Our Team
TakaHuman has gathered an elite team of scientists, engineers, and operators — spanning academia and industry across 4 continents. The team brings together world-class expertise in quantum AI, computational chemistry, knowledge graphs, drug discovery, laboratory automation, robotic systems, photovoltaics, computer vision, systems pharmacology, and software engineering.
Our scientists and engineers hold PhDs and senior positions from Yale, Cambridge, Caltech, IIT Delhi, IIT Kharagpur, University of Stuttgart, Michigan State University, University of Southern California, and UCSF. They have held leadership and senior roles at Amazon, GSK, Bristol-Myers Squibb, Plug Power, Tandem PV, Intel, Toyota Research Institute, KLA, POLARISqb, Astellas, and Microsoft.
Dan Takahashi
Founder & CEO
Dan graduated from Cornell University in approximately 3 years with
Magna Cum Laude honours. He previously founded
MNS International (a hedge fund) and PostPrime (a social media
platform that achieved an IPO within ~2.5 years on the Tokyo Stock Exchange).
He has lived in 7 countries and travelled to 62, and became a social media influencer almost
by accident — despite not being a fluent Japanese speaker — building over
1 million followers and appearing occasionally on national television in
Japan for interviews. He spent 1.5 years rigorously testing business ideas before committing
to TakaHuman — the path he identified as the highest-value contribution he could make to humanity.
Shayoni Dutta
Data & Semantic Science Lead · Knowledge Graph Specialist
Shayoni is a quantitative, semantic modeling, and data science leader with
14 years of experience (10+ years post-PhD) across pharma, biotech, and
healthcare analytics. She holds a PhD in Artificial Intelligence from IIT Delhi
and most recently served as Data Science Manager at GSK.
She is a specialist in knowledge graph architecture — lead architect of knowledge graph–driven and agentic AI frameworks integrating RDF knowledge graphs, causal inference, and LLM systems for decision intelligence. Her expertise further spans pharmacometrics, QSP/PK-PD/DMPK/ADME modelling, multiomics integration, Bayesian modelling, Monte Carlo simulation, reinforcement learning, and graph AI — applied across drug discovery, oncology, and enterprise-scale operational intelligence.
Shayoni has 8 published scientific papers, is a Stanford WiDS Regional Ambassador, an NVIDIA Deep Learning Institute certified practitioner, and a Sakura Fellow (Japan-Asia Youth Exchange Program in Science).
She is a specialist in knowledge graph architecture — lead architect of knowledge graph–driven and agentic AI frameworks integrating RDF knowledge graphs, causal inference, and LLM systems for decision intelligence. Her expertise further spans pharmacometrics, QSP/PK-PD/DMPK/ADME modelling, multiomics integration, Bayesian modelling, Monte Carlo simulation, reinforcement learning, and graph AI — applied across drug discovery, oncology, and enterprise-scale operational intelligence.
Shayoni has 8 published scientific papers, is a Stanford WiDS Regional Ambassador, an NVIDIA Deep Learning Institute certified practitioner, and a Sakura Fellow (Japan-Asia Youth Exchange Program in Science).
Sr. Director of Research Automation
Pharma Lab Automation · Robotics · Drug Discovery Operations
A research automation leader with 20+ years of experience in the
pharmaceuticals industry, spanning large molecules, B cell line development, NGS, molecular
rescue, genotyping, protein chemistry, CRISPR, cell line development, and drug discovery.
Expert in building labs with lean automation and turnkey systems for both large and small molecule workflows, with skills in Verification & Validation (V&V), Good Laboratory Practice (GLP), robot fleet management, change control, and oncology. This team member has chosen to keep their personal details private at this time.
Expert in building labs with lean automation and turnkey systems for both large and small molecule workflows, with skills in Verification & Validation (V&V), Good Laboratory Practice (GLP), robot fleet management, change control, and oncology. This team member has chosen to keep their personal details private at this time.
