2019-2020 Doctoral Dissertation Fellowship Awardees

ECE’s Ibrahim Ahmed, Chunhui Dai, and Vasileios Ioannidis are recipients of the 2019-2020 Graduate School Doctoral Dissertation Fellowship.

Vasileios Ioannidis has received the fellowship for his research titled “Deep Learning from Limited and Dynamic Graph Data.” He is working under the guidance of Prof. Georgios Giannakis.

Vasileios Ioannidis standing in front of a white screen
Vasileios Ioannidis, doctoral candidate

Vasileios’ fellowship-winning dissertation research is focused on addressing some of the critical challenges associated with recognizing patterns in network data to provide dependable analytics. Social media sites offer an example of where such research could be applied. Users of Facebook for instance generate massive amounts of data (4 million likes each minute, 350 million photos each day are just a couple of sample statistics)*. In addition to this torrent of data, there are other factors to take into account. Users may conceal personal data, social links can change over time, and malicious users can feed social networks with “fake news” to sway public opinion. Other challenges include the complex nature of users’ connections with overlapping circles of relationships (for instance being connected to the same individual as a coworker as well as a friend). The volume of data and the speed at which it is generated necessitates on the fly analytics.

Vasileios’ dissertation leverages advances in graph signal processing, optimization, data science, tensor decomposition, and machine learning to tackle the challenges facing robust learning tasks over large-scale dynamic networks. His work takes a three-pronged approach: development of novel learning algorithms, modelling multi-relational tensors combined with graph data, and designing graph neural networks for scalable learning of multi-relational data. Together, his research develops  tools for machine learning with application to computational social and data sciences.

Vasileios earned his bachelor’s degree in electrical and computer engineering from National Technical University of Athens, Greece, in 2015. He moved to the University of Minnesota in 2015 as a graduate student to pursue his research interests in machine learning, artificial intelligence, optimization, data, and network science. Vasileios has previously received student travel awards from the IEEE Signal Processing Society, and from IEEE. From 2014 to 2015, he worked as a middleware consultant for Oracle in Athens, Greece, and received a Performance Excellence award for his work. 

Chunhui Dai standing against a backdrop of mountains
Chunhui Dai, doctoral candidate

Chunhui Dai has received the fellowship for his research titled “Self-assembly of graphene-based three-dimensional nanostructures for ultra-sensitive biomolecular sensing.” He is working on his dissertation under the guidance of Prof. Jeong Hyun Cho. 

Chunhui earned his bachelor’s degree in electrical engineering from State University of New York, Binghamton in 2014. He then moved to the University of Minnesota to pursue his interests in programmable self-assembly, 3D graphene-based nanostructures, and plasmonic biosensing. His dissertation research addresses some of the diagnostic and treatment challenges that currently prevail in the healthcare industry. 

Hemoglobin is one of the vital components of blood, and abnormalities in it can cause diseases such as sickle cell disease (SCD). SCD leads to cumulative organ damage, acute painful episodes called “crises”, and reduced survival. The current detection method, high-performance liquid chromatography (HPLC), requires expensive instruments and experienced practitioners, which is neither affordable nor accessible in developing areas. And the scenario is not particularly unique to the SCD. In fact, providing affordable and timely disease diagnosis and treatment are critical problems facing the healthcare industry and practitioners today. Chunhui’s work aims to develop a cost-effective, highly sensitive, and selective diagnostic techniques using self-assembled plasmonic 3D graphene and gold based nanostructures as a sensor. 

To fabricate the 3D structures, Chunhui, in collaboration with other researchers has invented a novel in-situ monitored origami-like assembly process, which is triggered by electron irradiation induced crystallization. The 3D plasmonic nanostructures can efficiently confine incident light, creating a strong electromagnetic field. This enhanced field allows for the detection of hemoglobin with ultra-high sensitivity.

Chunhui has been the recipient of several awards and honors, including best poster awards at multiple Materials Research Society fall meetings, NSF poster competitions, and the 3M Science and Technology Fellowship. 

Ibrahim Ahmed’s fellowship-winning dissertation is titled Computing with Oscillators: From Concept to Practice,” and he is working on his research under the guidance of Prof. Chris Kim. 

Ibrahim Ahmed, doctoral candidate

Ibrahim’s work addresses the challenges that will arise as CMOS technology reaches its limits while the demand for increased computational power and lower energy devices continues to rise. Besides, traditional methods of solving newer and novel computational problems are proving to be prohibitively expensive and time intensive. Ibrahim focuses his research on finding an alternative system, oscillation based computation, that can meet these challenges, and portable to various applications such as autonomous vehicles, drone delivery systems, 3D mapping, network design, data clustering, supply chain optimization, and vehicle routing.

He also provides an in-depth analysis of spintronic memory, a promising alternative to CMOS memory. The goal of Ibrahim’s dissertation research is to optimize, evaluate, design, and validate novel computation architecture and memory solutions for “beyond CMOS” technology. Such technology would enable smaller and more efficient integrated circuits for a range of applications such as consumer electronics, autonomous vehicles, and artificial intelligence.