Kemal Özkırşehirli / Resume

Resume

Education, recognition, research, engineering, teaching, computational skills, languages, interests, and hobbies.

Kemal Özkırşehirli

MIT B.Sc. Candidate — Computer Science and Engineering: AI/ML; Physics: Chemical Physics; Mathematics: Mathematical Biology. Minors in Philosophy and Writing. Cambridge, MA · kemalozk@mit.edu · LinkedIn · GitHub · Google Scholar

I am an international student from İstanbul, Türkiye, pursuing a rare triple major in Computer Science and Engineering: AI/ML, Physics: Chemical Physics, and Mathematics: Mathematical Biology, with minors in Philosophy and Writing. I am on track to graduate one year early, completing all requirements for three majors and two minors in three total years of undergraduate education. I leverage AI/ML frameworks, generative and diffusion models, free-energy methods, atomistic molecular dynamics, Monte Carlo, density-functional-theory simulations, inference, and robust optimization to turn scientific complexity into parsable, compilable, and computable representations and extract their governing rules for critical scientific decision-making.

Education

Massachusetts Institute of Technology — Cambridge, MA B.Sc. expected May 2027. GPA: 5.0/5.0. On track to graduate one year early after completing all requirements for a rare triple major and two minors in three total years of undergraduate education.

  • Majors: Computer Science and Engineering: AI/ML; Physics: Chemical Physics; Mathematics: Mathematical Biology.
  • Minors: Philosophy, Writing.
  • Research interests: biomolecular simulation; free-energy methods; CADD; statistical mechanics and renormalization-group theory; reaction–diffusion chemistry; high-performance computing; generative models; scientific workflow verification; scientific false-success evaluation; DNA–RNA engineering; agentic AI; inference.
  • Computational skillset: Python, C/C++, CUDA, PyTorch, Transformers, GNNs, HPC/GPU programming, Linux, Bash, Git, SLURM/PBS/qsub.
  • Coursework: 6.7960 Deep Learning; 6.5060 Algorithm Engineering; 6.4610 Natural Language Processing; 6.3930 AI and Decision Making in Medicine: From Disease to Therapy; 6.1220[J] Advanced Algorithm Design-Analysis; 18.404[J] Theory of Computation; 18.615 Stochastic Processes; 6.5240 Sublinear Time Algorithms; 6.1910 Computer Structures and Architectures; 6.100B, 6.1903, and related Python/C/C++/Assembly programming labs; 18.600 Probability Theory and Random Variables; 18.06 Linear Algebra; 5.43 Advanced Organic Chemistry; 6.1200[J] Discrete Mathematics.
  • Humanitas Core: 24.245 Theory of Models; 21W.747 Rhetoric; 21W.745 Advanced Essay Workshop; 21W.762 Poetry Workshop; 24.251 Philosophy of Language; 24.05 Philosophy of Religion; 17.01[J]/24.04[J] Justice; 24.121 Metaphysics; 21W.738[J] Memoir.

Activities

  • AI@MIT.
  • Schwarzman College of Computing Advisor.
  • MIT BioMakers.
  • MIT Student Council Representative.
  • ClubChem.
  • MIT Gala Model/Designer.

Recognition

  • Medalist in the 56th International Chemistry Olympiad, representing Türkiye; ranked 105th internationally among 327 participants.
  • Medalist in the 58th International Mendeleev Chemistry Olympiad, representing Türkiye; ranked 73rd internationally among 151 participants.
  • Silver Medal in the TÜBİTAK National Chemistry Olympiad; ranked 3rd nationally among 62 finalists and 2,500+ students to represent Türkiye.
  • Gold Medal in the Istanbul Science Olympiads 2022 in Chemistry; ranked first in Istanbul among 50 finalists.
  • Columbia Science Research Fellow Named Scholar; awarded a full-ride early with a Likely Letter in recognition of scientific accomplishment and talent.
  • World Science Scholar, 2023 cohort; selected among 52 scholars from 22 countries for exceptional mathematical talent and as the fourth Turkish scholar in program history.
  • AIME qualification and AMC Hall of Fame acknowledgement; 7th place in Türkiye and a top-5% AMC 12 score among 300,000+ participants.
  • Selected to attend AlgoTrade Hackathon 2026 with free travel and accommodation from 1,000+ applicants across 51 countries and 148 universities.
  • Transferred to MIT from Columbia as one of 22 selected from 764 applicants, a 2.5% acceptance rate.
  • CHEM13 Certificate of Distinction, University of Waterloo International Exam; ranked 2nd among 1,551 participants.
  • Certificate of Distinction, Galois International Mathematics Contest; ranked 7th among 3,071 international students and 20th globally among 6,595.
  • Book Prize, Sir Isaac Newton International Physics Contest, University of Waterloo; ranked in the top 50 globally among 2,000+ participants.

