All our percepts and behavior are generated from the concerted activity of a billion neurons in the brain. To understand the neural basis of perception and behavior, we need to measure, analyze, and interpret brain activity together with measurements of behavior, such as hand and eye movements. Such complex datasets are typically large, high-dimensional, noisy and hard to interpret, and must be approached with a deep understanding of neurophysiology, experimental design, and the entire suite of data science tools. I specialize in computational modeling and algorithm development, bringing insights from electrical engineering, high-performance computing, machine learning, and statistics. I am experienced in the three most widespread techniques — primate neurophysiology, human electromagnetic imaging (EEG/MEG), and functional MRI — deployed to study neural systems across scales of ~100 to ~1,000,000 neurons. I have applied these tools to study the somatosensory, visual, eye-movement and movement systems. My work has been highly collaborative (~20 co-authors from ~10 institutions) and impactful, resulting in 8 peer-reviewed journal and 5 peer-reviewed conference publications, 240 citations, 25 conference presentations, and ~10 invited talks.

Research Associate @ I work on projects spanning three major focus areas, two of which are funded by NIH R-01 grants. I specialize in neural data analysis, in particular, encoding models of, and functional connectivity between populations of spiking neurons recorded from chronic arrays in monkeys. My projects include: (1) Testing biological plausibility of computational models of eye-movement guidance in the frontal eye fields. (2) Seeking evidence for the representation of uncertainty during reaching in premotor cortices. (3) Modeling the representation of hue in visual area V4 during natural scene viewing. From September 2012 to Present (3 years 4 months) Greater Chicago AreaResearcher @ In my doctoral thesis, I developed methods to characterize oscillatory brain dynamics and their long-range cortico-cortical interactions over time-scales of minutes. I made multichannel MEG recordings over time scales of minutes during rest and perception of natural stimuli, and applied distributed inverse modeling and source separation techniques to characterize oscillatory networks [five peer-reviewed publications]. Independently of my doctoral work, I applied classical machine learning methods such as multivariate pattern analysis (MVPA), to characterize the _information resolution_ of non-invasive electrophysiology (EEG/ MEG). Resolving information previously thought unresolvable from such data, would portend well for the applicability of EEG/ MEG to make detailed characterizations of neural systems in terms of their information processing. I applied MVPA to study both low-level visual feature encoding, as well as semantic encoding of natural scenes [two peer-reviewed publications]. I analyzed resting state fMRI data from chronic pain patients, particularly in the functional connectivity analyses of very low frequency signals [one peer-reviewed publication]. I also co-developed and validated a statistical mathematical technique which proposes a principled test to reject components obtained using independent component analysis of fMRI data [one peer-reviewed publication]. From September 2006 to September 2012 (6 years 1 month) Vice President, Executive Board Member @ I was a founding member of a university-wide body with the open-ended goals of promoting student-driven social entrepreneurship in the Helsinki region and served as vice-president for two semesters. In the first semester, I wrote the vision and mission statements around which volunteers could rally. I developed our web and social media presence. Within a quarter, I co-led the team to organize 15+ public lectures and incubate 15+ social-impact projects. Our social media visibility grew to 1500+, spread across 3 continents. In the second semester, I created more structure within the organization, identifying functions such as event heads, bloggers, and social media managers. At the end of this quarter, I also co-led the transition management by recruiting a new set of committee members and handing it over to a new leadership. Today, Aalto Social Impact has transformed into Aalto Global Impact, which primarily focuses on delivering educational resources for international development within Aalto University. From April 2010 to December 2010 (9 months) Research Intern @ Design and implementation of a machine vision and tracking algorithm on an industrial robot. From May 2005 to July 2005 (3 months) Summer Intern @ Algorithm development for stereo-vision guided tracking with a robotic arm. From May 2004 to July 2004 (3 months) Bengaluru Area, India

PhD, Neuroscience, Signal Processing, Machine Learning @ Aalto University School of Science From 2009 to 2012 MSc (Tech), Bioinformatics @ Helsinki University of Technology From 2007 to 2009 B. Tech., Electronics and Communication Engineering @ Indian Institute of Technology, Guwahati From 2002 to 2006 Pavan Ramkumar is skilled in: Machine Learning, Statistics, Data Analysis, Signal Processing, Magnetoencephalography, Systems Neuroscience, Numerical Analysis, Pattern Recognition, Neuroscience, EEG, fMRI, Neuroimaging, LaTeX, Electrophysiology, Science