Udari Madhushani Sehwag’s journey from the University of Peradeniya to the absolute forefront of global artificial intelligence is a powerful testament to the caliber of our engineering graduates. Armed with a Ph.D. from Princeton University, her groundbreaking career spans roles at some of the world's most elite institutions, including a postdoctoral tenure at Stanford University and highly coveted research stints at DeepMind and Meta (FAIR). Today, she serves as an AI Research Scientist at Scale AI, where she leads critical efforts on agentic AI, frontier safety, and scalable oversight. Her trajectory reflects the extraordinary global opportunities available to Peradeniya students, proving that a strong foundation built here can launch an impactful career shaping the future of safe and capable AI on an international scale.

Building upon her robust undergraduate foundation in Electrical and Electronic Engineering at Peradeniya—where she specialized in Robotics and Control Theory—Udari advanced her doctoral studies at Princeton under the advisement of Prof. Naomi Leonard. Her academic vision centers on multi-agent reinforcement learning and intelligent computer systems, specifically embedding AI agents with the ability to enhance their capabilities through collective intelligence. Ultimately, her research aims to enable advanced AI models to seamlessly and safely coexist with humans. This deep theoretical focus paved the way for her to expand her expertise at Stanford's Department of Computer Science, bridging the gap between academic theory and real-world application.

This profound technical expertise has seamlessly translated into high-impact academic and global industry recognition. In addition to collaborating with world-leading AI labs like DeepMind and Meta, she has also driven innovation as a Research Scientist at JPMorgan AI Research and Siemens. She is a highly prolific scholar, with her cutting-edge work on AI safety, alignment, and multi-agent coordination consistently published in top-tier venues like NeurIPS, ICML, and PNAS. Udari's rapid rise, profound industry impact, and dedication to the safe development of modern AI technologies stand as a brilliant inspiration to the Peradeniya community, demonstrating the versatility, innovation, and global leadership of our alumni.

Dr. Chaminda Bandara's journey from the halls of the University of Peradeniya to the cutting edge of global technology is a powerful example of what our engineering graduates can achieve. Today, he is a Computer Vision Scientist and AI/ML Engineer at Apple, developing next-generation solutions for one of the world's most valuable tech companied. Armed with a recently completed Ph.D. from Johns Hopkins University, his groundbreaking work in artificial intelligence has already earned him recognition as a top-3 finalist for a major international automation award and secured his place as a leading researcher in his field.

Building upon the strong engineering foundation he established at Peradeniya, Dr. Bandara pursued his Ph.D. in Electrical and Computer Engineering at Johns Hopkins under the mentorship of Prof. Vishal M. Patel. Remarkably, he successfully defended his dissertation in February 2024, completing his doctorate in an exceptionally rapid three and a half years. His research delves into highly complex computer vision tasks like image segmentation and super-resolution, alongside a strong focus on Generative AI and diffusion models.

This deep technical expertise quickly translated into high-impact academic and industry success. Dr. Bandara is a prolific scholar whose innovative work was consistently accepted at the world's most elite computer vision conferences including CVPR, WACV, and MICCAI—even while completing his fast-tracked degree. Notably, his paper on extracting roads from aerial images for autonomous driving, "SPIN Road Mapper," was selected as a finalist for the prestigious ICRA Outstanding Automation Paper award. During his doctoral studies, his talent was recognized by industry leaders, leading to highly competitive summer internships at GetZippin and Apple. His exceptional performance at Apple in 2023 led directly to his current full-time role, standing as a brilliant reminder to the Peradeniya community of the global impact our students can make.

Dr. Yasitha Liyanage is an electrical engineering researcher and applied scientist whose work focuses on machine learning, statistical signal processing, cloud computing, and optimization theory. He completed his Bachelor of Science degree in Electrical and Electronic Engineering from the University of Peradeniya, Sri Lanka, in 2016, graduating with a GPA of 3.95/4.00 and standing 3rd in a batch of 110. He then pursued doctoral studies at the State University of New York at Albany, where he received his PhD in Electrical and Computer Engineering in 2022. His doctoral dissertation, titled Dynamic Instance-Wise Decision-Making for Machine Learning, was supervised by Dr. Daphney-Stavroula Zois.