B. Maurice Benson
Principal Software Engineer · Quantum Computing & AI
Maurice is a senior software engineer with 17+ years of experience,
including 10+ years in AI-driven solutions and 5 years in quantum
computing. He holds an MS in Computer Science from the
University of North Carolina Wilmington, where his thesis paper won the
2010 SE DSI Best Article Award.
He currently serves as Principal Software Engineer at POLARISqb, where he collaborates with industry leaders to revolutionise preclinical drug discovery — reducing lead times from 5 years to just 4 months by leveraging D-Wave quantum annealers, Google Cloud, and AI-driven quantum optimisation pipelines to accelerate drug blueprint generation at scale.
Previously at Fearless (Baltimore) as Technical Lead and Senior Software Developer, he led machine learning and NLP projects across distributed computing platforms, designed containerised NLP pipelines for large-scale unstructured data processing, and collaborated with interdisciplinary teams of scientists and supply chain analysts. He is expert in Python, Docker, GCP, CI/CD, vector databases, MLOps, and quantum optimisation, and is recognised as a thought leader in quantum computing and AI with multiple publications, presentations, and awards.
He currently serves as Principal Software Engineer at POLARISqb, where he collaborates with industry leaders to revolutionise preclinical drug discovery — reducing lead times from 5 years to just 4 months by leveraging D-Wave quantum annealers, Google Cloud, and AI-driven quantum optimisation pipelines to accelerate drug blueprint generation at scale.
Previously at Fearless (Baltimore) as Technical Lead and Senior Software Developer, he led machine learning and NLP projects across distributed computing platforms, designed containerised NLP pipelines for large-scale unstructured data processing, and collaborated with interdisciplinary teams of scientists and supply chain analysts. He is expert in Python, Docker, GCP, CI/CD, vector databases, MLOps, and quantum optimisation, and is recognised as a thought leader in quantum computing and AI with multiple publications, presentations, and awards.
Heather Quigley
Head of Data Sales · Strategic Partnerships & Multi-Omics
Heather is a business development and strategic partnerships leader with
a decade across genomics, molecular diagnostics, and multi-omics.
She originates and closes population-scale data partnerships, pairing deep scientific
fluency with AI and data-platform skills to position discovery workflows for clinical
and translational markets.
She currently serves as Director of Strategic Partnerships at Panome Bio, where she drives go-to-market strategy for a mass-spectrometry multi-omics platform and has built and manages an $8M+ pipeline of population-scale data partnerships with academic medical centres, NIH-funded cohorts, global biobanks, and pharma/biotech organisations. She designed Panome Bio’s data-marketplace partnership strategy and negotiated a term sheet structuring a zero-custody, controlled-access licensing model — de-identification to HIPAA Safe Harbor / GA4GH standards, role-based access, audit traceability, and IP protections — enabling third-party evaluation and licensing of multi-omics datasets without raw data transfer.
Previously she was Business Development Executive for Population Health at Metabolon, closing multi-million-dollar contracts with international multi-omics consortia, and Business Development Manager for Archer NGS at Invitae, selling custom NGS oncology panels to clinical diagnostic labs and launching a minimal residual disease liquid-biopsy IVD. Earlier roles at Bionano Genomics and Thermo Fisher Scientific spanned regional business management, corporate communications, and DNA synthesis. She presents at AACR, ASHG, and HLTH, and holds a BS in Neurobiology, Physiology & Behavior from UC Davis.
She currently serves as Director of Strategic Partnerships at Panome Bio, where she drives go-to-market strategy for a mass-spectrometry multi-omics platform and has built and manages an $8M+ pipeline of population-scale data partnerships with academic medical centres, NIH-funded cohorts, global biobanks, and pharma/biotech organisations. She designed Panome Bio’s data-marketplace partnership strategy and negotiated a term sheet structuring a zero-custody, controlled-access licensing model — de-identification to HIPAA Safe Harbor / GA4GH standards, role-based access, audit traceability, and IP protections — enabling third-party evaluation and licensing of multi-omics datasets without raw data transfer.
Previously she was Business Development Executive for Population Health at Metabolon, closing multi-million-dollar contracts with international multi-omics consortia, and Business Development Manager for Archer NGS at Invitae, selling custom NGS oncology panels to clinical diagnostic labs and launching a minimal residual disease liquid-biopsy IVD. Earlier roles at Bionano Genomics and Thermo Fisher Scientific spanned regional business management, corporate communications, and DNA synthesis. She presents at AACR, ASHG, and HLTH, and holds a BS in Neurobiology, Physiology & Behavior from UC Davis.
