WELCOME TO MATH LABS
Applied Computational Mathematics & Artificial Intelligence
Math Labs Research is a London based premier Computational Mathematics and Artificial Intelligence lab, that builds upon its rapidly expanding multi-disciplinary team of exceptional mathematicians, scientists and engineering researchers - to solve 'hard' business problems that are not commonly solved.
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Our mission is to help accelerate an enterprise journey towards becoming Data and AI Optimal - starting with significantly enhancing its abilities in leveraging AI & advanced mathematics to outperform traditional competition.
The firm focusses on solving problems for data-sensitive sectors such as financial services and healthcare.
Math Labs combines a strong research core - continually developing/optimising mathematical / AI components, with its close engagement with prestigious client organizations using several industry-first approaches, to deliver solutions that significantly enhance the probability of AI success and adoption within these clients.
ENGAGING ORGANIZATIONS WITH
'ENHANCED AI'
At the heart of Math Labs' relationship with you - is our optimized 'Enhanced AI' engagement methodology, which combines several industry_first approaches
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A first of her kind - a Dual capability 'PhD AI Engagement Manager' - who is essentially a fusion highly mathematical background with the ability to shape the right algorithmic/mathematical pathway, and a strong leadership dna with the ability to help her team execute for success.
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SAMPLE PROJECTS
Partnering With Our Clients To Deliver Computational Math / Artificial Intelligence enabled Impact
MARKET RISK DETECTION AND EXPLANATION FOR A TIER 1 EUROPEAN BANK
A banking client was looking to capture spikes in perceived risk in companies within its portfolio of securities (bonds) and explain them via external financial newsfeed data.
Math Labs used its own advanced NLP frameworks to explain the risk spikes relating them to concepts embedded in news on risk anomalies and their intensity of impacts.
PERSONALIZATION FOR A TOP TIER HEALTHCARE OPERATOR
Building of a robust advanced analytics engine to help our client create actionable insights on a series of personalization levers that potentially impacted the delivery quality to their target patient cohort
EVALUATION OF A QUANTUM MATHEMATICS BASED R&D FIRM
Helping a top 10 global technology major unwrap and undertake an 'under the hood' assessment of the R&D firm's key computational engine for a strategic investment.
This was done via a techno-mathematical due-diligence of the investee firm's end to end quantum mathematics and AI based algorithm pathway.
A world class, multi-disciplinary and objective 'Research based Build' methodology - that ensures project success even in highly complex and otherwise 'difficult to break' problems - away from the traditional narrow 'data-science' pathway. Importantly, this takes away the individual data-scientist's own 'bias' away from problem solving.
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CUTTING EDGE RESEARCH IN ENTERPRISE ARTIFICIAL INTELLIGENCE IS CORE TO MATH LABS
CURRENT RESEARCH
ANOMALY ANALYSIS
Understanding ‘anomalous patterns’ represents a significant area of interest to Math Labs, given the range of use-cases it represents.
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At our lab, we look at anomalies such as 'financial fraud', 'machine failure' through the lens of 'evolution', away from the more common approach of considering these a single step phenomenon - requiring us to unify singular approaches from across mathematical fields, rather than limiting ourselves to traditional AI.
AUTO DATA SPACE OPTIMIZATION
Math Labs leverages multi-disciplinary approaches to transform data to its highly optimized version (technically - to a more optimal subspace), which 'significantly amplifies' the power of machine learning models applied on it.
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This does away with the painfully manual & ad-hoc process of decisions that determine what final data enters a clustering / supervised learning model - not only optimizing the pre-model 'data science expert' stage, but also enhancing the efficiency of a typical data science life-cycle by over 50%
SUPERVISED FEATURE EXTRACTION FROM TEXTUAL DATA
Extraction of an optimal, hidden 'concepts-space' from text - that maximizes the ability to explain a response variable - could expand the usage of experts/analysts' opinions and even world / social media data in enterprise supervised learning.
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Math Labs is working to bring about a robust, trust-able methodology to this exciting area, by approaching this as a manifold optimization problem - that requires combining natural language processing with, algebraic-mathematics and statistics.
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OUR KEY PARTNERS
Collaborative Efforts
A prominent NHS England Foundation trust - owning and responsible for public health & social care services for several critical sections of UK's population.
FutureLabs is a distinctive tech VC & VentureBuilder established by former Partners of McKinsey Ventures.
The firm focuses on identifying best-of-breed B2B big data, cybersecurity & AI start-ups and scale them into regional and global leaders.
Hitachi Consulting - a business consulting & technology major, with a significant expertise in IT and IOT - co-creates with client organisations around the world to accelerate their digital transformation and respond to dynamic global change.
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Math Labs is working with Hitachi consulting in delivering AI / Computational mathematics expertise and services to the latter's marquee global clients.
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FinMechanics provides software solutions and services to global Investment Banks, Regional and Commercial Banks, Asset Managers, Central Banks, Foreign Exchange services, Precious Metal dealers and Corporate Treasuries.
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OUR AI TEAM
MARIJA RUŠČIĆ, PHD
Lead AI Scientist
Marija – a Geo-Physics PhD - specializes in predictions related to extreme ‘events’ and brings her considerable knowledge in Seismology and geo-physics into building tools for complex 'Anomaly' related problems
RUDOLF KOHULAK, PHD
Lead AI Scientist
Rudolf – an applied mathematics PhD from UCL – leverages his strengths in computational geometry to bring a range of unique perspectives in adding new capabilities to 'traditional' AI.
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Member of a Chalkdust magazine editorial team. Author and co-author of several popular science articles. Recreational Rubik’s cube solver.
