Class of 2027 · Aspiring physician & student researcher

I look for the signal
before it becomes
the symptom.

I'm curious about how small signals can reveal something much bigger, whether they come from a brainwave, a heartbeat, or a conversation with a patient. That curiosity has led me into research, oncology and nephrology shadowing, community healthcare and service, leadership, and athletics. Together, those experiences have shaped how I think about medicine as a field that requires scientific precision, empathy, humility, and the ability to listen closely to people.

Explore the researchDownload résumé
260+
Volunteer hours
35+
Patient consultations during oncology observership
40
Oncology shadowing hours
5
Days in oncology clinic
3
Research projects
99.5
Weighted GPA
Portrait of Akif Khan
Akif KhanEEG · ECG · R
01 / Research · Hofstra University, 2025

Finding Seizure Patterns in EEG Brainwaves

Seizure activity hides inside ordinary EEG noise. Working in R with the CHB-MIT Scalp EEG Database, I compared Butterworth, Kolmogorov–Zurbenko and an adaptive hybrid filter, then used FFT and periodogram estimation to ask which one reveals seizure structure without inventing it. Every figure below is rebuilt live from the poster's data.

chb01_03 seizure · chb01_04 non-seizure · channel FP1–F7 · RStudio 4.2.1 · kza, TSA, imputeTS
Original poster PDF ↓

Research question

How can EEG filtering reduce noise while preserving the brainwave patterns that distinguish seizure activity from non-seizure activity?

Research approach

Using the CHB-MIT Scalp EEG Database, I compared seizure and non-seizure EEG recordings in R. I applied three filtering methods, Butterworth, Kolmogorov–Zurbenko (KZ), and an adaptive hybrid filter, and then used FFT and periodogram analysis to examine how each method affected the frequency content of the signal. I also calculated relative power across the delta, theta, alpha, beta, and gamma frequency bands to compare seizure and non-seizure activity.

Research Leadership

Working under the mentorship of Dr. Antonios E. Marsellos, I led this research project from raw EEG recordings through analysis and final presentation. Using R, I processed CHB-MIT EEG data, implemented Butterworth, Kolmogorov–Zurbenko, and adaptive hybrid filtering methods, performed FFT and periodogram analyses, calculated frequency-band power, evaluated signal preservation, and created the figures used to present my findings.

EEG preprocessingFilter implementation in RFFT and periodogram analysisFrequency-band power analysisData visualizationPoster development and presentation

Faculty mentor

Dr. Antonios E. Marsellos

Hofstra University

Faculty mentorship and scientific guidance throughout the Hofstra University Summer Science Research Program.

Key findings

The seizure recordings showed a clear shift in frequency-band activity. Delta power increased by 27 percentage points during seizure segments, while alpha power decreased by 11 points. The filtering methods also differed substantially in the amount of noise they removed, with KZ reaching the highest observed noise reduction at 38.1%, compared with 34.0% for the adaptive filter and 16.1% for Butterworth.

Delta power, 0.5–4 Hz

+0 pts

Relative low-frequency power rises consistently in seizure segments, channel after channel.

Alpha power, 8–13 Hz

−0 pts

Alpha is suppressed during seizures: the mirror image of the delta shift.

Peak noise reduction

0.0%

Upper bound for the KZ filter, versus 16.1% for Butterworth and 34.0% adaptive.

Fig. 1–2

Time domain, FP1–F7

Non-seizureSeizure
Raw seizure EEG carries sudden amplitude spikes on top of ordinary-looking noise. Switch to the filtered view to see Butterworth and KZ smoothing pull the underlying rhythm forward.
Fig. 3–4

Spectral density, 0.5–50 Hz

Seizure PSDNon-seizure PSD
FFT and periodogram overlay. Seizure power collapses toward the delta band while non-seizure activity spreads across alpha and beta. Hover a band chip to isolate it.
Fig. 5

Relative band power, seizure vs non-seizure

Non-seizureSeizure, band gainsSeizure, band losses
Delta climbs 27 points and alpha drops 11 during seizure segments. Theta and gamma shift moderately; beta softens. This delta-up / alpha-down signature repeats channel to channel.
Table E2