Tridip Das
Computational Material Scientist · Staff Scientist, Caltech
Tridip is a computational material scientist at Caltech developing new
materials from first principles. He holds a PhD in Chemical Engineering from
Michigan State University (GPA 3.71) and an M.Tech from IIT Kharagpur
(GPA 9.70/10).
His expertise spans DFT, molecular dynamics, thermodynamics modelling, and ML applied to materials — batteries, semiconductors, catalysts, and CO2 capture. He previously worked as Process Engineer at Intel Corporation and Co-op Research Scientist at the Toyota Research Institute. He has 16 published papers including in Nature Communications, supported by a $694K DOE grant. He holds a Six Sigma Black Belt.
His expertise spans DFT, molecular dynamics, thermodynamics modelling, and ML applied to materials — batteries, semiconductors, catalysts, and CO2 capture. He previously worked as Process Engineer at Intel Corporation and Co-op Research Scientist at the Toyota Research Institute. He has 16 published papers including in Nature Communications, supported by a $694K DOE grant. He holds a Six Sigma Black Belt.
Stephen A. Adeoye, Ph.D.
Thermal Packaging Engineer · Semiconductor Packaging & Electronics Cooling
Stephen is a thermal packaging engineer with a Ph.D. in Mechanical Engineering and 8+ years of
combined professional and academic experience in semiconductor packaging, advanced package
thermal management, electronics cooling, and system-level thermal architecture. He currently
works at Intel Corporation as a Thermal Packaging Engineer on Optical & Advanced Packaging
Systems, owning end-to-end Gen 2 optical packaging development supporting heterogeneous
integration and chiplet-based systems for high-volume manufacturing.
At Intel, he has reduced insertion loss by 30% through package redesign and process optimisation, cut cure-temperature error by ~30% through CFD/FEA simulation, and reduced package delamination risk by 50% through thermal cycling and reliability qualification. He holds a Ph.D. in Mechanical Engineering (ThermoFluids | Heat and Mass Transfer) from the University of Central Florida, where his doctoral research included designing a high-pressure, high-temperature supercritical CO₂ test rig for microelectronics thermal management and proposing a new correlation for area-averaged Nusselt number. He previously interned at Rivian Automotive on HVAC and defog/defrost thermal control models. Stephen is skilled in CFD/FEA simulation (ANSYS Fluent, Icepak, COMSOL), thermal interface engineering, and manufacturing scale-up.
At Intel, he has reduced insertion loss by 30% through package redesign and process optimisation, cut cure-temperature error by ~30% through CFD/FEA simulation, and reduced package delamination risk by 50% through thermal cycling and reliability qualification. He holds a Ph.D. in Mechanical Engineering (ThermoFluids | Heat and Mass Transfer) from the University of Central Florida, where his doctoral research included designing a high-pressure, high-temperature supercritical CO₂ test rig for microelectronics thermal management and proposing a new correlation for area-averaged Nusselt number. He previously interned at Rivian Automotive on HVAC and defog/defrost thermal control models. Stephen is skilled in CFD/FEA simulation (ANSYS Fluent, Icepak, COMSOL), thermal interface engineering, and manufacturing scale-up.
Mamdudur R.
Mechanical Engineer · Structural Dynamics & Advanced Manufacturing
Mamdudur is a mechanical engineer and PhD candidate in Mechanical Engineering at the
University of South Carolina, specialising in structural dynamics, solid
mechanics, phononics, and advanced manufacturing. His research focuses on mechanical
simulation, dynamic behaviour of engineered structures, and advanced materials systems,
with an emphasis on connecting computational modelling to practical engineering applications.