Experiences

Principal Investigator — Order-Agnostic MeshAnyOrder for Life Sciences

Özkırşehirli Group — independent 7-member research collaboration with a Google-affiliated research lead — Apr 2026–Present

  • Leading MeshAnyOrder, an order-agnostic autoregressive transformer for point-cloud-conditioned 3D mesh generation that represents faces as quantized tokens and predicts unvisited adjacent faces from arbitrary traversal seeds.
  • Extending the architecture with 3D rotary positional encoding for translation-invariant attention, heterogeneous triangle/quad tokenization, topology-aware validity constraints, frontier-parallel decoding, and local mesh completion/remeshing.
  • Designing publication-grade ablations across random, axis-based, BFS/DFS traversal and causal, adjacency-aware, and bidirectional masking; evaluating reconstruction quality, manifoldness, watertightness, inference latency, memory consumption, and high-resolution scaling against leading autoregressive and diffusion-based mesh generators.

Principal Investigator — Özkırşehirli Group; Team Leader, TBXT Small-Molecule Hackathon

MIT and onepot — Apr 2026–Present

  • Leading an 11-person chordoma-focused TBXT/brachyury computational hit-identification project targeting PDB 6F59 chain A / TBXT G177D site F; compressed 2,274 prior-art compounds plus 737 raw analogs into 503 filtered analogs, 30,000 BRICS recombinations, 67 novel QSAR-pass proposals, and a 570-compound novelty-filtered pool using site-F/A/G grids, Tanimoto novelty control, and sourceability-aware generation.
  • Integrated a multi-orthogonal AI/CADD stack: Vina ensemble docking, GNINA CNN pose/pKd scoring, Vina-trap detection, RF/XGBoost TBXT QSAR, Boltz-2 co-folding, MMGBSA/FEP scaffolding, T-box paralog selectivity, Rowan IC50/affinity analysis, RDKit descriptors/BRICS, onepot/muni catalog checks, and Bash/HPC automation. Trained QSAR on 650 RDKit-valid SPR-derived compounds from 14 decrypted XLSX files, 15 campaigns, and 1,620 Kd fits, achieving Spearman ρ ≈ 0.49 and MAE ≈ 0.5 pKd; GNINA screened 569 of 570 candidates and flagged 40 Tier-A, 51 Tier-B, and 73 Vina-trap candidates.
  • Prioritized a final funnel of 570 → 137 strict-pass → 24 submission-ready → 4 judge-facing site-F picks under exact-match, non-covalent chemistry, PAINS/forbidden-motif, lead-likeness, ESOL/logS, Tanimoto, cost, chemistry, supplier-risk, and 16-paralog selectivity gates. The final four showed Boltz Kd values of 3.2–8.8 µM, Jack/SCC agreement of 1.01–1.34×, GNINA Vina scores of −5.01 to −6.19, pKd values of 3.94–4.69, and Rowan IC50-style predictions of 1.82–6.11 µM.

PRISM AI Safety Fellow — Protein Foundation Model Red-Teaming and Evaluation

PRISM, W2D2, Siemens — May 2026–Present

  • Selected from 800+ applicants for PRISM 2026 to develop adversarial evaluation methods for protein foundation models, testing when biologically plausible outputs fail sequence–structure–function constraints, uncertainty calibration, and protein-design reliability checks.
  • Creating methods to evaluate high-stakes protein-design reliability across biological plausibility, structural consistency, developability, uncertainty calibration, claimed mechanisms, and failure behavior.
  • Building a safety-bounded benchmark and failure taxonomies for next-generation protein models using prior work in EVEdesign, A*STAR V2M, antibody/protein models, CADD, and uncertainty-aware candidate ranking.

Summer UROP Scholar — ChromoGen-Engine V2

MIT Chemistry, Computational Systems Biology; Prof. Zhang — May 2026–Present

  • Developing ChromoGen-Engine V2 as a conditioned 3D genome generation and evaluation pipeline for single-cell chromatin coordinate ensembles under sequence, regulatory-state, and chemical/regulatory perturbation conditions.
  • Building preprocessing and conditioning machinery for 3D structural data, sequence windows, perturbation labels, regulatory readouts, metadata, leakage-aware splits, and ablation controls.
  • Designing a benchmark that compares conditional generation with sequence-only, shuffled-label, and ablated baselines using contact-map agreement, P(s) decay, radius of gyration, ensemble diversity, condition separation, calibration, leakage checks, failure-mode analysis, and interpretable attribution.