Absolute band power, log scale

Delta1.59e+8 – 2.30e+9 µV²

Dominant band across recordings

Theta1.31e+6 – 2.39e+8 µV²

Elevated during seizure segments

Alpha1.75e+7 – 5.49e+7 µV²

Moderate presence

Beta2.49e+6 – 2.07e+8 µV²

Reduced after filtering

Gamma2.55e+5 – 1.96e+8 µV²

Mostly suppressed after filtering

Delta dominates every recording by an order of magnitude. Gamma is almost entirely suppressed once filtering is applied, which is exactly the trade-off the AP Research paper set out to quantify.

Conclusion

This research showed me that identifying seizure-related EEG patterns requires more than simply removing as much noise as possible. Seizure activity consistently shifted power toward the lower-frequency delta band while reducing alpha activity, making spectral and band-power analysis especially useful for distinguishing changes in the signal. Filtering helped make those patterns easier to examine, but preserving the underlying EEG structure remained just as important as reducing noise.

Reliable workflow

Filtering combined with spectral and band-power analysis reliably surfaces seizure-related EEG features.

Clinical case

Strengthens the argument for automated detection systems that support neurologists and improve patient safety.

Not a one-off

A reproducible pipeline in R rather than a single hand-tuned result, so anyone can rerun it.

Next

Multi-channel comparisons, entropy-based classifiers, and integration with machine learning.

02 / Research · Hofstra University, 2026

Detecting Arrhythmias Using Heart Rate Variability

Five cardiologist-annotated recordings from the MIT-BIH Arrhythmia Database, RR intervals built straight from expert beat labels, six HRV features over sliding 30-beat windows, and a 400-tree Random Forest. Then the part most papers skip: holding two patients out entirely and reporting what happens.

100train106train119train103held out208held out
360 Hz · MIT-BIHOriginal poster PDF ↓

Research question

Can heart-rate-variability features distinguish arrhythmia from normal rhythm, and will a model that performs well still maintain that performance when tested on patients it has never seen before?

Research approach

I used five cardiologist-annotated recordings from the MIT-BIH Arrhythmia Database and converted the expert beat annotations into RR-interval sequences. From sliding 30-beat windows, I engineered six heart-rate-variability features: RMSSD, SDNN, CVrr, pNN50, mean RR interval, and mean heart rate. I then trained a 400-tree Random Forest classifier and evaluated it in two different ways: a random-window split and a stricter patient-level split in which two patient records were held out entirely from training.

Research Leadership

For this project, I wanted to understand not only whether an arrhythmia classifier could perform well, but whether that performance would hold up on patients it had never seen before. With guidance from Dr. Antonios E. Marsellos, I transformed MIT-BIH ECG annotations into RR-interval series, engineered six heart-rate-variability features across sliding 30-beat windows, implemented a 400-tree Random Forest classifier, and designed the validation strategy.

I then compared patient-level testing with random-window validation and found a substantial difference in performance. That comparison became one of the most important parts of the project because it demonstrated how strongly validation design can influence reported model accuracy.

MIT-BIH data processingHRV feature engineeringRandom Forest implementationValidation designModel evaluationResults interpretationPoster development and presentation

Faculty mentor

Dr. Antonios E. Marsellos

Hofstra University

Faculty mentorship and scientific guidance throughout the Hofstra University Summer Science Research Program.

Key findings

The validation method changed the result dramatically. Random-window validation produced 97.3% accuracy, while testing on patients held entirely out of training produced 79.6% accuracy, a 17.7-point difference using the same model and features. On the patient-level test set, the classifier achieved 100% specificity and 64.6% sensitivity. Feature-importance analysis also showed that measures of beat-to-beat irregularity, especially RMSSD, SDNN, CVrr, and pNN50, contributed far more to the model than mean heart rate or mean RR interval.

The central question

Does the train/test split change reported accuracy? It does, by 17.7 points.

