Category: News

Husky Innovate Students Win Top Prizes in New Venture Online Competition

by Husky Innovate

For the 11th year running, Central Michigan University and Michigan Tech collaborated to offer Tech students a chance to compete at CMU’s New Venture Competition. 2021 marked the second year the pitch competition was held online as the New Venture Online Competition (NVOC).

Despite the challenges of a pandemic and a virtual platform, our students persevered, honed their pitches and won top prizes. This year’s NVOC winners were also winners at the 2021 Bob Mark Business Model Pitch Competition held at Tech in January. All of their hard work and effort paid off!

Congratulations to this year’s MTU winners:

  • In the 2020-track 10-minute pitch category, Team Focus with Ranit Karmakar won the Best Overall Venture Award for $25,000. Watch Karmakar’s pitch.
  • In the two-minute pitch category, Team The Fitting Room with Jordan Craven won third place for $1,000. Watch Craven’s pitch.
  • Team Recirculate with Hunter Malinowski won an honorable mention award for $750. Watch Malinowski’s pitch.

Read more in the NVOC 2021 Booklet.


Congratulations Class of 2021!

It has been a challenging academic year, to say the least. As part of the Class of 2021, you are an exceptional group of graduates. Your final academic year presented you with unforeseen and unprecedented challenges, yet you persevered.

We are all proud to have mentored, instructed, and supported you on your educational journey. We know you’ll do well. You are a Husky, after all!

Please stay in touch!


Nathir Rawashdeh Publishes Paper at SPIE Conference

Nathir Rawashdeh (AC) led the publication of a paper at the recent online SPIE Defense + Commercial Sensing / Autonomous Systems 2021 Conference.

The paper, entitled “Drivable path detection using CNN sensor fusion for autonomous driving in the snow,” targets the problem of drivable path detection in poor weather conditions including on snow-covered roads. The authors used artificial intelligence to perform camera, radar and LiDAR sensor fusion to detect a drivable path for a passenger car on snow-covered streets. A companion video is available. 

Co-authors include Jeremy Bos (ECE).


Jingjing Yao, New Jersey Institute of Technology, to Present Talk May 11


Jingjing Yao, a PhD candidate in computer engineering at New Jersey Institute of Technology, will present a talk on Tuesday, May 11, at 3:00 p.m.

Her talk is titled, “Intelligent and Secure Fog-Aided Internet of Drones.”

Yao’s research interests include Internet of Things (IoT), Internet of Drones (IoD), Deep Reinforcement Learning, Federated Learning, Cybersecurity, Mobile Edge Computing/Caching, and Energy Harvesting.

Join the virtual talk here.

Talk Title

Intelligent and Secure Fog-Aided Internet of Drones

Talk Abstract

Internet of drones (IoD), which deploys several drones in the air to collect ground information and send them to the IoD gateway for further processing, can be applied in traffic surveillance and disaster rescue. Fog-aided IoD provisions future events prediction and image classification by machine learning technologies, where massive training data are collected by drones and analyzed in the fog node. However, the performance of IoD is greatly affected by drones’ battery capacities. Also, aggregating all data in the fog node may incur huge network traffic and drone data privacy leakage.

The speaker will share her vision and research to address these two challenges. In this talk, the speaker utilizes energy harvesting technology to charge drone batteries and investigate wireless power control to adjust the drone wireless transmission power to reduce drone energy consumption. The joint optimization of power control and energy harvesting scheduling is investigated in time-varying IoD networks to minimize the long-term average system energy cost constrained by the drone battery capacities and quality of service (QoS) requirements.

A modified actor-critic deep reinforcement learning algorithm is designed to address the joint optimization problem in time-varying IoD networks. To prevent the privacy leakage of IoD, the speaker utilizes federated learning (FL) by performing local training in drones and sharing all training model parameters in the fog node without uploading drone raw data.

However, drone privacy can still be divulged to ground eavesdroppers by wiretapping and analyzing uploaded parameters during the FL training process. The power control problem is hence investigated to maximize the FL system security rate constrained by drone battery capacities and the FL training time requirement. An algorithm with low computational complexity is then designed to tackle the security rate maximization problem and its performance is demonstrated by extensive simulations.

Biography

Jingjing Yao is currently a Ph.D. candidate in Computer Engineering with the Department of Electrical and Computer Engineering at the New Jersey Institute of Technology (NJIT). She will receive her Ph.D. degree from NJIT in May 2021. She received the M.E. degree in Information and Communication Engineering from the University of Science and Technology of China (USTC), and the B.E. degree in Information and Communication Engineering from the Dalian University of Technology (DUT).

She has published 13 first-author journal articles and seven first-author conference papers. Her research interests include Internet of Things (IoT), Internet of Drones (IoD), Deep Reinforcement Learning, Federated Learning, Cybersecurity, Mobile Edge Computing/Caching, and Energy Harvesting.