Summer AI/ML Researcher — Autoimmune Target Discovery

*Experimental Drug Development Centre, ASTAR** — May 2026–Present

  • Building V2M-Engine as a provenance-first, leakage-controlled variant-to-mechanism evidence graph for autoimmune/IBD target discovery, linking disease, locus, QTL/colocalization evidence, effector genes, immune cell contexts, mechanisms, and target-control tiers.
  • Developing source-pinned manifests, normalized schemas, curation logs, graph exports, target-control evaluation tiers, and transparent classical baselines before adding single-cell foundation-model or sequence-effect layers.
  • Defining downstream extensions for Perturb-seq validation, Evo 2/EVEE-style sequence-effect scoring, single-cell foundation-model ablations, and targetability modules after the inspectable evidence graph is stable.

Head of Chemistry Research-Implementation; Backend Software Engineer for LangGraph AI Agent Pipelines

Pedal AI — Jul–Dec 2025

  • Designed Python/LangGraph backend architecture and FastAPI interfaces connecting stateful chemistry agents with molecular-prediction engines, including persistent execution state, custom tool orchestration, scalable control flow, and model/data evaluation workflows.
  • Built chemist- and researcher-facing UI/UX for Pedal AI's Agentic Chemistry Platform, incorporating Coley Lab retrosynthesis models with ASKCOS-style route search, inspectable retrosynthesis trees, reaction-level provenance, and decision-ready synthesis workflows.

VeriQSM + QSMBench — Scientific Workflow Verification

Columbia University — Dec 2025–Present

  • Developing VeriQSM, a verification-first scientific-agent framework that converts scientific intent into typed, auditable workflows for quantum chemistry and statistical mechanics while separating nominal completion from scientifically verified success and false success.
  • Building typed workflow IR, static validators, physics verifiers, reference-style numerical checks, provenance ledgers, and bounded repair/refusal policies across PySCF-compatible electronic-structure tasks and trajectory/statistical-mechanics analyses.
  • Focusing the project on whether explicit physical verification and recovery policies make scientific agents more reliable than constrained planning alone. GitHub repository.

Project Co-Lead — Deep Reinforcement Learning for Antibody–Antigen Interactions

AI@MIT / AIM Labs — Jan 2026–Present

  • Co-led a six-person research effort and developed a risk-constrained, structure-guided antibody sequence optimization scaffold combining ESM-2-compatible antigen embeddings, structure-informed cross-attention decoding of antibody CDR sequences, and OAS/SAbDab/IEDB-style data curation.
  • Implemented supervised warm starts and selective PPO with clipped policy loss, value learning, reference-policy KL, uncertainty penalties, abstention, conservative developability screens, and audit-ready evidence-lineage controls.
  • Structured evaluation around contamination-aware splits, matched-budget baselines, reward-hacking checks, candidate-panel blinding, and reproducible run manifests. GitHub repository.

Protein Design Algorithm Engineer — EVEdesign Collaborative Project

Harvard Medical School; Prof. Marks — Apr 2026–Present

  • Working on EVEdesign's open-source, method-independent protein-design platform as part of a 21-person, 18-institution, 8-country collaboration, supporting composable multi-objective antibody/protein design across evolutionary, structural, developability, and experimental-feedback constraints.
  • Developing uncertainty-aware candidate selection, sequence–structure–function objective integration, protein-language-model/evolutionary-prior scoring, and agent-based tool orchestration for scalable lab-in-the-loop biosequence design.

Additional Experiences

AI/ML for Statistical Mechanics Simulations Researcher and Teaching Fellow

MIT Physics · Prof. A. Nihat Berker — Jan 2022–Present

  • Developing Kadanoff-GNN-RG, a machine-learning-augmented renormalization-group framework using symmetry-adapted coarse-graining, direct continuum RG operators, finite-spin distribution comparisons, typed-edge graph neural networks, and calibrated phase classification for spin systems.
  • Using empirical RG-flow reconstruction, Wasserstein/MMD-style distribution distances, uncertainty-aware phase topology, and conventional observables to study finite-size, sampler-dependent, and representation-dependent phase-boundary estimates.
  • Mentored 200+ students regarding college applications, decisions, and career paths while teaching condensed Augmented Chemistry and Classical/Quantum Mechanics courses and grading daily problem sets and exams.