Honest

Subject-level split

Records 103 and 208 held out entirely

0.0%
Naive

Random-window split

Windows from the same patients on both sides

0.0%
0.0

Points of accuracy added by data leakage alone, with the same data, same 400-tree Random Forest, same features. Only the split changed.

Fig. 4

Confusion matrix, subject-level split

Actual normal
Actual arrhythmia
Pred.
normal

71

True normal

34

Missed arrhythmia

Pred.
arrhythmia

0

False alarm

62

Caught arrhythmia

Accuracy

79.6%

Specificity

100%

Sensitivity

64.6%

Trained on records 100, 106 and 119; tested on 167 windows from held-out records 103 and 208. Zero false alarms, but 34 arrhythmia windows slipped through, making this a conservative classifier.
Fig. 2

Averaged beat morphology, record 106

Normal, n=271PVC, n=60
271 normal beats against 60 PVCs, aligned on the R spike. The average PVC is wider, dips deeper, and arrives without the preceding P wave. That shape difference is what the HRV features detect indirectly, through timing.
Fig. 3

RR tachograms

Record 100Record 208
Record 100 holds a steady beat-to-beat rhythm. Record 208 shows the short-long couplet signature: an early PVC followed by a compensatory pause. That irregularity is exactly what RMSSD and SDNN measure.
Fig. 5

Random Forest feature importance

Irregularity featuresRate features
Mean decrease in Gini. The four irregularity features dominate; mean RR interval and mean heart rate contribute almost nothing. The classifier reads beat-timing irregularity, not heart rate.

Conclusion

The most important result was not the higher accuracy. It was understanding why that number was higher. When windows from the same patients appeared in both training and testing, the model reached 97.3% accuracy. When I tested it on patients it had never seen before, performance fell to 79.6%. This showed me that how a model is validated can matter as much as the model itself. Patient-level testing provided a more realistic measure of generalization and exposed limitations that the random-window split had hidden.

Validation design is a result

A 17.7-point gap between honest and naive accuracy means the split matters as much as the model.

Conservative by nature

100% specificity with 64.6% sensitivity: it rarely cries wolf, but it misses some true arrhythmia windows.

Ground truth, not re-detection

Beat positions came from cardiologist annotations rather than a custom R-peak detector.

Next

Scale to more of the 48 MIT-BIH records, benchmark against a neural network, and ship an R Shiny app for real-time classification.

03 / AP Research · 5,478 words

How EEG Filtering Can Alter Seizure Signals

My AP Research paper isolated preprocessing as the only variable and compared Butterworth, KZ and an adaptive hybrid across CHB01–CHB05. The finding that mattered most was uncomfortable: noise reduction percentage is a misleading standalone metric, because a filter can score well while erasing the very spectral peaks that make a seizure identifiable.

Research question

How do Butterworth, Kolmogorov–Zurbenko, and adaptive hybrid filtering methods affect spectral integrity and waveform preservation in seizure and non-seizure EEG, and which approach best balances noise reduction with preservation of meaningful signal features?

Research approach

For my AP Research study, I analyzed EEG recordings from CHB01 through CHB05 and kept the underlying data and analysis framework consistent while changing the preprocessing method. I compared Butterworth, KZ, and adaptive hybrid filtering by measuring noise reduction, signal variability, waveform preservation, and changes in frequency-domain structure. I then examined where each method performed well, where information was lost, and why the adaptive approach behaved differently during high-variance seizure segments.

Independent Research

Building on questions that emerged from my earlier EEG research, I designed an independent AP Research study examining how different filtering methods affect the preservation of clinically meaningful EEG signals.

I analyzed CHB01–CHB05 recordings, implemented and compared three filtering approaches, evaluated spectral integrity and waveform preservation, and investigated why the adaptive filtering method performed poorly during high-variance seizure activity.

The project culminated in a 5,478-word single-author research paper with six appendices documenting my methodology, analysis, findings, and limitations.

Study designEEG preprocessingComparative filter analysisSpectral analysisWaveform evaluationResults interpretationAcademic research writing

Context

I completed this project independently for AP Research, extending questions I had previously explored through Hofstra University's Summer Science Research Program.

