NSF Research Traineeship (NRT)

Past practicum projects:

2017-18 Traineeship

Students participated in a one-semester course consisting of three one-month long modules, entitled "Methods In Data- Enabled Research Into Human Behavior And Its Cognitive And Neural Mechanisms." This year consisted of the following three course modules:

  1. Probabilistic (often Bayesian) Models of Human Perception and Cognition taught by Professor Robert Jacobs. In particular, we used probabilistic models to develop so-called "ideal observers" which perform a perceptual or cognitive task. The task can also be performed based on the task inputs, task constraints, and modeling assumptions built into the observer. Through three programming projects, students gained hands-on experience implementing probabilistic ideal observers, and comparing observers' performances with human performances.
  2. Inference Implementation in the Brain taught by Professor Ralf Haefner. Simulating a simple biophysical model of an individual neuron, students showed how neurons can support linear-nonlinear operations that are at the core of deep neural networks in machine learning (programming assignment 1). Both learning and inference in such a model using an MCMC- sampling-based algorithm gives rise to a neural network (in the biological sense) with neural response properties similar to those found neurophysiologically (programming assignment 2).
  3. Language Processing taught by Professor James Allen. This module included general background on the structure of language, an introduction to basic statistical methods used for a variety of language applications (e.g., part of speech tagging), and an overview of semantics, semantic parsing and natural language dialogue/systems.

The following semester, the students participated in "Practicum In Data-Enabled Research Into Human Behavior And Its Cognitive & Neural Mechanisms".  In this course, trainees work in mixed teams of CS and BCS PhD students to learn the application of Data Science algorithms to find meaningful patterns in human brain data, collected using fMRI (functional magnetic resonance imaging). Funding to pay for MRI scan time is being provided by the NRT grant. This year, the course was led by Professor Rajeev Raizada, who is a faculty member of the BCS department working on computational methods of fMRI analysis.”

Bios of the 2017-18 Class Members


Wednesday Bushong

I'm a third year PhD student in the Brain and Cognitive Sciences department advised by Florian Jaeger. I use a combination of computational and experimental methods to study how people learn the statistics of their language and strategically use this knowledge during real-time processing.

Email: bushong@hartford.edu

Project contributions: Our group of brain and cognitive sciences and computer science students investigated whether introducing visual information about the face aids in speech perception in humans and neural networks. I contributed to the experimental side, designing and running experiments to test how visual information changes speech perception in humans and I compared this to how visual information aided speech perception in neural networks.

What did I learn from the course? The biggest takeaway for me was the ability to collaborate, especially with people who have very different skillsets.

How might this course affect my career? As I mentioned, I felt teamwork and collaboration skills were the main benefit of the course and will have a very positive impact on my career as I collaborate more with researchers outside of my subfield. The computational skills I learned from the first part of the course are also applicable to my research and to current industry standards if I decide to leave academia.


UPDATE June 2020: Wednesday Bushong completed her PhD and will be starting a tenure-track faculty position at University of Hartford as an Assistant Professor of Psychology.


Sam Cheyette

I graduated from Carnegie Mellon in 2016 with a BS in cognitive science. I'm now a brain and cognitive sciences PhD student advised by Steve Piantadosi, studying numerical cognition and conceptual development. I participated in NRT my 1st and 2nd year (2016-2018). 


Project contributions: I was involved in numerous projects, but my favorite was probably developing a tool to integrate speech with lip-reading. At first I was involved in developing the tool (a deep recurrent neural network), but my primary focus became figuring out how well non-deaf people can integrate speech with lip-reading.

What I learned: On a "factual" level, I learned a lot of useful machine learning methods, including how to train large deep neural networks using publicly available tools (and other people's pre-trained networks); and useful statistical techniques, such as how to infer people's priors in hierarchical Bayesian models. But, unique to this class, I learned how to work effectively in a team on larger-scale projects requiring the implementation of multiple co-dependent pieces.

How it might affect my career: Many of the machine learning tools that I learned in this class are widely used or becoming widely used in my field. Having some experience with those tools will certainly be useful.

