Review – Speakers 2019

Dr. Chakrabarti is a Senior Research Scientist in Hitachi's Financial Innovation Lab. in Santa Clara. He works at the intersection of data science and finance, and has been doing industrial research for over ten years . He did his MS-PhD in Electrical and Computer Engineering at Rice University, and afterwards did his Master of Financial Engineering at U C Berkeley. Dr. Chakrabarti's research publications and patents have collectively been cited over one thousand times.

Matt serves as the CEO at QuantHub, spearheading the drive to help companies overcome the extreme analytics talent shortage and build exceptional data science and engineering teams. Matt has a passion for developing authentic relationships with customers to truly understand what drives them, and then crafting creative solutions to their most critical problems. Prior to joining QuantHub, Matt spent the last 15 years running product and tech at PE-backed companies, including building a product and engineering organization at Daxko to deliver 10x revenue growth, 7 acquisitions, and 3 enormously successful recapitalizations in just 10 years. While at Daxko, Matt led the team to deliver the first machine learning/AI solution to the market, predicting customer membership churn and also propensity to donate. 

Dr. Dyann Daley, MD,  is an experienced pediatric anesthesiologist and child maltreatment prevention executive, specializing in location-based predictive modeling, systems thinking, and development of practical solutions for community-influenced children’s issues.  

Dr. Daley founded and was the executive director of Cook Children’s Center for Prevention of Child Maltreatment in Fort Worth, Texas where she demonstrated the effectiveness of place-based predictive analytics for child maltreatment using spatial risk modeling.  She went on to found Predict Align Prevent, a national nonprofit advancing geospatial machine learning predictions in child welfare, strategic alignment of prevention resources, and implementation of accountable prevention programs to prevent child maltreatment before it happens.  Her organization is committed to open science for social good. 

Dinakar Deshmukh is the VP of data science and analytics for GE Aviation. He has been working in aviation industry for the last 20 years. In his current role as data science leader for GE Aviation, he is responsible for developing AI based solutions for engine health management, supply chain and MRO operations. His team is also developing ML based applications to help airline operations to improve operational efficiencies.

Siddha Ganju, whom Forbes featured in their 30 under 30 list, leads AI innovation in LLM and Guardrails along with the deployment of Medical Instruments for Nvidia partners at Nvidia. Siddha previously worked in the self-driving teams for simulation, perception, scalable training, and inference along with global automotive partnerships and go-to-market strategies.

Dr. Kunling Geng is currently the Lead Data Scientist and AI Architect at Decision Engines Inc.  His expertise spans across Deep Learning, Computer Vision, Natural Language Processing, Knowledge Graph, and Relationship Reasoning. He designed and implemented various AI models to natively understand unstructured business documents during the intelligent automation of business processes such as account payable process, fraud detection, financial reconciliation, decision making, risk assessment, etc.

Kunling revived his Ph.D. in Biomedical Engineering at USC, where his research focuses on utilizing machine learning and neural network based models to analyze the time-series neural data recorded in the human hippocampus regions.

Luba Gloukhova leads and executes advanced machine learning projects for high tech firms and major research universities in Silicon Valley. She also preaches what she practices, serving as the founding chair of Deep Learning World – the premier conference covering the commercial deployment of deep learning – and delivering highly-rated talks at many other events as well. Luba previously supported Stanford faculty as an internal consultant at the university's Graduate School of Business, conceiving and generating innovative solutions to accelerate research.

Before that, Luba gained industry experience in high frequency trading analysis, catastrophe risk modeling, and marketing analytics. She received her master’s in analytics from the University of San Francisco and two bachelors degrees from Berkeley: applied mathematics and economics. Luba also teaches yoga and enjoys an active lifestyle.

