Amel Lageat, Johnson & Johnson, Institute-Industry-Innovation Seminar @ColumbiaDataScience

Uploaded November 2018 | Updated September 2026, 2 weeks ago

"People centric approach to optimize Data Science, Commercial impact and Leadership"

Abstract: In a world of Infobesity, analysts, engineers, professionals, executive leaders, and people now have access to more data and analytics opportunities that we can ever make sense of. However, a genuine people centric approach can provide the sharpest guidance in designing relevant strategies and solutions: it makes data, models, and analytics more meaningful and purposeful, and also leads to marketing and commercial impact in global organizations.

In this talk, I will use various business examples to discuss this premise, including negative targeting (voluntarily deciding to not advertise to a person), the UX of marketing mix modeling (who is the real user?), and servant leadership to data experts (what does it look like?).

Bio: Amel Lageat is heading up Global Analytics for the Johnson &Johnson Consumer Division. She has spent the last 20 years leading various commercial organizations within J&J (Marketing, Market research and analytics), in France, the UK and the US. Her recipe for impact is a fruitful combination of data, curiosity and empathy.

Amel Lageat, Johnson & Johnson, Institute-Industry-Innovation Seminar

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$On the Power of Randomization for Scheduling Real-Time Traffic in Wireless Networks On the Power of Randomization for Scheduling Real-Time Traffic in Wireless Networks Guest Speaker: Javad Ghaderi, Associate Professor of Electrical Engineering, Columbia University Hosted By: Debasis Mitra, Professor of Electrical Engineering, Columbia University; and Chair of DSI Sense, Collect and Move Data Center Abstract: Much of the prior work on scheduling algorithms for wireless networks focuses on maximizing throughput. However, for many real-time applications (e.g., vehicular networks, IoT, edge computing) delays and deadline guarantees on packet delivery can be more important than just long-term throughput. In this talk, we consider the problem of scheduling deadline-constrained packets in wireless networks, under a conflict-graph interference model. The objective is to guarantee that at least a certain fraction of packets of each link are delivered within their deadlines, which is referred to as delivery ratio. This problem has been studied before under restrictive frame-based traffic models, or greedy maximal scheduling schemes like LDF (Largest-Deficit First). In this talk, we pursue a different approach through a careful randomization over the choice of maximal links that can transmit at each time. We design randomized policies that can achieve delivery ratios much higher than what is achievable by deterministic policies such as LDF or MWS. Further, our results apply to traffic (arrival and deadline) processes that evolve as an unknown Markov chain. Hence, this work is an improvement with respect to both efficiency and traffic assumptions compared to the past work. Joint work with PhD student Christos Tsanikidis. Bio: Javad Ghaderi is an Associate Professor of Electrical Engineering at Columbia University. His research interests include algorithms, optimization, and stochastic processes with application to wireless networks and data centers. He received his B.Sc. from the University of Tehran, Iran, in 2006, M.Sc. from the University of Waterloo, Canada, in 2008, and Ph.D. from the University of Illinois at Urbana-Champaign in 2013. He spent a one-year Simons Postdoctoral Fellowship at the University of Texas at Austin before joining Columbia. He has received several awards including Best Student Paper Finalist at ACC 2013, Best Paper Award at ACM CoNEXT 2016, NSF CAREER Award in 2017, Best Student Paper Award at IFIP Performance 2020, and Best Paper Award at IEEE INFOCOM 2020.$

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