Before his doctoral studies, he worked as a Mechanical Design Engineer at CAE SoftSys, Inc. (Texas), leading mechatronic design projects across aerospace, automotive, and HVAC sectors — including upgrading the Boeing 737 Landing Gear Door Retract System from a mechanical mechanism to a mechatronic control system in CATIA V5, and delivering a $100K project that improved operational efficiency by 50%. He also conducted research at the University of Texas at Arlington Research Institute (UTARI), contributing to the development of SLA 3D-printed coaxial nozzles for piezoelectric nanofibre manufacturing, while pursuing a BSc in Mechanical Engineering with a Nuclear Engineering minor. Mamdudur is proficient in SOLIDWORKS, CATIA, ANSYS, ABAQUS, Python, and MATLAB, and holds Dassault Systèmes certifications in CATIA, Aerospace & Defense, Automotive, and Industrial Equipment.
Before his doctoral studies, he worked as a Mechanical Design Engineer at CAE SoftSys, Inc. (Texas), leading mechatronic design projects across aerospace, automotive, and HVAC sectors — including upgrading the Boeing 737 Landing Gear Door Retract System from a mechanical mechanism to a mechatronic control system in CATIA V5, and delivering a $100K project that improved operational efficiency by 50%. He also conducted research at the University of Texas at Arlington Research Institute (UTARI), contributing to the development of SLA 3D-printed coaxial nozzles for piezoelectric nanofibre manufacturing, while pursuing a BSc in Mechanical Engineering with a Nuclear Engineering minor. Mamdudur is proficient in SOLIDWORKS, CATIA, ANSYS, ABAQUS, Python, and MATLAB, and holds Dassault Systèmes certifications in CATIA, Aerospace & Defense, Automotive, and Industrial Equipment.
Principal Scientist · Drug Discovery & Screening
High-Throughput Screening · Biochemistry · Translational Research
A discovery team leader and screening scientist with over two decades of industry and
institutional experience in drug discovery, translational research, and core facility
administration. Holds a PhD in Biochemistry from the University of Cambridge and an MSc in
Biological Research Methods from the University of Exeter.
Previously spent nearly 12 years at a leading US academic medical centre as Academic Administrator and Senior Biologist, coordinating more than 20 national and international academic screening projects — with one programme spawning a venture-funded start-up — and leading 6 industry partnerships in high-throughput screening, high-content image analysis, and biophysical fragment screening. Earlier, served as Senior Scientist at a drug discovery biotech and as a Postdoctoral Research Fellow at a leading US university (School of Medicine). Currently serving as Principal Scientist at an early-stage biotech. This team member has chosen to keep their personal details private at this time.
Previously spent nearly 12 years at a leading US academic medical centre as Academic Administrator and Senior Biologist, coordinating more than 20 national and international academic screening projects — with one programme spawning a venture-funded start-up — and leading 6 industry partnerships in high-throughput screening, high-content image analysis, and biophysical fragment screening. Earlier, served as Senior Scientist at a drug discovery biotech and as a Postdoctoral Research Fellow at a leading US university (School of Medicine). Currently serving as Principal Scientist at an early-stage biotech. This team member has chosen to keep their personal details private at this time.
Joseph Kangas
Neural ODE & PINNs Specialist · Computational Biophysics · University of Minnesota
Joseph is a senior research faculty member at the University of Minnesota
with a PhD in Mechanical Engineering and more than
10 years of experience developing computational and mathematical models
for biological systems.
His work focuses on the development and application of PDEs, ODEs, Neural ODEs, and Physics-Informed Neural Networks (PINNs) to problems in crystallisation, glass formation, biotransport, cellular toxicity, biostabilisation, and cryopreservation. He also develops multiphysics models, optimisation methods, and data-inversion techniques that connect mathematical theory with experiments in physical and biological systems.
His work focuses on the development and application of PDEs, ODEs, Neural ODEs, and Physics-Informed Neural Networks (PINNs) to problems in crystallisation, glass formation, biotransport, cellular toxicity, biostabilisation, and cryopreservation. He also develops multiphysics models, optimisation methods, and data-inversion techniques that connect mathematical theory with experiments in physical and biological systems.
Basel Mansour
Computational & Medicinal Chemist · Drug Design · ADMET · Free Energy Perturbation
Basel is a computational and medicinal chemist with dual doctoral training — a PhD in Computational & Medicinal Chemistry and a PharmD — combining deep molecular science with frontline clinical pharmacology. With 10+ years of experience spanning pharmaceutical research, hospital practice, and biotech, he operates at the rare intersection of bench, computation, and AI.