Dr. Alex Glushkovsky is a Principal Data Scientist at BMO Financial Group. He has over 30 years of diverse industrial, consulting and academic experience. Alex holds a PhD in mathematical modeling and optimization of technological processes and an Honours MSEE. He is a Fellow Member of The Royal Statistical Society and a Senior Member of the American Society for Quality. Alex holds CRE and CQE by ASQ, and the PRMTM professional certifications. He has been awarded for outstanding instruction of the Economics for Managers course, Ellis MBA, NYIT. Alex has published/presented over 30 research papers on the statistical analysis, machine learning and analytical management in International editions.

Martin Görner works in developer relations at Google, where he focuses on parallel processing and machine learning. Passionate about science, technology, coding, algorithms, and everything in between, Martin’s first role was in the Computer Architecture Group at STMicroelectronics. He also spent 11 years shaping the nascent ebook market, starting at Mobipocket, which later became the software part of the Amazon Kindle and its mobile variants. He holds a degree from Mines Paris Tech. He is best known today for the "Tensorflow without a PhD" series.

Jeff Heaton, Ph.D., is a vice president and data scientist at Reinsurance Group of America (RGA) and an adjunct faculty member at Washington University in St. Louis.  With 20 years of life insurance industry experience, Jeff has crafted a variety of InsurTech solutions using machine learning technologies such as deep learning, random forests, and gradient boosting, among others. Utilizing electronic health records (EHR) data such as FIHR, ICD10, SNOMED, and RxNorm, he works closely with all stages of model development from inception to ultimate deployment.  Jeff is the author of several books and has published peer-reviewed research through IEEE and the ACM. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and Fellow of the Life Management Institute (FLMI).  Jeff holds a Ph.D. in computer science from Nova Southeastern University in Ft. Lauderdale, FL.

Steve Hoeg is the Senior Engineering Manager responsible for Adobe’s line of professional audio and video products - Premiere Pro, After Effects, Audition, Prelude and Media Encoder. Personal big-screen Hollywood movie credits include Deadpool, Only the Brave, Hail Caesar and Gone Girl.

Steve has a background in image processing, and was previously Project Lead, guiding technical direction and architecture of Premiere Pro. Author of the GPU accelerated Mercury playback engine, implementing a large scale image processing pipeline in CUDA. Speaker at conferences including NVIDIA GTC and AMD Developer Fusion.

Anne Hunt is the Manager, Data Science, joining the team 7 years ago. She is an annual presenter at the Trimble Dimensions Conference focusing on analytics topics. Anne applies statistical theories and methods to collect, analyze and interpret quantitative data using tools such as python, Tableau, R, and SAS. She has provided analytics to hundreds of clients, improving their fuel efficiency and monitoring fuel theft. Anne holds degrees in Mathematics and Statistics from the University of Minnesota, Duluth. She also has a Masters in Analytics from Villanova University.   

Armin Kappeler received his M.S. and Ph.D. degree in Electrical Engineering from Northwestern University in 2016 where he worked on different deep neural networks applications for Image and video classification and recovery. He was among the first researchers to apply deep neural networks to video super-resolution. In 2015, he joined Verizon Media Group ( former Yahoo ) as a researcher, where he is currently working on automated model building, active learning and other related computer vision applications.

Meher Kasam is a seasoned software developer at Square working on building engaging experiences on their iOS Point of Sale application. He worked previously at Microsoft, where he shipped features used by tens of millions of users everyday, from Cortana to the award winning Bing app. He also has experience designing and building scalable cloud APIs serving millions of requests daily. He’s was the mobile lead for Seeing AI app, using computer vision and deep learning for blind users. With a flair for fast prototyping, he has won close to two dozen hackathons, and then turned them to ship features in several products. He is also co-author of the upcoming book, ‘Practical Deep Learning for Cloud and Mobile’.