Kupcinet-Getz Scholar in Computational Biochemical Diffusion

Weizmann Institute of Science — Jun–Aug 2025

  • Selected as one of 22 scholars worldwide with an acceptance rate below 10%; developed stochastic and learned models for nonlinear biochemical reaction–diffusion systems, including Euler–Maruyama, Runge–Kutta, Gillespie SSA, PINN, neural-ODE, GNN, transformer, and trajectory-clustering components.
  • Expanded the direction into Kupcinet–Getz Reaction–Diffusion AI, a solver-faithful scientific-computing framework spanning nonlinear chemical oscillators, traveling waves, and synthetic morphogenesis with deterministic, chemical-Langevin, Gillespie, and spatial-RDME solvers.
  • Added convergence audits, solver-agreement checks, calibration, abstention, matched-compute evaluation, and reproducibility manifests to study when numerical representation and intrinsic reaction noise change inferred morphology or phase behavior.

Chemistry and AI Consultant and Scientific Data and Evaluation Specialist

Sepal AI, Mercor — Sept 2024–Feb 2025

  • Developed model-training, testing, and evaluation data for LLM workflows that convert graduate-level retrosynthesis, reaction-mechanism, and method-selection problems into tasks that can be checked against known solutions; built grading rubrics and chemical-validity constraints.
  • Developed instructional data, prompt-engineering workflows, error analysis, decomposition, and failure-mode annotation for advanced chemistry reasoning, converting the work into instruction/response, evaluation/grading, and feedback formats for training and benchmarking.

Foundation Research Scholar in Computational Organic Chemistry

Lumiere Research Inclusion Foundation — Aug 2023–Feb 2024

  • Engineered a multiscale computational-organic-chemistry workflow linking density-functional-theory transition-state thermochemistry to stochastic kinetic Monte Carlo across solvent pathways; the historical project summary reports screening 20+ transition states and 500+ trajectories across five solvent pathways with a 22% predicted yield optimization.
  • Expanded the direction into a mechanism-aware DFT → kMC implementation scaffold that parses Gaussian/ORCA thermochemistry, checks stationary points and frequencies, converts activation free energies to Eyring rates, compiles exact stochastic propensities, and runs deterministic ODE, exact CME, and Gillespie/SSA kinetic analyses.
  • Uses uncertainty-aware pathway and solvent ranking, provenance, and report generation to ask whether mechanistic conclusions remain stable under declared energetic, mechanistic, and model-form uncertainty.

Advanced Organic Synthesis Researcher

MIT Chemistry; Prof. Stephen L. Buchwald — Dec 2025–May 2026

  • Supported development of a CuH catalyst for selective asymmetric methylation and alkylation of vinyl boronate esters; used 1H-NMR and chiral-HPLC to relate catalyst structure to activity, expanded substrate scope, and scaled synthesis from 0.1 mmol to multi-gram quantities.

Teaching Fellow and Grader for 6.1200[J]

MIT EECS — Discrete Mathematics / Mathematics for Computer Science — Jan 2026–Present

  • Selected after receiving an A+ and ranking in the top 1% of MIT EECS's largest foundational theoretical-computer-science course; grades proof-intensive assignments in logic, graph theory, recurrence relations, asymptotic analysis, and cryptography.
  • Supports a 250+ student cohort through recitations, office hours, midterms, and finals.

Personal

  • Computer-Aided Drug Discovery skillset: molecular docking; virtual screening; MMGBSA/FEP/free-energy methods; AutoDock Vina; GNINA CNN scoring; Boltz-2 protein–ligand co-folding; QSAR with RF/XGBoost; ADMET/Rowan; Morgan fingerprints; Tanimoto/PAINS/ESOL filtering; BRICS/generative chemistry; onepot/muni catalog screening; PyMOL.
  • Additional computational skillset: distributed training; Conda; NumPy; molecular dynamics; OpenMM; OpenFF; OpenFE; MDTraj; ParmEd; PDBFixer; RDKit; SMILES/SDF/PDB/PDBQT; PySCF; SciML; LLM embeddings; sequence modeling; PPO; SQL; Java; MATLAB; LaTeX.
  • Languages: English native; Turkish native; German professional; Spanish beginner; Hebrew beginner.
  • Interests: philosophy of science, epistemology, logic, metaphysics, psychology, language, history, aesthetics, ethics, politics, power, biopolitics, sociocultural anthropology, Foucauldian studies, love, Judaism, Islam, and queer, postmodern, psychoanalytic, post-structuralist, race, and feminist critical theories.
  • Hobbies: modelling, fashion, essay writing, volleyball, competitive Pokémon, figure skating, chess, poker, meditative and labyrinth walking, and FNAF.