Key findings

The filters revealed a clear trade-off between noise reduction and signal preservation. KZ produced the greatest observed noise reduction, ranging from 12.6% to 38.1%, but also reduced high-frequency detail and flattened some peak amplitudes. Butterworth removed less noise, ranging from 6.5% to 16.1%, but preserved more of the original waveform structure. The adaptive method fell between the two, with noise reduction ranging from 9.2% to 34.0%, but it did not eliminate the trade-off.

One of the most important findings appeared in CHB02 seizure segments. Because variance remained elevated throughout the seizure window, the adaptive filter’s variance-based switching mechanism stayed in Butterworth-dominant mode and KZ smoothing largely dropped out. In that setting, high variance was part of the seizure signal itself, making time-domain variance alone a poor way to distinguish meaningful activity from noise.

Table E1

Filter performance ranges, CHB01–CHB05

Floating bars show the observed range per filter. KZ suppresses the most noise but flattens peak amplitudes; Butterworth preserves structure and removes the least; adaptive sits between, without eliminating the trade-off.

Where the adaptive filter broke

In CHB02 seizure segments, variance stayed elevated across the whole window, so the variance-based switch locked into Butterworth-dominant mode and KZ smoothing dropped out entirely. When the pathology is itself high-variance, amplitude statistics cannot separate signal from noise. Future adaptive frameworks need frequency-domain criteria, such as spectral entropy or band-specific power ratios, rather than time-domain variance alone.

Read the full paper (PDF)6 appendices · 5,478 words
Raw

High variability and noise present

Butterworth

Preserves structure, maintains fluctuations

KZ

Moderate smoothing, reduced high-frequency detail

Adaptive

Balanced noise reduction and preservation

Conclusion

My results showed that noise-reduction percentage alone is not enough to judge the quality of an EEG filter. A method can remove more noise while also flattening spectral peaks or waveform features that are part of the signal being studied. The better question is not simply which filter removes the most noise, but which one preserves the information needed for interpretation. The adaptive filter’s behavior during high-variance seizure activity also showed that future adaptive approaches should consider frequency-domain information, rather than relying on signal variance alone.

04 / WHY MEDICINE

Research taught me how much information can be hidden inside a biological signal. Clinical experiences showed me that data is only one part of medicine. During oncology and nephrology shadowing, I observed how physicians connect test results with the experiences of the people sitting in front of them. At RotaCare, Ronald McDonald House, and North Shore University Hospital, I learned how much patients and families depend on clear communication, trust, and the feeling that someone is genuinely listening. I want to become a physician who can understand the evidence behind a medical decision while never losing sight of the person behind the diagnosis.

MENTORSHIP

Guidance that shaped how I approach both research and medicine.

Research Mentor

Antonios E. Marsellos, Ph.D.

Associate Professor, Hofstra University

Research Mentor, Hofstra Summer Science Research Program

Dr. Marsellos mentored my EEG and HRV research through Hofstra University’s Summer Science Research Program, providing scientific guidance, feedback, and direction as I carried out each project.

Clinical Mentor · Oncology

Syed Salman Ali, MD

Board-Certified Hematologist & Medical Oncologist

LMG Cancer & Infusion Center

Shadowing Dr. Ali showed me how careful observation, clear communication, and empathy come together in patient care. Watching how he listened to patients and families shaped the way I think about the kind of physician I hope to become.

Clinical Shadowing · Nephrology

Max Pommier, D.O.

Nephrologist

Suffolk Nephrology Associates

Shadowing Dr. Pommier introduced me to the careful decision-making involved in managing chronic kidney disease. I observed how physicians connect laboratory results, medications, and symptoms with practical recommendations for patients. It showed me that understanding the science is only part of medicine; explaining it clearly matters just as much.

View Physician Profile
05 / Clinical experience

Where the data stops and the person starts

I observed oncology and hematology care with Dr. Syed Salman Ali at the LMG Cancer & Infusion Center and nephrology care with Dr. Max Pommier, D.O., at Suffolk Nephrology Associates. Alongside shadowing, I serve patients and families through a free clinic, Ronald McDonald House, and a hospital.

