Lab Members

Dr. Ching-Yi Lin earned his bachelor's degree from National Tsing Hua University. He has received the Dean’s Fellowship, CMLH Fellowship, and IEEE CASS Student Travel Grant. He is currently a postdoctoral associate at the University of Maryland. His research interests include ML hardware and biosensor circuits.

Vineeta V Nair is currently a Postdoctral associate at Department of Electrical and Computer Engineering at the University of Maryland, College Park. Her PhD thesis related to in-memory implementations of Echo state networks at the School of Electronic Systems and Automation, Digital University Kerala. Prior to this she completed her masters and bachelors degree in Electronics Engineering. She has a background in the area of neural networks and quantum circuits, with a specific focus on echo state networks, ESN implementations, and quantum-hybrid neural networks. She is a IEEE member. 

Charana is a Ph.D. Student at the Department of Electrical and Computer Engineering at the University of Maryland, College Park. He received her B.Sc.(Hons) degree specializing in Electronics and Telecommunication Engineering from the University of Moratuwa, Sri Lanka. in 2019. Before joining the University of Maryland, he worked in the industry as a software engineer related to machine learning and big data systems.

​Currently, his research interests include energy-efficient mixed-signal hardware for computing machine-learning algorithms and real-time signal processing algorithms. 

Sayma received the B.Sc. degree in Electrical and Electronic Engineering from University of Dhaka in 2016 and the M.Sc. degree in Communications and Signal Processing from University of Dhaka in 2018. She worked on early detection of epileptic seizures using SVM and NN as her undergraduate research project. She received NIST fellowship from Ministry of Science and Technology of Bangladesh for her M.Sc. thesis on Multi-class Cancer Classification using Genetic Algorithms. She joined the Department of Electrical Engineering at the University of Maryland, College Park as a Ph.D. student. Her research interests includes energy-efficient hardware for computing machine learning algorithms. Currently, she is exploring novel architectures for learning efficiently on-chip. 

Utku Noyan, pursuing his Ph.D. in Electrical and Computer Engineering at the University of Maryland, College Park, specializes in biochemical sensing using CMOS chips, focusing on DNA and phospholipid detection, among other pivotal biological events. He is working from the sensor and circuit interface design to biochemical surface functionalization to empirical measurements and analysis of experimental results. Committed to fostering inclusivity, Utku has excelled as a teaching assistant in undergraduate and graduate courses, notably in mixed-signal VLSI design, earning multiple departmental accolades for his teaching and mentorship. Prior to UMD, Utku graduated in the top 10% from Koc University in 2016 with dual B.Sc. degrees in Electrical and Electronic Engineering and Computer Engineering. His diverse experience spans research labs, corporations, and startups. Notably, he was selected by the Young Guru Academy as one of fifty standout volunteers, dedicating over 7,500 hours to social innovation projects, exemplifying his dedication to community service.

Irem completed her undergraduate degree in Electrical and Electronics Engineering at Middle East Technical University (METU) as a high honor student in 2024. During her undergraduate studies, she focused on designing power transmission networks with near-zero index meta-materials. In Industry, she collaborated on the design and fabrication process of mid-wave infrared (MWIR) detectors in the clean room environment. Outside her academic pursuits, Irem enjoys analogue photography and art history. Currently, she is a Ph.D. student in the Department of Electrical Engineering at the University of Maryland, College Park. Her research interest includes developing energy-efficient neuromorphic systems through 3D integration of silicon-based artificial neurons and synapses and development of low-power solid-state circuits and architectures for on-chip learning at edge devices. 

Kaushik is a Ph.D. candidate at the Department of Electrical and Computer Engineering at the University of Maryland, College Park. His areas of expertise include development of embedded systems, optics-based system development, and electronic systems design. He received his bachelor (B. Tech.) in Electronics and Communication Engineering from SASTRA University, Thanjavur in 2022. During his time at the Shafiee Lab, Harvard Medical School, Boston, he worked on developing Point-of-Care diagnostic devices. Before his tenure at UMD, he also worked as a research associate at the BEES Lab, Indian Institute of Science, focusing on the development of electronic systems for neural interfaces, research in the characterization of cancerous and non- communicable diseases, fluorescence-based imaging systems for margin detections during surgery and ultrasound acquisition systems. Currently, at UMD, he is interested in exploring low-power mixed-signal Brain Machine Interface (BMI) systems aimed at studying and decoding neural networks to actuate kinematics. His research interests include working with advancements in biomedical devices, Neuro-MEMS, and Motor- Neuron Assisting Mechatronics.

Yihui is currently a Ph.D. student in the Department of Electrical and Computer Engineering at the University of Maryland, College Park. He earned his bachelor's degree in Photoelectron Information Science and Engineering from Yanshan University in 2020. Subsequently, he completed his master's degree in Electrical Engineering at George Washington University in 2023. During his undergraduate studies, he focused on Embedded Systems and Digital Signal Processing, and his graduation thesis involved designing a Gesture Identification CNN on an Edge Device. In his master's degree research, he delved into Digital VLSI Design, FPGA, and Spiking Neural Networks, culminating in the design and implementation of a simplified digital neuron and synapse model for large-scale SNN on an FPGA at the ADAM Lab, GWU. His research interests at Shah Lab include Mixed-Signal Circuit Design, Neuromorphic Computation, and Brain-Machine Interface.