His core expertise spans the full modern drug design toolkit: from structure-based drug design (SBDD) using receptor crystal structures, molecular docking, and binding site analysis, to ligand-based drug design (LBDD) through pharmacophore modelling, shape similarity, and SAR-driven optimisation. He carries this through reaction enumeration and combinatorial library generation, enabling rapid chemical space exploration, all the way to free energy perturbation (FEP) for high-accuracy binding affinity prediction, applying both physics-based and non-physics-based computational experiments.
What distinguishes Basel is his ability to work fluidly alongside medicinal chemists — translating computational insights into actionable chemistry decisions and, equally, grounding computational models in the synthetic and pharmacological realities that medicinal chemists navigate daily. He speaks both languages: the mathematics of molecular simulation and the intuition of structure–activity relationships. This fluency allows him to serve as a true bridge between computational predictions and experimental design, shortening the feedback loop between virtual hypotheses and real-world chemistry outcomes.
Basel has authored 7 publications (5 peer-reviewed, 2 in submission), with contributions spanning enzyme mechanisms, ADMET prediction, and computational lead optimisation. He believes the future of medicine is built at the boundary of rigorous science and intelligent automation — and he is building it.
His core expertise spans the full modern drug design toolkit: from structure-based drug design (SBDD) using receptor crystal structures, molecular docking, and binding site analysis, to ligand-based drug design (LBDD) through pharmacophore modelling, shape similarity, and SAR-driven optimisation. He carries this through reaction enumeration and combinatorial library generation, enabling rapid chemical space exploration, all the way to free energy perturbation (FEP) for high-accuracy binding affinity prediction, applying both physics-based and non-physics-based computational experiments.
What distinguishes Basel is his ability to work fluidly alongside medicinal chemists — translating computational insights into actionable chemistry decisions and, equally, grounding computational models in the synthetic and pharmacological realities that medicinal chemists navigate daily. He speaks both languages: the mathematics of molecular simulation and the intuition of structure–activity relationships. This fluency allows him to serve as a true bridge between computational predictions and experimental design, shortening the feedback loop between virtual hypotheses and real-world chemistry outcomes.
Basel has authored 7 publications (5 peer-reviewed, 2 in submission), with contributions spanning enzyme mechanisms, ADMET prediction, and computational lead optimisation. He believes the future of medicine is built at the boundary of rigorous science and intelligent automation — and he is building it.
Kuei-Ling T.
Cell Biology Scientist · Organoids, CRISPR & Functional Genomics
Kuei-Ling is a cell biology scientist with 10+ years across academia and
industry in organoid culture, CRISPR engineering, and next-generation sequencing.
She holds a PhD and an MS in Biological & Environmental Engineering from
Cornell University, and is Bionano-certified in Optical Genome Mapping.
She served as Scientist II at BlueRock Therapeutics (Cambridge, MA), where she led CRISPR off-target analysis by NGS, and previously as Research Scientist at Bayer in the San Francisco Bay Area.
As a Visiting Scholar at Duke University she developed and ran high-throughput drug screening of 3D patient-derived cancer organoids to identify therapeutic targets for personalised therapy, performed ATAC-seq and RNA-seq for integrative epigenomic and transcriptomic profiling of intestinal stem cells and patient-derived colorectal cancer cells, and carried out genome-wide dCas9 screening in intestinal organoids. Her wet-lab range spans lentiviral shRNA and CRISPR knockdown, flow cytometry, qPCR, Western blot, IP, ELISA, IHC, FISH, and confocal imaging. She has mentored graduate students and managed teams of research associates.
She served as Scientist II at BlueRock Therapeutics (Cambridge, MA), where she led CRISPR off-target analysis by NGS, and previously as Research Scientist at Bayer in the San Francisco Bay Area.