Dr. Liyanage is currently a Senior Data and Applied Scientist at Microsoft Corporation, where he works on applied machine learning and data-driven systems. Before this role, he served as a Data and Applied Scientist II at Microsoft from 2022 to 2024. He also gained industry research experience through applied scientist internships at Amazon in 2020 and 2021. His research combines theoretical machine learning, optimization, feature selection, and decision-making methods, with applications across high-dimensional and real-world data problems.

His research contributions include work on on-the-fly joint feature selection and classification, dynamic instance-wise classification, and machine learning methods that adaptively select the most useful information for each prediction. These ideas are important for efficient and interpretable AI systems, especially when data collection or feature evaluation is costly. Through his academic journey from the University of Peradeniya to doctoral research in the United States and his current role at Microsoft, Dr. Liyanage represents a strong example of how a foundation in electrical and electronic engineering can lead to impactful careers in AI, machine learning, and large-scale applied data science.

Harshana Weligampola exemplifies the dynamic and evolving career paths accessible to graduates of the Faculty of Engineering, University of Peradeniya. After completing his B.Sc. in Computer Engineering in 2020, he progressed into advanced research by pursuing a Ph.D. at Purdue University, where world-renowned astronaut Neil Armstrong was once a student. His academic journey reflects a strong commitment to innovation in computational imaging, supported by deep interests in computer vision, machine learning, optimization, and image processing. Through this focus, Harshana has positioned himself at the intersection of theory and application, tackling complex challenges in modern visual computing.

His research portfolio spans impactful and diverse areas. He has contributed to a comprehensive review of AI techniques for spatio-temporal modeling and forecasting in the context of COVID-19, highlighting their broader relevance for future global applications. His work on an optical physics-inspired CNN for intrinsic image decomposition demonstrates his ability to merge physical principles with advanced machine learning. More recently, he has been involved in pioneering projects such as a diffusion algorithm for metalens optical aberration correction, MetaTele; a compact refractive metasurface computational telephoto camera, and GenMFSR, a generative multi-frame image restoration and super-resolution framework. Through these interdisciplinary contributions, Harshana continues to advance the frontiers of intelligent imaging systems, reflecting the global impact and versatility of Peradeniya engineering graduates.

Shirantha Welikala entered the Faculty of Engineering at the University of Peradeniya from Kingswood College, Kandy, and quickly distinguished himself within its rigorous academic environment. During his undergraduate studies, he engaged in research on autonomous navigation algorithms for reconnaissance robotics, employing non-linear control techniques and classical computer vision methods at a time when deep learning had not yet become prevalent in the field. The intellectually rich and collaborative setting at Peradeniya, together with its strong emphasis on first principles and practical implementation, played a pivotal role in shaping his analytical thinking and long-term research interests.

Following his graduation, he continued at the Faculty of Engineering, University of Peradeniya as a Research Assistant, contributing to smart grid research with a particular focus on Non-Intrusive Load Monitoring (NILM). His work during this period received multiple awards and Q1 publications for both its technical merit and real-world relevance. He later pursued his PhD at Boston University, USA, where his research focused on swarm robotics, distributed optimisation, and multi-agent systems. His doctoral work addressed key challenges in coordination, scalability, and decision-making among large groups of autonomous agents, leading to several high-impact research contributions supported by competitive fellowships.

After completing his PhD, he undertook postdoctoral research at the University of Notre Dame, further advancing his work in robotics and intelligent systems. He currently serves as an Assistant Professor at Stevens Institute of Technology, New Jersey, where he leads research in swarm robotics, optimisation, and distributed autonomous systems, while contributing to teaching, mentorship, and international research collaborations.