I am a 4th year Ph.D. student studying stochastic computing at the University of Michigan. My PhD adviser is Dr. John P. Hayes. Besides research, I have a keen interest in endurance running and teaching.
Computers are built to process data in the form of bits (0s and 1s) and usually data is translated into bits according to a deterministic encoding. It may sound a bit absurd to consider replacing the standard encoding with a random one, but the encoding of data into random bits is exactly what stochastic computing proposes. In fact, the first stage in a stochastic computing circuit often converts perfectly good deterministic bits into their randomized counterparts. Random bits are desirable because the hardware needed to perform multiplication on random bits is much simpler than the hardware needed for multiplication on deterministic bits. Simpler multiplication hardware can lead to smaller device size and longer battery life which is important for portable devices like cell phones or hearing aids.
Using random bits can lead to smaller computing circuits, but a drawback of using random bits is that the computation result is approximate rather than exact. Approximate results are useful in some applications like neural networks and signal processing, but they are unsuitable for other applications like scientific computing. Consequently, stochastic computing has an application niche. Identifying suitable applications and demonstrating the benefits of stochastic circuits is one of my research focuses. My other research focus is on developing a new mathematical framework for quantifying the accuracy of stochastic circuits.
There will soon be a publication list on this website, but in the meantime please refer to my Google Scholar for a list of my publications. Also, don't hesitate to contact me at firstname.lastname@example.org with any comments!
In 2017, I completed Bachelor of Science degrees in physics and in computer science at Rowan University (Glassboro, NJ). I was awarded Rowan's Medallion for Excellence in Physics for my contribution to Rowan's Physics and Astronomy department. Following Rowan, I enrolled in the University of Michigan's computer science and engineering (CSE) Ph.D. program. My master's coursework was completed in 2019 and the classes I took focused mainly on A.I. and machine learning. Presently, I am in my fourth year at Michigan and I expect to complete the requirements for a Ph.D. sometime in late 2022.
While at Rowan University, I served as an undergraduate tutor and teaching assistant where I learned how to guide students with various learning abilities, habits, and motivations. My main duty was to lead drop-in physics tutoring sessions, but I also had opportunities to assist with lecture planning. For instance, I helped develop a new kinesthetic approach for teaching electric circuit phasor diagrams. The new approach helps students visualize how the phasor's components rotate with time.
At Michigan, I have served as a graduate student instructor (GSI) four times where my main duties were to help plan homework, hold office hours and lead discussion (recitation) sessions. Michigan's Center for Researching on Teaching and Learning (CRTL) has been a great resource for developing effective pedagogical techniques. For example, I've attended CRTL workshops on topics like developing a teaching philosophy and on effective assessment practice. Below are two student testimonies from endterm instructor evaluations:
“I could go on and on about how amazing Tim was as a GSI. He dedicated probably 50+ hours of his own personal time throughout the semester into helping explain material, whether it be through email or in person, to students who wanted 1on1 communication. He truly cares about how well we understood the material, and he was always very careful not to give away answers while being as helpful as possible. His communication skills are outstanding, especially for a topic as complicated as logic design. He is by far, undoubtedly, without question, the best GSI I’ve ever had…”
“Tim was the best GSI I have ever had. Being an undergraduate student in this class, I was a little overwhelmed and frequently had questions. Tim always answered my questions in a detailed and respectable manner; I would send Tim one question, and he would send me back 3 paragraphs worth of an explanation. Tim not only boosted my confidence in taking this class, he also increased my understanding of the material and motivated me to want to do better…”
Growing up, I ran during random efforts lose weight and did a couple 5K's to support charitable causes. At age 23, I bought a book titled "Faster Road Racing: 5K to Half Marathon" in hopes of improving my 5K race time. To great surprise, following the training schedules in the book improved my longest run from 4 miles to 13.1 miles in just 10 weeks. One year after buying the book, I ran the 2019 Detroit marathon, a feat that I would've deemed impossible a year prior. Although running is very different from academic work, I've found that the two activities complement one another. Running improves my mental well-being, reinforces the importance of consistency and offers an opportunity for broad learning via audiobooks and podcasts. On the other hand, through research and teaching, academia provides a creative outlet, offers an opportunities for community impact and enables one to study something deeply.
If you're interested in learning more about fitness, my research or other interesting content (books, podcasts, videos, etc.), please check out my blog!