smartphone industry (Qualcomm, Samsung R&D) as a computer architect.

I was fortunate to witness the explosive growth of the smartphone and tablet markets first-hand and was a member of high visibility + growth teams that collectively delivered over 1 Billion chips to users.

Computer Science Engineer, specializing in Hardware and Network Systems from SUNY Buffalo. Highly passionate about Research in the field of VLSI , Computer Architecture and Hardware Security. Also, I have a great interest in the field of Routers and Switches, MIMO technology and VLSI Digital Circuit designing. Along with my Master's I am also working as a Graduate Assistant in HPVSA Laboratory, in the Dept. of Computer Science Engineering , SUNY Buffalo. My current research is on design and optimization of Physically Unclonable Functions (PUF), which could act as a fingerprint for semiconductor devices.

Furthermore, I have obtained my Bachelor's degree in Electronics and Communication Engineering from SRMIST,Chennai,India. During my undergrad I sincerely worked under my Professor for the research project - on Tunnel Field Effect Transistor, which we presented at the National Conference Silicon'18. Our paper "Design Optimization of Tunnel Field Effect Transistor" was published in International Journal of Recent Technology and Engineering (IJRTE) in March 2019.

The amalgamation of Electronics, Computers and Communications have helped me to pave stronger base for the skills needed in the field of Computer Hardware and Computer Networks.

My research interests lie in field-programmable technology and electronic design automation, with increasing focus on the hardware security vulnerabilities of today’s heterogeneous and intelligent computing systems.

Aninda Manocha received her Ph.D student in Computer Science at Princeton University. She was advised by Professor Margaret Martonosi in the broad area of Computer Architecture. More specifically, she worked on the design of reconfigurable hardware to accelerate a variety of software applications with a focus on the application and compiler interface. She also worked on memory hierarchy designs and operating system techniques tailored to irregular, memory-intensive graph applications. She received her B.S. in Electrical and Computer Engineering as well as Computer Science from Duke University in 2018. She now works at Rivos.