Chandra Khatri is a Senior AI Scientist at Uber AI, where he is driving fundamental and applied research in the field of Deep Learning, Reinforcement Learning, and Conversational AI. He is developing novel Conversational Interfaces for drivers and riders at Uber. Prior to Uber, he was the Lead Scientist for Alexa Prize, which is a $3.5 Million on-going University Competition from Amazon to advance the state of Conversational AI. Chandra along with the Alexa Prize participants, which includes some of the best research groups in the world, have advanced the state of Conversational AI through this competition. 

Prior to Alexa, Chandra was a Researcher at eBay’s Applied Research Group, wherein he led various Deep Learning and NLP initiatives such as Automatic Text Summarization, Automatic Content Generation and Recommendation Systems within the e-Commerce domain. The models that he built led to significant gains for eBay. Chandra holds degrees in Machine Learning and Computational Science & Engineering from Georgia Tech and BITS Pilani.

Anirudh is the Head of AI & Research at Aira (Visual interpreter for the blind), and was previously at Microsoft AI & Research where he founded Seeing AI - Talking camera app for the blind community. He is also the co-author of the upcoming book, ‘Practical Deep Learning for Cloud and Mobile’. He brings over a decade of production-oriented Applied Research experience on Peta Byte scale datasets, with features shipped to about a billion people. He has been prototyping ideas using computer vision and deep learning techniques for Augmented Reality, Speech, Productivity as well as Accessibility. Some of his recent work, which IEEE has called ‘life changing’, has been honored by CES, FCC, Cannes Lions, American Council of the Blind, showcased at events by White House, House of Lords, World Economic Forum, on Netflix, National Geographic, and applauded by world leaders including Justin Trudeau and Theresa May.

Alex is a physicist/mathematician turned computer scientist, and then later turned machine learning enthusiast. Through his years working as a data scientist, he developed and deployed machine learning solutions to solve real world business problems, such as using LSTM to forecast staffing needs, using xgboost models to execute real-time online customer behavior classifications. We live in an amazing era, where machine learning algorithms 60 years ago can be put into reality with a few lines of python code. As one of the first two data scientists in company history to join American Tire Distributors, Alex helped grow the data science team to a size of 12 within a year; and they are now developing machine learning solutions to help the company in supply chain, sales, warehousing, as well as eCommerce.

Przemek Maciołek, PhD is a VP of R&D at Collective Sense, where he leads development efforts on the company's next-gen log and network monitoring platform. Previously, Przemek's work focused on using machine and deep learning for business intelligence, data mining, and big data solutions for IBM, Oil & Gas exploration companies, Toptal, Base (now part of Zendesk), and others. Outside of his professional duties, he concurrently worked on research in the field of machine learning, anomaly detection, intrusion detection systems, and natural language processing, and he earned his PhD in 2015. Przemek is a frequent public speaker and active in the technology meet-up community, managing the second largest data-centric group in Poland.

Polina Mamoshina is a senior research scientist at Insilico Medicine, Inc, a Baltimore-based bioinformatics and deep learning company focused on reinventing drug discovery and biomarker development and a part of the computational biology team of Oxford University Computer Science Department.  Insilico Medicine, Inc headquartered at the Johns Hopkins University - Montgomery County Campus in Rockville. The company is focusing on applying deep learning and advanced signalling pathway activation analysis to biomarker discovery, drug discovery and drug repurposing for ageing and age-related diseases. Through a partnership with the BitFury Group, the company is working on a range of AI solutions for blockchain to help return the power over life data back to the individual.  A brief video explaining Insilico Medicine research and commercial focus is available on Youtube. Her primary research interests include artificial intelligence, deep learning, biomarkers of ageing and disease and drug responses, healthcare data management,  healthcare data economy.

Polina graduated from the Department of Genetics of the Moscow State University. She was one of the winners of GeneHack a Russian nationwide 48-hour hackathon on bioinformatics at the Moscow Institute of Physics and Technology attended by hundreds of young bioinformaticians. Polina is involved in multiple deep learning projects at the Pharmaceutical Artificial Intelligence division of Insilico Medicine working on the drug discovery engine and developing biochemistry, transcriptome, and cell-free nucleic acid-based biomarkers of ageing and disease.

