01
Start with the workload
Choose algorithms and data structures from the actual access pattern. Complexity is a tool for reasoning, not a scorecard in isolation.
COMPUTATIONAL SCIENTIST & ENGINEER
Build systems that hold up.
I work where algorithms, scientific software, AI/ML, and real-world constraints meet — designing computational systems that are explainable, efficient, and built to be verified.
See the technical lens01 / WORK
A technical lens across layers
The interesting problems are rarely confined to one layer. I move between the algorithm, the data, the device, and the software — and try to keep the whole system legible.
01 · DATAFLOW
Continuous signal → usable state
SENSORstream
→
ALGORITHMfilter · detect · classify
→
STATEinterpretable
02 · SYSTEM BOUNDARY
Real-world constraints → verified computation
REAL WORLD
device / data
device / data
CONSTRAINTS
memory / latency / power
memory / latency / power
→
COMPUTE
algorithm / software
algorithm / software
✓
tests / verification01
Algorithms
Data structures, algorithmic complexity, signal processing, detection, classification, and reasoning about the shape of a workload before choosing an implementation.
CORE
02
Scientific Software
Turning research questions into maintainable software — from computational analysis and prototypes to tools that can be used repeatedly and explained clearly.
SYSTEMS
03
Real-time & Edge
Working under device, memory, latency, power, and data-stream constraints, with wearable and embedded systems as real-world testbeds.
PHYSICAL
04
AI / ML
Machine learning and neural methods applied to high-dimensional, multimodal, and behavioral data — with an emphasis on useful systems rather than models in isolation.
INTELLIGENCE
05
AI-assisted Engineering
Using AI where it creates leverage — generation, exploration, testing, debugging, and automation — while keeping architecture, constraints, and final judgment in human hands.
EMERGING
Interactive Demo
Live Algorithm Sandbox: Real-time Signal Filtering
Experience on-device signal processing. Toggle the noise frequency below to see how a real-time low-pass dataflow filter extracts the clean biological signature in real-time.
02 / THINKING
Engineering is constraint management
A theoretically elegant implementation is not automatically the right implementation. Hardware, data volume, latency, memory, reliability, and the cost of being wrong all change the answer.
02
Make the trade-off explicit
Time, space, I/O, power, latency, and engineering complexity compete with one another. A good design states what it is optimizing.
03
Design for verification
Especially in consequential systems, correctness is not “the code looks right.” Build tests, invariants, review points, and failure handling into the design.
AI changes implementation economics. The value shifts toward specifying the problem, decomposing the work, choosing the right tools, and knowing which parts require deep human review.
Understanding still matters. Code you rely on should be explainable, modifiable, and predictable when it fails — whether it was typed by a person or generated by a model.
03 / EXPERIENCE
Research, engineering, teaching
A career built around moving between computation and the real world — and teaching the fundamentals that make that movement possible.
2019 — PRESENT
Staff Scientist · Computational Scientist & Engineer
National Institute of Mental Health · NIH
Computational analysis, software design, algorithm development, and real-time sensing work across research problems in behavioral and neuroscience settings.
2023 — PRESENT
Visiting Professor · Biocomputational Engineering
University of Maryland, College Park
Teaching undergraduate-level C++ object-oriented programming in spring and algorithms in fall — helping students build a practical mental model of computation, data structures, and algorithmic reasoning.
2019
Postdoctoral Researcher
University of Maryland School of Medicine
Research spanning computational methods, machine learning, and neural/behavioral data analysis.
2015 — 2018
Ph.D. Research · Electrical & Computer Engineering
University of Maryland, College Park
Research in dataflow-oriented signal processing, embedded computing, algorithm implementation, and hardware/software co-design.
2015 — 2018
Research Fellow
U.S. Army Research Laboratory
Real-time sensing, detection, and tracking systems under embedded and resource-constrained conditions.
Teaching at UMD: ENBC312 Object-Oriented Programming in C++ · ENBC322 Algorithms & Data Structures
SPRING / FALL
04 / LINKS
Elsewhere
The work lives in different places. Start with the one that matches what you want to inspect.
Good problems welcome.
Research collaborations, engineering conversations, algorithms, software, or a hard systems problem worth thinking through.