Watson College welcomes eight new faculty members for fall 2020

The refurbished Engineering Building surrounded by changing leaves in fall 2020. Image Credit: Jonathan Cohen.
The refurbished Engineering Building surrounded by changing leaves in fall 2020.
The refurbished Engineering Building surrounded by changing leaves in fall 2020. Photography: Jonathan Cohen.

Binghamton University’s Thomas J. Watson College of Engineering and Applied Science welcomed eight new assistant professors for the fall 2020 semester.

Zeynep Ertem

Department of Systems Science and Industrial Engineering

Ertem’s expertise is in data analytics in healthcare systems, epidemic disease modelling, network optimization, discrete optimization, social networks and health systems optimization. Prior to joining Binghamton University, she was an adjunct professor in the Department of Data Sciences and Operations at Marshall School of Business at the University of Southern California. She also served as a post-doctoral fellow at the University of Texas at Austin in the Department of Data Science and Statistics.

She completed her PhD in industrial and systems engineering at Texas A&M University after obtaining her BS degree in industrial engineering from the Middle East Technical University in Turkey, with a minor in mathematics. Her recent papers were published in high-impact journals such as PLOS Computational Biology, Social Networks and the Journal of Global Optimization.

Hoda Naghibijouybari

Department of Computer Science

Naghibijouybari received her PhD in computer science from the University of California, Riverside, after earning her BS at Amirkabir University of Technology in Tehran, Iran, and her MS at the Iran University of Science and Technology in Tehran. Her primary research interests are in the areas of computer architecture and security.

Her current research focuses on architecture support for security, microarchitectural attacks, GPU security and heterogeneous systems. Her research has resulted in the discovery of new attacks that have been disclosed to GPU companies and received coverage from technical news outlets. Her paper on GPU security was selected for Top Picks in Hardware and Embedded Security in 2019.

Yu “Chelsea” Jin

Department of Systems Science and Industrial Engineering

Jin received her BS degree in 2014 from the Department of Information Science and Technology at Jinan University in Guangzhou, China. In 2015, she received her ME degree in manufacturing engineering from the University of Michigan at Ann Arbor. She earned her PhD degree in industrial engineering at the University of Arkansas - Fayetteville in May 2020. Her research focuses on sensing and analytics, advanced manufacturing, data mining and machine learning for manufacturing and service applications.

She received the IISE Gilbreth Memorial Fellowship in both 2018 and 2019; the Kuroda Graduate Fellowship in Engineering and Graduate Research Award in 2019; and the Outstanding Graduate Student Award in 2020 from the University of Arkansas. Her work has been published in IISE Transactions and ASME Journal of Manufacturing Science and Engineering. She is an active member of IISE and INFORMS, and she served as the president of the INFORMS student chapter at the University of Arkansas.

Xudong Liang

Department of Mechanical Engineering

Liang received his bachelor’s degree in theoretical and applied mechanics from Sun Yat-Sen University in China (2010), a master’s degree in engineering mechanics from Tsinghua University in China (2013) and a doctoral degree in mechanical engineering from the University of California, San Diego (2018). His thesis was focused on the mechanical instability of soft materials and soft active materials. He has published research articles in Physical Review Letters, Applied Physics Letters and the International Journal of Solids and Structures.

His current research interests include mechanics of soft materials, high-speed deformation, and mechanical metamaterials, with applications to energy conversion and absorption, biomechanics and biomaterials, and next-generation advanced materials.

Sujoy Sikdar

Department of Computer Science

Sikdar received his PhD and MS in computer science at the Rensselaer Polytechnic Institute (RPI). He has broad interests across artificial intelligence, mechanism design, problems at the intersection of computer science and microeconomics, machine learning, and computational social science.

Before coming to Binghamton University, he worked as a postdoctoral research associate at Washington University in St. Louis, Mo.

His research focuses on algorithmic decision-making in the allocation of resources, and voting, with an eye at learning and faithfully representing preferences. He is the recipient of a best paper award at SocialCom2013.

Zimo Wang

Department of Systems Science and Industrial Engineering

Wang obtained his PhD in 2020 in industrial engineering from Texas A&M University and he also holds an MS degree in mechatronics engineering and a BS in industrial engineering, both from Harbin Institute of Technology in China. Wang conducted research projects in broad aspects of smart manufacturing with strengths in sensors and AI for additive manufacturing and precision manufacturing processes.

His research focuses on bridging sensor techniques, manufacturing processes and data science to create smart sensing approaches, develop machine learning approaches and integrate them in the cyber-physical platform to allow in-process characterizations of materials, diagnosis/prognosis of the processes to realize smart manufacturing processes and autonomous systems. Wang is a member of IISE, ASME and INFORMS.

Hyunsoo Yoon

Department of Systems Science and Industrial Engineering

Yoon joins Watson College from a post-doctoral fellow experience at the ASU-Mayo Center for Innovative Imaging. He received an MS in statistics from the Georgia Institute of Technology and earned a PhD in industrial engineering from Arizona State University in 2018. His research focuses on practice, algorithm and theory of statistical machine learning. Recent interests are the transfer learning of heterogeneous data sources for predictive analytics, knowledge discovery in the application of imaging-based diagnosis, and smart and connected health.

He is engaged with interdisciplinary research with the Mayo Clinic, Columbia University, the University of Pennsylvania, Banner Health and others. He received the best paper award at the INFORMS Data Mining and Decision Analysis Workshop in 2019. He is a member of IISE, INFORMS and IEEE, and a research affiliate at the Mayo Clinic.

Yingge “Gary” Zhou

Department of Systems Science and Industrial Engineering

Zhou received his PhD in the Department of Industrial, Manufacturing and Systems Engineering (IMSE) at Texas Tech University in 2020. He earned his BS in mechanical engineering and business administration at Xiangtan University, China, in 2014, and his master’s degree in industrial engineering from Texas Tech University in 2016. His current research focuses on microfabrication and hybrid manufacturing of 3D biomimetic architecture for biomedical applications.

His most recent work focuses on rapid fabrication of nanoporous microtubes as artificial capillary vessels for tissue engineering. He has published more than 20 refereed journal papers and conference papers. His teaching interests include manufacturing systems, additive manufacturing processes and systems, and biomedical design and manufacturing. He is a member of IISE and ASME.