0+

Patient consultations during oncology observership

0

Oncology shadowing hours

0

Days in oncology clinic

ONCOLOGY OBSERVERSHIP

July 2026 · 5 days · 40 hours

40 hoursDr. Syed Ali · LMG ↗

Clinical Reflections: These reflections focus on lessons from my observership. Patient-specific details have been generalized or omitted to protect privacy.

The moment that stayed with me

During a routine follow-up, Dr. Ali spent several minutes talking with a patient about life outside the clinic before discussing test results. I watched the tension in the room ease.

What it taught me

Making a patient feel heard can change the entire tone of a medical visit.

End-of-week synthesis · the story I would tell

What stayed with me was how Dr. Ali followed the difficult news with a clear plan for what would happen next, giving the patient direction in an overwhelming moment.

Oncology reminded me of my EEG research in one important way: both depend on recognizing subtle signals early, whether it is a change in a brainwave or a shift in blood counts, before the larger problem becomes obvious.

NEPHROLOGY SHADOWING

August 2026 – Present

Dr. Max Pommier, D.O.

Suffolk Nephrology Associates · Bay Shore, NY

I observed outpatient nephrology consultations involving chronic kidney disease, hypertension, diabetes, congestive heart failure, and electrolyte abnormalities. I learned how physicians evaluate laboratory results, medications, blood pressure, and symptoms when making treatment and follow-up decisions, and documented observations about physician-patient communication, medication safety, dietary recommendations, and managing chronic conditions.

Focus Areas

  • Chronic kidney disease and hypertension
  • Interpreting laboratory results and monitoring kidney function
  • Medication management and electrolyte abnormalities
  • Patient education and chronic disease follow-up

Clinical Reflection

During nephrology shadowing, I started paying closer attention to how a physician interprets laboratory results. Potassium levels, kidney function, and blood pressure were important, but Dr. Pommier considered them alongside the patient's symptoms, medications, and daily routines. I began to understand how much careful listening and explanation matter when managing conditions that patients live with every day.

Key takeaway

A laboratory result tells part of the story. Understanding the patient helps a physician decide what comes next.

View Physician Profile

0

Volunteer placements

July 2026 – Current

RotaCare NY Free Clinic

Volunteer · Hempstead, NY

20 hours

  • Free primary and specialty care for uninsured and low-income patients across Nassau County
  • Patient intake, registration and navigation during clinic sessions
  • Working alongside volunteer physicians, nurses and staff in direct patient care

November 2025 – Current

Ronald McDonald House

Volunteer & Youth Ambassador · Queens, NY

80 hours

  • Selected for the 2026–27 Youth Ambassador Program, a year-long leadership cohort
  • Support for families of hospitalized children, front desk and guest check-ins
  • Prepared and served meals, supported fundraising and outreach

November 2024 – July 2025

North Shore University Hospital

Jr. Volunteer · Manhasset, NY

100 hours

  • Greeted patients and visitors, supported front desk check-ins and visitor logs
  • Maintained an organized, welcoming lobby environment

July 2023 – August 2023

National Inventors Hall of Fame

CAMP Invention Student Counselor · Merrick, NY

60 hours

  • Taught students from kindergarten through sixth grade the fundamentals of wiring circuits
  • Assisted in building robots during interactive STEM workshops, promoting hands-on learning and problem solving
  • Strengthened self-awareness and leadership skills through feedback from adult mentors
06 / Outside the lab

Outside the Classroom, Still Learning

Two school teams and a travel season, a student association I co-founded and now lead, and a habit of showing up for things that are not on a transcript.

Akif Khan in a Calhoun baseball uniform standing in the outfield with his glove

Baseball

Sanford H. Calhoun High School

A sport of small margins where preparation shows up only in the moments that count.

Akif Khan in a Calhoun volleyball jersey at the net during a match

Volleyball

Sanford H. Calhoun High School

Reading the play a beat before it happens, the same pattern recognition the research rewards.

Akif Khan at the plate mid at-bat, bat loaded, during a travel baseball game

Travel Baseball

Ghost Travel Baseball

Year-round competition: long seasons, early mornings, and the discipline they demand.

