Applied R&D in engineering and technology to tackle cross-cutting challenges with societal and global impacts

Integrating AI/ML into practice-ready solutions to improve productivity, effectiveness, and reliability of processes

Translational R&D

Implement cutting-edge engineering and technology research into practice in energy, transportation and health sciences; transfer technology from the state of the art to the standardized state of the practice; Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) projects

US DOT SBIR project

Applied AI/ML

Implement and integrate practice-ready AI techniques, such as machine learning for prediction & classification, evolutionary computation for optimization, federated ML for security & efficiency, physics-guided AI for improved explainability, NLP/LLM search & productivity tools, Edge/IoT AI/ML, and MLOps

Computational Modeling

Numerical simulation and computational modeling of multi-physics phenomena in micro-, meso-, and macro- scales; simulation-based design optimization; AI/ML surrogate modeling; physics-integrated AI/ML; finite element analyses; computational fluid dynamics; materials science; mechanics of composites

Systems Engineering

Employ computational intelligence in systems engineering to make data-driven decisions for asset preservation and maintenance; from forecasting asset conditions to optimizing project portfolios

Applied AI/ML Projects

Callentis offers practice-ready AI/ML development for prediction, control, optimization, and automation tasks that improve productivity, effectiveness, and reliability of business processes.

Federated Learning

Callentis has developed federated/distributed AI frameworks to train models based on large remote data sources or IoT/Edge devices with high efficiency, reliability, privacy, and security.

Hybrid Models

Callentis has engineered hybrid and physics-informed AI/ML to develop explainable and interpretable models that incorporate both theoretical and experimental data.

Generative AI

Callentis has leveraged pre-trained NLP/LLM models to deliver customized search and productivity tools via RAG and transfer learning. Callentis engineers have also used GANs to generate synthetic data for data augmentation.

Deep Learning

Callentis has fine-tuned foundational deep learning models via transfer learning on context-sensitive data to extract information from sensor data including images and LiDAR point clouds.

Surrogate Models

Callentis engineers have conducted simulation-based design optimization, leveraging ML surrogate modeling to increase efficiency and reliability of design processes.

MLOps

Callentis deploys practice-ready AI solutions into MLOps pipelines for continuous updates while being used for inference. MLOps monitors model success metrics and triggers updates to avoid model drift.

What Drives Us

Mission: use emerging technologies to drive quantifiable process improvements in transportation, energy, and health sciences.

Strategy: we do the D in R&D; advance technologies from the state-of-the-art research to the standardized state of the practice.

Tactic: deliver practice-ready solutions that integrate emerging technologies with context-sensitive subject matter expertise.

People: experienced civil, mechanical, and systems engineers, materials scientists, solution architects, creative designers, software developers and data scientists.

WHO WE ARE

SBIR Phase III Contract Vehicle: any Government agency can issue a sole-source contract to Callentis for purchasing concrete durability sensors (developed under US DOT SBIR Phase II) or for further R&D to customize the sensor technology for other specific applications. Please contact us for more information.

Who We Are

Callentis Consulting Group is a small woman-owned research, development, and consulting company, focused on integrating the latest proven technologies into existing business processes to create growth and opportunity. With extensive background in engineering and computational science, our team offers software and hardware development for a variety of science and technology applications, and diagnostic support for data-driven decision processes. We thrive in problem solving and love tackling complex challenges that have global impacts. We have mainly worked in transportation, energy, and health science industries but would love a challenge where ever we can make a positive impact.

Meet the Callentis leadership team, supported by experienced civil, mechanical, and systems engineers, materials scientists, solution architects, creative designers, software developers and data scientists.

Andrew Drach, PhD

Co-Founder & President

Dr. Drach has over fifteen years of experience in numerical modeling techniques, applied statistics, data science, custom software and hardware development, and renewable energy engineering. He has published over 40 peer-reviewed articles, presented at over 20 international conferences, and gave 4 invited talks. He did his postdoctoral training in Computational Engineering and Sciences at the University of Texas at Austin. He holds a PhD in Mechanical Engineering from the University of New Hampshire.

Monika Drach, MA

Founder & CEO

Mrs. Jociunaite has over ten years of experience in business data analysis, creative communication, data visualization, and user experience design. She has extensive experience working in 5 different countries, and assisting large multinational firms with their regional marketing strategies. She has successfully helped more than 15 companies with the creative design of their product and functionality to offer a seamless user experience. Mrs. Jociunaite holds two bachelors degrees in Economics and Business Administration, and two masters degrees in International Business and Marketing.

Nima Kargah-Ostadi, PhD, PE, PMP

Vice President

Dr. Kargah-Ostadi has over fifteen years of experience in applied statistics, data science, machine learning, evolutionary computation, and transportation infrastructure asset management. He has published over 15 peer-reviewed articles, and presented at over 20 conferences, workshops, and webinars. He holds a PhD in Civil Engineering and a doctoral minor in Computational Science from Penn State University. He is a registered Professional Engineer and a certified Project Management Professional.