THEERAWAT BHUDISAKSANG, PHD CANDIDATE
AI Research Scientist
Theerawat, pursuing a Doctorate in Financial Mathematics at Oxford, specialises in stochastic control and its application to financial, statistical inference in a diffusion process, Ergodic theory, and stochastic analysis.
He is a keen competitive-mathematician with several successes in the International Math Olympiads.
MERCEDEH REZAEI, PHD CANDIDATE
AI Research Scientist
Mercedeh, pursuing her Doctorate in Machine Learning & AI - brings to client situations additionally a strong background in computer science & network security.
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Interested in speed reading, nature, motivational speakers.
MANESH HALAI
Associate AI Scientist
Manesh holds a degree in Physics and a masters in Analytical Chemistry. His strengths lie in combining mathematics and investigative reasoning based on intensive data analysis, including the analysis of signatures via mass spectra to identify key patterns and anomalies.
He is a keen gamer, an avid sports fan and recreational lacrosse player.
RYAN SINGH
Associate AI Scientist
Ryan holds a Master in Mathematics from Oxford University. A keen mathematician, his focus has primarily been on graph theory and discrete algebra, alongside probability and statistics.
His non-mathematical pursuits include among others learning languages, rock climbing, as well as the occasional cryptic crossword.
GEORGIA PETROULEA
Associate AI Scientist
Georgia holds a degree in Physics and a masters in Astrophysics. Her strengths lie in conducting data analysis and using numerical methods incl. Monte-Carlo simulations to predict natural physical phenomena - such as Geomagnetic storms on Earth.
She is passionate about teaching and has been helping undergraduate students solve complex Quantum and Modern physics problems since completing her degree.
She is interested in recreational cooking and cycling
PETER KENNY, PHD
AI Research Scientist
Peter, currently completing his PhD in Statistical Science at UCL, is primarily interested in applied statistics and machine learning. His undergraduate degree was in Physics.
Peter's PhD is on the analysis of X-ray signatures, using linear un-mixing and supervised learning methods on high dimensional data sets for material detection and quantification.
Outside of work he plays and follows football and likes to cycle.
JACK SKIPPER, PHD
AI Research Scientist
Jack, with a PhD in mathematics from the University of Warwick - studying fluid dynamics, is an experienced mathematician, interested in modelling physical systems and understanding their evolution.
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His PostDoc experience consists of leading several research projects in regularity theory, calculus of variations and convex integration theory as well as undertaking graduate teaching and advising.
A sports enthusiast who enjoys sailing, ultimate frisbee and boardgames.
AHMAD KHAZAIE, PHD
AI Engineering Researcher
Ahmad holds a Masters in Computer Science (Decision Systems) from Ecole Centrale Paris and is currently completing his PhD in Computer Science at Imperial College London.
He brings to the firm a significant range of engineering approaches from across high performance computing and large-scale data management in AI projects. Ahmad’s current research is on 'Worst-case Optimal Joins' within relational and factorized databases.
Outside of academia, he plays and follows football and likes traveling and photography.
JOEY REINESS, PHD
AI Engineering Researcher
Joey, after his bachelors and masters MSci in Physics from Cambridge University, has completed his PhD in Particle Physics Phenomenology from Durham, in which he applied computational methods and advanced mathematics to investigate the Higgs Boson. He has presented his work at international conferences, taught undergraduates and lectured master’s-level physics.
He specializes in building software applications for involved mathematical and computational-physics pathways.
He is a keen advocate of scientific outreach, working with charities and university outreach teams for the past seven years. His non-academic pursuits include making music, working on his Spanish, exploring the great outdoors and playing ultimate frisbee.
OLIVER SARGENT, PHD
AI Engineering Researcher
Oliver brings to the firm a wide range of mathematical & computational interests including ergodic theory and number theory, along with a keenness for converting complex mathematical pathways into highly efficient software applications.
He has a PhD in Mathematics and recently worked as a PostDoc at the Weizmann Institute of Science, a leading multidisciplinary basic research institution in Israel, studying amongst other things, novel classes of random walks and the interplay between randomness and structure.
An enthusiastic juggler, Go player and cyclist.
ASHWANI ROY
Practice Head - Financial Services
Ashwani, an alumnus of the London Business School, is an Ex Microsoft Programmer.
He has spent over 10 years developing enterprise risk, derivatives trading and e-commerce Systems, and over 5 years - as a Director with Citigroup London - helping institutional clients manage Market and Credit risk via structuring quantitative hedging and financing solutions.
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An avid cricketer, golfer and a car racing enthusiast.
EREZ RAANAN
Co-Founder
Global leader of Analytics Ventures with McKinsey & Co., and an experienced Analytics Transformation Officer working with top tier enterprises to deliver Data and Analytics programmes . 10+ years’ building and scaling technology product companies.
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He is an MBA from Harvard Business School, and holds a bachelors in Electrical Engineering.
PRODIPTO GHOSH
Chief Scientist
Prodipto, an alumnus of the Indian Institute of Technology, has been a Lead AI Scientist with McKinsey & Co. in their London office, working on developing & optimising AI algorithmic pathways & toolkits for interesting, non-standard enterprise problems, across verticals. A practitioner with 10+ years in computational mathematics.
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He is a recreational badminton player and trekker.
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Matteo Altorio, PHD
AI Engineering Researcher
Matteo is an experienced physicist interested in reducing complex problems to first causes and principles, in order to achieve simple solutions and make it comprehensible to everyone via the right technology media - whether algorithmic libraries, easy to use APIs, or stand-alone applications.
He enjoys bringing together various strains of applied & computational physics / mathematics to fit the best solutions to problems at hand.
His background consists of a PhD in Physics studying inertial sensor using Cold Atoms. He had earlier undertaken his masters with a concentration on Condensed Matter Physics.
On a more personal front, he enjoys hiking outdoors and exploring new places. Passionate about technology, Formula1.