As a Visiting Scholar at Duke University she developed and ran high-throughput drug screening of 3D patient-derived cancer organoids to identify therapeutic targets for personalised therapy, performed ATAC-seq and RNA-seq for integrative epigenomic and transcriptomic profiling of intestinal stem cells and patient-derived colorectal cancer cells, and carried out genome-wide dCas9 screening in intestinal organoids. Her wet-lab range spans lentiviral shRNA and CRISPR knockdown, flow cytometry, qPCR, Western blot, IP, ELISA, IHC, FISH, and confocal imaging. She has mentored graduate students and managed teams of research associates.
Binil Benny
QSP/PK/PD & PBPK Modelling Scientist · Clinical Pharmacology
Binil is a Clinical Pharmacologist and QSP/PK/PD Modelling Scientist with 5+ years
of experience in pharmacometrics, PBPK/QSP modelling, and translational drug
development. He holds a Doctor of Pharmacy (PharmD) from Teerthanker Mahaveer
University and an MSc in Pharmacology and Drug Discovery from Coventry
University.
He currently serves as a PBPK & QSP Modelling Consultant at Vial, developing mechanistic models to support drug development and regulatory decision-making. His expertise includes QSP modelling, PBPK modelling, population pharmacokinetics (PopPK), model-informed drug development (MIDD), and multi-omics data integration.
He currently serves as a PBPK & QSP Modelling Consultant at Vial, developing mechanistic models to support drug development and regulatory decision-making. His expertise includes QSP modelling, PBPK modelling, population pharmacokinetics (PopPK), model-informed drug development (MIDD), and multi-omics data integration.
Karolina Pearson, Ph.D.
Chemical Process Engineer · Hydrogen & Electrochemical Systems · Program Management
Karolina is a Chemical Process Engineer and Technical Program Manager with
10+ years of experience leading complex hydrogen, electrochemical,
and catalytic process projects from R&D through pilot- and commercial-scale deployment.
She holds a Dr.-Ing. (magna cum laude) in Thermal Process Technology from the
University of Stuttgart and an Engineering Diploma in Environmental Engineering.
At Plug Power (Massachusetts), she served as Program Manager across electrolyzer systems and giga-factory automation — overseeing a $83M, 120 MW electrolyzer system delivery, managing a $47M DOE-funded gigawatt-scale electrolyzer manufacturing programme, and leading a 400 MW renewable-powered electrolyzer plant design spanning teams in the US, Europe, UAE, and India. She achieved a 60% manufacturing cost reduction and 25% improvement in testing capacity through automated assembly and in-process quality control.
Earlier, she spent 5 years as a Research Engineer at the German Aerospace Center (DLR) in Stuttgart, developing thermochemical fuel conversion and hydrogen-rich syngas processes for PEM fuel cell systems in aerospace applications, managing an €800K international research project, and publishing in peer-reviewed journals. Her core toolkit includes ASPEN Plus, UniSim, P&ID development, HAZOP/LOPA, and digital twin & data analytics.
At Plug Power (Massachusetts), she served as Program Manager across electrolyzer systems and giga-factory automation — overseeing a $83M, 120 MW electrolyzer system delivery, managing a $47M DOE-funded gigawatt-scale electrolyzer manufacturing programme, and leading a 400 MW renewable-powered electrolyzer plant design spanning teams in the US, Europe, UAE, and India. She achieved a 60% manufacturing cost reduction and 25% improvement in testing capacity through automated assembly and in-process quality control.
Earlier, she spent 5 years as a Research Engineer at the German Aerospace Center (DLR) in Stuttgart, developing thermochemical fuel conversion and hydrogen-rich syngas processes for PEM fuel cell systems in aerospace applications, managing an €800K international research project, and publishing in peer-reviewed journals. Her core toolkit includes ASPEN Plus, UniSim, P&ID development, HAZOP/LOPA, and digital twin & data analytics.
Founding Engineer
AI Infrastructure & Computational Biology · Drug Engineering
A founding engineer with a rare blend of computational biology and AI infrastructure
expertise, holding a PhD in Integrative Systems Biology and a BS in
Mathematics, with 9 peer-reviewed publications in leading cancer-research
and neuroscience journals.