Greatest challenge: Working with many other people towards an ambitious goal can be frustrating (and rewarding). It's tough trying to keep everyone in a team on the same page and motivated toward a common purpose.



Joseph German

Joseph is a first-year PhD student in the Brain and Cognitive Sciences department working with Robert Jacobs. He received his BM in Viola Performance and double MM in Viola Performance and Musicology (with a focus on music cognition) from the Peabody Conservatory of Johns Hopkins University. He then pursued coursework in mathematics, logic, and computer science at the University of Maryland, Baltimore County. His current research interests include the computational modeling of cognition and perception, biologically-plausible machine learning, and mathematical optimization.


Project contributions: In addition to representing Linux systems in the data exploration and analysis, I raised a number of methodological issues with the experimental design and statistical analysis of fMRI.

What did I learn from the course? The course was a great overview of the strengths and limitations of fMRI, both technical and philosophical. Any cognitive scientist, whether they plan to work with fMRI or not, needs to know how to properly interpret the results. The course also covered basic tools to streamline the analysis of neuroimaging data.

How might this course affect my career? fMRI is one of the most powerful experimental tools at our disposal. Knowledge of neuroimaging techniques, including both their strengths and limitations, is essential for any cognitive scientist.

What was surprising or the greatest challenge? Often, more sophisticated-seeming approaches to analyzing fMRI data that take certain subtleties of experimental design into account perform no better than more basic techniques that ignore those complexities.


Lisa Jin

Lisa is a second-year computer science PhD student, advised by Professor Daniel Gildea. Her research interests include natural language generation and machine translation. She received her bachelor’s degree in computer science from the University of Michigan in 2017.


What did I learn from the course? I gained insight into statistical models of human cognition, neuroscience, and natural language.

How might this course affect my career? Learning about the interplay between multiple levels of brain function and human reasoning showed me how AI is shaped (and limited) by biological ideas. I will look into these domains for context in future research problems.

Updates since the class ended:

  • Internship at Lawrence Livermore National Laboratory
  • Attended NAACL

Nathan Kent

Nathan Kent is a second year PhD student in the Computer Science department at the University of Rochester. He is a member of the Robotics and Artificial Intelligence Laboratory under Thomas Howard. His major interest is in epigenetic robotics. He received his BS in computer engineering from Iowa State University.

Email: nate@nkent.net OR nkent2@cs.rochester.edu

What did I learn from the course? The things I found to be the most interesting were the subtle differences between doing research as a student in brain and cognitive sciences and doing research as a student in computer science. The final project was an interesting exercise in crossing that gap.

How might this course affect my career? This course provided me with the opportunity to make contacts in another department that I may not have made otherwise. Hopefully, I will be able to use this experience to work with the BCS department in the future.

What was surprising or the greatest challenge? The writing workshops. The difference between the workshops in the first year and the second year seemed like night and day. I did not expect them to be as useful and interesting as they were.

Updates: Since the class ended, I have attended the Epirob 2017 and RSS 2018 conferences.



Samuel Lerman

I’m a second-year PhD student at the University of Rochester, having graduated with a BS in computer science and a BA in mathematics. My passion is the study of learning — how learning happens, how phenomena are experienced, and how they’re integrated into an intelligent system. It’s a passion for understanding the nature of the mind. For that reason, my research is heavily inspired by the brain and cognitive sciences. My goal is to work within the fields of reinforcement learning, neuroscience, and psychology to illuminate the inner workings of these mysteries. Together with my advisor, Henry Kautz, and the support of the NRT (an interdisciplinary program between artificial intelligence and brain and cognitive sciences) I hope to do just that.

Personal Webpage
Email: slerman@ur.rochester.edu

Project contributions: Broadly speaking, I contributed to medical predictive modeling using biologically inspired, novel neural network architectures (and hopefully it worked, but we are still building them!)

What did I learn from the course? I learned about the methods and popular mathematical and medical tools used to study the brain, such as Bayesian modeling and fMRI. 

How might this course affect my career? This course will consolidate the cognitive aspect of my research focus in my career, allowing me to understand and investigate neuroscientific perspectives with more aptitude.