Jidong Xiao, Boise State University, to Present Talk May 12


Jidong Xiao, an assistant professor in the computer science department at Boise State University, will present a talk on Wednesday, May 12, at 3:00 p.m.

Dr. Xiao’s research focuses on computer security, especially computer system security and cloud security.

In his talk, “Identifying New Threats in Cloud Environments,” Dr. Xiao will present two research projects focusing on a concept called virtual machine extrospection and a new type of rootkit, which allows attackers to perform active or passive attacks in a nested virtualization environment.

Join the virtual talk here.

Talk Title

Identifying New Threats in Cloud Environments

Talk Abstract

Cloud computing has become prevalent over the past decade. While individuals and organizations rely on cloud computing more and more, various security problems in cloud platforms are discovered. In this talk, I will present two research projects. In the first project, I will present a concept called virtual machine extrospection, in which attackers or cloud customers collect sensitive information about the physical machine from within a virtual machine. In the second project, I will present a new type of rootkit, which allows attackers to perform active or passive attacks in a nested virtualization environment, and then I will describe our detection approach. At the end of the talk, I will briefly discuss my future research projects and plans.

Biography

Dr. Jidong Xiao is an assistant professor in the computer science department at Boise State University. His research focuses on computer security, especially computer system security and cloud security. He received his PhD degree in computer science from the College of William and Mary. Prior to joining Boise State University, he spent approximately 5 years in industry working at Intel and Symantec.

Dr. Xiao’s research was recognized in different venues, including publications that won the best paper award at the USENIX Large Installation System Administration Conference (LISA) 2015, won the distinguished poster award at the Network and Distributed System Security Symposium (NDSS) 2016, and won the best paper award nomination at the International Conference on Dependable Systems and Networks (DSN) 2020. Dr. Xiao has been awarded several grants by the NSF, NSA, and the Army Research Office (ARO).


Dr. Dukka KC, Wichita State, to Present Talk May 5


Dr. Dukka KC, Electrical Engineering and Computer Science, Wichita State University, will present a talk on Wednesday, May 5, 2021, at 3:00 p.m.

Dr. KC will discuss some past and ongoing projects in his lab related to machine learning/deep learning-based approaches for an important problem in Bioinformatics: protein post-translational modification.

Join the virtual talk here.

Talk Title

Bioinformatics as an emerging field of Data Science: Protein post-translation modification prediction using Deep Learning

Talk Abstract

In this talk, I will be presenting about some of the past and ongoing projects in my lab especially related to Machine Learning/Deep Learning based approaches for one of the important problems in Bioinformatics – protein post-translational modification.

Especially, I will focus on our endeavors to get away from manual feature extraction (hand-crafted feature extraction) from protein sequence, use of notion of transfer learning to solve problems where there is scarcity of labeled data in the field, and stacking/ensemble-based approaches.

I will also summarize our future plans for using multi-label, multi-task and multi-modal learning for the problem. I will highlight some of the ongoing preliminary works in disaster resiliency. Finally, I will provide my vision for strengthening data science related research, teaching, and service for MTU’s college of computing.

Biography

Dr. Dukka KC is the Director of Data Science Lab, Director of Data Science Efforts, Director of Disaster Resilience Analytics Center and Associate Professor of Electrical Engineering and Computer Science (EECS) in the Department of EECS at Wichita State University. His current efforts are focused on application of various computing/data science concepts including but not limited to Machine Learning, Deep Learning, HPC, etc. for elucidation of protein sequence, structure, function and evolution relationship among others.

He has received grant funds totaling $4.25M as PIs or Co-PIs, spanning 17 funded grants. He was the PI on the $499K NSF Excellence in Research project focused on developing Deep Learning based approaches for Protein Post-translational modification sites.

He received his B.E. in computer science in 2001, his M.Inf. in 2003 and his Ph.D. in Informatics (Bioinformatics) in 2006 from Kyoto University, Japan. Subsequently he did a postdoc at Georgia Institute of Technology working on refinement algorithms for protein structure prediction. He then moved to UNC-Charlotte and did another postdoc working on functional site predictions in proteins. He was a CRTA Fellow in National Cancer Institute at National Institutes of Health where he was working on intrinsically symmetric domains.

Prior to his arrival at WSU, he was associate professor and graduate program director in the Department of Computational Science and Engineering at North Carolina A&T State University.

Dr. KC has published more than 30 journal and 20 conference papers in the field and is associate editor for two leading journals (BMC Bioinformatics and Frontiers in Bioinformatics) in the field. He also dedicates much of his efforts to K-12 education, STEM workforce development, and increasing diversity in engineering and science.