My research focuses on developing diagnostic methods for Traumatic Brain Injuries (TBIs), stroke, and migraine, by synthesizing ideas from AI, machine-learning, and signal processing. I have recently developed SilenceMap, an open-source algorithm based on scalp electroencephalography (EEG). SilenceMap permits rapid detection and localization of regions of silence in the brain, e.g., ischemic, necrotic, or lesional tissue in stroke, TBIs, or even tumors in the brain, using only 3 min of EEG data. Recipient of CMU BrainHub Fellowship (2018-19), and CMU Swartz Center Innovation Fellowship (2020-21). 

 My research interests are in information flow and causal inference in the brain. I am especially interested in understanding how these tools can be used to determine neural interventions via brain-machine interfaces. I like hiking, climbing, skiing, and reading.

 My research primarily deals with designing algorithms for neurostimulation, by using techniques from machine learning and information theory. Other than that, I am a typical engineering post-graduate. Yes, I do fit all the stereotypes.

 I am an undergraduate research assistant, my main role in the lab is braiding the hair of the participants. In addition to that I am a photographer and stylist.

 Jasmine is a post-doctoral fellow joint between the GroverLab, NeuralWeb, and the Lab in Multisensory Neuroscience, mentored by Barb Shinn-Cunningham. Her work merges engineering, cognitive science, and neuroimaging with the aim of developing quantitative measures of behavioral phenomena in human cognition using non-invasive methods. Her ongoing post-doctoral research seeks to understand the neural basis of individual differences (population heterogeneity) in top-down attention using high-density electroencephalography (EEG) and machine learning. In parallel, Jasmine is working to develop neurotechnologies, such as EEG and fNIRS, optimized for coarse, curly hair and dark pigmentation (melanin). Finally, she writes about inclusive neurotech and the history of racial bias in neuroscience, medicine, and technology. Jasmine has received several honors throughout her training, including being named an NSF GRFP, a Ford Foundation Fellow, a Society for Neuroscience NSP fellow, and a “Rising Star in Biomedical" by MIT. In her free time, Jasmine is a dance fitness instructor and enjoys travel and time with her enormous family.

 Mats Forssell is a postdotocral researcher in the Grover and Chamanzar labs. His current research focuses on electrial brain stimulation, in particular how to improve the accuracy and effectiveness of non-invasive stimulation. He earned his PhD from Carnegie Mellon University working on fabrication and characterization of flexible ploymer neural probes for long-term applications

 I am currently interested in non-invasive neural recording modalities such as HD-sEMG, EEG, and fMRI. I explore applications of statistical ML and deep learning to understand and treat conditions caused by spinal cord injury. I am also focused on fusion techniques for simultaneous EEG-fMRI recording which leverage temporal and spatial resolution of both to more precisely map functional connectivity in resting states. In my free time, I enjoy rock climbing, playing guitar, and traveling back and forth between Pittsburgh and Lisbon, PT!

 Vishal joined the team in Spring 2019 as a postdoc researcher after completing postdoc and research positions in University of Delhi, India and National Inst of Physiological Sciences, Japan. He received his PhD in Neuroscience from the Institute of Physiology and Allied Sciences, India. He is now working to design and perform neurobiology experiments to test and validate next generation non-invasive neural interfaces. He is jointly advised by Profs. Maysam Chamanzar and Pulkit Grover.  

• Ex-vivo stimulation and recording (2021~)
• Developing portable embedded cardiovascular healthcare systems (2021)
• Developing platforms to detect EEG from adult and larval zebrafish (2020)
• Efficient channel selections in fNIRS (2018)

 I am a PhD student in the BME department, jointly advised by Prof. Pulkit Grover and Prof. Jana Kainerstorfer. I am generally interested in neuroengineering, especially enhancing the methodologies used therein. When not doing research, I enjoy photography (both digital and analog), cooking different cuisines, and creating music. I'm also interested in learning about linguistics. Currently, my projects mainly focus on joint source localization using near-infrared spectroscopy (NIRS) and electroencephalography (EEG), as well as optimal placement of neuromonitoring sensors.

 Arnelle Etienne is a young innovator and recent graduate of Carnegie Mellon University's School of Engineering, with a self-defined degree in Technology and Humanistic Studies. She was a 2018 Undergraduate Research Fellow for the Center for the Neural Basis of Cognition in Pittsburgh, PA. Her emerging work addresses a one hundred-year-old problem in EEG, collecting unbiased data from Black patients. Her passion is to illuminate and eradicate bias by designing hardware and protocol to address disparities in tech. In her free time, she enjoys cooking Caribbean food, arguing about if music genres even exist, and having conversations about community and healing.

 With the Grover Lab, I have mainly been working on two research problems. The first, analyzing reverse engineering problems in neuroscience through a computational lens. I am also working in the area of partial information decomposition: understanding the feasibility of various measures of dependence in joint random variables. In other research, I proved some optimal probabilistic inequalities for my math masters thesis. I spend much of my free time making high-effort jokes with the CMU KGB. I spend the rest on more! extra-curricular! math! fun!

 As part of the Grover Lab, I am currently exploring how EEG technology can be improved and made more inclusive. My research interests lie at the intersection of brain and technology. I am passionate about find technological solutions for people affected by neurological disorders like epilepsy and autism. I am also working towards spreading  awareness about epilepsy through a social media initiative called IndiaGoesPurple.

 My research interests lie broadly in Machine Learning Theory. Currently, I am working on a problem in the area of foundations of fair and explainable AI systems applying Information theoretic techniques. At Cornell, I worked on problems in online learning for LQR control and designed algorithms for online packing problems. Even before this, I used to work on Combinatorial problems during my undergraduate studies and I still maintain an interest in the area. Outside research, I enjoy watching movies, playing chess, and solving Math/Chess puzzles.

 I am a undergraduate in Electrical and Computer Engineering and Biomedical Engineering. I work on 3D design of sevo EEG electrode adapters as well as adapters for fNIRS fibers. I also have designed and tested PCBs for EEG signal processing and data logging. I play tennis, play guitar, and love watching Formula 1 racing.