James McCaffrey, who annually leads the full-day deep learning workshop at Machine Learning Week, works for Microsoft Research in Redmond, Wash. James has a PhD in cognitive psychology and computational statistics from the University of Southern California, a BA in psychology, a BA in applied mathematics, and an MS in computer science. James learned to speak to the public while working at Disneyland as a college student, and he can still recite the entire Jungle Cruise ride narration from memory.

Anand Oka is a Principal Group Program Manager in the Business Applications Group of Microsoft, where he drives innovative applications of connected customer knowledge and cutting-edge AI to solve challenges faced by enterprises who are making the Digital Transition, such as protection from Fraud and Abuse. Previously Anand worked on User Value Measurement and Data Science for Controlled Feature Exposures and AB Experimentation on Microsoft Office, Bing and Cortana, and Machine Intelligence for search, recommendation, and systems optimization at Blackberry. He original training is in physical layer Wireless Engineering and its applications in broadband and ultrabroadband communication systems.

Anand holds a PhD from the University of British Columbia, and an MSc from The Technion – Israel Institute of Technology, both in Electrical Engineering. He is a Senior Member of the IEEE.

Miles Porter has held various roles over his 20+ year career including positions in software engineering, database engineering, network engineering, systems engineering, and quality assurance.  He has worked as an individual contributor, tech lead as well as in management.  In early 2017 Miles shifted his focus to data science and is currently part of the Trimble data science team.  He has a bachelor's degree in mathematics from the University of Northern Colorado and has done graduate work in pattern analysis and applied mathematics at Colorado State University.  He is currently working towards a master's degree in analytics from Georgia Tech.  When he is not doing geeky math and computer stuff, Miles enjoys playing upright and electric bass in various musical groups, practicing martial arts and going on interesting road trips with his wife and two kids.  Miles lives in Minneapolis, Minnesota.

Aashish Sheshadri has a master's degree in Computer Science from the University of Texas at Austin. He is currently part of strategic machine learning enablement at PayPal. His interests lie in machine learning enablement and research.

Mohammad is currently a Senior Data & Applied Scientist at Microsoft, and Instructor at Stanford University. He is a former Data Scientist at Apple and previously worked for Samsung, Bosch, General Electric and UCLA Research Labs. He received a PhD in Computer Science from the University of California, Riverside and B.Sc. from University of Tehran. Mohammad is the author of the book, ‘Applications of Mining Massive Time Series Data'. He has also been a keynote speaker at more than 40 Data Summits/Conferences around the globe. 

Tech and Team leader for three divisions: autonomous driving and machine learning/deep learning, simulations at Isuzu technical Center of America. First authored multiple technical papers on autonomous driving, Machine learning and simulations. Speaker at various conferences, including  Autonomous Vehicle Test & Development Symposium 2018, Autonomous Vehicles Silicon Valley 2019, SAE world congress 2018/2019, Autonomous driving and future mobility 2019.

Luming is Head of Data at Millennium Management where he leads an effort to build the world’s leading data platform to help the firm to deliver the alternative investment industry's highest quality returns to investors.

Previously, Luming was the head of deep learning at Uber where his team built the most advanced deep learning platform in the world. Prior to Uber, Luming held various leadership positions at Microsoft and Amazon. Luming holds a Master’s degree of Computer Science from the Graduate School of the Chinese Academy of Sciences.

Ryan is an experienced Data Scientist and Statistician who utilizes advanced machine learning techniques to provide innovative solutions to the transportation space. He received his Masters of Science in Applied Economics at the University of North Dakota, and previously worked in the healthcare industry with United Health Group doing advanced pharmaceutical research. Currently, he works with Trimble as a part of Trimble Transportation Mobility in the Minnetonka Minnesota office.