Session Abstract:
From bearings and drivetrains to radios and grids, modern operations generate high-rate streams that don’t wait for the cloud. This panel gathers domain experts to explain how they model high-frequency data, what problems they’re solving (early-fault detection, spectral anomalies, power events, precision motion), and the use cases delivering value today. We’ll contrast common challenges—time sync/jitter, sensor drift, label scarcity, concept drift, rare-event detection, and edge compute limits—with domain-specific hurdles in manufacturing, energy, mobility, and healthcare.
Speakers:
Zohreh leads a team of researchers and developers at SAS R&D, where she focuses on developing tools for Internet of Things applications. She is passionate about designing and implementing accurate, scalable methods to analyze high-frequency, high-dimensional IoT data. Her work also centers on making statistical and machine learning techniques more accessible and effective for real-time data analysis. Zohreh holds a Ph.D. in Operations Research from North Carolina State University.
Suresh Venkatesh is an Assistant Professor at North Carolina State University, Electrical and Computer Engineering department. He received his M.S degree in Electrical and Computer Engineering from North Carolina State University in 2010 and his PhD in Electrical and Computer Engineering from University of Utah in 2017. His PhD dissertation received the ECE Outstanding Dissertation Award, 2016. Prior to joining NC State, he was an Associate Research Scholar at Electrical and Computer Engineering Department, Princeton University. He was also a Lead Antenna Technology consultant for Massachusetts based start-ups namely, Wafer LLC and E-Space where he developed SATCOM technologies for high-speed low-latency communications. He was also a Research Project Assistant at Molecular Astronomy Laboratory, Raman Research Institute, Bangalore during 2007-08, where he worked on millimeter-wave radio telescope. His research interests are in electromagnetics, metamaterials, antenna design, high frequency integrated circuits, computational imaging, and transformation optics design. He has authored/co-authored more than 70 journal and conference publications including one US patent. He is a recipient of the 2021 Mistletoe Research Fellowship from Momental Foundation. He is an affiliate member of MTT-23 Wireless Communication and MTT-21 Terahertz Technology and Applications committees.
Priya Sharma is a Principal Solutions Advisor in the Internet of Things (IoT) Division at SAS Institute Inc., where she brings over a decade of experience in advanced analytics to transform sensor-generated data into actionable intelligence. Her work focuses on architecting scalable IoT analytics solutions that enable organizations to harness real-time data from connected devices, machines, and environments. At SAS, Priya has led numerous end-to-end IoT initiatives—spanning edge data ingestion, signal preprocessing, predictive modeling, and deployment into operational systems—across industries such as manufacturing, energy, and healthcare. Her core expertise includes IoT analytics, machine learning, artificial intelligence, in-memory and in-database processing, and model lifecycle management. She also provides strategic guidance to clients on embedding analytics into business workflows and designing resilient architectures for real-time insight and automated decision-making.
Aparajithan Sampath (Ajit) is a geospatial data scientist and systems engineer specializing in Lidar analytics, remote-sensing data quality, and decision-theoretic modeling for environmental risk mitigation. With over 15 years of technical leadership experience, he has developed scalable, cloud-native workflows for 3DEP lidar validation, hyperspectral/Earth Observation cross-calibration, flood-monitoring decision tools, and advanced machine-learning pipelines for point-cloud and imagery analysis. His work integrates open-source geospatial architectures, AI/ML automation, and uncertainty-aware analytics to bridge the gap between remote-sensing science and operational decision-making. Ajit holds a Masters in Engineering and Management from MIT and a PhD in Geomatics from Purdue University, and collaborates widely across academic, federal, and industry partners on next-generation geospatial intelligence and Earth-observation applications.