What was surprising or the greatest challenge? The greatest challenge was the overwhelming amount of uncertainty challenging researchers in both fields. There was difficulty in estimating which discoveries and observations were useful to the actual process of learning and not just incidental byproducts of more essential features or evolutionary circumstances. In other words, it was difficult filtering through what information was meaningful and important! This process strengthened my skill of discernment in these research areas.


Parker Riley

Parker is a second-year computer science PhD student, working on natural language processing with Daniel Gildea. His primary research interests are in machine translation and multilinguality, with a focus on unsupervised and semi-supervised methods. He received his BSc in computer science from the University of Kansas.


Project contributions: Parker primarily worked on the face detection and facial feature extraction components.

What I learned: "This course was an excellent way to form interdisciplinary connections and get a broader perspective on research in the field."

Career impact: "With the recent boom in deep learning, getting familiar with it early on in the program is an excellent way to be set up for success after graduation."

Surprise/challenge: "I was most surprised to see how much work there is happening in other fields that can be applied to my own; we need programs like this to increase cross-disciplinary communication."

Updates since participation: I attended ACL 2017, presented published work at ACL 2018 (both trips with support from the NRT), and interned at Google in New York City.


Berhan Senyazar

Berhan Senyazar

Berhan Senyazar is a first-year PhD student in the Brain and Cognitive Sciences department advised by Robert Jacobs. His main research interests are the computational and information theoretic limitations of cognitive functions and how they are organized to work effectively under such restrictions. He received his MA in Cognitive Science and his BSc in Computer Engineering from Bogazici University, Istanbul, Turkey.


What did I learn from the course?  I learned how to think about cognitive and neuroscientific phenomena in terms of basic computational terms, model their workings using advanced statistical and computational methods, and test the validity of the models with appropriate behavioral and neuroimaging experiments.

How might this course affect my career? I gained the right perspective to mathematically model and analyze real-world data. This is not only a fundamental skill for academic research but also for industry with the increasing importance of data science.

What was surprising or the greatest challenge? Even though I have a multi-disciplinary background, the greatest challenge was to learn the strategies to communicate my research to a diverse group of scientists.


Sabyasachi (Sabya) Shivkumar

Sabyasachi (Sabya) Shivkumar is a first year PhD student pursuing his PhD in the Brain and Cognitive Sciences department under Dr. Ralf Haefner. Shivkumar is interested in understanding the working of the brain from a normative perspective. Coming from an electrical engineering undergraduate background, he has adopted a theoretical and mathematical approach to studying the brain. His main interests are in studying perception and decision making in a Bayesian probabilistic inference framework. Shivkumar received his Bachelors and Masters in Electrical Engineering from Indian Institute of Technology, Madras.

Email: sshivkum@ur.rochester.edu

What did I learn from the course? The course gave me a unique chance to study topics in neuroscience and computer science with a highly interdisciplinary cohort. This led to a lot of discussions which provided a new perspective in studying these ideas.
How might this course affect my career? As a computational neuroscientist, the course provided exposure to tools in data science which would help me in my future research. The invited lectures gave a good insight into industrial opportunities which would be useful to look at in the future.
What was surprising or the greatest challenge? The most surprising thing for me was discovering the similarity in research done in the industry to that in academia and the possible benefits this would have on future collaborations.


Bin Yang

Bin Yang is a first year PhD student working with Michele Rucci in the Brain and Cognitive Sciences department. He is mostly interested in the mysteries of human visual perception. Yang received his Bachelor’s in Software Engineering in Nankai University and then worked as an RA with Dr. Mingsha Zhang in the National Key Lab in Cognitive Neuroscience and Learning, Beijing Normal University. With his interdisciplinary background, he is pursuing a career combining experimental methods and computational approaches to study brains as well as intelligent machines.

Current CV
Email: bin.yang@rochester.edu

What did I learn from the course? I learned a lot about statistical and computational approaches such as Bayesian Inference and LNP model, and their applications to understand cognition and neural processing.

How might this course affect my career? This course introduced me to computational neuroscience, which will be a key part of my future research career.

What was surprising? People talked about Bayesian Inference all the time. However, it is hard for me to believe that we are just behaving as Bayesian decoders, as hard as it is for me to say anything against this idea!