Muslim Student Association

President & Co-Founder

  • Co-founded the chapter, led as Vice President then President
  • Organized "Chai and Chat" on navigating life in America as a Muslim student
  • Coordinated a food drive raising over $400 in donations

Medical Club

Secretary

  • Manage meeting records, communications and event coordination
  • Bake sale fundraisers for cancer research, roughly $300 raised

Project FurGood

Volunteer

  • Helped organize a bake sale raising roughly $500
  • Helped build the non-profit's website to increase outreach

AP exam scores

99.5 weighted GPA

  • AP Seminar5
  • AP English Language5
  • AP World History5
  • AP Biology4
  • AP Physics 14
  • AP US History4
  • AP Research4

Coursework also includes AP Biology, AP Physics 1, Chemistry Honors, Algebra 2 Honors and Foundations of Engineering.

08 / Academic Record

Academic Distinctions

National academic recognition earned through sustained performance across advanced coursework and research.

AP Capstone Diploma

College Board

National Recognition

AP Scholar with Distinction

College Board

National Recognition

College Board National Recognition Award

College Board

National Recognition
07 / The record

Everything, and what came of it

A running log of the labs, clinics, and classrooms, newest first, with the source documents behind each one.

  1. June 2027

    Expected graduation, Sanford H. Calhoun HS

    On track to finish with a 99.5 weighted GPA while carrying a full AP load alongside research and clinic work.

  2. August 2026 – Present

    Nephrology Physician Shadowing: Suffolk Nephrology Associates

    Shadowed Dr. Max Pommier, D.O., observing how physicians interpret kidney function, laboratory results, medications, and patient symptoms to guide treatment decisions and long-term care.

  3. June 2026 – August 2026

    HRV arrhythmia poster presented at Hofstra

    Showed that patient-wise validation drops accuracy 17.7 points versus a naive split, making the case against leakage-inflated results.

  4. July 2026 – Current

    RotaCare NY free clinic volunteer

    Ran intake and navigation for uninsured Nassau County patients, shortening the path from door to physician each session.

  5. July 2026

    Oncology observership, LMG Cancer & Infusion Center

    40 hours over five days with Dr. Syed Ali, observing 35+ consults and translating each into written reflections on how bad news is delivered.

  6. September 2025 – May 2026

    AP Research paper on EEG filtering, 5,478 words

    Quantified the trade-off between noise suppression and signal distortion, documenting where adaptive filters fail on seizure onset.

  7. November 2025 – Current

    Ronald McDonald House volunteer

    Served families of hospitalized children as a Youth Ambassador, running meals and activities that gave parents hours back at bedside.

  8. October 2025

    EEG poster presented; featured in the Hofstra newspaper

    Work reached beyond the symposium. The university newspaper covered the findings on delta/alpha power shifts.

  9. June 2025 – August 2025

    EEG seizure research at Hofstra (HUSSRP)

    Built a full filtering and spectral pipeline from raw EEG, turning a summer program into a reproducible seizure-detection study.

  10. November 2024 – July 2025

    Jr. Volunteer, North Shore University Hospital

    First hospital floor experience: patient transport, supply runs and wayfinding across busy inpatient units.

  11. July 2023 – August 2023

    Camp Invention student counselor

    Led elementary STEM build teams, the first taste of teaching a method rather than just an answer.

Toolkit

R programmingEEG signal analysisFFT & periodogram estimationButterworth & KZ filteringECG arrhythmia detectionHRV feature engineeringRandom Forest classificationSubject-level validationData visualizationScientific poster designPatient communicationCPR & First Aid certified

Source documents

Every figure on this site was rebuilt from these. The originals are here unmodified.

First page preview of EEG seizure poster
EEG seizure posterPDF · 48×36 in↓
First page preview of HRV arrhythmia poster
HRV arrhythmia posterPDF · 48×36 in↓
First page preview of AP Research paper
AP Research paperPDF · 5,478 words↓
First page preview of Résumé
RésuméPDF · 2 pages↓