Most recently served as founding engineer at a healthcare AI start-up, architecting a 100% infrastructure-as-code, HIPAA-compliant AWS environment and building a secure multi-agent AI backend that cut hallucinations by 70% through text-to-SQL grounding. Previously co-founder & CEO of a pharma-screening AI company, building a proprietary high-throughput computational screening platform that produced the company’s foundational patent, and worked as a computational biologist at a cell-therapy biotech and as a postdoctoral researcher at a leading US university and two major cancer-research institutes, contributing to a widely used cancer dependency map. This team member has chosen to keep their personal details private at this time.
Most recently served as founding engineer at a healthcare AI start-up, architecting a 100% infrastructure-as-code, HIPAA-compliant AWS environment and building a secure multi-agent AI backend that cut hallucinations by 70% through text-to-SQL grounding. Previously co-founder & CEO of a pharma-screening AI company, building a proprietary high-throughput computational screening platform that produced the company’s foundational patent, and worked as a computational biologist at a cell-therapy biotech and as a postdoctoral researcher at a leading US university and two major cancer-research institutes, contributing to a widely used cancer dependency map. This team member has chosen to keep their personal details private at this time.
Machine Learning Specialist
LLM & VLM Expert · Proprietary Model Development
A machine learning specialist with deep expertise in large language models (LLMs)
and vision-language models (VLMs). This team member leads the development of
TakaHuman's proprietary AI models — including the custom LLM and VLM at the core of the
platform's scientific reasoning and multi-modal simulation capabilities.
Their background spans cutting-edge model architecture, fine-tuning, and deployment at scale. They have chosen to keep their personal details private at this time.
Their background spans cutting-edge model architecture, fine-tuning, and deployment at scale. They have chosen to keep their personal details private at this time.
Senior Machine Learning & Full-Stack Engineer
Computer Vision · NLP · Generative AI · Full-Stack Development
A senior machine learning and full-stack engineer with 7+ years of experience
across computer vision, NLP, robotics, and full-stack AI development. Currently based in Tokyo,
this team member brings hands-on expertise in generative AI, deep learning, and
real-world deployment of vision and language systems.
They have built AI vision systems for autonomous robotics, published 13 research papers in computer vision and NLP, and hold industry experience spanning pharma AI, VR product development, and enterprise software engineering. They have chosen to keep their personal details private at this time.
They have built AI vision systems for autonomous robotics, published 13 research papers in computer vision and NLP, and hold industry experience spanning pharma AI, VR product development, and enterprise software engineering. They have chosen to keep their personal details private at this time.
Ch. Omar
Full Stack Engineer · Python, React & AWS
Omar is a full-stack engineer with 8+ years building scalable, production-grade
applications across healthcare, fintech, and SaaS, using React, Python (Django, FastAPI,
Flask), and AWS. He is comfortable owning features end-to-end in fast-moving, ambiguous
environments, bringing a founding-team mindset to every engagement — including leading a
$500K research platform recognized as a gold standard by the client and doubling client
revenue through AI/ML algorithm optimization.
He currently serves as Lead Software Engineer at Eco-Green Developers, architecting Django and PostgreSQL backend systems and leading a distributed team of Python developers, boosting team productivity by 55% and cutting delivery time by 40% through workflow automation. Previously, as Senior Software Engineer & Team Lead at AlignerBase, he built a full-stack platform on Django, Next.js, and PostgreSQL, and as Senior Engineer at Wings Intranet, he built a React Native mHealth app on AWS with a HIPAA-compliant Django REST backend that drove a 50% increase in daily active users. Omar holds a BS in Computer Science from the University of Central Punjab.
He currently serves as Lead Software Engineer at Eco-Green Developers, architecting Django and PostgreSQL backend systems and leading a distributed team of Python developers, boosting team productivity by 55% and cutting delivery time by 40% through workflow automation. Previously, as Senior Software Engineer & Team Lead at AlignerBase, he built a full-stack platform on Django, Next.js, and PostgreSQL, and as Senior Engineer at Wings Intranet, he built a React Native mHealth app on AWS with a HIPAA-compliant Django REST backend that drove a 50% increase in daily active users. Omar holds a BS in Computer Science from the University of Central Punjab.
